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The Innovation Process: 10-Step Process to Successful Innovation

Published: 06 December, 2023

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In the modern business environment, where change is the only constant, embracing innovation is not just a choice; it’s a strategic imperative. As businesses navigate through the intricacies of the digital era, the importance of a well-defined innovation process becomes not only evident but indispensable for staying ahead of the curve. Innovation is not a one-time event but a continuous journey that reshapes organizations, propelling them toward growth, adaptability, and sustainability.

What is Innovation?

Innovation involves the methodical approach to creating and introducing groundbreaking products and services to gain acceptance from customers. It’s a term often used but not always clearly understood and marks the inception of transformation. At its core, innovation is the process of utilizing digital technologies and creative thinking to fundamentally change how organizations operate, enhance value creation , and interact with their stakeholders. It’s not just about adopting new technologies; it’s a strategic shift in the business and operating models of an organization.

Why Innovation is Important for Business?

Innovation is crucial for business success as it drives growth, enhances competitiveness, and ensures long-term sustainability. By constantly adapting to changing market dynamics, technology, and customer needs, innovation enables businesses to stay relevant, differentiate themselves, and seize new opportunities. Embracing innovation fosters creativity, efficiency, and resilience, positioning a business to navigate challenges and emerge as a leader in its industry.

What is Innovation Process

The innovation process entails a series of strategic steps characterized by the adoption of creative and novel methods, aimed at attaining predefined organizational objectives within a designated timeframe. The innovation process definition is an efficient and well-managed systematic series of strategic steps tailored and characterized by the application of creative and novel approaches to align with a company’s structure and aspirations in innovation and achieve specific organizational goals within a defined timeline. It’s about introducing groundbreaking methods, and diverse sources of supply, exploring new markets, creating innovative goods, and reshaping organizational structures within an industry. The allure of the innovation process lies in its transformative power, turning a mere idea into a tangible and successful reality.

A successful innovation process is the key to staying ahead in the dynamic landscape of business. It involves translating knowledge into tangible products, solutions, or services that resonate in the market. Companies leading in innovation have a distinct edge, often driven by a robust process that efficiently transforms ideas into impactful concepts.

Establishing a reliable innovation process demands a strategic approach, and The UNITE Innovation Approach is a valuable resource in this journey. It not only structures the innovation process but also facilitates the creation of meaningful networks and maximizes the potential of intellectual property.

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The UNITE Innovation Approach | Overview

Why is the innovation process important.

The innovation process is not a one-time project but an ongoing journey. Organizations often go for major transformative changes only every 8.5 years on average. Hence, as you embark on the innovation process, it’s crucial to make business plans for the day after tomorrow. This holistic approach, which we refer to as the Digital Leadership approach, ensures flexibility in initiating, pausing, or scaling projects seamlessly.

1) Strategic Problem Solving:

The innovation process serves as a powerful tool for strategic problem-solving by identifying and addressing challenges within the business ecosystem. Companies, guided by a customer-centric approach and a deep understanding of customers’ jobs to be done , can proactively enhance their operations. This results in improved customer satisfaction as the products or services align more closely with the specific needs and desires of the customers. The outcome is not just increased sales but sustained revenue growth, as the organization consistently meets and exceeds the expectations tied to the fundamental “jobs” that customers are trying to accomplish.

In the intricate landscape of the innovation process , the Business Model Innovation (BMI) canvas plays a pivotal role in the problem-solving step. As organizations navigate challenges and seek solutions.

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The UNITE Business Model Innovation Patterns

This process begins with the identification of core problems, utilizing the canvas’s capabilities for a comprehensive examination of existing digital business models .

2) Global Market Maximization:

In an era of increasing globalization, the innovation process becomes instrumental in helping businesses not only tap into new markets but also identify opportunities and leverage emerging trends worldwide. Adopting a tailored innovation strategy allows companies to not just adapt their products and services but proactively identify and capitalize on diverse cultural and market demands. This approach not only fosters international success but positions the organization as a proactive player in identifying and seizing opportunities on a global scale.

3) Agility Through Change:

Change is an inevitable constant in the business landscape. A robust innovation process empowers businesses to not only adapt to change but thrive in dynamic environments. By embracing change through innovation, organizations enhance their resilience and agility, ensuring they remain adaptable in the face of evolving market trends.

4) Optimized Workplace Dynamics:

The innovation process plays a pivotal role in navigating the evolving dynamics of the workplace. As demographics shift and employee expectations change, innovation ensures that businesses can create adaptive work environments, attracting and retaining top talent while fostering a culture of continuous improvement.

5) Customer-Centric Evolution:

Understanding and responding to customer preferences are central to a business’s success. The innovation process allows companies to develop customer-centric solutions, aligning products and services with evolving preferences. This customer-focused approach not only satisfies current needs but anticipates future demands, ensuring sustained relevance.

6) Competitive Edge Retention:

In a fiercely competitive business landscape, retaining a competitive edge requires more than routine operations. A well-crafted innovation process enables companies to stand out by developing unique digital business models, making strategic moves, and continuously evolving to meet and exceed customer expectations.

The Business Model Canvas (BMC) plays a pivotal role in the innovation process, offering a dynamic framework for visualizing and communicating key aspects of a business model. Its simplicity fosters collaboration, aids in identifying innovation opportunities, and ensures organizations adapt efficiently to evolving landscapes, contributing to successful and competitive innovation.

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The UNITE Business Model Canvas

7) future-proofing strategies:.

Innovation is an investment in the future. Companies with a robust innovation process are better equipped to navigate uncertainties, foresee industry trends, and proactively shape their destinies. This forward-looking approach ensures long-term sustainability and positions organizations as leaders in their respective fields.

8) Transformation of Ideas into Value:

At its core, the innovation process is the alchemy that turns raw ideas into tangible value, crafting a compelling value proposition. It’s the systematic journey from concept to reality, ensuring that creative sparks translate into products, services, or solutions that resonate in the market.

9) Enhanced Efficiency and Effectiveness:

An innovation process isn’t just about introducing something new; it’s about doing things better. It streamlines operations, enhances efficiency, and ensures that every facet of the organization is optimized for success.

10) Cultivation of a Progressive Innovation Culture:

Embracing innovation fosters a culture of progress within an organization. Innovation culture encourages a mindset of continuous improvement, where employees are empowered to contribute ideas, experiment with new approaches, and actively participate in the organization’s growth journey.

11) Future-Proofing:

Innovation isn’t just about the present; it’s an investment in the future. Organizations with a well-established innovation process are better positioned to navigate uncertainties, foresee trends, and proactively shape their destiny.

Steps of the Innovation Process

Understanding the steps of the innovation process is key to successful implementation. Let’s break down the process into its essential components:

Step 1: Innovative Idea Generation

Embarking on the innovation journey initiates a quest for innovative potential through robust idea generation . This pivotal step involves actively seeking out new and groundbreaking ideas that have the potential to redefine products, services, or processes. The subsequent evaluation phase separates the wheat from the chaff, identifying ideas with the highest innovation quotient.

Idea Generation Techniques:

Creativity Techniques:

Employing structured creativity techniques stimulates ideation. These methods, ranging from brainstorming sessions to mind mapping, encourage teams to think outside the box and explore unconventional possibilities.

Design Thinking:

Rooted in empathy and a human-centric approach, design thinking is a powerful technique for innovative idea generation . It involves understanding user needs, ideating solutions, and prototyping to address real-world challenges.

Creativity techniques and design thinking are employed to stimulate ideation, ensuring ideas align with customer needs and address unmet requirements. JTBD enhances the customer-centric perspective, increasing the likelihood of generating transformative ideas that resonate authentically with evolving customer expectations.

The UNITE Jobs to Be Done (JTBD) Customer’s Job Statement plays a pivotal role. This framework emphasizes understanding the fundamental business goals and motivations of customers, guiding the generation of innovative ideas that address specific needs and challenges. By crafting precise Job Statements through UNITE, the process gains clarity on customer requirements, enabling a targeted approach to ideation.

Jobs to be Done Customer's Job Statement

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The UNITE Jobs-To-Be-Done Statement & Map

Tracking these innovative potentials varies in approach. Some methods involve targeted searches, delving into specific fields and aligning with the overall innovation strategy . Platforms such as idea generation contests, LEAD user workshops, or creativity workshops serve as channels for uncovering these hidden gems.

Reviewing and evaluating ideas regularly is crucial to ensure swift progress and provide immediate feedback to ideators. This practice prevents ideas from languishing without acknowledgement, fostering a supportive environment for ongoing participation in your innovation program. Lack of feedback can demotivate contributors and discourage their involvement in the innovation process. This emphasizes the importance of actively managing and nurturing the ideas generated to maintain a dynamic and participative innovation program.

Step 2: Advocacy Screening and Experimentation

The second crucial phase in the innovation process is advocacy screening and experimentation. Following the inception of innovative ideas, this stage involves an in-depth analysis to gather comprehensive information for evaluating feasibility. The goal is to scrutinize the potential challenges and benefits of a business idea, enabling informed decisions about its future. This phase plays a pivotal role in shaping the destiny of an innovation by subjecting it to rigorous scrutiny. Thorough analysis and experimentation provide valuable insights, identifying potential roadblocks and identifing opportunities for refinement.

One of the critical components is the feasibility analysis, assessing the practicality of the proposed innovation within the existing operational framework. Evaluating risks and benefits helps understand the innovation’s potential impact, and establishing a decision-making framework guides future development. Aligning the innovation with the organizational culture ensures seamless integration. Crucially, this phase involves meticulously analysing customer requirements, and validating the innovation against real-world demands.

Implementing best practices for business growth, such as fostering open communication and encouraging cross-functional collaboration, contributes to building a resilient innovation culture . Advocacy screening and experimentation propel the innovation process from ideation to strategic evaluation, ensuring concepts are grounded in practicality and market relevance. Navigating this phase sets the stage for visionary innovations with a solid foundation for success.

Step 3: Develop a Solution

Moving into the third phase of the innovation process , the focus now shifts to crafting a viable and deployable solution poised for market entry. This stage involves the meticulous development of solutions, including the construction of prototypes and the execution of various tests. Beyond conceptual and laboratory assessments, real-world market tests are conducted to garner firsthand insights and comprehensive feedback.

Throughout this phase, the emphasis is on refining the solution, addressing any identified shortcomings, and ensuring its readiness for implementation. Prototypes undergo scrutiny, and iterative testing ensures that the final product aligns seamlessly with customer expectations. The integration of market tests in authentic conditions serves as a crucial step in validating the solution’s practicality and market appeal.

Upon reaching maturity, the solution proceeds to the next pivotal stage: commercialization and marketing. Simultaneously, marketing and implementation strategies are further honed and developed to maximize impact. This synchronized approach ensures a smooth transition from solution development to market deployment, laying the groundwork for successful implementation and customer adoption. The third step in the innovation process is a critical juncture, where ideas evolve into tangible solutions ready to make a meaningful impact in the market.

Step 4: Commercialization and Marketing

Entering the crucial phase of commercialization and marketing marks a pivotal step in the innovation process. This stage is dedicated to creating market value for the developed idea, product, or service by focusing on its real-world impact. A key element within this step involves the meticulous establishment of specifications for the given idea, ensuring clarity and alignment with market needs.

Commercialization is a multifaceted endeavour that extends beyond the conceptual realm. It involves the physical manifestation of the product, making it available to potential customers through mass production, procurement, and logistics—all guided by well-defined concepts. Successful commercialization hinges on effective strategies that navigate the intricate landscape of consumer demand, competition, and market dynamics.

As the innovation transitions from organizational development to the commercial realm, the fourth step ensures a seamless integration of the product into the market, setting the stage for widespread adoption and success.

Marketing plays a central role in this phase, encompassing activities that drive awareness, engagement, and adoption. Crafting compelling narratives, establishing brand presence, and strategically positioning the product are integral components of a robust marketing strategy. The goal is to not only introduce the innovation to the market but also to generate enthusiasm, trust, and a compelling value proposit i on.

The value proposition canvas plays a crucial role. It helps in clearly defining and communicating the unique value the innovation brings to customers. By identifying customer pains and gains, the value proposition canvas guides marketing efforts, ensuring that the product’s benefits align with customer needs.

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The 4 Types of Innovation and the Problems They Solve

by Greg Satell

Summary .

Innovation is, at its core, about solving problems — and there are as many ways to innovate as there are different types of problems to solve. Just like we wouldn’t rely on a single marketing tactic for the life of an organization, or a single source of financing, we need to build up a portfolio of innovation strategies designed for specific tasks. Leaders identify the right type of strategy to solve the right type of problem, just by asking two questions: How well we can define the problem and how well we can define the skill domain(s) needed to solve it. Well-defined problems that benefit from well-defined skills fall into the category of “sustaining innovation.” Most innovation happens here, because most of the time we’re trying to get better at something we’re already doing. “Breakthrough innovation” is needed when we run into a well-defined problem that’s just devilishly hard to solve. In cases like these, we need to explore unconventional skill domains. When the reverse is true — skills are well-defined, but the problem is not — we can tap into “disruptive innovation” strategies. And when nothing is well-defined, well, then we’re in the exploratory, pioneering realm of basic research. There are always new problems to solve; learn to apply the solution that best fits your current problem.

One of the best innovation stories I’ve ever heard came to me from a senior executive at a leading tech firm. Apparently, his company had won a million-dollar contract to design a sensor that could detect pollutants at very small concentrations underwater. It was an unusually complex problem, so the firm set up a team of crack microchip designers, and they started putting their heads together.

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4.3 Developing Ideas, Innovations, and Inventions

Learning objectives.

By the end of this section, you will be able to:

Describe and apply the five stages of creativity
Discuss innovation as a system for problem solving and much more
Outline the sequence of steps in developing an invention

The previous section defined creativity, innovation, and invention, and provided examples. You might think of creativity as raw; innovation as transforming creativity into a functional purpose, often meant to eradicate a pain point or to fulfill a need; and invention as a creation that leaves a lasting impact. In this section, you will learn about processes designed to help you apply knowledge from the previous section.

The Creative Process: The Five Stages of Creativity

Raw creativity and an affinity for lateral thinking may be innate, but creative people must refine these skills in order to become masters in their respective fields. They practice in order to apply their skills readily and consistently, and to integrate them with other thought processes and emotions. Anyone can improve in creative efforts with practice. For our purposes, practice is a model for applied creativity that is derived from an entrepreneurial approach ( Figure 4.11 ). 34 It requires:

Preparation

Elaboration.

Preparation involves investigating a chosen field of interest, opening your mind, and becoming immersed in materials, mindset, and meaning. If you have ever tried to produce something creative without first absorbing relevant information and observing skilled practitioners at work, then you understand how difficult it is. This base of knowledge and experience mixed with an ability to integrate new thoughts and practices can help you sift through the ideas quicker. However, relying too heavily on prior knowledge can restrict the creative process. When you immerse yourself in a creative practice, you make use of the products or the materials of others’ creativity. For example, a video-game designer plays different types of video games on different consoles, computers, and online in networks. They may play alone, with friends in collaboration, or in competition. Consuming the products in a field gives you a sense of what is possible and indicates boundaries that you may attempt to push with your own creative work. Preparation broadens your mind and lets you study the products, practice, and culture in a field. It is also a time for goal setting. Whether your chosen field is directly related to art and design, such as publishing, or involves human-centric design, which includes all sorts of software and product design efforts, you need a period of open-minded reception to ideas. Repetitive practice is also part of the preparation stage, so that you can understand the current field of production and become aware of best practices, whether or not you are currently capable of matching them. During the preparation stage, you can begin to see how other creative people put meaning into their products, and you can establish benchmarks against which to measure your own creative work.

Incubation refers to giving yourself, and your subconscious mind in particular, time to incorporate what you learned and practiced in the preparation stage. Incubation involves the absence of practice. It may look to an outsider as though you are at rest, but your mind is at work. A change of environment is key to incubating ideas. 36 A new environment allows you to receive stimuli other than those directly associated with the creative problem you are working on. It could be as simple as taking a walk or going to a new coffee shop to allow your mind to wander and take in the information you gathered in the previous stage. Mozart stated, “When I am, as it were, completely myself, entirely alone, and of good cheer—say, traveling in a carriage, or walking after a good meal, or during the night when I cannot sleep; it is on such occasions that my ideas flow best and most abundantly.” 37 Incubation allows your mind to integrate your creative problem with your stored memories and with other thoughts or emotions you might have. This simply is not possible to do when you are consciously fixated on the creative problem and related tasks and practice.

Incubation can take a short or a long time, and you can perform other activities while allowing this process to take place. One theory about incubation is that it takes language out of the thought process. If you are not working to apply words to your creative problems and interests, you can free your mind to make associations that go deeper, so to speak, than language. 38 Patiently waiting for incubation to work is quite difficult. Many creative and innovative people develop hobbies involving physical activity to keep their minds busy while they allow ideas to incubate.

Insight or “illumination” is a term for the “aha!” moment—when the solution to a creative problem suddenly becomes readily accessible to your conscious mind. The “aha!” moment has been observed in literature, in history, and in cognitive studies of creativity. 39 Insights may come all at once or in increments. They are not easily understood because, by their very nature, they are difficult to isolate in research and experimental settings. For the creative entrepreneur, however, insights are a delight. An insight is the fleeting time when your preparation, practice, and period of incubation coalesce into a stroke of genius. Whether the illumination is the solution to a seemingly impossible problem or the creation of a particularly clever melody or turn of phrase, creative people often consider it a highlight in their lives. For an entrepreneur, an insight holds the promise of success and the potential to help massive numbers of people overcome a pain point or problem. Not every insight will have a global impact, but coming up with a solution that your subconscious mind has been working on for some time is a real joy.

Evaluation is the purposeful examination of ideas. You will want to compare your insights with the products and ideas you encountered during preparation. You also will want to compare your ideas and product prototypes to the goals you set out for yourself during the preparation phase. Creative professionals will often invite others to critique their work at this stage. Because evaluation is specific to the expectations, best practices, and existing product leaders in each field, evaluation can take on many forms. You are looking for assurance that your standards for evaluation are appropriate. Judge yourself fairly, even as you apply strict criteria and the well-developed sense of taste you acquired during the preparation phase. For example, you might choose to interview a few customers in your target demographics for your product or service. The primary objective is to understand the customer perspective and the extent to which your idea aligns with their position.

The last stage in the creative process is elaboration , that is, actual production. Elaboration can involve the release of a minimum viable product (MVP) . This version of your invention may not be polished or complete, but it should function well enough that you can begin to market it while still elaborating on it in an iterative development process. Elaboration also can involve the development and launch of a prototype, the release of a software beta, or the production of some piece of artistic work for sale. Many consumer-product companies, such as Johnson & Johnson or Procter & Gamble , will establish a small test market to garner feedback and evaluations of new products from actual customers. These insights can give the company valuable information that can help make the product or service as successful as possible.

At this stage what matters most in the entrepreneurial creative process is that the work becomes available to the public so that they have a chance to adopt it.

Link to Learning

Test marketing can reveal much information about the potential users of a product. Visit the Drive Research site on test markets for more information.

Innovation as More than Problem Solving

Innovative entrepreneurs are essentially problem solvers, but this level of innovation—identifying a pain point and working to overcome it—is only one in a series of innovative steps. In the influential business publication Forbes , the entrepreneur Larry Myler notes that problem solving is inherently reactive. 40 That is, you have to wait for a problem to happen in order to recognize the need to solve the problem. Solving problems is an important part of the practice of innovation, but to elevate the practice and the field, innovators should anticipate problems and strive to prevent them. In many cases, they create systems for continuous improvement, which Myler notes may involve “breaking” previous systems that seem to function perfectly well. Striving for continuous improvement helps innovators stay ahead of market changes. Thus, they have products ready for emerging markets, rather than developing projects that chase change, which can occur constantly in some tech-driven fields. One issue with building a system for constant improvement is that you are in essence creating problems in order to solve them, which goes against established culture in many firms. Innovators look for organizations that can handle purposeful innovation, or they attempt to start them. Some innovators even have the goal of innovating far ahead into the future, beyond current capacities. In order to do this, Myler suggests bringing people of disparate experiential backgrounds with different expertise together. These relationships are not guarantees of successful innovation, but such groups can generate ideas independent of institutional inertia. Thus, innovators are problem solvers but also can work with forms of problem creation and problem imagination. They tackle problems that have yet to exist in order to solve them ahead of time.

Let’s examine one multilevel approach to innovation ( Figure 4.12 ). The base is problem solving. The next level up in the pyramid, so to speak, is prevention. The next level is working toward continuous improvement, and at the top of such efforts is creating the capacity to direct the future of your industry or multiple industries so that you can weather disruption in your career or even to create it.

Even if you are not interested in shaping the future of whole industry sectors, developing future-focused innovation practices still is a good idea. It will help you prepare for disruption. The pace of technological change is such that workers at all levels need to be prepared to innovate. Innovation leaders, such as the marketing guru Guy Kawasaki , have built on psychological principles to suggest new ways to approach innovation. According to Kawasaki, innovative products include five key qualities: deep, indulgent, complete, elegant, and emotive— DICEE ( Figure 4.13 ). 41 You can strive to infuse individual innovations with these qualities in practical ways.

Deep products are based on the logic of innovation that we’ve just established and anticipate users’ needs before they have them. These types of innovations often have masterful designs that are intuitive for new users while still being capable of completing complex tasks. Adobe is an innovative corporation working in several fields, such as software, marketing, and artificial intelligence. Adobe often creates software applications with basic functions that are easily accessible to new users but that also enable experienced users to innovate on their own. 42 Creating a platform for innovation is a hallmark of deep, forward-thinking innovation.

Innovations with lasting power engage users in ways that make them feel special for having purchased the product or for having found the service. Indulgence refers to a depth of quality that does not come from being the fastest solution to a problem. Indulgence may even sound like a negative trait. In humans, it certainly can be, but for someone using an innovative product, feeling indulgent can relate to a richness of experience with the user interface (UI). The UI of a product, particularly a software product, is what the user sees and interacts with. A feeling of indulgence imbues your product with a sense of value and durability that reassures users and encourages them to use your product confidently.

Kawasaki’s vision of a complete product includes the services wrapped around it and underlying it such that users understand the product well enough to be comfortable using it. Information about how it works and how it is meant to work is readily available. Thus, product innovation must include marketing and other communication efforts. For Kawasaki, this builds the “ total user experience .” 43 If you truly have solved a problem in the marketplace, users will understand what that problem is and how your product and related services deliver.

Elegance also is part of a product’s UI. It refers to intuitive design that immediately makes sense to consumers. Elegance conveys more information with fewer words. Elegant design is not afraid of negative space or of the occasional pause. Elegant innovations solve problems without creating new ones. For Kawasaki, elegance is the difference between a pragmatic, good innovation and something great.

Emotive innovations evoke the intended emotion and demand to be admired and shared. In other words, truly great innovations create fandoms, not just consumer bases. You can’t force people to love your product, but you can give them experiences that create a sense of excitement and anticipation of what you might come up with next.

Developing an Invention

The general process of inventing involves systematic and practical steps that might include linear and nonlinear thinking. You might think that only people with innate artistic skills are creative and that only geniuses become innovators and inventors, but much of creativity is driven by being immersed in a practice. You can build and foster your own creativity. Your idea of an inventor might be someone like Johannes Gutenberg , who developed the printing press. The spread of printing ultimately redrew the map of Europe and resulted in the foundation of new centers of learning. Gutenberg’s supposed spark actually was more of a slow burn. He was creative and innovative—one of history’s most famous inventors—but his printing press, like all other inventions, was a synthesis of existing technologies. Gutenberg’s most important innovation was his use of moveable, interchangeable metal type instead of entire hand-carved wooden blocks of text ( Figure 4.14 ). Perfecting his printing process took decades and left him all but broke. 44 The notion of the inventor’s single stroke of genius is mostly myth. The people that history remembers usually worked very hard to develop their creativity, to become familiar with the processes and tools that were ripe for innovation in their time, and ultimately to make something so unique that society recognizes it as an invention.

The old adage claims that “necessity is the mother of invention,” but an innovator needs experience in a field, creative effort, and knowledge to be a successful inventor. Entrepreneurship means taking your efforts and knowledge, and finding a market where your invention can first survive, then thrive.

One model for developing an invention is the first five steps of a plan adapted from Sourcify.com , which specializes in connecting product developers with manufacturers. 45 This process is succinct and includes suggestions for building a team along the way ( Figure 4.15 ).

Step 1: Educate Yourself

Before your inventive product can do battle with other inventions, you will need to educate yourself. To prepare yourself to weather the competition, you need to learn as much as you can about the current investing climate, current product development opportunities, and current leadership approaches. Even if you are not deeply interested in leadership dogma, it helps to know what the current trends are in leadership and product development. To succeed as an inventor in a vast marketplace, you need to understand the rules, written and unwritten, of the industry and competitive landscape. The product development process can be quite involved. The process can vary by industry and by availability of resources.

Part of educating yourself is also gaining an understanding of your own strengths and weaknesses, and how those relate to your leadership style. A leadership style inventory can help you better understand your approach to leading others. This is just one example of many that exist to give you a starting point. As the inventor of your product or service, you will manage/lead others as you attempt to make your idea a reality. Also, the environment can constantly change. For this reason, it is important to understand the basic tenets of leadership and management in a dynamic work atmosphere. Many sources will give you insights into the challenges of management.

A leadership style inventory can help you understand your leadership style and how to adapt your own style to other situations and people.

Another key step in educating yourself is to find out which kinds of contributors you are going to need to build a successful entrepreneurial team. Building a team is essential to making your invention a reality. Even those who invent alone—and they are quite rare—must have a development team, a manufacturing and/or service team, a marketing team, and other members with specific skill sets such as coders, graphic designers, test marketers, and more.

Step 2: Stay Organized

Most tip sheets for inventors suggest that you find a method for organizing your creativity so that you don’t spend time trying to remember previous ideas, plans, and decisions. You must organize information related to your business idea, your business plan, and your potential teammates in the process.

Contact management software has been popular for decades. Nowadays, you can investigate many other productivity and team-chat tools. Research ways to organize information about the people you plan to work with and hope to work for. The team-chat program Slack (www.slack.com) enables you to create specific topics for team members to discuss and collaborate on. Slack offers several features to help keep employees connected. Insightly (www.insightly.com) is a customer relationship management tool to stay better connected to your customers. Ryver and Glip incorporate task management. Flock and Microsoft Teams offer a host of features, with Microsoft leveraging its corporate position to bring about deployment in more than 200,000 organizations. Select the tool set that works best for you and consider paying for the software that offers the precise team communication functionality and utility you need. 46

Step 3: Conduct Market Research

Market research is an obvious must, but many entrepreneurs fail to go as deeply as they should in researching their competition. You must be aware of current and future competitors so that you are prepared to compete in the marketplace when you are actually ready. Being the best on paper now won’t be much use when you enter the marketplace with an MVP in six to eight months in competition with competitors’ new products and updates.

What should you consider with regard to team development when you’re looking at the competition? Within the legal limits of any noncompete clauses, you should be shopping the competition for potential team members. The best leaders are always seeking talented people. If you sense that someone would be a good fit for your team, that they have not only the skill set but also the temperament that would help put your invention in the market, do not be afraid to reach out to them. How you reach out is something you must research for each industry. In some industries, you will have to be highly secretive. Part of market research is understanding the market well enough to understand the soft skills you need to find contributors who are already working in the industry or in an adjacent one

Step 4: Conduct Patent Research

If you expect to apply for a patent , take the time to read up on policies and procedures. Officials in the US Patent Office, or in similar bureaus in other countries, decide whether an invention is worthy of receiving a patent. A patentable invention must meet the criteria of being novel, useful, and nonobvious; it must be proven to be workable. 47 Those three standards—novel, useful, and nonobvious—are subjective. So is the concept of invention, but conceptualizing invention this way sets a high bar for entrepreneurs who truly wish to make a social impact. Developing an invention that is patentable also creates a barrier against competition, which can make the difference between business success and failure. There are two types of patents. Utility patents last twenty years, and design patents usually last fourteen years. If a patent is granted, the inventor has a window of time in which to secure further funding, work to produce the product, and try to gain mass-market adoption. 48 After all is said and done, you can apply your creativity to social innovations, product innovations, or service innovations. If you can combine enough innovations, add your unique creativity, and create something that survives the diffusion chasm, you can truly invent something new.

The patent basics page of the US Patent and Trademark Office’s website is roughly forty pages long. 49 The utility patent process includes a thirteen-step flow chart 50 that outlines the process. The patent office encourages you to use a registered patent attorney or agent. If you are skilled and diligent enough to secure a patent, you should expect to pay fees and file paperwork to maintain it for years after it is granted. We’ve already discussed the keys to securing a patent, but to reiterate, here is how an invention is defined in US patent law: “In the language of the statute, any person who ‘invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent,’ subject to the conditions and requirements of the law.” 51

When building a team to make your invention a reality, finding a patent lawyer or agent is key. Even those who have advocated for hiring patent attorneys in the past now suggest that hiring a patent agent might work. What’s the difference? Patent attorneys often bill by the hour, but they offer a full suite of legal advice. As the author Stephen Key indicates, patent agents are narrowly focused on helping you get and defend your patent. 52 The other limitation that Key mentions is that patent agents may write a patent application in such a way that you are less prepared to protect your invention against future legal challenges. Key quotes Gene Quinn , a leading attorney on intellectual property and patent law: “By the time you realize that you are sitting on a million-dollar invention it will be too late to do anything about it.…Patent agents as a general rule tend to be very good at describing what it is that you as an inventor show up with.” What they tend to be much less good at is describing what your invention could be. They also frequently will use terms that are more concrete and limiting than would a patent attorney. Attorneys are taught the art of being hyper-specific, which is necessary at times, but also the art of being anything but specific.” 53 Patents cannot be vague, but they can be written with just the right amount of specificity to protect against similar products that may arise and threaten your market share.

Step 5: Develop a Prototype

Developing a prototype can be the most fun or the most tedious part of inventing. Much of your attitude toward developing a prototype depends on available resources, technology, and expertise. In this text, we reference the concept of the lean startup from time to time. In the lean startup model, the prototype is most often an MVP. As we saw earlier, an MVP is a version of your invention that may not be polished or complete in terms of how you envisioned it, but it functions well enough and looks good enough that you can begin to market it with reasonable hopes that it will be adopted. For other inventions, you may need to build a more advanced prototype. This requires serious investment capital, but the payoff is that users will interact with a version of the product that looks and functions more like what you had in mind during your ideation phase. As an inventor, you are responsible for establishing quality control minimums for your product. You may have to compromise on your vision, but you should not compromise on basic functionality or basic levels of quality in materials.

You have many options at the prototype development stage. You can build the prototype yourself or with a small team. You can partner with design/invention firms that specialize in helping inventors create, but you must be very careful and involve your legal representation when working with such firms to be sure that you maintain the patents and other rights to your invention. Many inventors have partnered with such firms only to see their intellectual property stolen. Another option is to get funding for your invention on Kickstarter or some other crowdfunding site, but again you must beware that establishing such a campaign puts your idea in the public sphere. “Copycatters are monitoring crowdfunding platforms like Kickstarter and watching for trendy products to go viral,” according to Amedeo Ferraro , an intellectual property attorney. 54 Competing companies, particularly in foreign markets, actively scout Kickstarter and similar platforms for new ideas that they can manufacture and bring to market before your crowdfunding project has run its course. Perhaps most chilling is this comment regarding legal protections that do not function, even when inventors take precautions to protect their intellectual property when working with some Chinese firms: “But even with these protections, there’s no guarantee that you can stop someone from copycatting your product. [One U.S. intellectual property lawyer] said that the problem lies not in China’s courts but enforcing rulings. Winning a case against one factory is relatively easy. But suing every factory and winning is expensive and time consuming.” 55 For this reason, some inventors prefer to start small and local, if possible. It can be better for them to start with a trusted team striving for a small profit and a good market position than to see the market flooded with copycat products.

34 Aleza D’Agostino. “5 Stages of the Creative Process with James Taylor.” CreativeLive . December 28, 2015. https://www.creativelive.com/blog/5-stages-of-the-creative-process-with-james-taylor/
35 Eugene Sadler-Smith. “Wallas’ Four-Stage Model of the Creative Process: More Than Meets the Eye?” Creativity Research Journal 27, no. 4 (2015): 342–352.
36 Eugene Sadler-Smith. “Wallas’ Four-Stage Model of the Creative Process: More Than Meets the Eye?” Creativity Research Journal 27, no. 4 (2015): 342–352.
37 Neal Zaslaw, “Mozart As a Working Stiff,” in James M. Morris, ed., On Mozart (Cambridge: Cambridge University Press, 1994), 109.
38 Steven M. Smith, Thomas B. Ward, and Ronald A. Finke, eds., The Creative Cognition Approach . Cambridge, MA: MIT Press, 1995.
39 Eugene Sadler-Smith. “Wallas’ Four-Stage Model of the Creative Process: More Than Meets the Eye?” Creativity Research Journal 27, no. 4 (2015): 342–352.
40 Larry Myler. “Innovation Is Problem Solving . . . And a Whole Lot More.” Forbes. June 13, 2014. https://www.forbes.com/sites/larrymyler/2014/06/13/innovation-is-problem-solving-and-a-whole-lot-more/#3f11abe533b9
41 Guy Kawasaki. “Guy’s Golden Touch.” Guy Kawasaki . January 3, 2006. https://guykawasaki.com/guys_golden_tou/
42 Adobe Communications Team. “Adobe Named One of Fast Company’s ‘Most Innovative Companies’ for AI.” Adobe Blog . February 20, 2018. https://theblog.adobe.com/adobe-named-one-fast-companys-innovative-companies-ai/
43 Guy Kawasaki. “Guy’s Golden Touch.” Guy Kawasaki . January 3, 2006. https://guykawasaki.com/guys_golden_tou/
44 John Man. The Gutenberg Revolution . New York: Random House, 2010.
45 Natalie Peters. “9 Key Steps to Bring Your Invention to Life.” Sourcify . December 11, 2017. https://www.sourcify.com/bring-your-invention-to-life/
46 Aleksey Chepalov. “9 Slack Competitors in 2019: What Team Chat Tools Are Leading the Way?” Chanty . March 18, 2019. https://www.chanty.com/blog/slack-competitors/
47 “Patent Subject Matter Eligibility.” United States Patent and Trademark Office (USPTO). n.d. https://www.uspto.gov/web/offices/pac/mpep/s2106.html
48 “How Long Does Patent, Trademark, or Copyright Protection Last?” Intellectual Property Rights Information and Assistance. July 7, 2016. https://www.stopfakes.gov/article?id=How-Long-Does-Patent-Trademark-or-Copyright-Protection-Last
49 “General Information Concerning Patents.” United States Patent and Trademark Office . October 2015. https://www.uspto.gov/patents-getting-started/general-information-concerning-patents
50 “Process for Obtaining a Utility Patent.” United States Patent and Trademark Office . n.d. https://www.uspto.gov/patents-getting-started/patent-basics/types-patent-applications/utility-patent/process-obtaining
51 “What Can Be Patented” in “General Information Concerning Patents.” United States Patent and Trademark Office . October 2015. https://www.uspto.gov/patents-getting-started/general-information-concerning-patents#heading-4
52 Stephen Key. “Should You Hire a Patent Agent Instead of a Patent Attorney?” Inc . October 26, 2016. https://www.inc.com/stephen-key/should-you-hire-a-patent-agent-instead-of-a-patent-attorney.html
53 “Gene Quinn.” IP Watchdog . n.d. https://www.ipwatchdog.com/people/gene-quinn-3/
54 Jennifer Schlesinger, Andrea Day, Bianca Fortis, Eunice Yoon, and Lilian Wu. “How One Entrepreneur’s American Dream Turned into a Copycat Nightmare.” CNBC . April 30, 2019. https://www.cnbc.com/2019/04/30/how-one-entrepreneurs-american-dream-turned-into-a-copycat-nightmare.html
55 Josh Horwitz. “Your Brilliant Kickstarter Idea Could Be on Sale in China before You’ve Even Finished Funding It.” Quartz . October 17, 2016. https://qz.com/771727/chinas-factories-in-shenzhen-can-copy-products-at-breakneck-speed-and-its-time-for-the-rest-of-the-world-to-get-over-it/

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Creative Problem Solving: from complex challenge to innovative solution

Dr. Hannah Rose

Even if you usually excel at finding solutions, there will be times when it seems that there’s no obvious answer to a problem. It could be that you’re facing a unique challenge that you’ve never needed to overcome before. You could feel overwhelmed because of a new context in which everything seems to be foreign, or you may feel like you’re lacking the skills or tools to navigate the situation. When facing a difficult dilemma, Creative Problem Solving offers a structured method to help you find an innovative and effective solution.

The history of Creative Problem Solving

The technique of Creative Problem Solving was first formulated by Alex Osborn in the 1940’s. It was not the first time Osborn came up with a formula to support creative thinking. As a prolific creative theorist, Osborn also coined the term brainstorming to define the proactive process of generating new ideas.

With brainstorming, Osborn suggested that it’s better to bring every idea you have to the table, including the wildest ones, because with just a little modification, the outrageous ideas may later become the most plausible solutions. In his own words: “It is easier to tone down a wild idea than to think up a new one.”

Osborn worked closely with Sid Parnes, who was at the time the world’s leading expert on creativity and innovation. Together, they developed the Osborn-Parnes Creative Problem Solving Process. To this day, this process remains an effective way to generate solutions that break free from the status quo.

The Creative Problem Solving process, sometimes referred to as CPS, is a proven way to approach a challenge more imaginatively. By redefining problems or opportunities, it becomes possible to move in a completely new and more innovative direction.

Dr Donald Treffinger described Creative Problem Solving as an effective way to review problems, formulate opportunities, and generate varied and novel options leading to a new solution or course of action. As such, Treffinger argued that creative problem solving provides a “powerful set of tools for productive thinking”.

Creative Problem Solving can also enhance collective learning at the organisational level. Dr David Vernon and colleagues found that Creative Problem Solving can support the design of more effective training programmes.

From its invention by two creative theorists to its application at all levels of creative thinking — from personal to organisation creativity — Creative Problem Solving is an enduring method to generate innovative solutions to complex challenges.

The four principles of Creative Problem Solving

You can use Creative Problem Solving on your own or as part of a team. However, when adopted by multiple team members, it can lead to an even greater output of useful, original solutions. So, how do you put it into practice? First, you need to understand the four guiding principles behind Creative Problem Solving.

The first principle is to look at problems and reframe them into questions. While problem statements tend to not generate many responses, open questions can lead to a wealth of insights, perspectives, and helpful information — which in turn make it easier to feel inspired and to come up with potential solutions. Instead of saying “this is the problem”, ask yourself: “Why are we facing this problem? What’s currently preventing us from solving this problem? What could be some potential solutions?”

The second principle is to balance divergent and convergent thinking. During divergent thinking , all options are entertained. Throw all ideas into the ring, regardless of how far-fetched they might be. This is sometimes referred to as non-judgmental, non-rational divergent thinking. It’s based on the willingness to consider all new ideas. Convergent thinking, in contrast, is the thinking mode used to narrow down all of the possible ideas into a sensible shortlist. Balancing divergent and convergent thinking creates a steady state of creativity in which new ideas can be assessed and appraised to search for unique solutions.

Tangential to the second principle, the third principle for creative problem solving is to defer judgement. By judging solutions too early, you will risk shutting down idea generation. Take your time during the divergent thinking phase to give your mind the freedom to dream ambitious ideas. Only when engaged in convergent thinking should you start judging the ideas you generated in terms of potential, appropriateness, and feasibility.

Finally, Creative Problem Solving requires you to say “yes, and” rather than “no, but” in order to encourage generative discussions. You will only stifle your creativity by automatically saying no to ideas that seem illogical or unfeasible. Using positive language allows you to explore possibilities, leaving space for the seeds of ideas to grow into applicable solutions.

How to practice Creative Problem Solving

Now that you know the principles underlying Creative Problem Solving, you’re ready to start implementing the practical method devised by its inventors. And the good news is that you’ll only need to follow three simple steps.

Generating – Formulate questions. The first step is to understand what the problem is. By turning the problem into a set of questions, you can explore the issue properly and fully grasp the situation, obstacles, and opportunities. This is also the time to gather facts and the opinions of others, if relevant to the problem at hand.
Conceptualising – Explore ideas. The second step is when you can express your creativity through divergent thinking. Brainstorm new, wild and off-the-wall ideas to generate new concepts that could be the key to solving your dilemma. This can be done on your own, or as part of a brainstorming session with your team.
Optimising – Develop solutions. Now is the time to switch to convergent thinking. Reflect on the ideas you came up with in step two to decide which ones could be successful. As part of optimising, you will need to decide which options might best fit your needs and logistical constraints, how you can make your concepts stronger, and finally decide which idea to move forwards with.
Implementing – Formulate a plan. Figuring out how you’ll turn the selected idea into reality is the final step after deciding which of your ideas offers the best solution. Identify what you’ll need to get started, and, if appropriate, let others know of your plans. Communication is particularly important for innovative ideas that require buy-in from others, especially if you think you might initially be met with resistance. You may also need to consider whether you’ll need additional resources to ensure the success of complex solutions, and request the required support in good time.

Creative Problem Solving is a great way to generate unique ideas when there appears to be no obvious solution to a problem. If you’re feeling overwhelmed by a seemingly impossible challenge, this structured approach will help you generate solutions that you might otherwise not have considered. By practising Creative Problem Solving, some of the most improbable ideas could lead to the discovery of the perfect solution.

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In the early decades of the 20th century, automobiles, telephone service and radio (broadcast of the 1920 U.S. presidential election results from KDKA station in Pittsburgh is shown) were transforming life.

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From invention to innovation

In the 1940s, Vannevar Bush’s model for innovation was what’s now known as “linear.” He saw the wellspring of new scientific ideas, or what he termed “basic science,” as eventually moving in a more practical direction toward what he deemed “applied research.” In time, these applied scientific ideas — inventions, essentially — could move toward engineered products or processes. Ultimately, in finding large markets, they could become innovations.

In recent decades, Bush’s model has come to be seen as simplistic. The educator Donald Stokes, for instance, has pointed out that the line between basic and applied science can be indistinct. Bush’s paradigm can also work in reverse: New knowledge in the sciences can derive from technological tools and innovations, rather than the other way around. This is often the case with powerful new microscopes , for instance, which allow researchers to make observations and discoveries at tinier and tinier scales. More recently, other scholars of innovation have pointed to the powerful effect that end users and crowdsourcing can have on new products, sometimes improving them dramatically — as with software — by adding new ideas for their own use.

Above all, innovations have increasingly proved to be the sum parts of unrelated scientific discoveries and inventions; combining these elements at a propitious moment in time can result in technological alchemy. Economist Mariana Mazzucato, for instance, has pointed to the iPhone as an integrated wonder of myriad breakthroughs, including touch screens, GPS, cellular systems and the Internet, all developed at different times and with different purposes.

At least in the Cold War era, when military requests and large industrial labs drove much of the new technology, the linear model nevertheless succeeded well. Beyond AT&T and General Electric, corporate titans like General Motors, DuPont, Dow and IBM viewed their R&D labs, stocked with some of the country’s best scientists, as foundries where world-changing products of the future would be forged.

These corporate labs were immensely productive in terms of research and were especially good at producing new patents. But not all their scientific work was suitable for driving innovations. At Bell Labs, for instance, which funded a small laboratory in Holmdel, N.J., situated amid several hundred acres of open fields, a small team of researchers studied radio wave transmissions.

Karl Jansky, a young physicist, installed a moveable antenna on the grounds that revealed radio waves emanating from the center of the Milky Way. In doing so, he effectively founded the field of radio astronomy . And yet, he did not create anything useful for his employer, the phone company, which was more focused on improving and expanding telephone service. To Jansky’s disappointment, he was asked to direct his energies elsewhere; there seemed no market for what he was doing.

Above all, corporate managers needed to perceive an overlap between big ideas and big markets before they would dedicate funding and staff toward developing an innovation. Even then, the iterative work of creating a new product or process could be slow and plodding — more so than it may seem in retrospect. Bell Labs’ invention of the point-contact transistor, in December 1947, is a case in point. The first transistor was a startling moment of insight that led to a Nobel Prize . Yet in truth the world changed little from what was produced that year.

The three credited inventors — William Shockley, John Bardeen and William Brattain — had found a way to create a very fast switch or amplifier by running a current through a slightly impure slice of germanium. Their device promised to transform modern appliances, including those used by the phone company, into tiny, power-sipping electronics. And yet the earliest transistors were difficult to manufacture and impractical for many applications. (They were tried in bulky hearing aids, however.) What was required was a subsequent set of transistor-related inventions to transform the breakthrough into an innovation.

historical photo of John Bardeen, William Shockley and Walter Brattain working with technical equipment

The first crucial step was the junction transistor, a tiny “sandwich” of various types of germanium, theorized by Shockley in 1948 and created by engineering colleagues soon after. The design proved manufacturable by the mid-1950s, thanks to efforts at Texas Instruments and other companies to transform it into a dependable product.

A second leap overcame the problems of germanium, which performed poorly under certain temperature and moisture conditions and was relatively rare. In March 1955, Morris Tanenbaum, a young chemist at Bell Labs, hit on a method using a slice of silicon. It was, crucially, not the world’s first silicon transistor — that distinction goes to a device created a year before. But Tanenbaum reflected that his design, unlike the others, was easily “manufacturable,” which defined its innovative potential. Indeed, he realized its value right away. In his lab notebook on the evening of his insight, he wrote: “This looks like the transistor we’ve been waiting for. It should be a cinch to make.”

Finally, several other giant steps were needed. One came in 1959, also at Bell Labs, when Mohamed Atalla and Dawon Kahng created the first silicon metal-oxide-semiconductor-field-effect-transistor — known as a MOSFET — which used a different architecture than either junction or point-contact transistors. Today, almost every transistor manufactured in the world, trillions each second, results from the MOSFET breakthrough . This advance allowed for the design of integrated circuits and chips implanted with billions of tiny devices. It allowed for powerful computers and moonshots. And it allowed for an entire world to be connected.

Getting there

The technological leaps of the 1900s — microelectronics, antibiotics , chemotherapy, liquid-fueled rockets, Earth-observing satellites , lasers , LED lights, disease-resistant seeds and so forth — derived from science. But these technologies also spent years being improved, tweaked, recombined and modified to make them achieve the scale and impact necessary for innovations.

Some scholars — the late Harvard professor Clayton Christensen, for instance, who in the 1990s studied the way new ideas “disrupt” entrenched industries — have pointed to how waves of technological change can follow predictable patterns. First, a potential innovation with a functional advantage finds a market niche; eventually, it expands its appeal to users, drops in cost and step by step pushes aside a well-established product or process. (Over time the transistor, for example, has mostly eliminated the need for vacuum tubes.)

But there has never been a comprehensive theory of innovation that cuts across all disciplines, or that can reliably predict the specific path by which we end up transforming new knowledge into social gains. Surprises happen. Within any field, structural obstacles, technical challenges or a scarcity of funding can stand in the way of development, so that some ideas (a treatment for melanoma, say) move to fruition and broad application faster than others (a treatment for pancreatic cancer).

There can likewise be vast differences in how innovation occurs in different fields. In energy, for example, which involves vast integrated systems and requires durable infrastructure, the environmental scientist and policy historian Vaclav Smil has noted, innovations can take far longer to achieve scale than in others. In software development, new products can be rolled out cheaply, and can reach a huge audience almost instantly.

At the very least, we can say with some certainty that almost all innovations, like most discoveries and inventions, result from hard work and good timing — a moment when the right people get together with the right knowledge to solve the right problem. In one of his essays on the subject, business theorist Peter Drucker pointed to the process by which business managers “convert society’s needs into opportunities” as the definition of innovation. And that may be as good an explanation as any.

Even innovations that seem fast — for instance, mRNA vaccines for COVID-19 — are often a capstone to many years of research and discovery. Indeed, it’s worth noting that the scientific groundwork preceding the vaccines’ rollout developed the methods that could later be used to solve a problem when the need became most acute . What’s more, the urgency of the situation presented an opportunity for three companies — Moderna and, in collaboration, Pfizer and BioNTech — to utilize a vaccine invention and bring it to scale within a year.

person injecting a COVID-19 vaccine into someone's arm

“The history of cultural progress is, almost without exception, a story of one door leading to another door,” the tech journalist Steven Johnson has written. We usually explore just one room at a time, and only after wandering around do we proceed to the next, he writes. Surely this is an apt way to think of our journey up to now. It might also lead us to ask: What doors will we open in future decades? What rooms will we explore?

On the one hand, we can be assured that the advent of mRNA vaccines portends applications for a range of other diseases in coming years. It seems more challenging to predict — and, perhaps, hazardous to underestimate — the human impact of biotechnology , such as CRISPR gene editing or synthetic DNA. And it seems equally hard to imagine with precision how a variety of novel digital products ( robotics, for example, and artificial intelligence ) will be integrated into societies of the future. Yet without question they will.

Erik Brynjolfsson of Stanford and Andrew McAfee of MIT have posited that new digital technologies mark the start of a “second machine age” that in turn represents “an inflection point in the history of our economies and societies.” What could result is an era of greater abundance and problem-solving, but also enormous challenges — for instance, as computers increasingly take on tasks that result in the replacement of human workers.

If this is our future, it won’t be the first time we’ve struggled with the blowback from new innovations , which often create new problems even as they solve old ones. New pesticides and herbicides, to take one example, allowed farmers to raise yields and ensure good harvests; they also devastated fragile ecosystems. Social media connected people all over the world; it also led to a tidal wave of propaganda and misinformation. Most crucially, the discovery of fossil fuels, along with the development of steam turbines and internal combustion engines, led us into an era of global wealth and commerce. But these innovations have bequeathed a legacy of CO 2 emissions, a warming planet, diminished biodiversity and the possibility of impending environmental catastrophe.

The climate dilemma almost certainly presents the greatest challenge of the next 50 years. Some of the innovations needed for an energy transition — in solar and wind power, and in batteries and home heat pumps — already exist; what’s required are policies that allow for deployment on a rapid and more massive scale. But other ideas and inventions — in the fields of geothermal and tidal power, for instance, or next-generation nuclear plants, novel battery chemistries and carbon capture and utilization — will require years of development to drive costs down and performance up. The climate challenge is so large and varied, it seems safe to assume we will need every innovation we can possibly muster.

A solar thermal power plant in Morocco, with long rows of solar panels

Perhaps the largest unknown is whether success is assured. Even so, we can predict what a person looking back a century from now might think. They will note that we had a multitude of astonishing scientific breakthroughs in our favor at this moment in time — breakthroughs that pointed the way toward innovations and a cooler, safer, healthier planet. They will reflect that we had a range of extraordinary tools at our beck and call. They will see that we had great engineering prowess, and great wealth. And they will likely conclude that with all the problems at hand, even some that seemed fearsome and intractable, none should have proved unsolvable.

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A fluffy, orange fungus could transform food waste into tasty dishes

‘Turning to Stone’ paints rocks as storytellers and mentors

A Victorian-era book titled Mohun is propped up to show it's deep yellow cover, which is decorated by a paler flower with green leaves and vines.

Old books can have unsafe levels of chromium, but readers’ risk is low

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Astronauts actually get stuck in space all the time

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Published: 18 June 2021

Nobel Turing Challenge: creating the engine for scientific discovery

Hiroaki Kitano ORCID: orcid.org/0000-0002-3589-1953 1

npj Systems Biology and Applications volume 7 , Article number: 29 ( 2021 ) Cite this article

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Computational biology and bioinformatics

Scientific discovery has long been one of the central driving forces in our civilization. It uncovered the principles of the world we live in, and enabled us to invent new technologies reshaping our society, cure diseases, explore unknown new frontiers, and hopefully lead us to build a sustainable society. Accelerating the speed of scientific discovery is therefore one of the most important endeavors. This requires an in-depth understanding of not only the subject areas but also the nature of scientific discoveries themselves. In other words, the “science of science” needs to be established, and has to be implemented using artificial intelligence (AI) systems to be practically executable. At the same time, what may be implemented by “AI Scientists” may not resemble the scientific process conducted by human scientist. It may be an alternative form of science that will break the limitation of current scientific practice largely hampered by human cognitive limitation and sociological constraints. It could give rise to a human-AI hybrid form of science that shall bring systems biology and other sciences into the next stage. The Nobel Turing Challenge aims to develop a highly autonomous AI system that can perform top-level science, indistinguishable from the quality of that performed by the best human scientists, where some of the discoveries may be worthy of Nobel Prize level recognition and beyond.

Artificial intelligence and illusions of understanding in scientific research

AI-driven research in pure mathematics and theoretical physics

Accelerating science with human-aware artificial intelligence

Nobel turing challenge as an ultimate grand challenge.

Understanding, reformulating, and accelerating the process of scientific discovery is critical in solving problems we are facing and exploring the future. Building the machine to make it happen could be one of the most important contribution to society, and it will transform many areas of science and technology including systems biology. Since scientific research has been one of the most important activities that drove our civilization forward, the implications of such development will be profound.

Attempts to understand the process of scientific discoveries have a long tradition in the philosophy of science as well as artificial intelligence. Karl Popper introduced a concept of falsifiability as a criterion and process of solid scientific process but the process of hypothesis and concept generation do not have particular logic behind it 1 . Thomas Kuhn proposed a concept of a Paradigm shift where two competing paradigms are incommensurable and a set of knowledge, rather than a single knowledge, has to be switched with the transition of paradigm 2 . Imre Lakatos reconciles them by proposing actual science makes progress based on a “research program” composed of a hardcore that is immune to revision and flexible peripheral theories 3 . Contrary to these positions, Paul Feyerabend argued that there are no methodological rules in the scientific process 4 . Although these arguments are important thoughts in the philosophy of science, ideas are philosophical and not concrete to be implemented computationally. In addition, these studies are focused on how science is carried out as a part of human social activities. A rare example of implementing such concepts can be seen in the model inference system implemented by Ehud Shapiro that reflects Popper’s falsifiability 5 .

Not surprisingly, scientific discovery has been a major topic in artificial intelligence research that dates back to DENDRAL 6 and META-DENDRAL, followed by MYCIN, BEACON 7 , AM, and EURISKO 6 , 8 . It continues to be one of the main topics of AI 9 , 10 . Recently, an automated experimental system that closed-the-loop of hypothesis generation, experimental planning, and execution has developed for budding yeast genetics that clearly marks the next step towards an AI Scientist 11 , 12 , 13 . While these pioneering works have focused on a single data set or a specific task using limited resources, it signifies the state-of-the-art of technology today that can be the basis of more ambitious challenges.

The obvious next step is to develop a system that makes scientific discoveries that shall truly impact the way we do science and aim for major discoveries. Therefore, I propose the launch of a grand challenge to develop AI systems that can make significant scientific discoveries that can outperform the best human scientist, with the ultimate purpose of creating the alternative form of scientific discovery 14 . Such a system, or systems, may be called “AI Scientist” that is most likely a constellation of software and hardware modules dynamically interacting to accomplish tasks. Since the critical feature that distinguishes it from conventional laboratory automation is its capability to generate hypotheses, learn from data and interactions with humans and other parts of the system, reasoning, and a high level of autonomous decision-making, the term “AI Scientist” best represents the characteristics of the system to be developed. The best way to accelerate the grand challenge of this nature is to define a clear mission statement with an audacious yet provocative goal such as winning the Nobel Prize 14 . Therefore, I propose the Nobel Turing Challenge as a grand challenge for artificial intelligence that aims at “developing AI Scientists capable of autonomously carrying out research to make major scientific discoveries and win a Nobel Prize by 2050”. While the previous article 14 focused on rationales for such a challenge with emphasis on human cognitive limitations and needs for exhaustive search of hypothesis space, this article formulates the vision as the Nobel Turing Challenge and implications of massive and unbiased search of hypothesis space and verification, architectural issues, and interaction with human scientists are discussed as a transformative paradigm in science. The distinct characteristic of this challenge is to field the system into an open-ended domain to explore significant discoveries rather than rediscovering what we already know or trying to mimic speculated human thought processes. The vision is to reformulate scientific discovery itself and to create an alternative form of scientific discovery.

The accomplishment of this challenge requires two goals to be achieved that are: (1) to develop an AI Scientist that performs scientific research highly autonomously enabling scientific discoveries at scale, and (2) to develop an AI Scientist capable of making strategic choices on the topic of research, that can communicate in the form of publications and other means to explain the value, methods, reasoning behind the discovery, and their applications and social implications. When both goals are met, the machine will be (almost) comparable to the top-level human scientist as well as being scientific collaborators. The question and challenge is whether the machine will be indistinguishable from the top-level human scientists and most likely pass the Feigenbaum Test which is a variation of the Turing Test 15 , or whether it will exhibit patterns of scientific discovery that are different from human scientists.

It should be noted “winning the Nobel Prize” is used as a symbolic target illustrating the level of discoveries the challenge is aiming at. The value lies in the development of machines that can make discoveries continuously and autonomously, rather than winning any award including the Nobel Prize. It is used as a symbolism that triggers inspiration and controversy.

At the same time, the implication of the statement that explicitly aims to win the Nobel Prize poses a series of interesting questions whether such AI systems making a decisive discovery may also evolve to be indistinguishable from the top human scientist (passing the Feigenbaum Test 16 ). As witness in case of Satoshi Nakamoto’s blockchain and bitcoin, there is a case where a decisive contribution was simply published as a blogpost 17 and taken seriously, yet no one ever met him and his identity (at the time of writing) is a complete mystery. Given the possibility of creating a highly sophisticated virtual agent to interact with a human, with natural language capability to generate professional article, it will be non-trivial to distinguish whether such a scientist is human or AI. If a developer of an AI Scientist determined to create a virtual persona of a scientist with an ORCID iD, for demonstration of technological achievement, product promotion, or for another motivation, it would be almost impossible to distinguish between the AI and human scientist. The challenge shall be considered practically achieved when the Nobel Prize committee is alerted for any confusion on potential recipients. We might expect an AI Scientist detection system to be developed to identify who is an AI Scientist or not, that may resemble Deckard’s interrogation of Rachael in Blade Runner.

It shall be made clear that this goal does not state or imply that “all major discoveries will be made by AI Scientists”, nor that completeness of hypotheses or discoveries made by AI Scientist will be achieved. The challenge is fundamentally different from any attempts to prove the completeness of the system, including the Hilbert Program intended to prove axiomatic completeness of mathematic where feasibility is debunked by Incompleteness theorems by Kurt Gödel. It simply implies: “among discoveries made by AI Scientists, there should be discoveries that are considered very significant at the level worthy of the Nobel Prize or beyond”. The challenge is initiated based on the belief any significant acceleration of scientific discovery would benefits our civilization. This will be achieved by creating an alternative form of scientific discovery, and will change the form of science as we know as well as uncovering the essence of scientific discovery. The utility of such technology shall benefit broader areas of science, industry, and society.

The core of the research program shall be about “Science of Science” rather than “Science of the process of science by human scientists”. As in the case of past AI grand challenges, the best and perhaps the only way to demonstrate that scientific discovery can be reformulated computationally is to develop an AI system that outperforms the best human scientists. Furthermore, it is not sufficient to have AI Scientist make one discovery, it shall generate a continuous flow of discoveries at scale. The fundamental purpose behind this challenge is to uncover and reformulate the process of scientific discovery and develop a scalable system to perform it that may result in an alternative form of scientific discovery that we have not seen before.

Case studies: scientific discovery as a problem-solving

Herbert Simon argued that science is problem-solving in his article “The Scientist as Problem Solver” 18 . Scientists set themselves tasks of solving significant scientific problems. If this postulation holds, defining the problem and strategy and tactics to solve these problems is the essence of scientific discoveries.

An example of the discovery of cellular reprogramming leading to iPS cell and regenerative medicine by Shinya Yamanaka is consistent with this framework 19 , 20 . It has a well-defined goal with obvious scientific and medical implications, and search and optimization has been performed beautifully to discover cellular reprogramming capability using four transcription factors, now known to be Yamanaka factors 21 .

Another example is the discovery of conducting polymer by Hideki Shirakawa, Alan MacDiarmid, and Alan Heeger. It started with an accident that an intern researcher at Shirakawa’s Lab mistakenly used an abnormally high concentration of chemicals that formed thin film. Shikawaka noticed this accidental discovery and optimized the condition of thin-film formation. Then, with MacDiarmiad and Heeger, they identified a condition for conducting polymer formation. The initial experiments with an accidentally high dose of the chemical can be view as a stochastic search process where search space was extended beyond normal scope followed by extensive search and optimization for stable thin-film formation 22 .

The very simplified processes of these discoveries are shown in Fig. 1 . These examples, among many other cases, exemplify the process of scientific discovery as problems solving and typical tactics are search and optimization.

Search and optimization plays a critical role in the process of discovery. Yamanaka’s case is interesting because a search was conducted in bioinformatics followed by experiment-driven optimization that may be well suited for AI Scientist in the future.

Deep and unbiased exploration of hypothesis space as an alternative form of science

Discovery with an exhaustive search of hypothesis space is what characterizes AI Scientist. In the traditional approach, scientists wish to maximize the probability that the discovery they make will be significant under certain criteria. In other words, scientists are focusing on making significant discoveries and are not interested in the number of discoveries made. This is the value-driven approach. In the alternative approach, the system will learn to maximize the probability that discovery at any level of significance can be made without imposing any value-driven criteria. This is an exploration-driven approach that is an unbiased exploration of hypotheses and makes sharp contrast against the current practice of science. This approach subsumes the problem-solving approach because specific problems will be included in hypotheses generated by an unbiased search of hypothesis space and their verification. This means AI Scientist will generate-and-verify as many hypotheses as possible, expecting some of them may lead to major discoveries by themselves or be a basis of major discoveries. A capability to generate hypotheses exhaustively and efficiently verify them is the core of the system and it can be the engine that gives horsepower to the system.

This transition of a value-based approach to exploration-based approach driven by the unbiased search of hypotheses space may resemble a transition from the intuition-driven design of experiments to unbiased exhaustive measurements represented by omics-approach made possible with microarray and high-throughput genome sequencers, combined with bioinformatics supported by powerful computing resources. Unbiased hypotheses generation and verification will be built on the unbiased measurement of the well-established omics-approach, hypothesis generation system, a series of machine learning and reasoning systems, and robotics-based experimental systems. This is a logical evolution of the modality of science where a vast hypothesis space is searched in an unbiased manner rather than depending on human intuition.

A potential argument against this approach is that such a brute force approach is too inefficient and may not lead to any significant discoveries. Furthermore, one may argue that asking the right question is most important in science rather than brute force exploration. It is interesting to note that in the early days of AI research, it was widely accepted that a brute force approach would not work for complex problems such as chess, and that heuristic programming was essential for very large and complex problems 23 . The actual history of AI clearly demonstrated massive computing and machine learning is the key to success as seen in DeepBlue 24 and AlphaGo 25 .

Does that lessen apply to scientific discovery? There are three notable differences that are: (1) hypothesis space in scientific discovery is vast, open-ended, and possibly infinite as opposed to huge but finite space as in most games, (2) description in science, either knowledge or data, is not well-defined and often inaccurate whereas the well-defined description of game states and records exist in most games, and (3) evaluating hypothesis is likely to be more costly and time-consuming in science due to involvement of experiments. However, these issues can be made manageable and series of technologies to make them manageable will transform science and bring it to the next stage.

The exploration of hypothesis space in scientific discovery

The hypothesis space for scientific discovery is huge and complex as opposed to very big but finite, well-defined, and monolithic state-space in games. State-space for games such as Chess, Shogi, and Go are finite, quantized, completely observable, and monolithic. For example, the game of Go is known to have a state-space complexity in order of 10 170 and a game tree complexity in order of 10 360 . Every state of the game can be fully and unambiguously describable with a set of coordinates. There is no hierarchical structure in the state space. This is not the case in scientific discovery. The size of an entire hypothesis space is infinite or undefinable. States of objects involve substantial continuous values of higher-order dimensions. Nested hierarchical structures are prevalent. While it appears to be fundamentally different, much can be learned and adapted from experiences in building AI systems for gaming.

The most recent and significant success of building AI system for a board game is AlphaGo series that beat the best human players. AlphaGo combined deep learning, reinforcement learning, and Monte-Carlo Tree Search (MCTS) to explore possible state space and game tree to learn best possible play within an explored state space 25 . State spaces are explored based on predictions of possible next moves generated by networks trained through supervised learning of past records of Game of Go (SL policy network) and MCTS expanded a search space. Reinforcement learning using self-play improves policy network and a value network is trained to properly evaluate game status. This approach enabled AlphaGo to learn how humans played, and how humans may play in the possible game state that has proximity to the past game (Fig. 2a , orange circle). AlphaGo Zero starts from a random move and learns to play purely using reinforcement learning without human knowledge 26 . Interestingly, AlphaGo Zero not only outperforms the best human players, it outperforms AlphaGo as well. This demonstrates the strength of unbiased exploration of state space as AlphaGo Zero explores an entire state space of Go where AlphaGo incrementally searches the vicinity of human play styles (Fig. 2a , green space). AlphaZero 27 and MuZero 28 further extend such approaches to be able to learn and exhibit superhuman capability in multiple different games by learning game dynamics with model-free and mode-based reinforcement learning, respectively.

Search space structures for a perfect information games as represented by the Game of GO and b scientific discovery are illustrated with commonalities and differences. While the search space for the Game of GO is well-defined, the search space for scientific discovery is open-ended. A practical initial strategy is to augment search space based on current scientific knowledge with human-centric AI-Human Hybrid system. An extreme option is to set search space broadly into distant hypothesis spaces where AI Scientist may discover knowledge that was unlikely to be discovered by the human scientist.

A part of such an approach can be applied to scientific discovery. With AlphaGo levels of approach, a set of hypotheses can be generated using a body of knowledge accumulated to date, and it can be tested against a body of knowledge for their consistency and verified experimentally (Fig. 2b , orange circle). Enhancing the level of complexity of hypothesis and automation of experimental verification, exploration can be extended to hypothesis space where it was not practical with an incremental extension of current scientific practice (Fig. 2b , blue circle). The challenge would be to implement AlphaGo Zero strategy to randomly generate hypotheses for an entire hypothesis space because the hypothesis space can be infinite and undefinable (Fig. 2b , green zone). However, practical approaches may exist to solve this issue by leveraging the intrinsic structure of problem domains.

In biomedical sciences, any biological phenomena are the result of molecular interactions. It can be a simple interaction or involve a very complex network composed of very large numbers of molecules. Interactions among cells or even individuals can be attributed to molecular interactions. Information of any kind will be received by receptors to be meaningful for a biological system, therefore converted into molecular interactions. The exploration-driven approach in biomedical science leverages such intrinsic characteristics of application domains and may start from generating and testing hypotheses for basic biological mechanisms such as molecular interactions, genetic functions, metabolic reactions, material properties, and so forth and explore them at an unprecedented scale. Since most discoveries in biomedical science are on mechanisms behind diseases or specific biological phenomena exhaustive and unbiased exploration of molecular mechanisms shall be a building block for uncovering complex mechanisms for more complex biological phenomena (Fig. 3 ).

Most discoveries in biology and medicine are concerned with the identification of mechanisms behind important biological processes. It can be fundamental processes such as cell cycle and cellular reprogramming or clinically relevant processes such as mechanisms of disease outbreak and progression. In many cases, this basic structure will be nested into multiple levels. It should be noted that “Molecular mechanisms” are biological processes by themselves, thus multi-layer construction of this basic structure of discovery are inevitable.

Therefore, it is reasonable to assume that the first stage of the project focuses on the hypothesis of a particular form, rather than unlimited and complex forms, specifically to identify molecular mechanisms behind biological processes. By focusing on the specific type of canonical form of knowledge, the problem is now relatively well-defined which is important as an opening game of the challenge. While the omics-approach uncovered massive data on genomes, transcriptomes, metabolomes, and interactomes, detailed and exhaustive characterization and precision measurements using low-throughput methods are required to verify specific molecular characteristics and nature interactions. Such processes are generally time-consuming and often not automated thus experiments are performed only for high priority targets. Automating such processes to match omics-scale enables exhaustive search and verification of a broader range of hypotheses, which shall lead to discoveries with high-impact biomedical and biotechnology applications.

There are pioneering works to turn this idea into reality. Adam, the first closed-loop system for scientific discovery, is designed to execute the discovery of orphan enzymes in budding yeast 11 , 13 . Eve was designed to perform an automated drug repositioning screen for neglected diseases and identified TP-470, originally developed as an angiogenesis inhibiting anti-cancer drug for its irreversible binding to methionine aminopeptidase-2, to be as effective as an anti-Malaria drug as a DHFR inhibitor 29 . These systems automated low-throughput assay processes and enabled exhaustive verification based on the hypothesis generated. These systems are highly automated, but not autonomous, as the problem to be solved and the process are fully designed by humans to the detail. These are special-purpose machines optimized for specific types of problems.

This process can be applied iteratively (Fig. 4a ). A biological process in questions (h 1 ) may be explained by hypothesis h 2 or a combination of h 3 and h 4 , where h 2 , h 3 , and h 4 may have possible underlying molecular mechanisms of h 5 and h 6 , h 6 and h 7 , and h 8 , respectively. In such a case, a process of hypothesis generation and verification will be performed iteratively to verify or reject h 1 with a verified supporting mechanism either h 2 , h 3 , and h 4 . Generating experimental protocols and executing them is rather straightforward.

a Each hypothesis is dependent upon other hypotheses that are related to molecular mechanisms. b A hypothesis in question can be verified only at the system-level analysis of molecular interaction network behaviors, c a set of hypotheses and their dependency tree where each element is also a set (e.g., \(H_1 = \left\{ {h_1^0,h_1^1,h_1^2, \cdots ,h_1^n} \right\}\) ). In massive and exhaustive search of hypothesis space, a set of hypotheses, rather than a single hypothesis, is generated to cover specific hypothesis space and verified.

There are cases where a biological process can be only understood from a system dynamics perspective such as bifurcation and phase transition. A simple application of the iterative procedure to identify molecular mechanism is not sufficient. It requires reconstruction of molecular interaction network and analysis of their dynamical behaviors possibly underlying the process in question (Fig. 4b ). This is more challenging as it requires the generation of hypothesis that link biological process with mathematical concepts and verifying them through experimental verification of network behaviors and molecular mechanisms composing the network.

Exhaustive exploration of hypothesis means a set of hypotheses is generated and verified rather than a single hypothesis. Thus, nodes of a hypothesis dependency tree in Fig. 4 shall be sets, rather than an element (Fig. 4c ). Experiments shall be executed to verify an entire set of hypotheses, and protocols enabling such a process shall be generated. This may also include the generation of sub-hypothesis to be verified (Fig. 5 ).

A hierarchical generation of hypothesis sets and data to verify them will be automatically generated and executed. Verification of Hypothesis set C requires both Hypothesis sets A and B to be verified. Verification data for Hypothesis sets A and B shall be obtained from experiments in general. In general, multiple data sets are required to fill various parameters of elements in Hypothesis set before finally tested in the verification process. This requires Data Set 1 for Hypothesis set A, and Data Sets 2 and 3 for Hypothesis set B need to be collected. Data sets 1, 2, and 3 can be obtained from databases, or through automated experiments. Verified Hypothesis sets A and B mean a set of elements of Hypothesis sets A and B that are verified to be true or entire sets with a score for each element. Given the hypothesis set to be verified, this process automatically generates hypothesis sets that need to be verified first and specifies the data sets required.

The value of exhaustive generation of hypothesis and verification is in its potential capability to overcome the horizon problem. Assume that hypotheses are generated to maximize the expected significance of the discovery and tuned to focus highly expected value, such a strategy would work when the landscape is monotonically increasing (Fig. 6a ). However, it may avert exploring paths to significant discovery, when a series of discoveries precondition to the significant one was low in expected significance (Fig. 6b ). Searches may be terminated before reaching a significant discovery that may be located over-the-horizon. The landscape of discovery significance may be complex and non-monotonic. By enabling exhaustive exploration of hypothesis space, AI Scientist can go beyond the area that is over-the-horizon without it. At the same time, AI Scientist is not free from resource limitation. One of the most important areas of research would be to find out how to sample hypothesis space to effectively identify its landscape. Machine learning-guided experimental design was shown to be effective in chemistry 30 and some of the principles can be applied here.

a The landscape is monotonic, and b the landscape is non-monotonic. A simplified illustration on why there are cases that research outcomes not immediately recognized to be significant lead to a major discovery. “Estimated significance of discoveries” is used only as a conceptual index. There is no rigid method to estimate the significance of the discovery. The numbers of citations and their temporal changes can be an interesting index, but it may be biased toward short-term popularity unless the time horizon is set appropriately.

Knowledge of the world that science deals with is composed of multiple layers of abstraction, generally corresponding to the layers of systems in the domain. Discussions so far are centered around exploring and verifying hypothesis at molecular mechanisms, although it can be complex and nested. The next step shall be to uncover more complex phenomena and their dynamics that are interlinked with multiple layers of interaction, cells, organs, and individuals. This level further requires the identification of design principles and concepts behind complex systems (Fig. 7a ). As discussed already, system dynamics play a central role in discoveries of this level, hence mathematical concept shall be linked to biological processes (Fig. 4b ). At the same time, actual biological systems are constrained by fundamental principles such as biochemistry and physics, systems principles such as feedback theory and information theory, selected through evolution and manifested in the context of the environment it lives (Fig. 7b ). AI Scientist need to learn what are possible and impossible and what possible biology exist at present, and this could be potentially similar to learning models of game dynamics 28 , but in open-ended and a highly complex environment.

a In scientific discovery, knowledge is layered from tangible knowledge to conceptual knowledge. Properties of molecules and their interactions are tangible and knowledge of systems dynamics and design principles are conceptual as they are not directly associated with tangible objects such as molecules and cells. Conceptual knowledge is often backed by mathematical and system-oriented theories. b Biology that we observe (“existing biology”) is constrained by multiple factors such as fundamental principles, systems principles, environmental constraints, and evolutionary selection. “Possible biology” meets all constraints but has not been observed or realized yet.

The challenge is to find a way for the system to generate and test conceptual level hypotheses and test them. This shall be done in an unbiased manner. Methods such as model-based reinforcement learning and generative adversarial learning can be applied initially to investigate how to develop system that learn laws of nature at scale. Some recent studies demonstrate deep learning networks trained over millions of articles generate extensive molecular interactions 31 and the potential relationship between molecules and disease only using articles a year (or years) before such a relationship was discovered 32 . Deep learning was also used to uncover hierarchical structure and functions of cells 33 , deep generative models for discovering hidden structures 34 , precision phenotyping to predict genetic anomalies 35 , and many more. The outcome of such approaches is a set of hypotheses generated by deep learning and other AI methods from unbiased data, and hypotheses are generated in an unbiased manner. Such predictions can be a basis for the search for in-depth molecular relationships and functions. Furthermore, recent success in the Ramanujan Machine 36 in mathematics and the project Debater 37 in adversarial reasoning augmented possible approach that can be incorporated in the hypothesis generation process. Qualitative physics offers the opportunity to generate, match, and explain physical and mathematical concepts such as bifurcation and phase transition 38 , 39 . Combined with the capability of deep learning neural network to learn, classify, and generate non-linear dynamics 40 , 41 , qualitative physics approach can be a powerful method for hypothesis generation and verification at the level of dynamical system concept. There are studies to use qualitative physics for biological processes 42 , 43 . This illustrates the potential of AI to be able to generate conceptual model exhaustively, assemble basic knowledge to be consistent with the conceptual model, and experimentally verify them. This approach essentially forces us to create a set of possible substructures of systems and search for structural matching with reality. With unbiased exploration at this level, AI Scientist shall be capable of exploring the complex dynamical system and may be able to discover new knowledge that is less likely to be discovered by human scientists.

The multiverse of knowledge in scientific discovery

Generating hypotheses and maintaining a set of consistent body of knowledge in science is a formidable task due to the vast number of hypothesis generated and maintained, complexity and non-monotonicity, and unreliability of knowledge and data published. While publications and data already available today will be the initial foundation of hypothesis generation, the problem is that this initial foundation is not necessarily a solid ground; they contain substantial errors, missing information, and even fabrications. Manually checking statements with misinterpretation and biased interpretation of data individually and exhaustively is not practical given the volume of publications that shall be processed by AI Scientist. Currently, certain types of experimental results are known to be difficult to reproduce 44 , where some aspects of reproducibility issues shall be reduced by automated, transparent, and traceable experimental systems 45 , 46 , 47 . Intrinsic variability of biological systems due to noise and individual variations are treated as intrinsic features 48 , 49 and shall be treated separately from ambiguities and inaccuracy caused by the process of research itself. Aside from the immediate reproducibility problem, some observations may hold true in some contexts but may not be applicable in the other context as an intrinsic nature of the complex system. Scientific knowledge is probabilistic and non-monotonic and a representation system shall be able to reflect this reality 50 . Knowledge shall be contextualized, and a new context can be added incrementally. While this is a nature of scientific research, this poses a serious issue in the computational process as hypothesis will be generated using a body of knowledge that is sure to be revised constantly. It is like making reasoning in the twilight zone where what is correct or not is always ambiguous. In this regard, hypothesis and knowledge cannot be clearly distinguished. It is a matter of degree of confidence. Verification in the context of inductive reasoning means that “a certain hypothesis is still surviving against all falsifiability challenges, thus considered most likely so far”. This implies for all hypotheses, survived or not, the trace of tests and their outcome need to be recorded. Unless errors are obvious, every statement in publications shall be converted into knowledge graph and the knowledge graph shall be constantly updated (Fig. 8 ). Obviously, inconsistencies will emerge which will trigger forks of knowledge graph, each of them consistent internally. Whenever some of the assumptions are altered, the relevant hypothesis shall be automatically re-evaluated. This can be accomplished by maintaining a very large number of multiple consistent set of knowledge and data with explicit breaking point which set to be considered more probable. Truth maintenance system, brief revision system, and non-monotonic reasoning can be applied to maintain consistency with multiple contexts 51 , 52 , 53 . Given the nature of scientific knowledge that is essentially probabilistic, multiple sets of knowledge graph shall be maintained persistently, unless sets are proven to be inconsistent, and likelihood of each knowledge set to be most probable change dynamically.

The original knowledge graph (KG 1 0 ) is split into two incompatible KGs (KG 1 1 and KG 1 2 ) with the addition of a new data “d1” and associated arguments. Further addition of data “d2” resulted in the additional split of KGs. Data d3 and associated argumentation contextualized conflicting interpretations separating two KGs (KG 1 4 and KG 1 5 ) that resulted in the merger of them. Such a merge happens when KG 1 4 and KG 1 5 are not compatible due to conflicting interpretation of data d2, but data d3 and associated argumentation clarified conflict can be resolved that two interpretation of data d2 is context-dependent thus both interpretations hold in a different context. For two competing KGs (KG 1 6 and KG 1 7 ), d4 and associated argumentation eliminated KG 1 7 , and KG 1 6 survived and augmented to be KG 1 10 .

To evaluate the probability and possibly eliminate inconsistent knowledge sets, mechanisms to resolve ambiguities and falsify hypothesis need to be implemented. When a hypothesis is generated and verified, it always associated with data and justification why data support or reject the hypothesis. Theory of argumentation 54 and non-monotonic reasoning shall be the basis of argumentation structure generation and processing 55 , 56 . Hypothesis, or claim, can be rejected or needs further delineation with multiple cases such as (a) data is fabricated, inaccurate, or incomplete, or (b) interpretation of data/assumption is not sufficient to justify the hypothesis, (c) scope of the hypothesis shall be limited, and (d) effective rebuttle exists that denies reasoning connecting data/assumptions and the claim. It is possible that reasoning presented in publications are insufficient to justify hypothesis, and detailed justification may need to be re-generated or argument against the claim to be created to make knowledge set complete. Recent progress in computational debate may be a first step to implement mechanisms to generate such argumentations 37 , 56 , 57 . The argumentation module shall generate argumentation to support or falsify existing hypothesis thereby justification can be strengthened through additional experiments and reasoning. At the same time, argumentation generated need to be understood by the human scientist. Qualitative simulation 38 , 58 , 59 , 60 should be able to generate qualitative explanations consistent with human reasoning 39 . A closed-loop system involving such a process shall be developed that can incrementally improve confidence and consistency of knowledge and data thereby incrementally building up rigidly fortified data, argumentations, and hypotheses.

Challenges in technology platform: automation, precision, and efficiency

Development of high precision, fast, and low operation cost experimental system and data analysis system is mandatory for this challenge. Unbiased search of hypothesis space means an unprecedented number of hypotheses will be generated and tested. The test requires both computational and experimental tests. The volume of experiments required to execute unbiased exploration would be a magnitude larger than current scientific practices. The revolutionary precise, cost-effective, and fast experimental systems need to be developed and deployed. Since the cycle of hypothesis generation and verification is the rate-limiting factor of the entire process, how fast and accurately perform experiments will determine the chance of success of the challenge. Some experiments will involve hypothesis exploring unusual conditions such as 1000 times off from the conventional parameters such as the concentration of chemicals. The first step would be to make laboratories fully connected and automated. Then, equipment will be replaced over time for high precision and efficient devices including microfluidics, followed by the use AI modules for each process before reaching the high level of autonomy expected in the AI Scientist.

Therefore, experimental systems shall be less resource-demanding and accurate yet reliable, reproducible, and integrated. While automation of various experimental processes has been commercialized already these are fragments of an entire process. The challenge requires an entire process of various types of experiments to be automated and part of such system may be installed as robotics cloud laboratory 46 . Recently, a robotics experimental system successfully identified proper condition for cell culture of medical-grade iPS-derived retinal pigment epithelial (RPE) cells after searching 200 million possible parameter combinations through Bayesian optimization with local penalization 47 . Optimization of lycopene biosynthesis pathway and biofuels for synthetic biology-based bio-manufacturing are other examples automated search of design space was shown to be effective 61 , 62 . Such success demonstrates the introduction of robotics-AI systems for each process shall improve the quality and efficiency of experiments. Automated closed-loop system impacts synthetic biology as well due to its quality and reproducibility 63 . Currently, only a system closed-the-loop of hypothesis generation and experimental verification is on budding yeast genetics 13 . Variety of experiments and their complexity shall be significantly augmented to cope with an extensive set of hypotheses to be verified. A literature analysis of over 1628 papers indicates 86–89% of experimental protocols in these papers can be automated by readily available commercial robotics systems 64 . This implies the progress can be quick initially, and what matters will be how to integrate different processes, data management, and how to automate processes that are not automated at this moment as well as novel protocols in future.

To achieve this, precise process management shall be imposed for the flows of control, materials, data, and physical agents. Due to vast numbers of experiments required for verification, experimental systems shall be compact and requirements for experimental samples and reagents shall be minimized. Organs-on-Chips is a recent addition to technologies that can reproduce experimental context closer to in vivo condition while maintaining controllability, traceability, and requires smaller amounts of experimental materials 65 , 66 . A novel origami-inspired surgical robot has interesting characteristics of being compact and high precision that can be applied for a range of experiments 67 . In future, the combination of microfluidics and robotics system will be used extensively in biological experiments to meet the demands of large numbers of experiments and requirements for controllability, accuracy, and traceability 68 .

Experimental devices shall be controlled by a platform that combines software tools, data access, and experimental systems embedded in the closed loop. Machine learning-guided experimental design was shown to be effective in chemistry 30 and some of the principles can be applied to broader domains. Some of the technological platforms are readily available today, as seen in Garuda Connectivity and Automation Platform 69 , Wings workflow management tool 70 , 71 , and DISK Data Analysis and Hypothesis Evolution framework 72 , but many have to be developed as a part of the technology challenge. Extensive efforts are made to develop bioinformatics and systems biology analysis and modeling software and data standard that are fundamental to obtain data, analyze them properly, make accurate curation, and enabling dynamical simulations. Annual workshops such as COMBINE and HARMONY drive the development and adaptation of standards ( http://co.mbine.org/home ). Interoperability of software and data is mandatory to ensure connectivity of laboratory that is essential to automation of not only experimental processes but also analysis and modeling processes. More effort shall be made on the representation of hypothesis and knowledge reflecting the reality of scientific knowledge.

Evolving relationship between AI Scientists, human scientists, and society

How does AI Scientist evolve and transform scientific activities? It is clear superhuman-AI Scientist would not emerge out of blue. It will co-evolve with the scientific community over time. A possible, and logical, evolutional path of AI Scientist is to increase the level of automation first, followed by the increase of autonomy level (Fig. 9 ). Most current use of AI for research is a tool for specific tasks such as image classification, text-mining, and other tasks that are isolated and fully instructed by the human scientist. This is an AI tool stage. An early stage of AI scientist will take a form of a group of useful and highly competent software, including hypothesis generation module, and robotics to execute complex but pre-define tasks as instructed. Robot Scientist Adam and Eve are pioneering examples of this stage. Increasing repertoire of experiments and complexity of hypothesis are the next step. Substantial investment and user feedback are essential to make such systems useful and widely adapted.

AI Scientist requires a highly automated and connected laboratory to be able to design and execute experiments, as well as extensive access to databases and publication archives to process, extract, and evaluate current knowledge. Sophisticated laboratory automation is mandatory. Robot Scientist, Adam & Eve, is highly specialized automation with a certain level of intelligence for hypothesis generation and experimental protocol generation. The next step is to fully automate and connect laboratory equipment with layers of control for data flow, material flow, and physical control flow. Numbers of AI assistants shall be installed for each task initially, but need to be integrated as an integrated and highly autonomous system. The transition of automated system to autonomous system will be one of the most challenging part of the initiative.

Evolutionary pressure imposed on AI Scientist is whether it will be used by human scientists and widely adopted. Investment to develop AI Scientist, either by public funding or private sources, will be driven by the utility of such systems for human scientists. Therefore, AI Scientist will be inevitably interlocked with the research ecosystem of human scientists, and highly competent and user-friendly systems will survive for further development. This path inevitably make AI Scientist designed to be highly interactive with human scientists. Researchers will quickly understand the value and the power of AI Scientist, and will soon start asking questions that require the exhaustive generation of hypotheses and verification that exploits the full potential of AI Scientist at each stage of evolution. This will trigger the transformative change in biology as we witness in genomics when the unbiased measurement of genome sequence and transcriptome uncovered new realities in biology such as non-coding RNA 73 , 74 . Even without large-scale experiments, hypothesis generation capability of AI Scientist shall help researchers to explore hypotheses that may not be considered without such AI Scientist as well as being an extremely effective dialog-based creativity and discovery support system. Institutions without AI Scientist will no longer be competitive in science and technology.

With an increasing level of autonomy, AI Scientists are expected to make an autonomous decision on what to investigate next. While mechanisms to make it possible is yet to be seen, multiple strategies can be considered such as (1) goal-oriented approach of defining very high-level goals and find multiples paths to best achieve such goals or (2) bottom-up approach of exploring hypothesis search space based on discoveries already made by a specific AI Scientist. In either case, questions to be asked can be automatically extracted from publication, defined by human researchers, or randomly generating questions to be answered.

With an increased level of connectivity and flexibility to generate hypotheses and their verification process, instruction from human scientist will be more abstract, and AI Scientist will have an increased autonomous process to make decision of priorities of hypotheses to be tested and experimental protocols to be performed. This is a semi-autonomous stage because instruction on what to investigate is provided from outside although how to investigate them may be generated internally by AI Scientist. The level of abstraction of instruction by Human scientists need to be carefully chosen so that AI Scientist can execute the task with success. Instruction such as “find a set of protocols (transcription factors, chemicals, procedures) that can transform types of somatic cell (X) into defined cell types (Y)” is a difficult but tangible one. For such an instruction, multiple experimental protocol shall be generated, and prioritize choice of source and target cell types, interventions to use tested, and analysis procedures. However, much higher goals, such as “cure cancer”, “increase human life span to 150 years”, or “minimize climate change” would be problematic as some of these goals are too abstract, at least at the initial phase of AI Scientist. With the evolution of AI Scientist over years, some these questions may be addressed in future, still it requires user understanding on capability of AI Scientist to utilize its power.

With potentially expensive running cost of AI Scientists, especially when large-scale experiments are required, a certain level of monitoring will be enforced in most institutions. AI Scientist may include a function to generate questions more relevant to its owner or to the society. At least, it is highly plausible larger investment will be made to deliver high return on investment outcome. In this case, the choice of problem and evaluation of the significance of discoveries will reflect human-centric value system, most specifically the value of the stakeholders.

However, AI Scientists under this circumstance are less likely to make unexpected discoveries since the problems to be solved are pre-defined. Researchers with a priori expectations may sometime miss the big picture when one without such expectation may notice 75 . There are many cases discoveries initially received minor attention led to major discoveries later. It is extremely difficult, if not impossible, to evaluate the significance of the discovery when a few more discoveries may be needed to translate the discovery into high-impact outcome due to the over-the-horizon problem. The real value of AI Scientist is its capability to explore hypothesis space magnitude more efficiently into seemingly low-value domains with expectation that may eventually leads to major outcomes. Such systematic explorations into seemingly low-value hypothesis space are infeasible to be performed by human scientists. Both aspects of discovery are important that implies two roles for AI Scientist can be assumed that are “AI Scientist as a Problem Solver” aligned with the value of the stakeholders and “AI Scientist as an Explorer” that boldly explore hypothesis space nobody have gone before. However, in either cases, exhaustive hypothesis generation and verification will be the core of the AI Scientist that distinguishes it from the traditional approach.

AI Scientist will be a multiplexed multi-agent system generating multiple instances of AI Scientist (Fig. 10 ). It is comprised of many software and hardware agents (highly functional modules with a certain level of autonomy) with a high level of interactions, interoperability, and scalability in problem size and complexity. There may be two characteristics for an architecture of AI Scientist.

They evolve, merge, and interact with humans. Human experts can be a part of the system as human-in-the-loop system. Scientists who wish to work with AI Scientist are most likely to work with instances of AI Scientist.

First, it may be a multiplexing multi-agent system. It is possible multiple instances of AI Scientist are created each specialized in a certain area extensively exploring hypothesis space organically. They are almost identical in components but differ in hypothesis space exploring. Communication among AI Scientists may enable them to merge discoveries for further exploration. This may take a form of communication between AI Scientists through a series of inquiries or the creation of a new synthetic new AI Scientist. Therefore, AI Scientist as a whole entails multiple instances of AI Scientist with focused areas. In this case, discoveries may be made systematically centered around initial core domains and eventually as a combination of multiple domains forming specific path-dependencies in discoveries. In the community of AI Scientist, a series of discoveries and publications made by AI Scientist may resemble that of the successful scientist. Interaction between AI Scientists is equivalent to the search and exchange of new knowledge and style of discovery specialized by each AI Scientist. When the critical mass of knowledge and data is required to generate significant hypothesis combining multiple domains, forming the community of AI Scientist would make sense. The discovery of CRISPR–Cas9 may be one of the examples of revolutionary discoveries coming from the combination of basic research seemingly distant areas of research 76 . It is well recognized that many discoveries considered groundbreaking was triggered by connecting two or more seemingly unrelated ideas. If AI Scientist shall be able to make discoveries of this nature, it must be able to access and connect very broad and less related domains where there is already a sufficient accumulation of knowledge and data by each AI Scientist.

Second, it may be a human-in-the-loop system. From AI Scientist perspective, agents composing them do not have to be exclusively software or hardware, it can be human expert as far as it can interface with the rest of the system. Human experts can be in the role of domain experts or in the commanding and monitoring role. The commanding and monitoring role is important to avoid misuse of the system. Potentially, AI Scientist can make discoveries that are harmful to human and our planet. What to discover fully depends on how the owner uses such capability. A strict ethical guideline and enforcement may be required with increased level of autonomy of AI Scientist. Ultimately, it will impact national security of the highest level.

There will be multiple AI Scientists either by institutions, academic community, country, or other societal boundaries. Some of them may communicate each other, some may be configured in isolation, and some would form local networks. Such configuration may be decided based on ownership of data and intellectual properties generated. Some modules and databases will be publicly maintained, and some would be proprietary. Although the level of autonomy can be very high, intellectual property is still retained with human researchers who run this system because human researchers make the decision to run AI Scientist and monitor their progress, and are responsible for the outcome. Some institutions may run AI Scientist at free-run modes with very high levels of autonomy, to let it explore hypothesis space that human researchers may not think of. Even in such a case, completely autonomous may not be achievable, as the intention of the owner will influence the running of the system.

AI Scientist to transform systems biology and broader area of sciences

Automating the process of hypothesis generation and verification shall transform broad areas of sciences. Systems biology is one of the representative areas most affected by such technology, not only because it enables researchers to cope with massive data and publications otherwise under-utilized, but also it enables researchers to develop large-scale high-precision models as well as performing investigation significantly broader in scope and more extensively in parameter space than current approaches.

One of the initial expectations of systems biology was to develop a high-precision large-scale model of biological systems such as virtual humans that can be used as digital-twin of patients with in-depth molecular mechanisms behind 77 , 78 . While it is a holy grail of systems biology, it was proven to be extremely challenging as anticipated. There are fundamental difficulties for such a task partly due to the limitation of our cognitive capabilities and sociological constraints 14 . The research landscape of systems biology is clustered around two modalities that are; (1) a high-precision mechanistic model for the smaller and tractable system and (2) a large-scale network model based on omics data but less on detailed mechanisms. There is an inherent trade-off between these two modalities and attempts to overcome such trade-off have fallen short of expectations. First, there are human cognitive constraints. A vast amount of data and complexity of the system often goes beyond human comprehension and non-linear nature of biological process make things more difficult. Second, there are practical constraints as well. Building a large-scale precision model requires details of almost every interaction and molecular behavior to be investigated both computationally and experimentally which is beyond the capability of most research groups. Investigating each of such interactions and molecular behavior would require major efforts while many of them may not result in immediate major discoveries by itself. While interesting discoveries shall spring out from some of such efforts, tasks are designed to fill in every detail of a large model, rather than speculating the potential importance of interactions and molecules. It is not practical to assume dedicated efforts by members of the research group to be sustainable for many years unless most of such process is automated.

Perhaps, systems biology, particularly studies for large-scale precision models, is not a research field for human alone to investigate as possible causes of difficulties lies in human cognitive and sociological limitations. Once we accept the reality such a trade-off is inherent in human cognitive limitations and sociological constraints, the path to overcoming the trade-off is obvious. It is a field suitable for AI or AI-human hybrid system. Building high-precision large-scale models and efficiently exploit such models and aggregated knowledge to back it up requires powerful AI systems to support our scientific activities.

The AI system is not only useful for building large-scale in-depth models but will exhibit its power to discover new mechanisms and principles we have not imagined as well as discovering novel drug targets efficiently with a significantly extended search of target candidates. Extensive use of AI for drug discovery has been discussed with the implication of dramatically improving its efficiency and the transformation of the process 79 . Early successful cases including rapid identification of kinase inhibitor for DDR1 are encouraging 80 . A recent success of AlphaFold represents how AI technologies impact biomedical studies 81 . Studies on the relationship between drug target proteins and numbers of interactions of proteins demonstrate there is a low but reasonable probability that proteins with small numbers of identified interactions to be drug targets 82 . Although chances each protein can be a drug target may be small because the total numbers of such proteins are huge, exhaustive search of this class of proteins may result in abundant novel drug targets. With the same issues that arose in high-precision large-scale models, automation of the research process is essential to explore such opportunities.

Extending such an approach to synthetic biology to automate design and verification processes 63 , 83 , 84 .

Ultimately, a series of new discoveries will be integrated into an integrated model that is large-scale, high-precision, and in-depth. The implication is massive. It does not only mean researchers use AI Scientist as one of the tools, but it implies the practice of scientific discovery will be transformed dramatically with AI Scientist because discoveries will be made at scale and autonomously. At the same time, this will be a golden opportunity for systems biology since it will transform system biology into the next stage.

AI Scientist can be transformative not only in life science but also for broader science and technology domains. This is especially the case that requires hypothesis generation and verification to broader range parameter search of chemical synthesis and material discovery. Already, there are emerging interests in chemistry and material science for automation of experiments coupled with machine learning guide experimental design at various levels 30 , 46 , 85 , 86 , 87 , 88 , 89 , 90 . The idea of massive search of hypothesis space and verification applies to these domains as well. However, if such efforts can be applied to the discovery of novel concepts are yet to be seen. Recently, The Ramanujan Machine was announced for automated generation of conjectures in mathematics 36 . The Ramanujan Machine added a new perspective as it is not a parameter search and extensive generation of conjectures. With the rapid advances in robotics, sensors, AI with the increasing availability of computing powers, AI Scientists for broader domains of science will be inevitable. Research institutions without such capability will no longer be competitive in the coming decade.

The Nobel Turing Challenge is the ultimate challenge for AI and systems biology. Any progress toward achieving the goal will generate high utility technologies that shall accelerate science. Due to its breadth of expertise required and possible length of duration to achieve the goal, it may best be organized as a virtual big science 91 . Once the initiative taking off, it will uncover the essence of scientific discovery, and results in the creation of an alternative form of science. AI Scientist and human scientists will work together to solve formidable problems and to explore new intellectual territories where no one have gone before.

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Acknowledgements

I would like to thank members of the Systems Biology Institute, Okinawa Institute of Science and Technology Graduate School, Sony CSL, and Sony AI for fruitful discussions. Ross King and Yolanda Gil for working together in starting the challenge in reality. Special thanks to Ed Feigenbaum for insightful discussions and encouragements. This work was supported, in part, through an Office of Naval Research Global (ONRG) grant awarded to the Alan Turing Institute.

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Innovation | December 28, 2020

Ten Scientific Discoveries From 2020 That May Lead to New Inventions

From soaring snakes to surfing suckerfish, nature is an endless source of inspiration

One ultra-black fish is posed against a black background. Its head faces the upper left corner of the image. (Mobile image)

Rachael Lallensack

Former Assistant Editor, Science and Innovation

Many new inventions and technologies derive inspiration from nature. The practice of modeling artificial products after biological processes is called biomimicry or biomimetics . Janine Benyus, co-founder of the Biomimicry Institute, popularized the term in her 1997 book, Biomimicry . “Biomimicry,” she wrote, “is basically taking a design challenge and then finding an ecosystem that’s already solved that challenge, and literally trying to emulate what you learn.”

As scientists studying the natural world reveal their findings, inventors and engineers are drawing from these new revelations and applying nature’s solutions to new technology. Whether the problems researchers are looking to solve involve building better robots, tracking cancer cells more efficiently or improving telescopes to study space, a useful solution can be found in living things.

Here are ten findings from 2020 that could one day lead to new inventions.

Suckerfish Surf on the Backs of Other Sea Creatures

Remora feeding and skimming along whale body

Remoras are the ocean’s hitchhikers. Also known as suckerfish, whalesuckers or sharksuckers, the one-to-three-foot long swimmers anchor themselves to blue whales or zebra sharks with a suction cup-like disc that “sits on their head like a flat, sticky hat,” according to the New York Times . But these suckerfish aren’t just mooching a free ride. This year, researchers found that the fish can actually “surf” along their chauffeur’s back while the pair is in transit. The remoras glide along their host’s body, clustering near a whale’s blowhole and dorsal fin where there is minimal drag—all the while nibbling on dead skin and parasites.

Researchers Brooke Flammang, Jeremy Goldbogen and their teams found that the remora’s choice location is key to hanging on . The area between the blowhole and dorsal fin, especially on blue whales, has “much lower-velocity fluid” than if it were “just a few centimeters higher” on the whale’s body, Flammang tells the Times .

The fish’s “sucking disc” isn’t actually sticking up against the whale’s skin either. Instead it hovers just above, creating a low-pressure zone that sucks the fish close to the whale and prevents it from flying off into the abyss—most of the time.

Flammang, a biologist at the New Jersey Institute of Technology, has already gotten to work on an artificial suction disk inspired by the remora that she hopes will be used to attach cameras and tracking devices to endangered marine animals, like blue whales. Currently, researchers use regular suction cups to fasten cameras to their research subjects, but those only keep their grip from 24 to 48 hours. Flammang’s new device will stay on for weeks and reduce drag. She and her team are currently testing the disc on compliant surfaces as well as designing a remora-shaped casing for the camera. Eventually, they’ll field test the device on live animals, including whales, dolphins, sharks and manta rays.

“Bioinspired advances in attachment developed by Dr. Flammang's lab will revolutionize how we are able to get tags on animals with greater success and efficacy,” Goldbogen, a marine biologist at Stanford University, writes to Smithsonian magazine. “Maybe future tags could not only attach but also surf and crawl just like remoras to the ideal spot for specific physiological sampling.”

Fish Fins Are as Sensitive as Fingertips

Fish fins aren’t just for steering and swimming, University of Chicago neuroscientist Adam Hardy and his lab found this year. In fact, the researchers discovered that fins are as sensitive as primate fingertips . To reach this conclusion, the scientists studied round gobies, a type of bottom dwelling fish native to places like the Black Sea and the Caspian Sea, but invasive populations live anywhere from European rivers to the Great Lakes. These little critters are known to “perch” on rocks, brushing their fins along the rock bed of lakes.

To determine how sensitive the gobies’ fins were, the team injected euthanized fish with a saline solution that kept their nerves operating normally during their experiment. They then used a special device to record the patterns of electric impulses the nerves produced when the fish’s fins brushed up against a ridged wheel. This measure showed the team that fins’ were perceiving “really fine detail,” study coauthor Melina Hale, also a neuroscientist at the University of Chicago, told Science News .

The researchers hope this discovery can inspire advancements in robotic sensory technology, especially in underwater bots.

The Diabolical Ironclad Beetle’s Exoskeleton Is Indestructible

A Diabolical Ironclad Beetle faces the bottom right corner of the image as it scurries across gravel. Photographed in Irvine, CA.

The diabolical ironclad beetle absolutely lives up to its name. While most bugs live only a few weeks, these beetles have a lifespan of about eight years, which is roughly the equivalent of a human living several thousand years. To achieve such a feat, they’ve evolved some remarkable armor.

The roughly inch-long insect can survive being run over by a car—and if you can’t believe that, University of California, Irvine engineer David Kisailus and his team piled in a Toyota Camry and ran one over twice, and it lived. After several more technical experiments, the team found the beetle can withstand immense pressure —up to 39,000 times its own body weight.

Several factors contribute to the beetle’s sturdiness. The beetle’s exoskeleton is flat, not rounded, like a ladybug, for instance. Within the exoskeleton are protein-rich layers, which can shift individually without the entire shell breaking. The two halves of the shell are joined together like a puzzle piece. The layers follow the puzzle-like curves, reinforcing the thinnest part of the joint—the neck-like bit where the two halves are interlocked.

In their paper, the researchers suggest a beetle-inspired interlocking fastener could perhaps replace similarly-shaped, but layer-less, joints used to secure airplane turbines. The team created a 3-D printed model complete with “lamination,” or layers. They predict this finding could introduce “immediate benefit over aviation fasteners, providing enhanced strength and substantial increased toughness.” But really, this design could be used anytime two different materials—like metal and plastic—need to be conjoined, such as in bridges, buildings and vehicles, too.

The Ultra-Black Pigmentation of Sixteen Species of Deep-Sea Fish Is Explained

Against a black background, a Pacific blackdragon is coiled like a snake.

When National Museum of Natural History marine biologist Karen Osborn and her team accidentally pulled up a deep ocean fangtooth fish in their net of crabs, they tried to take its picture. But try as they might, details of the jet-black fish couldn’t be captured. The fish was literally unphotogenic, they later learned, because its tissue was absorbing 99.5 percent of the light from a camera’s flash.

The fangtooth, and 15 other species included in the study , sport ultra-black pigmentation that allows them to blend in to the pitch-dark environment of the deep ocean. Though light can’t reach this part of the ocean, some fish are bioluminescent. For sneaky predators, camouflaging into the dark abyss—or better yet absorbing light—is nature’s best invisibility cloak.

Plenty of animals on land and sea have very black coloring, but human-made color reflects around 10 percent of light and most other black fish reflect 2 percent of light. To cross the ultra-black threshold, these 16 species had to only reflect .5 percent of all light shining their way. These species achieved this feat with densely-packed, jumbo-sized, capsule-shaped melanosomes, or cells containing dark pigment. In other black, but not ultra-black, animals, melanosomes are loosely spread out, smaller and rounder in shape.

By imitating the shape, structure and dispersion of the ultra-black fish’s melanosomes, materials scientists may be able to create artificial ultra-black pigment. This pigment could be used to coat the inside of telescopes to get a better view of the night sky or improve light absorption on solar panels. It could even interest Naval researchers, Osborn told Smithsonian in July. “If you were to make, let’s say, armor that had melanin on the outside, you would be great for night ops,” she says.

When Soaring From Tree to Tree, Tropical Snakes Undulate for Stability

Flying Snake 95, Trial 618 by isaacyeaton on Sketchfab

As if ground snakes and swimming snakes aren’t enough, five species of snakes “fly.” To be fair, this flight is really more like highly-coordinated fall. It looks sort of similar to the wriggling and side-winding they do on land, but with the help of gravity. Or as Virginia Tech biomechanics researcher Jake Socha told the New York Times , snake flight resembles a “big, wiggly, ribbon thing.”

The snakes flatten their round torso into a flattened, triangular shape in order to catch more air and glide from one tree to another, sometimes dozens of feet away. But the whole side-to-side, loopy lunges they do in the air didn’t make as much sense to scientists. That is until Socha and his team rented out Virginia Tech’s four-story black box arena called the Cube. In it, they outfitted seven flying snakes in reflective tape and recorded their leaps on high speed cameras more than 150 times. (Don’t worry. The team had to pass snake safety protocol, and the arena was equipped with foam floors and fake trees.)

Snake flight happens really fast, so the reflective tape allowed the team to recreate the flight using 3-D computer modeling. The team found that the snakes undulated vertically twice as often as they did horizontally, moving their tail up and down as well. Virginia Tech mechanical engineer Isaac Yeaton told the Times , “Other animals undulate for propulsion. We show that flying snakes undulate for stability.”

The team hopes their findings can be used to create some kind of flying snake search-and-rescue robot. Yeaton says the advantage of snake-inspired robots is their stable locomotion and ability to sneak through tight spaces that might cause your typical bot to trip or fall. He’s got his sights set on perhaps one day creating a bot that can mimic all of the snake’s twists, flexes, swerves and wiggles into one single robot.

“Combining them together, you could have one platform that could move through complex environments: the robot can ascend a tree or building, quickly glide to another area, and then slither or swim somewhere else,” Yeaton tells Smithsonian magazine via email. “There are engineering challenges to doing this, but I’m inspired by how capable the real flying snakes are and recent advances in bioinspired design.”

Small, Tadpole-Like Sea Creatures Make Slimy Inflatable Filtration Systems

Giant larvaceans are shaped like tadpoles, only slightly larger; their bodies measure up to four inches in length. These tiny creatures live freely hundreds of feet below the sea surface, where food sources are scarce.

This year, researchers used laser scanning tools to unveil the complex “snot palaces” the creatures build, as study author and bioengineer Kakani Katija of Monterey Bay Aquarium Research Institute calls the structures. These tiny armless, legless creatures use their own secretions to construct elaborate clouds of snot complete with chambers, ribbed walls, tunnels, halls and chutes.

Much like spiders and their webs, larvaceans use these mucousy structures to capture tiny, sparse food particles floating by. Their little body sits in the middle of the “house,” while they wag their little tail to pump water through the labyrinth of channels and into their mouths—almost like an elaborate plumbing system of sorts. The cloud doubles as an invisibility cloak by concealing the critter’s motion in the dark depths where any false move is a death sentence.

Katija hopes to pull inspiration from these critters to one day create a biomimetic inflatable filtration system. Given that these animals can filter out particles smaller than viruses, perhaps medical-grade or HEPA filters could be improved with such a device.

“We’re still in the discovery phases of this project, and I’m hoping other researchers will pick up the torch,” Katija tells Smithsonian magazine via email.

An Iron-Packed Protein Is Key to a Tube Worm’s Glowing Blue Goo

Parchment tube worm photographed by day sports a yellowish tint (left) and a bluish glow by night (right)

The flashes of bioluminescent critters, like fireflies, typically last from less than a second to at most 10 seconds. But not the marine parchment tube worm—these ocean swimmers produce a bright blue goo that stays aglow for anywhere from 16 to 72 hours. Because the slime keeps shining outside of the worm’s body, it doesn’t waste the organism’s energy, which is great for the worm’s survival, but begs the question: How does it keep glimmering for so long?

University of California, San Diego researchers Evelien De Meulenaere, Christina Puzzanghera and Dimitri D. Deheyn examined the complicated chemistry of the worm’s mucus and found that it contains an iron-packed protein called ferritin, which emits ions, or electrically charged atoms. This form of ferritin reacts with the blue light, triggering more ion production, which in turn keeps the light glowing in a feedback loop.

The team hopes to replicate the tube worm’s unique photoprotein—or a protein linked to bioluminescence—to illuminate cancer cells during surgery. On a simpler note, Deheyn also says they could develop a synthetic biological battery of sorts that could be used in emergency situations when electricity is out. He compares the idea to glow-in-the-dark stickers.

“Glowing stickers keep glowing because they accumulated sunlight from the day and release it night,” he tells Smithsonian . “Now imagine you don’t need sunlight, you would just need to add iron. These kinds of applications could be used as portable biological lights for emergency use. For example, maybe you need light on a landing pad for helicopters or planes in a power outage.”

Bumblebees May Know How Big They Are

Bumblebees have a reputation for clumsiness , but perhaps that’s a bit of a misjudgment on our behalf. One summer day, engineer Sridhar Ravi of the University of New South Wales in Canberra was watching bees navigate around branches and shrubs with ease. He was shocked that an organism with a rather small brain is capable of overcoming these challenges.

To put the bees to the test, Ravi and his team connected a tunnel to a beehive in their lab. They placed a narrow gap inside the tunnel as an obstacle and made it smaller and smaller over time. When the gap was smaller than the bees’ wingspans, they paused to scan the opening and then turned sideways to get through the gap without damaging their wings. Accomplishing even this small feat requires some awareness of how big one’s body is from different angles, an aptitude that insects aren’t generally thought to possess.

But if small-brained bees can handle it, Ravi says robots may not need big complicated processors to get better at navigating their surroundings. “Complex perceptions do not need sophisticated, large brains and can be achieved at small size scales with much fewer neurons,” he tells Smithsonian . This idea is exciting to consider when thinking about developing less-clumsy robots. Hopefully, the researchers can use their findings to improve robotic flight or swimming abilities.

“The graduation from merely sensing to be able to perceive will be mark an epoch in the field of robotics,” Ravi says.

A Leaf-Cutter Ant’s Body Armor Has an Extra Mineral-Based Protective Coating

A high-resolution image of a leaf cutter ant's mineral coating covering its exoskeleton

When evolutionary biologist Hongjie Li realized the leaf-cutter ants he was studying had a thin layer of mineral body armor, he told his colleague: “ I found rock ants .”

To study the ant’s exoskeleton further, the coating would need to be removed, but how? Li had an epiphany while brushing his teeth, he tells Science News . Mouthwash removes plenty of junk from our teeth without damaging our cheeks, gums and tongue. His hunch did the trick, and mouthwash dissolved the mineral coating without damaging the exoskeleton. Through more traditional lab experiments, the team determined the mineral coating is made of calcite with a high concentration of magnesium. In sea urchins, this mixture of calcite and magnesium is thought to make the small “stone tip” of its tooth capable of grinding through limestone.

“Integration of magnesium in calcite could be especially beneficial for any nanotechnology that involves the use of calcite, such as in plastics, adhesives, construction mortar and dentistry,” explains study authors Cameron Currie and Pupa Gilbert in an email to Smithsonian magazine.

Furthermore, the mineral coating isn’t something the ants are born with, but something they can develop in a snap when they need it, Currie explains.

“It is incredible that our ants are able to massively improve on this projection by rapidly forming a thin and light nanocrystal coating,” he says. “This highlights the potential application of nanomaterial coating like this to improve body armor.”

Some Moths Have an Acoustic Cloak That Dampens Bat Sonar

A colorful computer model image of the moth's forked scales

To be a moth desperately hiding from a predator that uses sound to “see” is no easy feat, but some of these winged insects have evolved impressive features to shield themselves from bats.

In addition to sound-softening fur, two earless moth species have fork-shaped scales on their wings that help absorb bat sonar, researchers found earlier this year . Individual moth's wings are covered in tens of thousands of these tiny scales, each one less than millimeter long and just a few hundred micrometers thick. Each scale warps the sound of the wing, slowing down its acoustic energy and in turn, reflecting less sound back to the bats. The scales seems to resonate at a different frequency and as a whole, they can “absorb at least three octaves of sound,” reports Anthony King for Chemistry World .

“They are highly structured on a nanometre scale with strongly perforated corrugated top and bottom layers that are interconnected by a network of minute pillars,” study author Marc Holderied of the University of Bristol tells Chemistry World .

Holderied estimates moth-inspired soundproofing techniques could make materials “10 times more efficient at absorbing sounds.” Rather than installing bulky panels in homes and offices, he envisions sound-absorbing wallpaper coated with scale-like nanostructures.

Holderied could also see this finding having broader industry-level applications as well. “We are indeed very excited by the broad application prospects of this material,” he tells Smithsonian . “Any field from architectural to machine and transportation acoustics, where sound absorption with reduced footprint is of benefit, would profit from thinner moth-inspired solutions.”

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Rachael Lallensack is the former assistant web editor for science and innovation at Smithsonian .

Lesson Plan

July 13, 2023, 10:47 a.m.

Lesson plan: Solving problems through invention

Screenshot from PBS NewsHour

For a Google version of this lesson plan, click here . (Note: you will need to make a copy of the document to edit it).

In this lesson, students will learn the first key step in the invention process: how to identify and explore problems around them using current events. They’ll get to choose one story from a list of five that interests them and examine who is affected by a problem and what is being done about it.

This lesson is designed for humanities and STEM classrooms to help students understand how a deeper engagement with current events can inspire civic action, including developing their own inventions.

Students will identify problems that are worth solving using stories in the news.
Students will dig deeper into an identified problem before trying to find a solution.

Science, CTE, and humanities classes

Grade Levels

Grades 6-12

Estimated Time

One 50-minute class period

Supplemental Links

Google doc version of lesson
Teacher presentation
Warm-up handout
Main activity handout

Introduction

Thanks to modern technology like smartphones and social media, young people know more about the news than many adults did when they were their age. Recognizing and understanding problems in your community is actually a key step in finding — or inventing — solutions.

Problem identification is the first step of the invention process, the process by which an inventor creates an invention. After that, we can help think up creative inventions to help people, including those in our own communities.

Teacher preparation for the lesson

Adjust teacher presentation as needed to fit your classroom needs
Warm-up activity reference table (¼ sheet per student)
Main activity student worksheet (1 per student)

Essential question : How can we use current events in the news to identify and understand problems that we care about?

Internet connection and device (for each student)
Pen and paper (ability to take notes)
Student warm-up ¼ sheet of the chart can be found here (optional)
Student worksheet for the main activity can be found here (1 per student)

Warm-up activity (10 minutes)

Ask students where they get their news. Take a few minutes to share your responses together as a class. Then ask if any responses surprise them. Would you be surprised to learn that a vast majority (79%) of young people read or watch the news?

In a study by the Associated Press, young people were asked: What are the main reasons you, personally, use news and information? (Printable version here ) Source: AP/NORC University of Chicago

Source: AP/NORC University of Chicago

Take a few more minutes to visit AllSides.com for a list of news sources by ideology or find your local newspaper here (scroll down to newspapers by state). Click on your city/region’s newspaper and browse the headlines. Jot down a few headlines which spotlight problems in your community or neighboring communities, and share them with your class. (See the extension suggestions at the end of the lesson for a deeper dive into understanding problems in your community.)

Main Activity (30 minutes)

Your job in this lesson is to watch, or read, the news. Engaging with current events is one way to help you recognize problems in your community or problems in other parts of the country that interest you.

(20 min) Problem identification

Place students in small groups or with partners, and have them choose one story from the list below and write answers to the following news engagement questions. It’s important the story is something that interests the student!

Teacher note: you may also pre-choose a story for the class or individual groups. You may also browse through our list of invention stories .

Have students write their news story number on the board, so there are not too many repeats.

5 suggested invention-focused news stories:

Heatwaves are becoming more common. Here’s how the U.S. must plan for them . (4:56)
Plastics last more than a lifetime, and that’s a problem . (6:02)
Creating a concussion sensor . (2:18)
Living without limits — how one man has adapted to the disability with the help of technology . (3:49)
Helping student inventors turn big ideas into the next big thing . (6:54)

News engagement questions: ( student handout here )

What problem is discussed in this story?
Who is impacted by the problem and who is trying to solve the problem?
Where and When is this problem taking place? Do you think people elsewhere, including in your own community, might be affected?
Why is the need not met yet? What are the barriers to meeting the need?
How are experts trying to solve the problem?
Media literacy question : Where would you look to find out more about how other people are trying to solve the problem? What other news sources might provide different information? What experts might be able to help you understand the problem?

Students just completed a critical first step in the invention process! Recognizing the problem , in this case through current events, helps one understand why it’s a problem and how it’s affecting people you may know, including yourself. Take a look at the chart here:

(10 min) Dig deeper into the problem

Before an inventor sets out to research or brainstorm solutions to a problem, it’s important they have a strong grasp of the issue. Have students watch or read the news story a few more times to gain a fuller understanding of the issue, and answer the following questions. If students have not yet finished the initial questions, have them move on to experience the process of going back through the material.

How did the problem come about it in the first place?
Is there a solution to the need already proposed in the story you watched?
How could you improve that solution if there is one? Are there obstacles remaining that are keeping that solution from being available to everyone?
If there is no solution to the need proposed in the story you read or watched, what new breakthrough do you think would be needed to address the problem?

Debrief (10 minutes)

Share out the problem and discuss why it’s a problem and who it's affecting. Was there anything that really surprised you about the issues you learned about? What made you hopeful? What work needs to be done to address the problem?

Alternative: You can also have each group prepare a presentation at their workstation and have the rest of the class move to each station to learn about the problem each group took on. A jigsaw approach could also help groups share.

Looking back over the lesson, do you think you’ve gained a deeper understanding of how to use the news to find problems you care about? Why or why not? How did elements of the news story help you understand the problem better? Can you see why problem recognition is usually the first step of the invention process?

Extension activities

Discuss how the problem described in any story affects your own community. If so, how?
Review local connections and discuss: What (if any) solutions to the need discussed in the story could be used to address the local need students have just reviewed?
How could you find and speak directly with people affected by the problem to see what they think about the invention and how they might benefit (a.k.a. beneficiaries)?
How could you make the solution less expensive or easier to make (say, with materials you already have on hand)?
How can you adapt that solution to best fit the unique needs in your own community, if that applies?
What accessibility (design of inventions friendly to people with disabilities) and sustainability (ability to maintain or support a process over time) issues do you need to consider?
Media literacy lesson — If you are looking for a deeper dive into media literacy and source evaluation, take a look at our lesson here . By reviewing these basic questions about how to find high-quality news stories students care most about, you will be better informed to engage civically and help solve problems in your community.

Lesson link: https://www.pbs.org/newshour/classroom/lesson-plans/2023/07/lesson-plan-how-to-find-news-stories-on-issues-you-care-about

Each one of the five videos at the start of THIS LESSON has a full lesson plan associated with it. You can find them among our news-based invention lessons .
PBS NewsHour Classroom has developed a series of lessons to get your students started working through the invention process. Other lessons in the series include learning what it means to be an inventor , what an inventor does , pitching your invention and patenting your invention .
Here you will find a comprehensive list of invention education resources that support the work you are doing in the classroom.

NGSS (Next Generation Science Standards)

Engineering Design High School

HS-ETS1-1: Analyze a major global challenge

HS-ETS1-2: Design a solution to a complex real-world problem

HS-ETS1-3: Evaluate a solution to a complex real-world problem

Engineering and Design Middle School

MS-ETS1-1: Define the criteria and constraints of a design problem

MS-ETS1-2: Evaluate competing design solutions

MS-ETS1-3: Analyze data from tests to determine similarities and differences

*Note: Depending on what invention the students are working on, other NGSS will apply. You can follow our links for a highlighted PDF of the standards that could be applied for your specific classroom: Middle School NGSS and High School NGSS .

Common Core

Common Core: English Language Arts

RI.4: Interpret words and phrases as they are used in a text

SL.1: Prepare for and participate effectively in a range of conversations

SL.2: Integrate and evaluate information

SL.4: Present information, findings, and supporting evidence

SL.5: Express information and enhance understanding of presentations

L.4: Determine or clarify the meaning of unknown words

Common Core History/Social Studies, Science, and Technical Subjects

RH.4: Interpret words and phrases as they are used in a text

RH.7: Integrate and evaluate content presented in diverse formats

WHST.8: Gather relevant information and integrate the information

Common Core Math

MP3: Construct viable arguments and critique the reasoning of others

College, Career, and Civic Life (C3)

NCSS C3 Framework for Social Studies State Standards

Communications: D4.2.3-5 , D4.2.6-8 , D4.2.9-12 :

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According to all the available evidence, new ideas are getting harder to find.

Since the 1990s, the growth in novel patents — those that mention a new technology — has been negative in the United States. According to one widely cited study, U.S. research productivity declines 50% every 13 years, largely because new ideas are drying up. The implication is that the U.S. needs to double its research investment about every dozen years just to stand still in growth terms.

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But the growing difficulty of discovering new ideas — the next blockbuster drug for cancer, the next graphene — may come as no surprise when we consider that the odds of success are heavily stacked against U.S. scientists in the first place. Every material on Earth is made up of some unique combination of the 118 elements in the periodic table. Trillions of combinations remain undiscovered. Of these, most have no useful properties for industry. Finding the tiny subset of useful new materials is like trying to find a needle in a field of haystacks.

Even when promising candidates are identified, the vast majority fall at the later hurdles of testing, regulatory approval, and development. In health care, for example, only 1% to 2% of promising drugs make it to the market, with costs in the billions of dollars spiraling year on year.

New technologies such as machine learning, robotics, digital twins, and supercomputing can dramatically improve the odds of successful discovery — through faster and more efficient selection of the most promising new ideas and accelerated testing to get these ideas into development sooner. These technologies are paving the way to breakthrough advancements in fields as diverse as energy, medicine, and urban planning.

Machine-Powered Discovery of New Materials

“We’re used to thinking about machines as doing the jobs that are dull, dirty, and dangerous, whereas humans do the creative stuff,” technology writer Luke Dormehl, author of Thinking Machines , told us in an interview. But this line between humans as creators and machines as executors is blurring: “In practice, a lot of human creativity comes from previous ideas or brute-force experimentation — a musician might try scores of chords before finding a new tune, for example.�

About the Authors

Mark Purdy ( @mjpurdyecon ) is an independent economics and technology adviser. Max Klymenko ( @maxoklymenko ) is a technology and economics researcher.

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Scientific Discovery

Scientific discovery is the process or product of successful scientific inquiry. Objects of discovery can be things, events, processes, causes, and properties as well as theories and hypotheses and their features (their explanatory power, for example). Most philosophical discussions of scientific discoveries focus on the generation of new hypotheses that fit or explain given data sets or allow for the derivation of testable consequences. Philosophical discussions of scientific discovery have been intricate and complex because the term “discovery” has been used in many different ways, both to refer to the outcome and to the procedure of inquiry. In the narrowest sense, the term “discovery” refers to the purported “eureka moment” of having a new insight. In the broadest sense, “discovery” is a synonym for “successful scientific endeavor” tout court. Some philosophical disputes about the nature of scientific discovery reflect these terminological variations.

Philosophical issues related to scientific discovery arise about the nature of human creativity, specifically about whether the “eureka moment” can be analyzed and about whether there are rules (algorithms, guidelines, or heuristics) according to which such a novel insight can be brought about. Philosophical issues also arise about rational heuristics, about the characteristics of hypotheses worthy of articulation and testing, and, on the meta-level, about the nature and scope of philosophical reflection itself. This essay describes the emergence and development of the philosophical problem of scientific discovery, surveys different philosophical approaches to understanding scientific discovery, and presents the meta-philosophical problems surrounding the debates.

1. Introduction

2. scientific inquiry as discovery, 3. elements of discovery, 4. logics of discovery, 5. the distinction between the context of discovery and the context of justification, 6.1 discovery as abduction, 6.2 heuristic programming, 7. anomalies and the structure of discovery, 8.1 discoverability, 8.2 preliminary appraisal, 9.1 psychological and social conditions of creativity, 9.2 analogy, 9.3 mental models, other internet resources, related entries.

Philosophical reflection on scientific discovery occurred in different phases. Prior to the 1930s, philosophers were mostly concerned with discoveries in the broadest sense of the term, that is, with the analysis of successful scientific inquiry as a whole. Philosophical discussions focused on the question of whether there were any discernible patterns in the production of new knowledge. Because the concept of discovery did not have a specified meaning and was used in a very broad sense, almost all seventeenth- and eighteenth-century treatises on scientific method could potentially be considered as early contributions to reflections on scientific discovery. In the course of the 19 th century, as philosophy of science and science became two distinct endeavors, the term “discovery” became a technical term in philosophical discussions. Different elements of scientific inquiry were specified. Most importantly, the generation of new knowledge was clearly and explicitly distinguished from its validation, and thus the conditions for the narrower notion of discovery as the act of conceiving new ideas emerged.

The next phase in the discussion about scientific discovery began with the introduction of the so-called “context distinction,” the distinction between the “context of discovery” and the “context of justification”. It was further assumed that the act of conceiving a new idea is a non-rational process, a leap of insight that cannot be regulated. Justification, by contrast, is a systematic process of applying evaluative criteria to knowledge claims. Advocates of the context distinction argued that philosophy of science is exclusively concerned with the context of justification. The assumption underlying this argument is that philosophy is a normative project; it determines norms for scientific practice. Given these assumptions, only the justification of ideas, not their generation, can be the subject of philosophical (normative) analysis. Discovery, by contrast, can only be a topic for empirical study. By definition, the study of discovery is outside the scope of philosophy of science proper.

The introduction of the context distinction and the disciplinary distinction that was tied to it spawned meta-philosophical disputes. For a long time, philosophical debates about discovery were shaped by the notion that philosophical and empirical analyses are mutually exclusive. A number of philosophers insisted, like their predecessors prior to the 1930s, that the philosopher's tasks include the analysis of actual scientific practices and that scientific resources be used to address philosophical problems. They also maintained that it is a legitimate task for philosophy of science to develop a theory of heuristics or problem solving. But this position was the minority view during much of 20 th -century philosophy of science. Philosophers of discovery were thus compelled to demonstrate that scientific discovery was in fact a legitimate part of philosophy of science. Philosophical reflections about the nature of scientific discovery had to be bolstered by meta-philosophical arguments about the nature and scope of philosophy of science.

Today, however, there is wide agreement that philosophy and empirical research are not mutually exclusive. Not only do empirical studies of actual scientific discoveries inform philosophical thought about the structure and cognitive mechanisms of discovery, but researches in psychology, cognitive science, artificial intelligence and related fields have become an integral part of philosophical analyses of the processes and conditions of the generation of new knowledge.

Prior to the 19 th century, the term “discovery” commonly referred to the product of successful inquiry. “Discovery” was used broadly to refer to a new finding, such as a new cure, an improvement of an instrument, or a new method of measuring longitude. Several natural and experimental philosophers, notably Bacon, Descartes, and Newton, expounded accounts of scientific methods for arriving at new knowledge. These accounts were not explicitly labeled “methods of discovery ”, but the general accounts of scientific methods are nevertheless relevant for current philosophical debates about scientific discovery. They are relevant because philosophers of science have frequently presented 17 th -century theories of scientific method as a contrast class to current philosophies of discovery. The distinctive feature of the 17 th - and 18 th -century accounts of scientific method is that the methods have probative force (Nickles 1985). This means that those accounts of scientific method function as guides for acquiring new knowledge and at the same time as validations of the knowledge thus obtained (Laudan 1980; Schaffner 1993: chapter 2).

Bacon's account of his “new method” as it is presented in the Novum Organum is a prominent example. Bacon's work showed how best to arrive at knowledge about “form natures” (the most general properties of matter) via a systematic investigation of phenomenal natures. Bacon described how first to collect and organize natural phenomena and experimental facts in tables, how to evaluate these lists, and how to refine the initial results with the help of further experiments. Through these steps, the investigator would arrive at conclusions about the “form nature” that produces particular phenomenal natures. The point is that for Bacon, the procedures of constructing and evaluating tables and conducting experiments according to the Novum Organum leads to secure knowledge. The procedures thus have “probative force”.

Similarly, Newton's aim in the Philosophiae Naturalis Principia Mathematica was to present a method for the deduction of propositions from phenomena in such a way that those propositions become “more secure” than propositions that are secured by deducing testable consequences from them (Smith 2002). Newton did not assume that this procedure would lead to absolute certainty. One could only obtain moral certainty for the propositions thus secured. The point for current philosophers of science is that these approaches are generative theories of scientific method. Generative theories of scientific method assume that propositions can only be established and secured by showing that they follow from observed and experimentally produced phenomena. In contrast, non-generative theories of scientific method—such as the one proposed by Huygens—assumed that propositions must be established by comparing their consequences with observed and experimentally produced phenomena. In 20 th -century philosophy of science, this approach is often characterized as “consequentialist” (Laudan 1980; Nickles 1985).

Recent philosophers of science have used historical sketches like these to construct the prehistory of current philosophical debates about scientific discovery. The argument is that scientific discovery became a problem for philosophy of science in the 19 th century, when consequentialist theories of scientific method became more widespread. When consequentialist theories were on the rise, the two processes of conception and validation of an idea or hypothesis became distinct and the view that the merit of a new idea does not depend on the way in which it was arrived at became widely accepted.

In the course of the 19 th century, the act of having an insight—the purported “eureka moment”—was separated from processes of articulating, developing, and testing the novel insight. Philosophical discussion focused on the question of whether and to what extent rules could be devised to guide each of these processes. William Whewell's work, especially the two volumes of Philosophy of the Inductive Sciences of 1840, is an important contribution to the philosophical debates about scientific discovery precisely because he clearly separated the creative moment or “happy thought” as he called it from other elements of scientific inquiry. For Whewell, discovery comprised all three elements: the happy thought, the articulation and development of that thought, and the testing or verification of it. In most of the subsequent treatments of discovery, however, the scope of the term “discovery” is limited to either the first of these elements, the “happy thought”, or to the first two of these elements, the happy thought and its articulation. In fact, much of the controversies in the 20 th century about the possibility of a philosophy of discovery can be understood against the background of the disagreement about whether the process of discovery does or does not include the articulation and development of a novel thought.

The previous section shows that scholars like Bacon and Newton aimed to develop methodologies of scientific inquiry. They proposed “new methods” or “rules of reasoning” that guide the generation of certain propositions from observed and experimental phenomena. Whewell, by contrast, was explicitly concerned with developing a philosophy of discovery. His account was in part a description of the psychological makeup of the discoverer. For instance, he held that only geniuses could have those happy thoughts that are essential to discovery. In part, his account was an account of the methods by which happy thoughts are integrated into the system of knowledge. According to Whewell, the initial step in every discovery is what he called “some happy thought, of which we cannot trace the origin, some fortunate cast of intellect, rising above all rules. No maxims can be given which inevitably lead to discovery” (Whewell 1996 [1840]: 186). An “art of discovery” in the sense of a teachable and learnable skill does not exist according to Whewell. The happy thought builds on the known facts, but according to Whewell it is impossible to prescribe a method for having happy thoughts.

In this sense, happy thoughts are accidental. But in an important sense, scientific discoveries are not accidental. The happy thought is not a “wild guess.” Only the person whose mind is prepared to see things will actually notice them. The “previous condition of the intellect, and not the single fact, is really the main and peculiar cause of the success. The fact is merely the occasion by which the engine of discovery is brought into play sooner or later. It is, as I have elsewhere said, only the spark which discharges a gun already loaded and pointed; and there is little propriety in speaking of such an accident as the cause why the bullet hits its mark.” (Whewell 1996 [1840]: 189).

Having a happy thought is not yet a discovery, however. The second element of a scientific discovery consists in binding together—“colligating”, as Whewell called it—a set of facts by bringing them under a general conception. Not only does the colligation produce something new, but it also shows the previously known facts in a new light. More precisely, colligation works from both ends, from the facts as well as from the ideas that bind the facts together. Colligation is an extended process. It involves, on the one hand, the specification of facts through systematic observation, measurements and experiment, and on the other hand, the clarification of ideas through the exposition of the definitions and axioms that are tacitly implied in those ideas. This process is iterative. The scientists go back and forth between binding together the facts, clarifying the idea, rendering the facts more exact, and so on and so forth.

The final part of the discovery is the verification of the colligation involving the happy thought. This means, first and foremost, that the outcome of the colligation must be sufficient to explain the data at hand. Verification also involves judging the predictive power, simplicity, and “consilience” of the outcome of the colligation. “Consilience” refers to a higher range of generality (broader applicability) of the theory (the articulated and clarified happy thought) that the actual colligation produced. Whewell's account of discovery is not a deductivist system. It is essential that the outcome of the colligation be inferable from the data prior to any testing (Snyder 1997).

Whewell's theory of discovery is significant for the philosophical debate about scientific discovery because it clearly separates three elements: the non-analyzable happy thought or “eureka moment”; the process of colligation which includes the clarification and explication of facts and ideas; and the verification of the outcome of the colligation. His position that the philosophy of discovery cannot prescribe how to think happy thoughts has been a key element of 20 th -century philosophical reflection on discovery, and many philosophers have adopted the notion “happy thought” as a label for the “eureka moment” involved in discovery. Notably, however, Whewell's conception of discovery not only comprises the happy thoughts but also the processes by which the happy thoughts are to be integrated into the given system of knowledge. The procedures of articulation and test are both analyzable according to Whewell, and his conception of colligation and verification serve as guidelines for how the discoverer should proceed. A colligation, if properly done, has as such justificatory force. Similarly, the process of verification is an integral part of discovery and it too has justificatory force. Whewell's conception of verification thus comprises elements of generative and consequential methods of inquiry. To verify a hypothesis, the investigator needs to show that it accounts for the known facts, that it foretells new, previously unobserved phenomena, and that it can explain and predict phenomena which are explained and predicted by a hypothesis that was obtained through an independent happy thought-cum-colligation (Ducasse 1951).

Whewell's conceptualization of scientific discovery offers a useful framework for mapping the philosophical debates about discovery and for identifying major issues of concern in recent philosophical debates. First and foremost, nearly all recent philosophers operate with a notion of discovery that is narrower than Whewell's. In the narrower conception, what Whewell called “verification” is not part of discovery proper. Secondly, until the late 20 th century, there was wide agreement that the “eureka moment,” narrowly construed, is an unanalyzable, even mysterious leap of insight. The main disagreements concerned the question of whether the process of developing a hypothesis (the “colligation” in Whewell's terms) is or is not a part of discovery proper, and if it is, whether and how this process is guided by rules. Philosophers also disagreed on the issue of whether it is a philosophical task to explicate these rules. In recent decades, philosophical attention has shifted to the “eureka moment”. Drawing on resources from cognitive science, neuroscience, computational research, and environmental and social psychology, they have “demystified” the cognitive processes involved in the generation of new ideas.

In the early 20 th century, the view that discovery is or at least crucially involves a non-analyzable creative act of a gifted genius was widespread but not unanimously accepted. Alternative conceptions of discovery emphasize that discovery is an extended process, i.e., that the discovery process includes the reasoning processes through which a new insight is articulated and further developed. Moreover, it was assumed that there is a systematic, formal aspect to that reasoning. While the reasoning involved does not proceed according to the principles of demonstrative logic, it is systematic enough to deserve the label “logical”. Proponents of this view argued that traditional (here: Aristotelian) logic is an inadequate model of scientific discovery because it misrepresents the process of knowledge generation as grossly as the notion of “happy thought”. In this approach, the term “logic” is used in the broad sense. It is the task of the logic of discovery to draw out and give a schematic representation of the reasoning strategies that were applied in episodes of successful scientific inquiry. Early 20 th -century logics of discovery can best be described as theories of the mental operations involved in knowledge generation. Among these mental operations are classification, determination of what is relevant to an inquiry, and the conditions of communication of meaning. It is argued that these features of scientific discovery are either not or insufficiently represented by traditional logic (Schiller 1917: 236–7).

Philosophers advocating this approach agree that the logic of discovery should be characterized as a set of heuristic principles rather than as a process of applying inductive or deductive logic to a set of propositions. These heuristic principles are not understood to show the path to secure knowledge. Heuristic principles are suggestive rather than demonstrative (Carmichael 1922, 1930). One recurrent feature in these accounts of the reasoning strategies leading to new ideas is analogical reasoning (Schiller 1917; Benjamin 1934). In the 20 th century, it is widely acknowledged that analogical reasoning is a productive form of reasoning that cannot be reduced to inductive or deductive reasoning. However, these approaches to the logic of discovery remained scattered and tentative at that time, and attempts to develop more systematically the heuristics guiding discovery processes were eclipsed by the advance of the distinction between contexts of discovery and justification.

The distinction between “context of discovery” and “context of justification” dominated and shaped the discussions about discovery in 20 th -century philosophy of science. The context distinction marks the distinction between the generation of a new idea or hypothesis and the defense (test, verification) of it. As the previous sections have shown, the distinction among different features of scientific inquiry has a longer history, but in philosophy of science it became potent in the first half of the 20 th century. In the course of the ensuing discussions about scientific discovery, the distinction between the different features of scientific inquiry turned into a powerful demarcation criterion. The boundary between context of discovery (the de facto thinking processes) and context of justification (the de jure defense of the correctness of these thoughts) was now understood to determine the scope of philosophy of science. The underlying assumption is that philosophy of science is a normative endeavor. Advocates of the context distinction argue that the generation of a new idea is an intuitive, irrational process; it cannot be subject to normative analysis. Therefore, the study of scientists' actual thinking can only be the subject of psychology, sociology, and other empirical sciences. Philosophy of science, by contrast, is exclusively concerned with the context of justification.

The terms “context of discovery” and “context of justification” are often associated with Hans Reichenbach's work. Reichenbach's original conception of the context distinction is quite complex, however (Howard 2006; Richardson 2006). It does not map easily on to the disciplinary distinction mentioned above, because for Reichenbach, philosophy of science proper is partly descriptive. Reichenbach maintains that philosophy of science includes a description of knowledge as it really is. Descriptive philosophy of science reconstructs scientists' thinking processes in such a way that logical analysis can be performed on them, and it thus prepares the ground for the evaluation of these thoughts (Reichenbach 1938: § 1). Discovery, by contrast, is the object of empirical—psychological, sociological—study. According to Reichenbach, the empirical study of discoveries shows that processes of discovery often correspond to the principle of induction, but this is simply a psychological fact (Reichenbach 1938: 403).

While the terms “context of discovery” and “context of justification” are widely used, there has been ample discussion about how the distinction should be drawn and what their philosophical significance is (c.f. Kordig 1978; Gutting 1980; Zahar 1983; Leplin 1987; Hoyningen-Huene 1987; Weber 2005: chapter 3; Schickore and Steinle 2006). Most commonly, the distinction is interpreted as a distinction between the process of conceiving a theory and the validation of that theory, that is, the determination of the theory's epistemic support. This version of the distinction is not necessarily interpreted as a temporal distinction. In other words, it is not usually assumed that a theory is first fully developed and then validated. Rather, conception and validation are two different epistemic approaches to theory: the endeavor to articulate, flesh out, and develop its potential and the endeavor to assess its epistemic worth. Within the framework of the context distinction, there are two main ways of conceptualizing the process of conceiving a theory. The first option is to characterize the generation of new knowledge as an irrational act, a mysterious creative intuition, a “eureka moment”. The second option is to conceptualize the generation of new knowledge as an extended process that includes a creative act as well as some process of articulating and developing the creative idea.

Both of these accounts of knowledge generation served as starting points for arguments against the possibility of a philosophy of discovery. In line with the first option, philosophers have argued that neither is it possible to prescribe a logical method that produces new ideas nor is it possible to reconstruct logically the process of discovery. Only the process of testing is amenable to logical investigation. This objection to philosophies of discovery has been called the “discovery machine objection” (Curd 1980: 207). It is usually associates with Karl Popper's Logic of Scientific Discovery .

The initial state, the act of conceiving or inventing a theory, seems to me neither to call for logical analysis not to be susceptible of it. The question how it happens that a new idea occurs to a man—whether it is a musical theme, a dramatic conflict, or a scientific theory—may be of great interest to empirical psychology; but it is irrelevant to the logical analysis of scientific knowledge. This latter is concerned not with questions of fact (Kant's quid facti ?) , but only with questions of justification or validity (Kant's quid juris ?) . Its questions are of the following kind. Can a statement be justified? And if so, how? Is it testable? Is it logically dependent on certain other statements? Or does it perhaps contradict them? […]Accordingly I shall distinguish sharply between the process of conceiving a new idea, and the methods and results of examining it logically. As to the task of the logic of knowledge—in contradistinction to the psychology of knowledge—I shall proceed on the assumption that it consists solely in investigating the methods employed in those systematic tests to which every new idea must be subjected if it is to be seriously entertained. (Popper 2002 [1934/1959]: 7-8)

With respect to the second way of conceptualizing knowledge generation, many philosophers argue in a similar fashion that because the process of discovery involves an irrational, intuitive process, which cannot be examined logically, a logic of discovery cannot be construed. Other philosophers turn against the philosophy of discovery even though they explicitly acknowledge that discovery is an extended, reasoned process. They present a meta-philosophical objection argument, arguing that a theory of articulating and developing ideas is not a philosophical but a psychological theory.

The impact of the context distinction on studies of scientific discovery and on philosophy of science more generally can hardly be overestimated. The view that the process of discovery (however construed) is outside the scope of philosophy of science proper was widely shared amongst philosophers of science for most of the 20 th century and is still held by many. The last section shows that there were a few attempts to develop logics of discovery in the 1920s and 1930s. But for several decades, the context distinction dictated what philosophy of science should be about and how it should proceed. The dominant view was that theories of mental operations or heuristics had no place in philosophy of science. Therefore, discovery was not a legitimate topic for philosophy of science. The wide notion of discovery is mostly deployed in sociological accounts of scientific practice. In this perspective, “discovery” is understood as a retrospective label, which is attributed as a sign of accomplishment to some scientific endeavors. Sociological theories acknowledge that discovery is a collective achievement and the outcome of a process of negotiation through which “discovery stories” are constructed and certain knowledge claims are granted discovery status (Brannigan 1981; Schaffer 1986, 1994). Until the last third of the 20 th century, there were few attempts to challenge the disciplinary distinction tied to the context distinction. Only in the 1970s did the interest in philosophical approaches to discovery begin to increase. But the context distinction remained a challenge for philosophies of discovery.

There are three main lines of response to the disciplinary distinction tied to the context distinction. Each of these lines of response opens up a philosophical perspective on discovery. Each proceeds on the assumption that philosophy of science may legitimately include some form of analysis of actual reasoning patterns as well as information from empirical sciences such as cognitive science, psychology, and sociology. All of these responses reject the idea that discovery is nothing but a mystical event. Discovery is conceived as an analyzable reasoning process, not just as a creative leap by which novel ideas spring into being fully formed. All of these responses agree that the procedures and methods for arriving at new hypotheses and ideas are no guarantee that the hypothesis or idea that is thus formed is necessarily the best or the correct one. Nonetheless, it is the task of philosophy of science to provide rules for making this process better. All of these responses can be described as theories of problem solving, whose ultimate goal is to make the generation of new ideas and theories more efficient.

But the different approaches to scientific discovery employ different terminologies. In particular, the term “logic” of discovery is sometimes used in a narrow sense and sometimes broadly understood. In the narrow sense, “logic” of discovery is understood to refer to a set of formal, generally applicable rules by which novel ideas can be mechanically derived from existing data. In the broad sense, “logic” of discovery refers to the schematic representation of reasoning procedures. “Logical” is just another term for “rational”. Moreover, while each of these responses combines philosophical analyses of scientific discovery with empirical research on actual human cognition, different sets of resources are mobilized, ranging from AI research and cognitive science to historical studies of problem-solving procedures. Also, the responses parse the process of scientific inquiry differently. Often, scientific inquiry is regarded as having two aspects, viz. generation and validation of new ideas. At times, however, scientific inquiry is regarded as having three aspects, namely generation, pursuit or articulation, and validation of knowledge. In the latter framework, the label “discovery” is sometimes used to refer just to generation and sometimes to refer to both generation and pursuit.

The first response to the challenge of the context distinction draws on a broad understanding of the term “logic” to argue that a logic of scientific discovery can be developed ( section 6 ). The second response, drawing on a narrow understanding of the term “logic”, is to concede that there is no logic of discovery, i.e., no algorithm for generating new knowledge. Philosophers who take this approach argue that the process of discovery follows an identifiable, analyzable pattern ( section 7 ). Others argue that discovery is governed by a methodology . The methodology of discovery is a legitimate topic for philosophical analysis ( section 8 ). All of these responses assume that there is more to discovery than a “eureka moment.” Discovery comprises processes of articulating and developing the creative thought. These are the processes that can be examined with the tools of philosophical analysis. The third response to the challenge of the context distinction also assumes that discovery is or at least involves a creative act. But in contrast to the first two responses, it is concerned with the creative act itself. Philosophers who take this approach argue that scientific creativity is amenable to philosophical analysis ( section 9 ).

6. Logics of discovery after the context distinction

The first response to the challenge of the context distinction is to argue that discovery is a topic for philosophy of science because it is a logical process after all. Advocates of this approach to the logic of discovery usually accept the overall distinction between the two processes of conceiving and testing a hypothesis. They also agree that it is impossible to put together a manual that provides a formal, mechanical procedure through which innovative concepts or hypotheses can be derived: There is no discovery machine. But they reject the view that the process of conceiving a theory is a creative act, a mysterious guess, a hunch, a more or less instantaneous and random process. Instead, they insist that both conceiving and testing hypotheses are processes of reasoning and systematic inference, that both of these processes can be represented schematically, and that it is possible to distinguish better and worse paths to new knowledge.

This line of argument has much in common with the logics of discovery described in section 4 above but it is now explicitly pitched against the disciplinary distinction tied to the context distinction. There are two main ways of developing this argument. The first is to conceive of discovery in terms of abductive reasoning ( section 6.1 ). The second is to conceive of discovery in terms of problem-solving algorithms, whereby heuristic rules aid the processing of available data and enhance the success in finding solutions to problems ( section 6.2 ). Both lines of argument rely on a broad conception of logic, whereby the “logic” of discovery amounts to a schematic account of the reasoning processes involved in knowledge generation.

One argument, elaborated prominently by Norwood R. Hanson, is that the act of discovery—here, the act of suggesting a new hypothesis—follows a distinctive logical pattern, which is different from both inductive logic and the logic of hypothetico-deductive reasoning. The special logic of discovery is the logic of abductive or “retroductive” inferences (Hanson 1958). The argument that it is through an act of abductive inferences that plausible, promising scientific hypotheses are devised goes back to C.S. Peirce. This version of the logic of discovery characterizes reasoning processes that take place before a new hypothesis is ultimately justified. The abductive mode of reasoning that leads to plausible hypotheses is conceptualized as an inference beginning with data or, more specifically, with surprising or anomalous phenomena.

In this view, discovery is primarily a process of explaining anomalies or surprising, astonishing phenomena. The scientists' reasoning proceeds abductively from an anomaly to an explanatory hypothesis in light of which the phenomena would no longer be surprising or anomalous. The outcome of this reasoning process is not one single specific hypothesis but the delineation of a type of hypotheses that is worthy of further attention (Hanson 1965: 64). According to Hanson, the abductive argument has the following schematic form (Hanson 1960: 104):

Some surprising, astonishing phenomena p 1 , p 2 , p 3 … are encountered.
But p 1 , p 2 , p 3 … would not be surprising were an hypothesis of H's type to obtain. They would follow as a matter of course from something like H and would be explained by it.
Therefore there is good reason for elaborating an hypothesis of type H—for proposing it as a possible hypothesis from whose assumption p 1 , p 2 , p 3 … might be explained.

Drawing on the historical record, Hanson argues that several important discoveries were made relying on abductive reasoning, such as Kepler's discovery of the elliptic orbit of Mars (Hanson 1958). It is now widely agreed, however, that Hanson's reconstruction of the episode is not a historically adequate account of Kepler's discovery (Lugg 1985). More importantly, while there is general agreement that abductive inferences are frequent in both everyday and scientific reasoning, these inferences are no longer considered as logical inferences. Even if one accepts Hanson's schematic representation of the process of identifying plausible hypotheses, this process is a “logical” process only in the widest sense whereby the term “logical” is understood as synonymous with “rational”. Notably, some philosophers have even questioned the rationality of abductive inferences (Koehler 1991; Brem and Rips 2000).

Another argument against the above schema is that it is too permissive. There will be several hypotheses that are explanations for phenomena p 1 , p 2 , p 3 …, so the fact that a particular hypothesis explains the phenomena is not a decisive criterion for developing that hypothesis (Harman 1965; see also Blackwell 1969). Additional criteria are required to evaluate the hypothesis yielded by abductive inferences.

Finally, it is worth noting that the schema of abductive reasoning does not explain the very act of conceiving a hypothesis or hypothesis-type. The processes by which a new idea is first articulated remain unanalyzed in the above schema. The schema focuses on the reasoning processes by which an exploratory hypothesis is assessed in terms of its merits and promise (Laudan 1980; Schaffner 1993).

In more recent work on abduction and discovery, two notions of abduction are sometimes distinguished: the common notion of abduction as inference to the best explanation (selective abduction) and creative abduction (Magnani 2000, 2009). Selective abduction—the inference to the best explanation—involves selecting a hypothesis from a set of known hypotheses. Medical diagnosis exemplifies this kind of abduction. Creative abduction, by contrast, involves generating a new, plausible hypothesis. This happens, for instance, in medical research, when the notion of a new disease is articulated. However, it is still an open question whether this distinction can be drawn, or whether there is a more gradual transition from selecting an explanatory hypothesis from a familiar domain (selective abduction) to selecting a hypothesis that is slightly modified from the familiar set and to identifying a more drastically modified or altered assumption.

Another recent suggestion is to broaden Peirce's original account of abduction and to include not only verbal information but also non-verbal mental representations, such as visual, auditory, or motor representations. In Thagard's approach, representations are characterized as patterns of activity in mental populations (see also section 9.3 below). The advantage of the neural account of human reasoning is that it covers features such as the surprise that accompanies the generation of new insights or the visual and auditory representations that contribute to it. If all mental representations can be characterized as patterns of firing in neural populations, abduction can be analyzed as the combination or “convolution” (Thagard) of patterns of neural activity from disjoint or overlapping patterns of activity (Thagard 2010).

The concern with the logic of discovery has also motivated research on artificial intelligence at the intersection of philosophy of science and cognitive science. In this approach, scientific discovery is treated as a form of problem-solving activity (Simon 1973; see also Newell and Simon 1971), whereby the systematic aspects of problem solving are studied within an information-processing framework. The aim is to clarify with the help of computational tools the nature of the methods used to discover scientific hypotheses. These hypotheses are regarded as solutions to problems. Philosophers working in this tradition build computer programs employing methods of heuristic selective search (e.g., Langley et al. 1987). In computational heuristics, search programs can be described as searches for solutions in a so-called “problem space” in a certain domain. The problem space comprises all possible configurations in that domain (e.g., for chess problems, all possible arrangements of pieces on a board of chess). Each configuration is a “state” of the problem space. There are two special states, namely the goal state, i.e., the state to be reached, and the initial state, i.e., the configuration at the starting point from which the search begins. There are operators, which determine the moves that generate new states from the current state. There are path constraints, which limit the permitted moves. Problem solving is the process of searching for a solution of the problem of how to generate the goal state from an initial state. In principle, all states can be generated by applying the operators to the initial state, then to the resulting state, until the goal state is reached (Langley et al. 1987: chapter 9). A problem solution is a sequence of operations leading from the initial to the goal state.

The basic idea behind computational heuristics is that rules can be identified that serve as guidelines for finding a solution to a given problem quickly and efficiently by avoiding undesired states of the problem space. These rules are best described as rules of thumb. The aim of constructing a logic of discovery thus becomes the aim of constructing a heuristics for the efficient search for solutions to problems. The term “heuristic search” indicates that in contrast to algorithms, problem-solving procedures lead to results that are merely provisional and plausible. A solution is not guaranteed, but heuristic searches are advantageous because they are more efficient than exhaustive random trial and error searches. Insofar as it is possible to evaluate whether one set of heuristics is better—more efficacious—than another, the logic of discovery turns into a normative theory of discovery.

Arguably, because it is possible to reconstruct important scientific discovery processes with sets of computational heuristics, the scientific discovery process can be considered as a special case of the general mechanism of information processing. In this context, the term “logic” is not used in the narrow sense of a set of formal, generally applicable rules to draw inferences but again in a broad sense as a label for a set of procedural rules.

The computer programs that embody the principles of heuristic searches in scientific inquiry simulate the paths that scientists followed when they searched for new theoretical hypotheses. Computer programs such as BACON (Simon et al. 1981) and KEKADA (Kulkarni and Simon 1988) utilize sets of problem-solving heuristics to detect regularities in given data sets. The program would note, for instance, that the values of a dependent term are constant or that a set of values for a term x and a set of values for a term y are linearly related. It would thus “infer” that the dependent term always has that value or that a linear relation exists between x and y . These programs can “make discoveries” in the sense that they can simulate successful discoveries such as Kepler's third law (BACON) or the Krebs cycle (KEKADA).

AI-based theories of scientific discoveries have helped identify and clarify a number of problem-solving strategies. An example of such a strategy is heuristic means-ends analysis, which involves identifying specific differences between the present and the goal situation and searches for operators (processes that will change the situation) that are associated with the differences that were detected. Another important heuristic is to divide the problem into sub-problems and to begin solving the one with the smallest number of unknowns to be determined (Simon 1977). AI-based approaches have also highlighted the extent to which the generation of new knowledge draws on existing knowledge that constrains the development of new hypotheses.

As accounts of scientific discoveries, computational heuristics have some limitations. Most importantly, because computer programs require the data from actual experiments the simulations cover only certain aspects of scientific discoveries. They do not design new experiments, instruments, or methods. Moreover, compared to the problem spaces given in computational heuristics, the complex problem spaces for scientific problems are often ill defined, and the relevant search space and goal state must be delineated before heuristic assumptions could be formulated (Bechtel and Richardson 1993: chapter 1).

Earlier critics of AI-based theories of scientific discoveries argued that a computer cannot devise new concepts but is confined to the concepts included in the given computer language (Hempel 1985: 119–120). Subsequent work has shown that computational methods can be used to generate new results leading to refereed scientific publications in astronomy, cancer research, ecology, and other fields (Langley 2000). The most recent computational research on scientific discovery is no longer driven by philosophical interests in scientific discovery, however. Instead, the main motivation is to contribute computational tools to aid scientists in their research.

Many philosophers maintain that discovery is a legitimate topic for philosophy of science while abandoning the notion that there is a logic of discovery. One very influential approach is Thomas Kuhn's analysis of the emergence of novel facts and theories (Kuhn 1970 [1962]: chapter 6). Kuhn identifies a general pattern of discovery as part of his account of scientific change. A discovery is not a simple act, but an extended, complex process, which culminates in paradigm changes. Paradigms are the symbolic generalizations, metaphysical commitments, values, and exemplars that are shared by a community of scientists and that guide the research of that community. Paradigm-based, normal science does not aim at novelty but instead at the development, extension, and articulation of accepted paradigms. A discovery begins with an anomaly, that is, with the recognition that the expectations induced by an established paradigm are being violated. The process of discovery involves several aspects: observations of an anomalous phenomenon, attempts to conceptualize it, and changes in the paradigm so that the anomaly can be accommodated.

It is the mark of success of normal science that it does not make transformative discoveries, and yet such discoveries come about as a consequence of normal, paradigm-guided science. The more detailed and the better developed a paradigm, the more precise are its predictions. The more precisely the researchers know what to expect, the better they are able to recognize anomalous results and violations of expectations:

novelty ordinarily emerges only for the man who, knowing with precision what he should expect, is able to recognize that something has gone wrong. Anomaly appears only against the background provided by the paradigm. (Kuhn 1970 [1962]: 65)

Drawing on several historical examples, Kuhn argues that it is usually impossible to identify the very moment when something was discovered or even the individual who made the discovery. Kuhn illustrates these points with the discovery of oxygen (see Kuhn 1970 [1962]: 53–56). Oxygen had not been discovered before 1774 and had been discovered by 1777. Even before 1774, Lavoisier had noticed that something was wrong with phlogiston theory, but he was unable to move forward. Two other investigators, C. W. Scheele and Joseph Priestley, independently identified a gas obtained from heating solid substances. But Scheele's work remained unpublished until after 1777, and Priestley did not identify his substance as a new sort of gas. In 1777, Lavoisier presented the oxygen theory of combustion, which gave rise to fundamental reconceptualization of chemistry. But according to this theory as Lavoisier first presented it, oxygen was not a chemical element. It was an atomic “principle of acidity” and oxygen gas was a combination of that principle with caloric. According to Kuhn, all of these developments are part of the discovery of oxygen, but none of them can be singled out as “the” act of discovery.

In pre-paradigmatic periods or in times of paradigm crisis, theory-induced discoveries may happen. In these periods, scientists speculate and develop tentative theories, which may lead to novel expectations and experiments and observations to test whether these expectations can be confirmed. Even though no precise predictions can be made, phenomena that are thus uncovered are often not quite what had been expected. In these situations, the simultaneous exploration of the new phenomena and articulation of the tentative hypotheses together bring about discovery.

In cases like the discovery of oxygen, by contrast, which took place while a paradigm was already in place, the unexpected becomes apparent only slowly, with difficulty, and against some resistance. Only gradually do the anomalies become visible as such. It takes time for the investigators to recognize “both that something is and what it is” (Kuhn 1970 [1962]: 55). Eventually, a new paradigm becomes established and the anomalous phenomena become the expected phenomena.

Recent studies in cognitive neuroscience of brain activity during periods of conceptual change support Kuhn's view that conceptual change is hard to achieve. These studies examine the neural processes that are involved in the recognition of anomalies and compare them with the brain activity involved in the processing of information that is consistent with preferred theories. The studies suggest that the two types of data are processed differently (Dunbar et al. 2007).

8. Methodologies of discovery

Advocates of the view that there are methodologies of discovery use the term “logic” in the narrow sense of an algorithmic procedure to generate new ideas. But like the AI-based theories of scientific discovery described in section 6 , methodologies of scientific discovery interpret the concept “discovery” as a label for an extended process of generating and articulating new ideas and often describe the process in terms of problem solving. In these approaches, the distinction between the contexts of discovery and the context of justification is challenged because the methodology of discovery is understood to play a justificatory role. Advocates of a methodology of discovery usually rely on a distinction between different justification procedures, justification involved in the process of generating new knowledge and justification involved in testing it. Consequential or “strong” justifications are methods of testing. The justification involved in discovery, by contrast, is conceived as generative (as opposed to consequential) justification ( section 8.1 ) or as weak (as opposed to strong) justification ( section 8.2 ). Again, some terminological ambiguity exists because according to some philosophers, there are three contexts, not two: Only the initial conception of a new idea (the “eureka moment”) is the context of discovery proper, and between it and justification there exists a separate context of pursuit (Laudan 1980). But many advocates of methodologies of discovery regard the context of pursuit as an integral part of the process of justification. They retain the notion of two contexts and re-draw the boundaries between the contexts of discovery and justification as they were drawn in the early 20 th century.

The methodology of discovery has sometimes been characterized as a form of justification that is complementary to the methodology of testing (Nickles 1984, 1985, 1989). According to the methodology of testing, empirical support for a theory results from successfully testing the predictive consequences derived from that theory (and appropriate auxiliary assumptions). In light of this methodology, justification for a theory is “consequential justification,” the notion that a hypothesis is established if successful novel predictions are derived from the theory or claim. Generative justification complements consequential justification. Advocates of generative justification hold that there exists an important form of justification in science that involves reasoning to a claim from data or previously established results more generally.

One classic example for a generative methodology is the set of Newton's rules for the study of natural philosophy. According to these rules, general propositions are established by deducing them from the phenomena. The notion of generative justification seeks to preserve the intuition behind classic conceptions of justification by deduction. Generative justification amounts to the rational reconstruction of the discovery path in order to establish its discoverability had the researchers known what is known now, regardless of how it was first thought of (Nickles 1985, 1989). The reconstruction demonstrates in hindsight that the claim could have been discovered in this manner had the necessary information and techniques been available. In other words, generative justification—justification as “discoverability” or “potential discovery”—justifies a knowledge claim by deriving it from results that are already established. While generative justification does not retrace exactly those steps of the actual discovery path that were actually taken, it is a better representation of scientists' actual practices than consequential justification because scientists tend to construe new claims from available knowledge. Generative justification is a weaker version of the traditional ideal of justification by deduction from the phenomena. Justification by deduction from the phenomena is complete if a theory or claim is completely determined from what we already know. The demonstration of discoverability results from the successful derivation of a claim or theory from the most basic and most solidly established empirical information.

Discoverability as described in the previous paragraphs is a mode of justification. Like the testing of novel predictions derived from a hypothesis, generative justification begins when the phase of finding and articulating a hypothesis worthy of assessing is drawing to a close. Other approaches to the methodology of discovery are directly concerned with the procedures involved in devising new hypotheses. The argument in favor of this kind of methodology is that the procedures of devising new hypotheses already include elements of appraisal. These preliminary assessments have been termed “weak” evaluation procedures (Schaffner 1993). Weak evaluations are relevant during the process of devising a new hypothesis. They provide reasons for accepting a hypothesis as promising and worthy of further attention. Strong evaluations, by contrast, provide reasons for accepting a hypothesis as (approximately) true or confirmed. Both “generative” and “consequential” testing as discussed in the previous section are strong evaluation procedures. Strong evaluation procedures are rigorous and systematically organized according to the principles of hypothesis derivation or H-D testing. A methodology of preliminary appraisal, by contrast, articulates criteria for the evaluation of a hypothesis prior to rigorous derivation or testing. It aids the decision about whether to take that hypothesis seriously enough to develop it further and test it. For advocates of this version of the methodology of discovery, it is the task of philosophy of science to characterize sets of constraints and methodological rules guiding the complex process of prior-to-test evaluation of hypotheses.

In contrast to the computational approaches discussed above, strategies of preliminary appraisal are not regarded as subject-neutral but as specific to particular fields of study. Because the analysis of criteria for the appraisal of hypotheses has mostly been made with regard to the study of biological mechanism, the criteria and constraints that have been proposed are those that play a role in the discovery of biological mechanisms. Biological mechanisms are entities and activities that are organized in such a way that they produce regular changes from initial to terminal conditions (Machamer et al. 2000).

Philosophers of biology have developed a fine-grained framework to account for the generation and preliminary evaluation of these mechanisms (Darden 2002; Craver 2002; Bechtel and Richardson 1993; Craver and Darden 2013). Some philosophers have even suggested that the phase of preliminary appraisal be further divided into two phases, the phase of appraising and the phase of revising. According to Lindley Darden, the phases of generation, appraisal and revision of descriptions of mechanisms can be characterized as reasoning processes governed by reasoning strategies. Different reasoning strategies govern the different phases (Darden 1991, 2002; Craver 2002; Darden 2009). The generation of hypotheses about mechanisms, for instance, is governed by the strategy of “schema instantiation” (see Darden 2002). The discovery of the mechanism of protein synthesis involved the instantiation of an abstract schema for chemical reactions: reactant 1 + reactant 2 = product. The actual mechanism of protein synthesis was found through specification and modification of this schema.

It is important to appreciate the status of these reasoning strategies. They are not necessarily strategies that were actually used. Neither of these strategies is deemed necessary for discovery, and they are not prescriptions for biological research. Rather, these strategies are deemed sufficient for the discovery of mechanisms; they “could have been used” to arrive at the description of that mechanism (Darden 2002). The methodology of the discovery of mechanisms is an extrapolation from past episodes of research on mechanisms and the result of a synthesis of rational reconstructions of several of these historical episodes. The methodology of discovery is only weakly normative in the sense that the strategies for the discovery of mechanisms that have been identified so far may prove useful in future biological research. Moreover, the sets of reasoning strategies that have been proposed are highly specific. It is still an open question whether the analysis of strategies for the discovery of biological mechanisms can illuminate the efficiency of scientific problem solving more generally (Weber 2005: chapter 3).

9. Creativity, analogy, and mental models

The approaches to scientific discovery presented in the previous sections focus on the adoption, articulation, and preliminary evaluation of ideas or hypotheses prior to rigorous testing. They do not illuminate how a novel hypothesis or idea is first thought up. Even among philosophers of discovery, the predominant view has long been that there is an initial step of discovery that is best described as a “eureka moment”, a mysterious intuitive leap of the human mind that cannot be analyzed further.

The concept of discovery as hypothesis-formation as it is encapsulated in the traditional distinction between context of discovery and context of justification does not explicate how new ideas are formed. According to accounts of discovery informed by evolutionary biology, the generation of new ideas is akin to random, blind variations of thought processes, which have to be inspected by the critical mind and assessed as neutral, productive, or useless (Campbell 1960; see also Hull 1988). While the evolutionary approach to discovery offers a more substantial account of scientific discovery, the key processes by which random ideas are generated are still left unanalyzed.

Today, many philosophers hold the view that creativity is not mysterious and can be submitted to analysis. Margaret Boden has offered helpful analyses of the concept of creativity. According to Boden, a new development is creative if it is novel, surprising, and important. She distinguishes between psychological creativity (P-creativity) and historical creativity (H-creativity). P-creativity is a development that is new, surprising and important to the particular person who comes up with it. H-creativity, by contrast, is radically novel, surprising, and important—it is generated for the first time (Boden 2004).

The majority of recent philosophical studies of scientific discovery today focus on the act of generation of new knowledge. The distinctive feature of these studies is that they integrate approaches from cognitive science, psychology, and computational neuroscience (Thagard 2012). Recent work on creativity offers substantive analyses of the social and psychological preconditions and the cognitive mechanisms involved in generating new ideas. Some of this research aims to characterize those features that are common to all creative processes. Other research aims to identify the features that are distinctive of scientific creativity (as opposed to other forms of creativity, such as artistic creativity or creative technological invention). Studies have focused on analyses of the personality traits that are conducive to creative thinking, and the social and environmental factors that are favorable for discovery ( section 9.1 ). Two key elements of the cognitive processes involved in creative thinking are analogies ( section 9.2 ) and mental models ( section 9.3 ).

Psychological studies of creative individuals' behavioral dispositions suggest that creative scientists share certain personality traits, including confidence, openness, dominance, independence, introversion, as well as arrogance and hostility. (For overviews of recent studies on personality traits of creative scientists, see Feist 1999, 2006: chapter 5). Social situatedness has also been explored as an important resource for creativity. In this perspective, the sociocultural structures and practices in which individuals are embedded are considered crucial for the generation of creative ideas. Both approaches suggest that creative individuals usually have outsider status—they are socially deviant and diverge from the mainstream.

Outsider status is also a key feature of standpoint. According to standpoint theorists, people with standpoint are politically aware and politically engaged people outside the mainstream. Some standpoint theorists suggest exploiting this similarity for creativity research. Because people with standpoint have different experiences and access to different domains of expertise than most members of a culture, they can draw on rich conceptual resources for creative thinking. Standpoint theory may thus be an important resource for the development of social and environmental approaches to the study of creativity (Solomon 2007).

Many philosophers of science highlight the role of analogy in the development of new knowledge, whereby analogy is understood as a process of bringing ideas that are well understood in one domain to bear on a new domain (Thagard 1984; Holyoak and Thagard 1996). An important source for philosophical thought about analogy is Mary Hesse's conception of models and analogies in theory construction and development. In this approach, analogies are similarities between different domains. Hesse introduces the distinction between positive, negative, and neutral analogies (Hesse 1966: 8). If we consider the relation between gas molecules and a model for gas, namely a collection of billiard balls in random motion, we will find properties that are common to both domains (positive analogy) as well as properties that can only be ascribed to the model but not to the target domain (negative analogy). There is a positive analogy between gas molecules and a collection of billiard balls because both the balls and the molecules move randomly. There is a negative analogy between the domains because billiard balls are colored, hard, and shiny but gas molecules do not have these properties. The most interesting properties are those properties of the model about which we do not know whether they are positive or negative analogies. This set of properties is the neutral analogy. These properties are the significant properties because they might lead to new insights about the less familiar domain. From our knowledge about the familiar billiard balls, we may be able to derive new predictions about the behavior of gas molecules, which we could then test.

Hesse offers a more detailed analysis of the structure of analogical reasoning through the distinction between horizontal and vertical analogies between domains. Horizontal analogies between two domains concern the sameness or similarity between properties of both domains. If we consider sound and light waves, there are similarities between them: sound echoes, light reflects; sound is loud, light is bright, both sound and light are detectable by our senses. There are also relations among the properties within one domain, such as the causal relation between sound and the loud tone we hear and, analogously, between physical light and the bright light we see. These analogies are vertical analogies. For Hesse, vertical analogies hold the key for the construction of new theories.

Analogies play several roles in science. Not only do they contribute to discovery but they also play a role in the development and evaluation of scientific theories. Current discussions about analogy and discovery have expanded and refined Hesse's approach in various ways. Some philosophers have developed criteria for evaluating analogy arguments (Bartha 2010). Other work has identified highly significant analogies that were particularly fruitful for the advancement of science (Holyoak and Thagard 1996: 186–188; Thagard 1999: chapter 9). The majority of analysts explore the features of the cognitive mechanisms through which aspects of a familiar domain or source are applied to an unknown target domain in order to understand what is unknown. According to the influential multi-constraint theory of analogical reasoning developed by Holyoak and Thagard, the transfer processes involved in analogical reasoning (scientific and otherwise) are guided or constrained in three main ways: 1) by the direct similarity between the elements involved; 2) by the structural parallels between source and target domain; as well as 3) by the purposes of the investigators, i.e., the reasons why the analogy is considered. Discovery, the formulation of a new hypothesis, is one such purpose.

“In vivo” investigations of scientists reasoning in their laboratories have not only shown that analogical reasoning is a key component of scientific practice, but also that the distance between source and target depends on the purpose for which analogies are sought. Scientists trying to fix experimental problems draw analogies between targets and sources from highly similar domains. In contrast, scientists attempting to formulate new models or concepts draw analogies between less similar domains. Analogies between radically different domains, however, are rare (Dunbar 1997, 2001).

In current cognitive science, human cognition is often explored in terms of model-based reasoning. The starting point of this approach is the notion that much of human reasoning, including probabilistic and causal reasoning as well as problem solving takes place through mental modeling rather than through the application of logic or methodological criteria to a set of propositions (Johnson-Laird 1983; Magnani et al. 1999; Magnani and Nersessian 2002). In model-based reasoning, the mind constructs a structural representation of a real-world or imaginary situation and manipulates this structure. In this perspective, conceptual structures are viewed as models and conceptual innovation as constructing new models through various modeling operations. Analogical reasoning—analogical modeling—is regarded as one of three main forms of model-based reasoning that appear to be relevant for conceptual innovation in science. Besides analogical modeling, visual modeling and simulative modeling or thought experiments also play key roles (Nersessian 1992, 1999, 2009). These modeling practices are constructive in that they aid the development of novel mental models. The key elements of model-based reasoning are the call on knowledge of generative principles and constraints for physical models in a source domain and the use of various forms of abstraction. Conceptual innovation results from the creation of new concepts through processes that abstract and integrate source and target domains into new models (Nersessian 2009).

Some critics have argued that despite the large amount of work on the topic, the notion of mental model is not sufficiently clear. Thagard seeks to clarify the concept by characterizing mental models in terms of neural processes (Thagard 2010). In his approach, mental models are produced through complex patterns of neural firing, whereby the neurons and the interconnections between them are dynamic and changing. A pattern of firing neurons is a representation when there is a stable causal correlation between the pattern or activation and the thing that is represented. In this research, questions about the nature of model-based reasoning are transformed into questions about the brain mechanisms that produce mental representations.

The above sections show that the study of scientific discovery has become an integral part of the wider endeavor of exploring creative thinking and creativity more generally. Naturalistic philosophical approaches combine conceptual analysis of processes of knowledge generation with empirical work on creativity, drawing heavily and explicitly on current research in psychology and cognitive science, and on in vivo laboratory observations, and, most recently, on brain imaging techniques.

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Inquiry, Discovery, Invention, and Innovation—The Personal Experience of Technology Generation and Transfer in Engineering and Scientific Research

Information & authors, metrics & citations, science and truth, our usual experience, success, prediction, and knowing, inquiry, discovery, invention, and innovation, the insight experience, fine-tuning our personal instrument, seeing as engineering experience, the human self-organizing learning system, the importance of conflict and chaos in learning, birth of modern science, systems and chaos, basic learning processes, framework of discovery, innovation, and innovation, the question behind the challenge, stage 1: research, inquiry, and information saturation, stage 2: incubation, keystone of discovery, invention, and innovation, stage 3: synthesis, manifesting the idea in the world: development, demonstration, and beginning the work of invention, stage 4: verification, deployment in the marketplace and building the work of innovation, stage 5: implementation, suggested reading, information, published in.

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Innovation is problem solving...and a whole lot more.

Problem Solving. This is a reactive approach to innovation.
Heading off a problem before it happens is a more proactive practice than trying to solve it after it hits the fan.
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They say that necessity is the mother of invention, and I believe them, whoever they are. We can all agree that a problem can be a catalyst for a solution, and that many business innovations are born of business challenges. But is it really that simple? Perhaps innovation is more nuanced than that. As we examine four distinct levels of innovation, try to determine what your contribution to innovation is, and what it could be.

Level 1: Problem Solving. This is a reactive approach to innovation. If embraced in an orderly fashion—meaning, if the innovator is discriminating about which problems to take on and how solutions should be crafted and applied—this first level of innovation can be both powerful and prolific. However, the sometimes frenzied, stimulus-response nature of this style of innovation can often limit people to lower-value contributions. We all know co-workers who spend most of their time putting out fires, which is not necessarily a bad thing (fires can do a lot of damage), but the danger of constantly dealing with the urgent, at the expense of the important, is obvious. Joe Randolph, CEO of the Innovation Institute in La Palma, California, observes, “Problems do create the necessary urgency to innovate, but they can also overwhelm. In my experience, more than 90% of employees are fixated on dealing with day-to-day workload and emergencies, rather than looking toward the future.”

Level 2: Problem Prevention. What if a problem hasn’t happened yet, but lies in the future in the form of a risk? Heading off a problem before it happens is a more proactive practice than trying to solve it after it hits the fan. Innovators who can respond in advance to looming legislation, coming competitive threats, changing customer preferences, and technological advances, can be more valuable to an organization than a reactive problem-solver. But there is less squeaky-wheel urgency attached to potential problems, so innovators often find it difficult to rally the right resources to make change happen.

Level 3: Continuous Improvement. Rise above current and future problems and you will notice the processes, policies, and products that are working quite well. Not a problem in sight. But that doesn’t mean there is no need for innovation. This is where the myth, “all innovation comes from solving problems,” breaks down. In the opinion of Gregory Hicks, Director IT Innovation R&D at Optum , “Because the market is always moving, to rest when things are going well is exactly the wrong response. When the organization has comfortably adapted to its most recent improvements, that’s generally a signal to the innovator that it’s time to drive another set of innovations forward.”

This is also where what I call the Problem Paradox begins. If it ain’t broke, break it is a fun maxim, and an interesting book (you’re welcome, Robert Kriegel), but we should all be aware that breaking, streamlining, or replacing a perfectly good business system, albeit for the greater good in the end, creates real problems in the near-term. For a while, you’ll be back to Level 1.

Level 4: Creation of a New Future. This is the ultimate challenge for those who would innovate, and perhaps the greatest “problem” faced by every organization. With today’s unprecedented pace of change, the pressure on companies to reinvent themselves is relentless. It is sobering to consider the number of companies heralded in books like Built to Last and Good to Great that have since fallen into mediocrity or worse . Disruptive forces are constantly lurking in the shadows, ready to pounce on unwary, and therefore unprepared, successful companies. Staying relevant and competitive is no easy task. One remedy for this persistent problem can be found in the practice of creating Engineered Collisions, a term borrowed from Scott Dulchavsky, CEO of the Henry Ford Innovation Institute in Michigan. “We generate meetings between people who would not normally get together, and ask them to address issues they would not ordinarily talk about,” describes Dulchavsky. “When we put smart people with disparate backgrounds in the same room, they come up with always-wild and sometimes-brilliant solutions.”

If you know of leaders who are wild and brilliant innovators, let me know. I’d like to write about them.

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14 Major Tech Issues — and the Innovations That Will Resolve Them

Members of the Young Entrepreneur Council discuss some of the past year’s most pressing technology concerns and how we should address them.

The past year has seen unprecedented challenges to public-health systems and the global economy. Many facets of daily life and work have moved into the digital realm, and the shift has highlighted some underlying business technology issues that are getting in the way of productivity, communication and security.

As successful business leaders, the members of the Young Entrepreneur Council understand how important it is to have functional, up-to-date technology. That ’ s why we asked a panel of them to share what they view as the biggest business tech problem of the past year. Here are the issues they ’ re concerned about and the innovations they believe will help solve them.

Current Major Technology Issues

Need For Strong Digital Conference Platforms
Remote Internet Speed and Connections
Phishing and Data Privacy Issues
Deepfake Content
Too Much Focus on Automation
Data Mixups Due to AI Implementation
Poor User Experience

1. Employee Productivity Measurement

As most companies switched to 100 percent remote almost overnight, many realized that they lacked an efficient way to measure employee productivity. Technology with “ user productivity reports ” has become invaluable. Without being able to “ see ” an employee in the workplace, companies must find technology that helps them to track and report how productive employees are at home. — Bill Mulholland , ARC Relocation

2. Digital Industry Conference Platforms

Nothing beats in-person communication when it comes to business development. In the past, industry conferences were king. Today, though, the move to remote conferences really leaves a lot to be desired and transforms the largely intangible value derived from attending into something that is purely informational. A new form or platform for industry conferences is sorely needed. — Nick Reese , Elder Guide

3. Remote Internet Speed and Equipment

With a sudden shift to most employees working remotely, corporations need to boost at-home internet speed and capacity for employees that didn ’ t previously have the requirements to produce work adequately. Companies need to invest in new technologies like 5G and ensure they are supported at home. — Matthew Podolsky , Florida Law Advisers, P.A.

4. Too Much Focus on Automation

Yes, automation and multi-platform management might be ideal for big-name brands and companies, but for small site owners and businesses, it ’ s just overkill. Way too many people are overcomplicating things. Stick to your business model and what works without trying to overload the process. — Zac Johnson , Blogger

5. Phishing Sites

There are many examples of phishing site victims. Last year, I realized the importance of good pop-up blockers for your laptop and mobile devices. It is so scary to be directed to a website that you don ’ t know or to even pay to get to sites that actually don ’t exist. Come up with better pop-up blockers if possible. — Daisy Jing , Banish

6. Data Privacy

I think data privacy is still one of the biggest business tech issues around. Blockchain technology can solve this problem. We need more and more businesses to understand that blockchains don’t just serve digital currencies, they also protect people’s privacy. We also need Amazon, Facebook, Google, etc. to understand that personal data belongs in the hands of the individual. — Amine Rahal , IronMonk Solutions

7. Mobile Security

Mobile security is a big issue because we rely so much on mobile internet access today. We need to be more aware of how these networks can be compromised and how to protect them. Whether it ’ s the IoT devices helping deliver data wirelessly to companies or people using apps on their smartphones, we need to become more aware of our mobile cybersecurity and how to protect our data. — Josh Kohlbach , Wholesale Suite

8. Deepfake Content

More and more people are embracing deepfake content, which is content created to look real but isn ’ t. Using AI, people can edit videos to look like someone did something they didn ’ t do and vice versa, which hurts authenticity and makes people question what ’ s real. Lawmakers need to take this issue seriously and create ways to stop people from doing this. — Jared Atchison , WPForms

9. Poor User Experience

I ’ ve noticed some brands struggling with building a seamless user experience. There are so many themes, plugins and changes people can make to their site that it can be overwhelming. As a result, the business owner eventually builds something they like, but sacrifices UX in the process. I suspect that we will see more businesses using customer feedback to make design changes. — John Brackett , Smash Balloon LLC

10. Cybersecurity Threats

Cybersecurity threats are more prevalent than ever before with increased digital activities. This has drawn many hackers, who are becoming more sophisticated and are targeting many more businesses. Vital Information, such as trade secrets, price-sensitive information, HR records, and many others are more vulnerable. Strengthening cybersecurity laws can maintain equilibrium. — Vikas Agrawal , Infobrandz

11. Data Backup and Recovery

As a company, you ’ ll store and keep lots of data crucial to keeping business moving forward. A huge tech issue that businesses face is their backup recovery process when their system goes down. If anything happens, you need access to your information. Backing up your data is crucial to ensure your brand isn ’ t at a standstill. Your IT department should have a backup plan in case anything happens. — Stephanie Wells , Formidable Forms

12. Multiple Ad and Marketing Platforms

A major issue that marketers are dealing with is having to use multiple advertising and marketing platforms, with each one handling a different activity. It can overload a website and is quite expensive. We ’ re already seeing AdTech and MarTech coming together as MAdTech. Businesses need to keep an eye on this convergence of technologies and adopt new platforms that support it. — Syed Balkhi , WPBeginner

13. Location-Based Innovation

The concentration of tech companies in places like Seattle and San Francisco has led to a quick rise in living costs in these cities. Income isn ’ t catching up, and there ’ s stress on public infrastructure. Poor internet services in rural areas also exacerbate this issue. Innovation should be decentralized. — Samuel Thimothy , OneIMS

14. Artificial Intelligence Implementation

Businesses, especially those in the tech industry, are having trouble implementing AI. If you ’ ve used and improved upon your AI over the years, you ’ re likely having an easier time adjusting. But new online businesses test multiple AI programs at once and it ’ s causing communication and data mix-ups. As businesses settle with specific programs and learn what works for them, we will see improvements. — Chris Christoff , MonsterInsights

Recent Corporate Innovation Articles

Invention and Innovation as Creative Problem-Solving Activities

Reference work entry
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Frank Beckenbach 2 &
Maria Daskalakis 2

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4 Citations

Creativity ; Novelty creation

Background: Microeconomics of Novelty Creation and Problem Solving

Obviously, invention and innovation can be hardly analyzed from the usual cost/benefit perspective of economics. These processes are conjectural by their very nature:

Because ex ante results of the search endeavor cannot reasonably be anticipated (or even expected)

Because there is no guarantee for the social acceptance of a possible result

Because there is the risk that an accepted result cannot be used as a source of (additional) private yield (Nelson 1959a , b , 1982 )

Due to these intricacies, invention and innovation have previously been either considered as coming “out of the blue” (Kirzner 1979 ; Vromen 2001 ) or have been simply postulated as an outcome of mesopatterns in terms of paradigms, routines, and institutions (Dosi 1988 ; Lundvall 1992 ).

Notwithstanding these caveats and provisos, various attempts to conceptualize the novelty creating process from a microeconomic...

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Amabile TM. Creativity in context. Boulder/Oxford: Westview Press; 1996.

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Beckenbach, F., Daskalakis, M. (2013). Invention and Innovation as Creative Problem-Solving Activities. In: Carayannis, E.G. (eds) Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3858-8_370

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Sparking Ideas: Why Does One Invention Lead To Another?

By: Author Valerie Forgeard

Posted on Published: May 15, 2023 - Last updated: July 1, 2023

Categories Society

In the grand tapestry of human history, one thread that stands out is our unending journey of innovation. Each invention is a stepping stone from the wheel to the smartphone, leading to the next breakthrough. But have you ever wondered why this chain reaction of invention occurs?

Why does one discovery pave the way for another?

In this exploration, we will dive into the fascinating dynamics of human ingenuity, unraveling how and why each invention becomes a catalyst for the next.

Prepare to embark on an intellectual adventure that reveals the interconnectedness of our creative endeavors and the relentless progress of innovation.

Unleashing Creativity

Imagine standing at the edge of a vast ocean of human innovation, where each wave represents a new invention or idea. With every ebb and flow, these waves build upon one another, creating a seemingly unending sea of progress.

You might wonder why this continuous chain of invention occurs – what drives us to constantly seek new solutions and improvements?

The answer lies in our innate curiosity and creativity, our ability to learn from the past, and our ever-evolving challenges.

As you dive deeper into this ocean, you’ll discover that interconnectedness is crucial in sparking new inventions. Our natural competitive drive pushes us towards advancements that often open doors for even more discoveries.

This fascinating journey of exploration reveals how human ingenuity knows no bounds and will continue to flourish as long as we remain curious and open-minded. So let’s embark on an adventure through time and space, examining the intricate web of ideas that have shaped our world today – because understanding why one invention leads to another is essential in unlocking your limitless potential for creative freedom.

The Role of Curiosity and Creativity

It’s fascinating how curiosity and creativity play such crucial roles in driving the continuous chain of inventions! Curiosity catalyzes innovation by pushing you to explore the unknown, ask questions, and seek answers.

This innate desire to learn more about the world around you has led humankind to make discoveries and advancements throughout history. The combination of curiosity and creative exploration allows new ideas to emerge, inspiring others to build upon those concepts or create something entirely different.

Creative exploration goes hand-in-hand with curiosity as it encourages experimentation and problem-solving. When you dive into an unfamiliar territory or challenge existing norms, it often leads to breakthroughs that were previously unimaginable.

Exploring new possibilities, reimagining solutions, and constantly iterating helps propel innovations forward rapidly. Embracing this mindset fuels your inventiveness and creates a ripple effect that inspires others to approach their work with a similar sense of ingenuity.

The ongoing cycle of invention is genuinely remarkable – driven by people like you who are curious enough to ask ‘what if’ and brave enough to venture into uncharted territory. By fostering a culture that values curiosity and creativity, we can continue advancing our collective knowledge while unlocking new potential in ourselves and others.

Building Upon Existing Knowledge

Often, inventors rely on pre-existing knowledge to inspire and inform their creations, sparking further innovation. Building upon existing knowledge is a critical driver in the evolution of innovation.

As new ideas emerge and become integrated into society, they pave the way for future advancements, creating a cycle of perpetual progress. Knowledge accumulation fuels this ongoing process by providing a foundation that can be expanded upon and developed as time goes on.

Consider these examples of how innovations have built upon previous breakthroughs:

The invention of the printing press revolutionized the dissemination of information and set the stage for widespread literacy.
The development of the steam engine led to advances in transportation systems like steam locomotives and trains, which made it possible for people to travel greater distances easily.
The discovery of electricity enabled countless inventions, including electric lighting and communication devices such as telephones.
The creation of computers has given rise to an entirely new era marked by rapid technological progress and digital interconnectedness.

As you can see from these examples, inventions often serve as stepping stones by opening up new possibilities that were previously unimaginable. By expanding our understanding and capabilities through knowledge accumulation, we enable innovation evolution that continues to push boundaries and redefine what is possible.

This drive for freedom through exploration is an underlying motivation and creates an environment where even more radical ideas can take root. The beauty lies in that each invention builds upon its predecessors while simultaneously inspiring future creations.

With every advancement comes a wealth of potential applications just waiting to be discovered. So embrace curiosity, foster creativity, and continue seeking new knowledge; your contributions might spark the next wave of groundbreaking innovations that propel humanity into uncharted territory.

Solving New Problems and Challenges

As we tackle new problems and challenges, the innovations created along the way become crucial stepping stones for even more significant discoveries.

Problem identification is the first step in this journey – recognizing an issue or obstacle that needs to be overcome. From there, innovative solutions can be developed to address these challenges, often leading to new inventions or technologies.

As a result, each advancement paves the way for future growth and development.

In many cases, solving one problem may reveal additional obstacles or issues that were not previously apparent.

This continuous problem-solving cycle leads to steady inventions and improvements across various fields and industries.

For example, advancements in transportation have led to increased efficiency and environmental concerns, which in turn have inspired further innovations in clean energy and sustainable practices.

Similarly, breakthroughs in medical research often lead scientists down new paths of inquiry as they seek better treatments or cures for diseases.

As you continue on your quest for knowledge and freedom from limitations, remember that every challenge faced offers an opportunity for growth and progress.

Embrace these problems with curiosity and determination; by doing so, you will contribute to a legacy of invention that spans generations.

By pushing boundaries through innovation today, you’re setting the stage for tomorrow’s achievements – ultimately shaping our collective future as we build upon each other’s successes in pursuit of a brighter world.

Technological Advancements and Interconnectivity

As technology advances and our world becomes more interconnected, you’re exposed to many ideas, solutions, and opportunities that inspire further innovation. The breaking down of interconnectivity barriers has allowed for rapid advancement acceleration in various fields.

With access to more information than ever, people can build on each other’s work and knowledge at an unprecedented pace. This not only improves existing inventions but also paves the way for new technologies that address the evolving needs of society.

Interconnectivity allows you to collaborate with others from different industries or disciplines, providing fresh perspectives on problems and challenges inventors face. In this age of globalization, it’s now easier than ever for individuals across the globe to share their expertise to create groundbreaking innovations.

As a result, interdisciplinary collaboration has become essential for pushing the boundaries of what’s possible – both technologically and socially. These synergistic relationships create products and services that have a more significant impact on society.

The increased rate at which technology evolves also creates additional avenues for invention as new possibilities emerge from advancements in one field that can be applied or adapted into another domain altogether. For instance, developments in materials science may enable engineers to design lighter yet stronger structures; advancements in artificial intelligence could provide architects with tools that facilitate more brilliant building designs; while breakthroughs in biotechnology might allow medical professionals to develop innovative treatments for previously incurable diseases.

By staying informed about these advancements and understanding their potential applications beyond your field, you’re afforded greater freedom when exploring uncharted territory and developing inventive solutions that have lasting benefits.

The Competitive Drive for Progress

It’s no secret that competition fuels progress, pushing inventors to outdo themselves and strive for even more significant innovations constantly. The competitive drive for progress in the world of invention is a powerful force that benefits everyone involved – from the creators to the consumers.

This desire to stay ahead of the pack creates an environment where innovation flourishes, leading to advancements that can drastically improve our lives. Competitive innovation acts as a catalyst for change, providing inventors with progress incentives such as financial rewards or recognition within their industry.

Throughout history, competition has frequently catalyzed groundbreaking inventions. Here are two prominent examples that demonstrate this dynamic:

The Space Race Between the United States and the Soviet Union

This competition led to a series of significant advancements. It resulted in substantial rocket technology improvements and sophisticated satellite systems development. Ultimately, this rivalry propelled humanity into the era of space exploration, shaping our understanding of the universe and paving the way for technological innovations beyond Earth.

The ‘War of Currents’ Between Thomas Edison and Nikola Tesla

This intense rivalry revolved around competing electrical systems: Edison’s direct current (DC) and Tesla’s alternating current (AC). The competitive drive between these two inventors was instrumental in shaping how electricity powers our modern society. Both systems had their merits, and their adoption varied based on specific requirements, leading to a more diverse and efficient electrical infrastructure.

These instances underscore how competition can spur innovation, pushing the boundaries of what is technologically possible and driving societal progress.

Our Competitive Nature

The competitive nature of human beings often brings out their best qualities. When faced with challenges or competitors, people are driven to push themselves beyond their limits and achieve things they never thought possible.

As inventions continuously build upon one another, new opportunities arise for further discoveries and improvements. It’s important not to forget how interconnected technological advancements can be when considering why one invention leads to another.

Behind every life-changing device or system lies a web of other innovations that have contributed towards its creation – each spurred on by individuals seeking success and recognition amongst their peers. Embrace this spirit of friendly rivalry; it’s a driving force behind much of humankind’s most significant achievements thus far, promising countless more exciting breakthroughs.

The Double-Edged Sword of Continuous Invention

The phenomenon of one invention leading to another is a powerful engine of progress, driving us forward in leaps and bounds. On the one hand, it’s undoubtedly a positive force, propelling us towards better solutions, greater efficiency, and improved quality of life. Each new invention opens up a world of possibilities, offering us new tools to tackle challenges, enrich our experiences, and expand our horizons. Technological advancements in healthcare , for instance, have given us everything from life-saving medications to sophisticated diagnostic tools, dramatically improving human longevity and well-being.

However, like any mighty force, the chain of invention also has its potential downsides. Not all inventions are beneficial or benign. Some can have unintended negative consequences, primarily when misused or their implications are not fully understood. For instance, the invention of plastic has revolutionized numerous industries and made many aspects of daily life more convenient. But it has also led to widespread environmental pollution and related problems.

Moreover, the pace of invention can sometimes outstrip our ability to adapt to or manage the changes it brings about. Rapid technological advancements can lead to societal disruptions, job displacement due to automation, and a widening gap between those with access to new technologies and those without.

In essence, whether one invention leading to another is, a good or bad thing isn’t a black-and-white issue. It’s a complex dynamic with both positive and negative aspects. The key lies in managing this process and leveraging the benefits of new inventions while mitigating the risks and addressing the challenges they pose. As we continue this endless journey of innovation, our responsibility is to steer this powerful force in a direction that maximizes benefit, minimizes harm, and upholds our values.

Navigating the Ethical Compass: Invention in the Modern World

In the exhilarating journey of invention and innovation lies an often-overlooked yet profoundly important aspect: ethics. The connection between invention and ethics is crucial, serving as a moral compass guiding our relentless pursuit of progress.

As inventors push the boundaries of what’s possible, they also tread uncharted ethical territories.

Emerging technologies – artificial intelligence to genetic engineering – can offer promising solutions to pressing global issues. However, they also present new ethical dilemmas.

For instance, how should we address privacy concerns in an increasingly interconnected digital world? How do we ensure the responsible use of gene-editing technologies like CRISPR, which hold immense potential yet also risk unforeseen consequences?

The ethical considerations of the invention extend beyond the products to include the invention process. Questions arise concerning who benefits from these inventions and who might be left behind or harmed. Are these innovations accessible and equitable, or do they widen the socioeconomic divide?

Moreover, sustainability has become a critical ethical aspect of the invention in our era of environmental consciousness. Inventors today are urged to consider the long-term environmental impact of their creations, aiming not just for innovation but for sustainable innovation that respects and preserves our planet for future generations.

Balancing the pursuit of progress with ethical responsibility is no easy task, but it’s a challenge we must overcome.

Ensuring ethical considerations are woven into the fabric of the invention process is essential to building a future that is not only technologically advanced but also just, equitable, and sustainable. It’s not enough to ask if we can invent something. We must also ask if we should.

In this way, ethics provides a crucial counterbalance to innovation, helping steer the course of progress toward a future that reflects our highest values and ideals.

The Endless Cycle of Innovation

The intricate dance of invention and innovation is a testament to human ingenuity and our thirst for knowledge. Each invention is not an isolated event but a link in a continuous chain of progress stretching across human history’s tapestry. One invention sparks another, leading to innovations shaping and reshaping our world.

This process is driven by our inherent curiosity and a desire to solve problems. It’s our response to the ever-evolving challenges we encounter, both in our immediate surroundings and the broader context of our society and environment. As we’ve seen from historical examples, competition often serves as a catalyst, pushing us to strive for better, more efficient solutions.

Moreover, each invention opens up new realms of possibility, expanding our understanding and revealing fresh perspectives. As we learn more, we uncover new questions, sparking further exploration and discovery. This cycle of invention, discovery, and learning is self-perpetuating, fueled by our ceaseless drive to understand and improve the world around us.

While the invention process is complex and multifaceted, one thing is sure. As long as our curiosity endures, the chain of invention will continue to extend into the future, limited only by the boundaries of our imagination. Therefore, understanding why one invention leads to another isn’t just about appreciating our past—it’s also about unlocking our potential to innovate and shape the future. This insight is crucial for everyone, regardless of whether you’re an inventor, an entrepreneur, a student, or simply someone curious about the world, as it illuminates the path of progress and limitless possibilities.

The continuous and interconnected journey of human invention is like a river, with each breakthrough serving as a tributary feeding into the primary current of progress. As we’ve seen throughout history, from the Industrial Revolution to the Second Industrial Revolution, one invention invariably leads to another, creating a ripple effect that shapes the course of human civilization.

Consider the work of inventors like Eli Whitney and James Hargreaves, whose creations revolutionized the textile industry leading to mass production, laying the groundwork for the Industrial Revolution.

Their inventions not only transformed their era but also catalyzed future innovations. In a similar vein, Antonio Meucci’s early work in voice communication paved the way for the future work of Alexander Graham Bell and, eventually, the development of the telephone . This invention, born in New York, ignited a communication revolution that continues to evolve today with new technology.

Then, innovations such as nuclear weapons are born out of necessity and competition. While their development and use raise significant ethical issues, they have also sparked advancements in related fields such as nuclear energy and medicine.

Finally, it’s essential to highlight the role of recognition and incentive in spurring further invention. Awards like the Nobel Prizes encourage innovation by acknowledging groundbreaking work and inspiring others to push the boundaries of new knowledge and technology.

In conclusion, the journey of invention is an unending cycle driven by our innate curiosity, desire for improvement, and the challenges we encounter. Each new invention opens up fresh possibilities and lays the groundwork for further advancements. As long as our drive to explore, learn, and improve continues, this remarkable chain of invention will also shape our world in ways we can only begin to imagine.

Frequently Asked Questions

What does it mean when one invention leads to another.

This refers to the concept that an invention or a technological breakthrough often inspires or enables subsequent innovations. Each invention opens up new possibilities, catalyzing further technological or knowledge advancements.

What is an example of one invention leading to another?

A classic example is the invention of the internet. It started as a way to connect computers and share information, but it has since led to countless other inventions such as web browsers, social media platforms, and streaming services, to name a few.

What factors cause one invention to lead to another?

Factors can include the natural human desire for improvement, the need to solve new problems that arise, societal and economic demands, competition, and the evolution of knowledge and technology.

Does every invention lead to another?

Not every invention directly leads to another, but every invention contributes to the overall body of human knowledge and technological capability. This can inspire or enable future innovations, even if the connections are not immediately apparent.

How does understanding why one invention leads to another benefit us?

Understanding this concept can offer insights into the innovation process, the evolution of technology and society, and the potential future direction of scientific and technological advancement. It also underscores the value of investing in research, education, and innovation.

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COMMENTS

The Innovation Process: 10-Step Process to Successful Innovation
Step 3: Develop a Solution. Moving into the third phase of the innovation process, the focus now shifts to crafting a viable and deployable solution poised for market entry. This stage involves the meticulous development of solutions, including the construction of prototypes and the execution of various tests.
The 4 Types of Innovation and the Problems They Solve
Innovation is, at its core, about solving problems — and there are as many ways to innovate as there are different types of problems to solve. Just like we wouldn't rely on a single marketing ...
4.3 Developing Ideas, Innovations, and Inventions
Outline the sequence of steps in developing an invention; The previous section defined creativity, innovation, and invention, and provided examples. ... the entrepreneur Larry Myler notes that problem solving is inherently reactive. 40 That is, you have to wait for a problem to happen in order to recognize the need to solve the problem. Solving ...
Edisonian approach
Historian Thomas Hughes (1977) describes the features of Edison's method. In summary, they are: Hughes says, "In formulating problem-solving ideas, he was inventing; in developing inventions, his approach was akin to engineering; and in looking after financing and manufacturing and other post-invention and development activities, he was innovating."
Creative Problem Solving: from complex challenge to innovative solution
The Creative Problem Solving process, sometimes referred to as CPS, is a proven way to approach a challenge more imaginatively. By redefining problems or opportunities, it becomes possible to move in a completely new and more innovative direction. Dr Donald Treffinger described Creative Problem Solving as an effective way to review problems ...
What made the last century's great innovations possible?
What could result is an era of greater abundance and problem-solving, but also enormous challenges — for instance, as computers increasingly take on tasks that result in the replacement of human ...
Nobel Turing Challenge: creating the engine for scientific discovery
Case studies: scientific discovery as a problem-solving. Herbert Simon argued that science is problem-solving in his article "The Scientist as Problem Solver" 18. Scientists set themselves ...
Inventive Problem Solving (TRIZ), Theory
The first TRIZ application (reflected in the name of the methodology) - solving inventive problems in technological areas. However, inventive problem solving (IPS) is only one of the existing innovation needs. To address all needs and develop a complete innovation and problem-solving platform, the following steps have been taken: 1.
4.3: Developing Ideas, Innovations, and Inventions
In the influential business publication Forbes, the entrepreneur Larry Myler notes that problem solving is inherently reactive. 41 That is, you have to wait for a problem to happen in order to recognize the need to solve the problem. Solving problems is an important part of the practice of innovation, but to elevate the practice and the field ...
Invention and Innovation as Creative Problem-Solving Activities
This specific type of an ill-defined situation is called here a "strong ill-defined problem." ad (b): Given such a strong ill-defined problem, the first stage of the inventive process is the solution of the "problem" of problem finding.This problem is coped with by the above mentioned generative processes (section "Creative Cognition and Creative Problem Solving") leading to ...
PDF Technological Innovation, Invention and Problem-Solving
Students will be asked to try and figure out the connections, creative problem-solving steps and inter-related innovations, inventions and developments that occurred from one historical stage to the next, in the development of semiconductors. In other words, how did one person's 'invention' become the stepping stone for the next creative ...
Ten Scientific Discoveries From 2020 That May Lead to New Inventions
Whether the problems researchers are looking to solve involve building better robots, tracking cancer cells more efficiently or improving telescopes to study space, a useful solution can be found ...
Lesson plan: Solving problems through invention
In this lesson, students will learn the first key step in the invention process: how to identify and explore problems around them using current events. They'll get to choose one story from a ...
How Machine Discovery Can Accelerate Solutions to Society's Big Problems
New technologies such as machine learning, robotics, digital twins, and supercomputing can dramatically improve the odds of successful discovery — through faster and more efficient selection of the most promising new ideas and accelerated testing to get these ideas into development sooner. These technologies are paving the way to breakthrough ...
Scientific Discovery
Scientific discovery is the process or product of successful scientific inquiry. Objects of discovery can be things, events, processes, causes, and properties as well as theories and hypotheses and their features (their explanatory power, for example). Most philosophical discussions of scientific discoveries focus on the generation of new ...
Inquiry, Discovery, Invention, and Innovation—The Personal Experience
Inquiry generates from a curious state of mind about a segment of nature or reality. Questions are asked. Answers are sought. Discovery involves finding a new understanding about an aspect of nature. Behavior is newly comprehended. Material is newly generated or transformed. Invention uses this understanding to create a new way of working or ...
10 Breakthrough Technologies 2024
Every year, the reporters and editors at MIT Technology Review survey the tech landscape and pick 10 technologies that we think have the greatest potential to change our lives in the years ahead ...
Problem-Solving in Science and Technology Education
Many researchers proposed phases or steps to simplify the problem-solving process. However, according to Anderson (), problem-solving is an intuitive process, which is later checked analytically.He cites Bruner (1962, cited in Anderson, 1967) in saying that rather than using set formulas or patterns, intuitive problem-solving appears to be based on an implicit awareness of the entire issue ...
Unit 5 Invention and Innovation Flashcards
proactive problem-solving 1st step in making product or system includes concept, research, experimentation and development needed to prepare the product for manufacturing includes invention and innovation can refer to how something is made deals with design principles: rhythm, balance, proportion, emphasis ... could lead to innovation.
Innovation Is Problem Solving...And A Whole Lot More
Level 1: Problem Solving. This is a reactive approach to innovation. If embraced in an orderly fashion—meaning, if the innovator is discriminating about which problems to take on and how ...
14 Major Tech Issues & How To Solve Them
13. Location-Based Innovation. The concentration of tech companies in places like Seattle and San Francisco has led to a quick rise in living costs in these cities. Income isn ' t catching up, and there ' s stress on public infrastructure. Poor internet services in rural areas also exacerbate this issue.
Invention and Innovation as Creative Problem-Solving Activities
Invention and innovation are stages of the novelty creating process as a whole (which also includes the diffusion phase (Rogers 1995)). They are distinct in terms of general definition, cognitive resources, environmental conditions, process elements, and economic character. Taking into consideration these differences, the whole novelty creating ...
Sparking Ideas: Why Does One Invention Lead To Another?
Each invention is not an isolated event but a link in a continuous chain of progress stretching across human history's tapestry. One invention sparks another, leading to innovations shaping and reshaping our world. This process is driven by our inherent curiosity and a desire to solve problems.