Tag Archives: Front End of Innovation

Will CHATgpt make us more or less innovative?

Will CHATgpt make us more or less innovative?

GUEST POST from Pete Foley

The rapid emergence of increasingly sophisticated ‘AI ‘ programs such as CHATgpt will profoundly impact our world in many ways. That will inevitably include Innovation, especially the front end. But will it ultimately help or hurt us? Better access to information should be a huge benefit, and my intuition was to dive in and take full advantage. I still think it has enormous upside, but I also think it needs to be treated with care. At this point at least, it’s still a tool, not an oracle. It’s an excellent source for tapping existing information, but it’s (not yet) a source of new ideas. As with any tool, those who understand deeply how it works, its benefits and its limitations, will get the most from it. And those who use it wrongly could end up doing more harm than good. So below I’ve mapped out a few pros and cons that I see. It’s new, and like everybody else, I’m on a learning curve, so would welcome any and all thoughts on these pros and cons:

What is Innovation?

First a bit of a sidebar. To understand how to use a tool, I at least need to have a reasonably clear of what goals I want it to help me achieve. Obviously ‘what is innovation’ is a somewhat debatable topic, but my working model is that the front end of innovation typically involves taking existing knowledge or technology, and combining it in new, useful ways, or in new contexts, to create something that is new, useful and ideally understandable and accessible. This requires deep knowledge, curiosity and the ability to reframe problems to find new uses of existing assets. A recent illustrative example is Oculus Rift, an innovation that helped to make virtual reality accessible by combining fairly mundane components including a mobile phone screen and a tracking sensor and ski glasses into something new. But innovation comes in many forms, and can also involve serendipity and keen observation, as in Alexander Fleming’s original discovery of penicillin. But even this requires deep domain knowledge to spot the opportunity and reframing undesirable mold into a (very) useful pharmaceutical. So, my start-point is which parts of this can CHATgpt help with?

Another sidebar is that innovation is of course far more than simply discovery or a Eureka moment. Turning an idea into a viable product or service usually requires considerable work, with the development of penicillin being a case in point. I’ve no doubt that CHATgpt and its inevitable ‘progeny’ will be of considerable help in that part of the process too.   But for starters I’ve focused on what it brings to the discovery phase, and the generation of big, game changing ideas.

First the Pros:

1. Staying Current: We all have to strike a balance between keeping up with developments in our own fields, and trying to come up with new ideas. The sheer volume of new information, especially in developing fields, means that keeping pace with even our own area of expertise has become challenging. But spend too much time just keeping up, and we become followers, not innovators, so we have to carve out time to also stretch existing knowledge. But if we don’t get the balance right, and fail to stay current, we risk get leapfrogged by those who more diligently track the latest discoveries. Simultaneous invention has been pervasive at least since the development of calculus, as one discovery often signposts and lays the path for the next. So fail to stay on top of our field, and we potentially miss a relatively easy step to the next big idea. CHATgpt can become an extremely efficient tool for tracking advances without getting buried in them.

2. Pushing Outside of our Comfort Zone: Breakthrough innovation almost by definition requires us to step beyond the boundaries of our existing knowledge. Whether we are Dyson stealing filtration technology from a sawmill for his unique ‘filterless’ vacuum cleaner, physicians combining stem cell innovation with tech to create rejection resistant artificial organs, or the Oculus tech mentioned above, innovation almost always requires tapping resources from outside of the established field. If we don’t do this, then we not only tend towards incremental ideas, but also tend to stay in lock step with other experts in our field. This becomes increasingly the case as an area matures, low hanging fruit is exhausted, and domain knowledge becomes somewhat commoditized. CHATgpt simply allows us to explore beyond our field far more efficiently than we’ve ever been able to before. And as it or related tech evolves, it will inevitably enable ever more sophisticated search. From my experience it already enables some degree of analogous search if you are thoughtful about how to frame questions, thus allowing us to more effectively expand searches for existing solutions to problems that lie beyond the obvious. That is potentially really exciting.

Some Possible Cons:

1. Going Down the Rabbit Hole: CHATgpt is crack cocaine for the curious. Mea culpa, this has probably been the most time consuming blog I’ve ever written. Answers inevitably lead to more questions, and it’s almost impossible to resist playing well beyond the specific goals I initially have. It’s fascinating, it’s fun, you learn a lot of stuff you didn’t know, but I at least struggle with discipline and focus when using it. Hopefully that will wear off, and I will find a balance that uses it efficiently.

2. The Illusion of Understanding: This is a bit more subtle, but a topic inevitably enhances our understanding of it. The act of asking questions is as much a part of learning as reading answers, and often requires deep mechanistic understanding. CHATgpa helps us probe faster, and its explanations may help us to understand concepts more quickly. But it also risks the illusion of understanding. When the heavy loading of searching is shifted away from us, we get quick answers, but may also miss out on the deeper mechanistic understanding we’d have gleaned if we’d been forced to work a bit harder. And that deeper understanding can be critical when we are trying to integrate superficially different domains as part of the innovation process. For example, knowing that we can use a patient’s stem cells to minimize rejection of an artificial organ is quite different from understanding how the immune system differentiates between its own and other stem cells. The risk is that sophisticated search engines will do more heavy lifting, allow us to move faster, but also result in a more superficial understanding, which reduces our ability to spot roadblocks early, or solve problems as we move to the back end of innovation, and reduce an idea to practice.

3. Eureka Moment: That’s the ‘conscious’ watch out, but there is also an unconscious one. It’s no secret that quite often our biggest ideas come when we are not actually trying. Archimedes had his Eureka moment in the bath, and many of my better ideas come when I least expect them, perhaps in the shower, when I first wake up, or am out having dinner. The neuroscience of creativity helps explain this, in that the restructuring of problems that leads to new insight and the integration of ideas works mostly unconsciously, and when we are not consciously focused on a problem. It’s analogous to the ‘tip of the tongue’ effect, where the harder we try to remember something, the harder it gets, but then comes to us later when we are not trying. But the key for the Eureka moment is that we need sufficiently deep knowledge for those integrations to occur. If CHATgpt increases the illusion of understanding, we could see less of those Eureka moments, and the ‘obvious in hindsight ideas’ they create.

Conclusion

I think that ultimately innovation will be accelerated by CHATgpt and what follows, perhaps quite dramatically. But I also think that we as innovators need to try and peel back the layers and understand as much as we can about these tools, as there is potential for us to trip up. We need to constantly reinvent the way we interact with them, leverage them as sophisticated innovation tools, but avoid them becoming oracles. We also need to ensure that we, and future generations use them to extend our thinking skill set, but not become a proxy for it. The calculator has in some ways made us all mathematical geniuses, but in other ways has reduced large swathes of the population’s ability to do basic math. We need to be careful that CHATgpt doesn’t do the same for our need for cognition, and deep mechanistic and/or critical thinking.

Image credit: Pixabay

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Preserving Ecosystems as an Innovation Superpower

Lessons from Picasso and David Attenborough

Preserving Ecosystems as an Innovation Superpower

GUEST POST from Pete Foley

We probably all agree that the conservation of our natural world is important. Sharing the planet with other species is not only ethically and emotionally the right thing to do to, but it’s also enlightened self-interest. A healthy ecosystem helps equilibrate and stabilize our climate, while the potential of the largely untapped biochemical reservoir of the natural world has enormous potential for pharmaceuticals, medicine and hence long-term human survival.

Today I’m going to propose yet another reason why conservation is in our best interest. And not just the preservation of individual species, but also the maintenance of the complex, interactive ecosystems in which individual species exist.

Biomimicry: Nature is not only a resource for pharmaceuticals, but also an almost infinite resource for innovation that transcends virtually every field we can, or will imagine. This is not a new idea. Biomimicry, the concept of mimicking natures’ solutions to a broad range of problems, was first coined by Janine Benyus in 1997. But humans have intuitively looked to nature to help solve problems throughout history. Silk production in ancient bio-technology that co-opts the silk worm, while much of early human habitations were based on caves, a natural phenomenon. More recently, Velcro, wind turbines, and elements of bullet train design have all been attributed to innovation inspired by nature.

And Biomimicry, together with related areas such as biomechanics and bio-utilization taps into the fundamental core of what the front end of innovation is all about. Dig deep into virtually any innovation, and we’ll find it has been stolen from another source. For example, early computers reapplied punch cards from tapestry looms. The Beatles stole and blended liberally from the blues, skiffle, music hall, reggae and numerous other sources. ‘Uberization’ has created a multitude of new business from AirBNB to nanny, housecleaning or food prep services. Medical suturing was directly ‘stolen’ from embroidery, the Dyson vacuum from a sawmill, oral care calcium deposition technology was reapplied from laundry detergents, etc., etc..

Picasso – Great Artists Steal! This is also the creative process espoused by Pablo Picasso when he said ‘good artists borrow, great artists steal’. He ‘stole’ elements of African sculpture and blended them with ideas from contemporaries such as Cézanne to create analytical cubism. In so doing he combined existing knowledge in new ways that created a revolutionary and emergent form of art – one that asked the viewer to engage with a painting in a whole new way. Innovation incarnate!

Ecosystems as an Innovation Resource: The biological world is the biggest potential source of potential innovative ideas we have at our disposal anywhere.  Hence it is an intuitive place to go looking for ideas to solve our biggest innovation challenges. But despite many people trying to leverage this potential goldmine, including myself, it’s never really achieved its full potential. For sure, there are a few great examples, such as Velcro, bullet train flow dynamics or sharkskin surfaces. But given how long we’ve been playing in this sandbox, there are far too few successes. And of those, far too many are based on hindsight, as opposed to using nature to solve a specific challenge. Just look at virtually any article on biomimicry, and the same few success stories show up year after year.

The Resource/Source Paradox. One issue that helps explain this is that the natural world is an almost infinite repository of information. That potential creates a challenging signal to noise’ search problem. The result is enormous potential, but coupled with almost inevitably high failure rates, as we struggle to find the most useful insights

Innovation is More than Ideation: Another challenge is that innovation is not just about ideas or invention; it’s about turning those ideas into practice. In the case of biomimicry, that is particularly hard, as the technical challenge of converting natural technology into viable commercial technologies is hampered because nature works on fundamentally different design principles, and uses very different materials to us. Evolution builds at a nano scale, is highly context dependent, and is result rather than theory led. Materials are usually organic; often water based, and are grown rather than manufactured.  Very different to most conventional human engineering.

Tipping Point: But the good news is that materials science, technology, 3D printing and computational and data processing power, together with nascent AI are evolving at such a fast rate that I’m optimistic that we will soon reach a tipping point that will make search and translation of natural innovations considerably easier than today. Self-learning systems should be able to more easily replicate natural information processing, and 3D printing and nano structures should be able to better mimic the physical constructs of natural systems. AI, or at least massively increased computing power should make it easier for us to both ask the right questions and search large, complex databases.

Conservation as an Innovation Superpower: And that brings me back to conservation as an innovation superpower. If we don’t protect our natural environment, we’ll have a lot less to search, and a lot less to mimic. And that applies to ecosystems as well as individual species. Take the animal or plant out of its natural environment, and it becomes far more difficult to untangle how or why it has evolved in a certain way.

Evolution is the ultimate exploiter of serendipity. It does not have to understand why something works, it simply runs experiments until it stumbles on solutions that do, and natural selection picks the winner(s). That leads to some surprisingly sophisticated innovation. For example, we are only just starting to understand the quantum effects used in avian navigation and photosynthesis. Migratory birds don’t have deep knowledge of quantum mechanics; the beauty of evolution is that they don’t need to. The benefit to us is that we can potentially tap into sophisticated innovation at the leading edge of our theoretical knowledge, provided we know how to define problems, where to look and have sufficient knowledge to decipher it and reduce it to practice. The bad news is that we don’t know what we don’t know. Evolution tapped into quantum mechanics millennia before we knew what it was, so who knows what other innovations lie waiting to be discovered as our knowledge catches up with the nature – the ultimate experimenter.

Ecosystems Matter: But a species without the context of its ecosystem is at best half the story. Nature has solved flight, deep-water exploration, carbon sequestration, renewable energy, high and low temperature resilience and so many more challenges. And it has also done so with 100% utilization and recycling on a systems basis. But most of the underlying innovations solve very specific problems, and so require deep understanding of context.

The Zebra Conundrum: Take the zebra as an example. I was recently watching a David Attenborough documentary about zebras. As a tasty prey animal surrounded by highly efficient predators such as lions, leopards, cheetahs and hyenas, the zebra is an evolutionary puzzle. Why has it evolved a high contrast coat that grabs attention and makes it visible from miles away? High contrast is a fundamental visual cue that means even if a predator is not particularly hungry; it is pretty much compelled to take notice of the hapless zebra. But despite this, the zebra has done pretty well, and the planes of Africa are scattered with this very successful animal. The explanation for this has understandably been the topic of much conjecture and research, and to this day remains somewhat controversial. But more and more, the explanation is narrowing onto a surprisingly obvious culprit; the tsetse fly. When we think of the dangers to a large mammal, we automatically think of large predators. But while zebras undoubtedly prefer to avoid being eaten by lions, diseases associated with tsetse fly bites kill more of them. That means that avoiding tsetse flies likely creates stronger evolutionary pressure than avoiding lions, and that is proving to be a promising explanation for the zebras coat. Far less flies land on or bite animals with stripes.  Exactly why that is remains debatable, and theories range from disrupting the flies vision when landing, to creating mini weather fronts due to differential heating or cooling from the stripes. But whatever the mechanism ultimately turns out to be, stripes stop flies. It appears that the obvious big predators were not the answer after all.

Context Matters: But without deep understanding of the context in which the zebra evolved, this would have been very difficult to unravel. Even if we’d conserved zebras in zoos, finding the tsetse fly connection without the context of the complex African savannah would be quite challenging. It’s all too easy to enthusiastically chase an obvious cause of a problem, and so miss the real one, and our confirmation bias routinely amplifies this.

We often talk about protecting species, but if, as our technology evolves to more effectively ‘steal’ ideas from natural systems, from an innovation perspective alone, preserving context, in the form of complex ecosystems may likely turn out to be at least as important as preserving individual species. We don’t know what we don’t know, and often the surprisingly obvious and critical answer to a puzzle can only be determined by exploring a puzzle in its natural environment.

Enlightened Self-Interest. Could we use an analogy to the zebra to help control malaria? Could we steal avian navigation for gps? I have no idea, but I believe this makes pursuing conservation enlightened self-interest of the highest order. We want to save the environment for all sorts of reasons, but one of the most interesting is that one-day, some part of it could save us.

Image credit: Pixabay

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Join me at the Front End of Innovation 2013

Join me at the Front End of Innovation 2013From May 5-8, 2013 I will be in Boston, MA for the Front End of Innovation conference at the Seaport World Trade Center which takes place May 6-8, 2013.

Use discount code FEI13BRADEN to save 20% on the event and join me and 600+ innovation managers and thought leaders from around the world who are serious about learning more about the front-end of innovation or improving existing innovation efforts.

I’ll be there leading some thought provoking panel sessions, sharing new insights, and reconnecting with innovation friends (both old and new).

If you’d like to set up a meeting to explore your innovation efforts or needs while I’m there, please contact me.

I’m also willing to hold a FREE train the trainer session to go with all of the other FREE Nine Innovation Roles resources — if enough people are interested. To register your interest please fill out the contact form and make a note in the question field.


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