nhoeller's blog

Turning Great Ideas Into Viable Solutions

While trying to clean up my inbox (a fundamentally futile activity), I stumbled on https://www.wired.com/story/the-race-to-put-silk-in-nearly-everything/ that describes the early enthusiasm in emulating silk from spiders and silkworms, the years of research to understand the underlying principles, the trial-and-error of turning the concept into viable solutions, and a few success stories. 

Unfortunately, the road from idea to viable solution is often long and rocky - we covered some of the challenges and opportunities in the "Stories from the Trenches" series.  “Build a better mousetrap, and the world will beat a path to your door" rarely pans out, because it "focuses solely on the technology and not on the consumer. Consumers really don’t care about a better mousetrap. They care about fewer mice." (https://marketoonist.com/2011/08/a-better-mousetrap.html)  "The Race to Put Silk in Nearly Everything" covers the technical challenges but also the difficulty of finding Geoffrey Moore's "beachheads" (https://www.themarketingstudent.com/crossing-the-chasm-summary/) and building a "whole product" solution (watch the YouTube clip of Steve Jobs announcing the iPod technology but also how it fits into the Apple digital strategy).  

This requires extensive knowledge of the target client (their pain points. capabilities, size, and alignment with your solution), dogged persistence, and lots of patience.  Ed Catmull of Pixar emphasises the importance of "people from different disciplines working effectively together to solve a great many inherently unforeseeable problems." (https://hbr.org/2008/09/how-pixar-fosters-collective-creativity)  An obvious beachhead may prove too hard to breach because of the client's situation or unsurmountable issues discovered during in full lifecycle of the solution.  The knowledge gained will help identify better beachheads, and in time, the first beachhead may come around again.  More importantly, each beachhead achieved builds confidence in the innovation and also in the process of converting great ideas to solutions that make a meaningful impact.

What Might "Deep Biom*" Look Like?

Michael Helms distinguished between inspirational and deep bio-inspired design (BID) in ZQ25 (https://issuu.com/eggermont/docs/zq_issue_25final01/38).  Inspiration BID can provide practitioners with new ways of looking at problems and has low barriers to entry, but projects often get stalled in the ideation phase.  Deep BID produces a higher yield of implementable solutions but requires significantly more investment in expertise and time, along with approaches tailored to the specific situation.

I recently stumbled on “Description and composition of bio-inspired design patterns: a complete overview” (https://link.springer.com/article/10.1007%2Fs11047-012-9324-y) which describes a set of self-organisation design patterns.  Each low level pattern  is extensively documented and then combined into intermediate and higher level patterns (figure 4 in the paper), similar to the structure of Christopher Alexander’s pattern languages.  The authors included biological examples in their low level patterns and named the high level patterns using biological terms, but the patterns themselves seem to have been developed by studying computer science and robotics implementation.  This is not surprising – our detailed understanding of the mechanics behind self-organisation is in its infancy.  It may be that applying what we have learned from nature will help us define useful design patterns/principles that will accelerate broader practice. 

We can also evaluate how well our application of these design patterns/principles matches the functionality we observe in nature, creating a feedback loop that continually improves the patterns.  Artificial Intelligence has made great strides in pattern matching, but the high cost of training these AI systems and their susceptibility to even minor image tampering suggest that AI systems are a long way from emulating visual recognition in nature works.  “Machine vision that sees things more the way we do is easier for us to understand” (https://www.technologyreview.com/f/614870/ai-machine-vision-interpretable/) explores the challenges and also identifies pathways for improvement.  “Why even a moth’s brain is smarter than an AI” (https://www.technologyreview.com/s/610278/why-even-a-moths-brain-is-smarter-than-an-ai/) asks how moths are able to recognise new odors based on a few exposures while AI requires massive training sets, and describes research in building neural networks based on a deeper understanding of the moth’s olfactory learning system. 

These examples suggest that effective biom* requires more than a one-way transfer of knowledge from biology to technology.  There are a growing number of biom* examples involving people who brought novel expertise to both the biology and the technology, such as Annick Bay (https://issuu.com/eggermont/docs/zq_issue_25final01/90), Rolf Mueller (https://issuu.com/eggermont/docs/zq_issue_08_final/38), and John Dabiri (http://dabirilab.com/fieldlabs).  

Peter Niewiarowski pointed out in ZQ16 (https://issuu.com/eggermont/docs/zqissue16/44) that “It is crucial to create a ‘space’ that encompasses the important knowledge fields and ideally enhances all of them.”  The process of doing biom*, analysing the results, and comparing our best attempts with what nature can accomplish may not only improve our ability to do biom* in a reliable and scalable manner, but also deepen our understanding of nature.

Knowledge Transfer vs. Knowledge Mobilisation

We often describe biom* as the process of transferring or translating knowledge from biology into the technology domain.  Both the problem-driven and solution-driven pathways assume knowledge of the biological phenomenon exists in a form that can be efficiently transferred and applied to solve meaningful technical problems in a reliable/scalable manner.  Anecdotal evidence suggests that these assumptions may hold true in a small set of circumstances but break down in more complicated situations, sometimes resulting in shallow forms of biom* driven more by metaphor than a deeper understanding of nature.

There appears to be a third pathway involving expertise that straddles the domains of biology and technology.  In ZQ16, Peter Niewiarowski talked about "creat[ing] a 'space' that encompasses the important knowledge fields and ideally enhances all of them".  Some examples:

  • Rolf Mueller (physicist):  understanding the morphology and function of bat ears and the implications for range of technical challenges
  • John Dabiri (aerodynamics): understanding the fluid dynamics of fish schooling and the application to wind turbine positioning
  • Annick Bay (photonics) : understanding the anatomy of the firefly's anatomy and how the knowledge could increase LED efficiency

Building Robots That Can Go Where We Go (Jonathan Hurst) is an example of applying the latest engineering research methods to understand legged locomotion, develop mathematical models, and test the models by building bipedal robots.  Hurst's team also developed metrics (ability, power consumption, force levels) that assess the gap between our implementations and the natural analogues, creating a feedback loop between research and implementation.  This type of 'action research' can reduce timelines and increase credibility in the early stages.

'Knowledge mobilisation' encourages us to invest in expanding and integrating knowledge across domains and explore opportunities for transdisciplinary collaboration to deal with more complex problems.  If you are aware of other examples, please let me know.

Going Back to the Source

The first article in the "Stories from the Trenches" series included the observation that "Successful biomimetic innovation often benefits from repeatedly returning to the biological inspiration to validate assumptions and resolve challenges."  I received one comment that questioned this statement - as a biom* project develops and needs to integrate into the human domain, it may be more valuable to be open to a wider range of potential solutions from technology, basic engineering or physics.  

As always, the context is important.  "Going back to the source" can prove valuable if we lack a good understanding of the biological principles, or if the innovation process runs into into challenges that are not easily solved using current solutions.  An example is the Technology Review post Why even a moth’s brain is smarter than an AI.  We often assume that neural networks are inspired by our brains.  We have come a long way since the early attempts at artificial intelligence, but in many ways, our attempts at emulating nature fall far short of the mark.  

​The study described in the post looked at one aspect of learning - the speed at which moths can identify odors compared to the extensive training sets required by most of our artificial neural networks.  The research had two benefits - it increased our understanding of moth brains, and led to a new way of designing neural networks.  The key was to ask a deceptively simple question: how well do our attempts to emulate nature compare to the organism or phenomenon we are emulating?

Growing Up with Lucy: How to Build an Android in Twenty Easy Steps by Steve Grand has a similar message.  Grand argues that much of what we think we know about our brains is colored by our technology.  Trying to build an android using the best knowledge available to us, and then comparing how well it works compared to our (or a moth's) brain, can advance our understanding of biology and bootstrap our ability to emulate it.

The Business of Biom* - Part 2

 I recently read Blue Ocean Shift: Beyond Competing - Proven Steps to Inspire Confidence and Seize New Growth, an updated version of the earlier book Blue Ocean Strategy (also by Kim and Mauborgne).  Both books describe a business strategy based on creating new niches, rather than direct competition.  Key approaches include:

  • Identifying and eliminating costs for deliverables that do not provide value in the market.
  • Identifying and delivering untapped value that has been ignored by the market leaders.
  • Identifying and targeting ‘non-customers’ of the market leaders.

The Blue Ocean strategy creates potential opportunities for biom*.  Although the authors claim that they are not delivering disruptive innovation, they are clearly trying to dramatically change the game by increasing value, rather than competing on price or marginal features.  Blue Ocean Shift helps businesses manage risk by describing a step-by-step implementation process.  Showing the connections between biom* opportunities and the Blue Ocean strategy could help reduce the perceived risk of biom* projects.  In turn, biom* could help companies identify new Blue Ocean opportunities.  

In contrast to Fast Second and its focus on products/processes, the Blue Ocean strategy seems more aligned with systems biom*.  Rather than relying on a technological advance to create new markets, the focus is on building or reorganising relationships among existing actors, developing new business strategies, and strengthening innovation portfolios. 

Fast Second mentions strategic innovation as a way for businesses in mature markets to retain their competitive edge but almost as an afterthought.  Ideally, businesses should explore both approaches, depending on the specific circumstances.

The Business of Biom* - Part 1

ISO/TC 266 wants to engage business and industry to better understand how biom* could make a more significant impact.  One of the challenges is that we tend to speak the language of biology, rather than the language of business.  How does business go about innovating?

A colleague on the ISO/TC 279 Information Management project suggested Fast Second: How Smart Companies Bypass Radical Innovation to Enter and Dominate New Markets by Markides and Geroski.  This book describes the lifecycle of disruptive innovations:

  • Development of a new technological discovery or principle (usually in academic research or labs).
  • Market exploration/creation by entrepreneurs/startups (understand technology, create wide variety of products, identify target clients).
  • Market consolidation, typically by large companies that have the required production, delivery and support capabilities (develop and promote a dominant design).
  • Market expansion through strategic innovation (block competitors from grabbing market share). 

A recent example is artificial intelligence.  The underlying principles of modern AI were initially explored in the 1960s through the 1980s, but fell from favor in the latter part of the 20th century due to the limitations of current hardware and software.  The market exploration phase restarted early in the 21st century, driven by the availability of significantly more powerful hardware and ‘deep learning’ algorithms.  Numerous players sprung up hoping to grab market share.  Recently, large technology companies are beginning to consolidate the ‘big data’ markets, with some of them already exploring strategies for broadening market penetration through lowering barriers to client access.   

Fast Second suggests that most disruptive innovations are 'supply-push', and therefore relevant to biom* innovations that follow the 'biology to design' pathway.  A few biom* examples come to mind.  Self-cleaning surfaces (such as the Lotus Effect and SLIPs) as well as structural color have led to various products trying to enter a range of market niches, including Lotusan®, Sharlet, and Mirasol.  Velcro® is one of the few biomimetic innovations that has reached the consolidation/expansion phases, possibly because the ‘dominant design’ emerged early in the process.

Given that biom* promotes its disruptive potential, it would be worthwhile exploring the lifecycle of biom* innovation.  Are biom* concepts not sufficiently developed or generalized so that entrepreneurs can built startups around them?  Are there gaps in the supporting components, methods and tools required for market success, similar to what happened in the early days of AI?  Are biom* innovations perceived as too different/risky?  Are the market potentials unclear?   Stories from the Trenches of Biomimetic Innovation: Ideation and Proof of Concept ​ in ​ZQ21​ is the first in a series of articles following four biom* case studies from ideation through to commercialization.

What do you think?  

Making Biom* Real

One of the initiatives that I want to focus on in 2016 is 'making biom* real'.  The sixteenth issue of Zygote Quarterly ​includes three articles in this area. 

An issue with many biom* case studies is that they focus on the natural inspiration and the technological product but rarely discuss the 'secret sauce' in between.  Putting the Nosecone to Work describes the 'messy design process' required to develop a product that improves wind turbine efficiency, from understanding business trends and needs, identifying sources of inspiration, developing/testing solutions, and building partnerships among key stakeholders. 

The interview with Peter Niewiarowski describes how the University of Akron and Great Lakes Biomimicry were able to attract local industry and non-profits to support the Biomimicry Research and Innovation Center and its Biomimicry Fellows, not only financially but also by engaging the Fellows in real-life challenges.  Peter emphasizes the importance of collaboration to "create a ‘space’ that encompasses the important knowledge fields and ideally enhances all of them."

Expanding on Peter's comment,  Innovation Through Transdisciplinary Training ​contrasts intradisciplinary, interdisciplinary and transdisciplinary teamwork.  The article describes a novel approach to teaching transdisciplinary collaboration that culminates in the students solving a specific business problem.  Rather than teaching students to work across disciplines, the course emphasizes the skills required to be effective in transdisciplinary teams.

The BID Community Think Tank will shortly be tackling tangible initiatives around 'making biom* real'.  If you have any ideas, please let me know.

Thanks, Norbert

Implications of "Shoveling Water"

MIT Technology Review recently posted Shoveling Water: Why does it take so long to commercialize new technologies? that explored the challenges faced commercializing microfluidic devices, also called "lab on a chip" technology.  Although the potential is clear and significant progress has been made overcoming the challenges of manipulating liquids at the micro scale, the technology has not yet made the leap from the laboratory to commercial success.  According to David Weitz "It is a wonderful solution still looking for the best problems."

When new technologies emerge, potential users many not fully understand how the technology will solve their problems better than existing solutions.  Even if the need is compelling, the technology may be hard to use.  The article describes how automated genome sequencing only became popular when a sample preparation kit was developed.  Adoption can be particularly slow when a new technology domain is introduced that opens up potential applications that users may not be familiar with.  Sometimes the ability to deliver is outpaced by hype, which can leave a promising technology languishing in the Gartner "trough of disillusionment".  Although these challenges are shared by all innovation, they seem particularly applicable to bio-inspired design.

I have done a first pass of The Nature of Technology by W. Brian Arthur, referenced in the MIT post.  We tend to focus on specific 'breakthrough' technologies, like the steam engine, digital computing or the Internet.  Arthur argues that we should consider technology as a hierarchical web of technological components, some satisfying human needs, others playing a supporting rule.  These technologies have a lineage going back millennia to the days of fire, pottery and simple tools.  Technological advances often arise through new combinations of existing technologies or incremental improvements in supporting technologies.  Arthur argues that technology is advancing at an ever increasing rate because the number of components and combinations is increasing and solutions bring new problems. 

Revolutionary technologies spring out of our understanding of natural phenomena, but only if that understanding can be transformed into principles and implemented in technology that solves compelling problems.  New 'domains' of technology often take a long time before they become commonly used, especially if the needs are not self-evident or supporting technologies are lacking.  Arthur also describes the feedback loop where advances in technology allow us to explore and understand new phenomena. 

There are a lot of details in Arthur's book that I still need to digest.  I see a number of ideas that may help us advance bio-inspired design.  One of the reasons that bio-inspired design may be slow to catch on is that it has not had time to build a sufficiently complete web of components.  The technology web has grown organically and even Arthur has not tried to map it at any level of detail.  One strategy may involve consciously mapping the web of bio-inspired design technologies and filling in critical gaps.  Another may be to hook bio-inspired design into existing technology by:

  • proposing new combinations of components
  • identifying new approaches to finding valuable combinations (evolutional algorithms come to mind)
  • providing new support components or enhancing existing ones 
  • exploring the process by which new natural phenomena (typically from physics or chemistry) are integrated into technology

Watch for additional comments based on further exploration of Arthur's book.  I will also repost some discussions I have had about Arthur's book.  

Complexity and Bio-Inspiration

There is growing interest in exploring how bio-inspiration could tackle more complicated challenges.  A key goal of bio-inspiration is to re-balance our relationship with nature and the ecosystems that support us – simple solutions are unlikely to have a significant impact.  However, Ashok pointed out in the May 27th B3D Webinar that the tools and methods we have used to emulate organisms or simple natural phenomena are not well suited to dealing with systems.

The Cynefin framework (David Snowden and Mary Boone) is a useful way of looking at different degrees of complexity, from simple through complicated to complex and finally chaotic.  Each level has specific characteristics and requires unique approaches.  Incorrectly assessing the complexity of a situation or using the wrong approach can lead to poor, unexpected or catastrophic results.  As the complexity increases, understanding the situation and using an appropriate process become increasingly important, in contrast to simpler situations where specific knowledge is more effective.  Rather than looking for specific solutions in complex situations, the goal is to find ‘safe-fail’ interventions, constantly adjusted based on results. 

This message is reiterated in Donella Meadow’s Thinking in Systems.  Again, rather than proposing solutions, Meadows emphasizes finding the right leverage points.  The book provides extensive examples of modeling systems and seeing how the model reacts to changes.  Terry Love has taken this further to develop very complex models that made unexpected predictions.

Where does bio-inspiration fit it?  Although programs like Cosmos: A Spacetime Odyssey emphasize the interconnectedness of all life, we would benefit from a clearer appreciation of how our existence depends on natural systems.  Another approach is to look at how evolutionary processes could be applied to design, although time scales, history and context can make this challenging.  We could also apply the ideas of Snowden/Boone and Meadows to understanding the evolving dynamics and relationships between humans and the natural world. 

In addition, there may be generalizable attributes of complex systems that can be deduced from studying a wide range of human and natural systems.  Meadows in Thinking in Systems (pages 51-58) describes the influence of delays on systems with multiple feedback loops.  Although it seems intuitively obvious that reducing delays would improve the overall efficiency of the system, Meadows shows how this can actually result in instability.  The Complex Mathematics of Robot Wrestling describes how the researchers modeling wrestlers discovered that introducing a short delay into the response to inputs reduced the level of complexity in the simulation.  Perhaps the germ of an underlying principle?

Stuart Kauffman in At Home in the Universe  claims to have identified optimal degrees of ‘connectedness’ as the number of actors in the system increases.  Too few connections (as determined by the number of influencers on each actor) and the system is unable to adapt.  Too many connections and the system becomes unstable.  Julian Vincent speculated in A Comparison of Biological and Technological Systems that being embedded in a system reduces complexity because you only need to deal with a limited part of the system. 

Are you aware of other examples that suggest ‘lag’ and ‘connectedness’ might be useful attributes to explore?  Are there other examples of attributes identified in natural systems that might be applied to technological systems?

Biomimetics Mailing List Discussion on Biomimetics Case Studies

A recent discussion on the Biomimetics mailing list showed the challenges not only of assessing the merits of individual biomimetic case studies but also the diversity of perspectives on definitions and approaches to doing biomimetic/bio-inspired design.  The lack of a comprehensive and verifiable record on past biomimetic innovations is a challenge that many other disciplines face.  Paleontology and archaeology come to mind: much can be inferred from a fragment of bone or pottery but only based on a body of sound research, an understanding of the context and a willingness to explore other hypotheses.  A strength of the discussion was its grounding in specific examples rather than taking a theoretical/philosophical approach. 

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