Does Innovation Theater + Innovation Choir = Innovation Compounding ? Often.

For almost 25yrs, a simple seminal study tied innovation ( and in it's loosest definition ) to the capacity that in order to increase the acceptance of what was considered " breakthrough "  when future backward consistent group coordination was at play, the reality became that this is innovation compounding. And, when it's executed as a group multiple ecosystem effort where this can signal a dilution often it becomes a further concentration of said breakthrough / collapse of value chains thus very closely mirroring the exact definition of innovation. Most of the time.

In Competition, Innovation And Increasing Returns we see the underpinnings of the reality that ( and from many other past articles about similar topics ) that the idea of consortia, vertical ( and known ) where specific functional user groups that share a collective need and also acceptance of risk in the face of future specific 3, 5, 10+ year time frames, mean category-defining transformational capabilities have a tendency to become real world applicability much sooner than expected. With Futures Design tools and foreseen futures that codify the barely define into future functionality that is buildable ( if not fully usable ) today the process feeds upon itself if and only if end users see " the inevitable " that is on the way, eg. connected computing, wireless communications, neural networks, non von Neumann computing architectures, leading in no short form to synthetic biological computing.

Where this has been referred to as Skunkworks Singularity efforts, it is the capability and necessity to broaden and codify the unknown into the shortly doable ( even in it's most rudimentary forms ) that allows a choir of similar voices ( and which can easily be competitors ) to push acceptable use into broader awareness. What become particularly interesting is how organizations ( internally as in the capability of intrapreneurship ) also past a certain size become, themselves ( due to their reach across multiple and diverse areas of industrial reach ) become a choir of their own coordinated capability.

In the case of  Amendola, Gaffard and Musso's findings we see that this can not only be something that takes place inside one organization but sometimes across multiple organizations and even when incrementalism is lauded as breakthrough but only when ( seemingly ) these innovations are consistent. Consistency itself becomes a form of innovation realization., What can be ( and is often the case ) ignored are how these " breakthroughs " are a function of acceptable ( in relation to Physics ) doable output that can be tricked out as if a golden path were already laid. Examples of this can not only be seen in the evolution and adoption ( in the sense of the long tail model that is investigation and discovery ) of neuromorphic computing ( a completely different form of the current, silicon based, von Neumann computing architectures ) and, clearly, the use of standard silicon based computing architectures. Where Neuromorphic computing diverges from the standard paradigm a new future, a wholly, self contained 3rd self operating system ( and the hardware to support it ) is just one example of the compounding that that is present and evolving as we speak.

 

                    

   

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Collaborative Cognition = Unblocking Uncertainty

When the absence for consensus becomes the limiting factor to hurdling innovation roadblocks, what other mechanisms ease collaborative cognition ? Here's several for innovation plasticity.

In the race for interconnected innovation instances that continually lead to further convergence ( as divergences are a daily occurrence and also part of the design, engineering and innovation process ) we see that so to enable alternative mechanisms for lab based breakthroughs and to make it past the gauntlet of ( within large organization processes ) well honed, ' hold on, explain how that will work with / within my group ' situations, the key to success often resides in not only collaborative underpinnings but also the unblocking of known or subconsciously accepted risks. This is often separate and unrelated to enumerating the steps and changes necessary to make a specific modules work, solving technically ( and temporarily ) " impossible " situations and dealing with lab bench challenges. It is the steps in-between lab bench science and manufacturable usability that these challenges ultimately rear their ugly head.

Where we see this particularly well articulated is how Systemic Innovation Designers Through Informal and Collaborative Activities drive formal and specific processes which allows for the quantization of ever changing user attitudes often reflected as ' needs ' in certain circumstances: sometimes early on in the design processes. This is embodied in the further process of transversal competencies mediated by digital tools: telepresence, simultaneous collaboration, and synchronous and asynchronous communication which ultimately ( if done properly ) lead of convergences through a systematic peristalsis. When pushed or more aptly in today's language, " enabled " through effort.

This has been echoed for more than 20 years ( ten proceeding the original publication and then after ) where in Facilitating Innovation Through Cognitive Mapping of Uncertainty we see the systematic need for specific Skunkworks frameworks that can encompass the incredible level of uncertainty in early stages of defining breakthrough efforts ( from lab bench science ) through the engineering process. This mixing and cognitive separation, refinement, leads to the formation of what to do, what not to do weavings allowing the underpinning of what some have referred to as likeness lillypads - allowing the further connection between what has worked, may work, cannot work, and will not work environments. Moreover we see how an originally seemingly impermeable footpath to alternative future scenario directions can and often does allow for existing and transitionary system to take hold to forge those paths, and which some refer to as innovation plasticity.

With the notion of breaking barriers ( both conscious and unconscious ) in not only end users but to those involved in the delivery of specific components / end user products as the key factor to fostering the highest levels of convergence capabilities inside and post lab bench science validation, it seems there are many keys needed to increase the unblocking of uncertainty and which means an adherence to the need to foster ( at every level of the innovation effort ) a constant and clear mode of collaboration as well as a willingness to bend the rules - yet another type of innovation definition.

 

                    

   

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When Consensus is Conceded Innovation Isn't Interrupted

What is the biggest hurdle for innovation to overcome? Consistent convergence. But how in the face of constant concern, even when it's concern for what has already been done, when it's still said it can't be done ? Here's how.

Design, Design Thinking, Design Futures, Detailed Design and Design Engineering is, together, the basis for, and most flexible tools of, constraint based morphing of input parameters into working end solutions. In other, what could be considered " unhelpful ways " we separately see the advent of Ai capabilities ( and especially as related to non-physics based output ) which can turn just about anything ( image and video wise ) into anything else and connect them together. It is here that the terms " hallucinations " for given data output from search engines / search engine data comes into being. It is also here where these Ai systems output demonstrate that the fundamental vocabulary ( and specifically via the multitude of search engine URL results produced ) can easily become obsurified and more, where In the past we saw a ( roughly ) well documented history of where data " results " came from, where now however the tables have completely turned. But how? And how can we utilize this for the further efforts of innovation activities ?

In the past the idea of innovation ( and again from the root definition of the word enumerated by Schumpeter ) we see that invention (conceiving a new idea or process - that works on a lab bench and as governed by the laws of physics ), turns into innovation (arranging the manufacturing and producibility requirements for implementing an invention - and where Schumpeter seems to have just redefined entrepreneurship in his original description ), and onto diffusion (whereby people observing the new discovery adopt [ purchase via entrepreneurship ] or imitate [ by copying to also sell for their own ] where Schumpeter has it correct ) seems now to have been upended. Ai and specifically generative Ai ( in many forms ) lets anyone jump right from invention to an infinite number of diffusions ( and call them all new ), and, at the expense of perpetuating concern for adoption which in fact is the exact limiter to innovation as discussed above. That which is not known for long enough to be encoded with the guise of safety is quite [ as the baby is ] often thrown out with the bathwater as the saying goes and where innovation fails.

When time frames for diffusion ( as in the above definition ) are extended and slowed down, when users are unable to discern viability and usability easily, it is here that the limiting factor to uptake is effected and thus barriers are further created that is at the constant forefront of innovation efforts. It is here where it is necessary to diverge from Schumpeter's definition of " innovation " as the entrepreneurial process, and, a more accurate form of the definition of innovation comes into play: the effort of diluting and dispersing concern so as to actually reach consistent consensus and thus increasing the capability for value in a new something to take hold. Simple examples are that of the Xerox / xerography photo copying system started several years before 1938 when the technology was finally made to " work " in 1938 and thus the lab science / bench science invention phase was completed. It wasn't until 1944 that it was " noticed " as important and where it took until 1948 where it was deemed a " successful " development effort ( most likely due to the innovation phase to be able to work within the context of a large manufacturable manner was completed ) and yet not available for sale until 1950, possibly 15 years after it's conception, design and initial " working " mode. It is here we see the efforts of consistent consensus even after the math and physics were validated. Was it consensus pressures leading groups to reject this useful ( and thus patentable ) idea rather than move forward with them the issue? In many cases absolutely and as detailed in Greater Variability In Judgements Of The Value Of Novel Ideas in Nature Magazine. How then can these situations be mitigated so as to close such gaps in a better way? Interestingly it is as simple as unwavering concession.

Design and thus it's constraint based counterpart, concession, is inherently limited by ( as in the case case of raw physics ) the necessity that some configurations of materials cannot operate in a way that may have been initial conceived - " there are somethings you just can't make plastic do " as Steve Jobs famously said. However when it's possible to keep 5000 things in play at the same time and still be able to reach functionality that increases usability and within the context of efficient manufacturability we see 2nd order breakthroughs / usability and the elevation of concern happen. And, it is this relentless process of configuring and re-configuring constraints that ultimately allows innovations to persist in the face of " concern " being often veiled within individual confidences of need that may not ( and often are not ) a form of the actual evolution of innovation to be addressed and delivered upon. Regardless these hurdles cannot be ignored and thus it's Design, Design Thinking, Design Futures, Detailed Design and Design Engineering the bring about the quickest convergence capability to occur.

 

                    

   

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MemRistors + MemGTP + MemOS Might Mean MemInnovation

With ( possibly ) mathematically, the most radical technological upheaval in adoption that humanity has seen, Ai is poised to make MemInnovation happen shortly. Can such a thing be said ? Yes.

Via the creation and culmination of years of research into non-silicon and semi-silicon chip fabrication designed brain like, neuronal processor and processing technology, and as detailed in Dynamical Memristors For Higher-Complexity Neuromorphic Computing we see the capability that now ( and even in some software systems alone ) full personalized AI with long-term memory ( thus the 3rd self ); self-editing memory; infinite context windows; access to unlimited data; customizable tools; and thus, essentially, long-term memory is already here. With the stitching together of technologies such as MemRistors / Neuromorphing computing and interfaces such as MemGTP a new fabric of systems and what is being called MemOS and thus gtpOS or as referenced in the past the idea of an ownable version of The Third Self has come into usability in it’s early forms. Think the 1st version of Douglas Engelbart’s ” The Mother of All Demos ” or Xerox PARC’s Alto GUI that made it’s way into the creation of the 1st Mac computers by Apple.

With the specific ability of MemRistors + MemGTP + MemOS to move designer and innovation practitioners into a capability can that can be considered MemInnovation, we see the further the capability of participatory innovation & generative design system can directly effects the way advanced R&D and design efforts evolve. More specifically when non-linear and novel technological and design centric integration takes place Skunkworks models of R&D, innovation and design efforts can evolve at a pace faster than the technology produced – all due to the design oriented aspect of the technology. In this case there is the possibility to say that systems and technologies such as MemRistors + MemGTP + MemOS create integrations, platforms, that perform, essentially, second order derivative thus almost creating a rate of change to find ( utilize ) the acceleration of an object ( when velocity – the uptake of such specific technologies ) are given by first derivative.

Further in Disruptive Science Has Declined – Even As Papers Proliferate in Nature magazine, and with an incredible sample size of 45 million manuscripts along with utilizing the data from 3.9 million patents we see the notion ( where the long held bastion of what is determined to be ” innovative ” ) that converging technologies such as MemRistors + MemGTP + MemOS even more rapidly than before are pushing out the notion ( and acceptance ) of what is disruption and thus true innovation. Where this then becomes particularly interesting is the way in which firms that are investing in said patents almost to create an unbreakable ( if Patented fast enough ) barrier to creativity as thus inadvertently discouraging radical design and innovation efforts unless the value of the output to the larger owned system takes place and which is of incredible value to the firms that focus on the creation and use of such efforts. Thus the idea of ” innovation is dead, long live innovation ” is fully rooted in the development of Neuromorphic Computing / MemRistors + MemGTP + MemOS and which is here / right around the corner.

 

                    

   

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Does Ai Enabled Design Re-Imaginations Mean Instant Innovation ?

Re-Imaginations ( Ai or not ) drive ROI when Innovation is Integrated. Here's how....

When the nature of breakthroughs is a direct function of measurable firm impact, a key variable of patience is often overlooked but more specifically often not factored into what we might call the bamboo delta - the time difference between the start of an unknown internal growth cycle necessary before a massive and overwhelming growth stage takes place similar to the way bamboo seems to be completely dormant and then grows at such a radical pace it could be considered to the uninitiated other worldly, in many cases with bamboo shoots growing at 5.5 inches per hour or almost 1mm every 90 seconds.

 Where typical Innovation efforts ( often realistically defined at NPD or New Product Development work ) look to have an output measure of dynamic technological capabilities, when firms undertaking either radical product innovation, radical process innovation, or both and are often measured against domestic patent applications only ( but not necessarily being granted / assignment said patents ) or as marked by industry awards ( in the case of design and manufacturing awards ) a specific form of balance can be missed: the re-interpretation / re-imagination of internal firm capability to in fact, push out ( and deliver utility ) before the acceptance of said " innovation " becomes known. But how?

Via a focus on Dynamic capabilities to mean the collective capacities to learn and adapt ( within the firm ) with the help of dynamic competencies ( i.e., individuals' abilities to learn and solve problems where a system of stated or unstated champions inside an organization assist with this effort is grown and supported ) is where innovation aggressive firms are able to be "visionary" enough to perceive opportunities which necessitate investment in knowledge-intensive strategies through the R&D process, and which ( when focused properly ) allow for the development of knowledge and investigation internally that is also absorbed from the outside so that when it comes time for incredible & rapid growth the firm / organization / individuals are ready for that zeitgeist to begin.

Simultaneously making sure that employees who are capable of re-designing, using, applying, re-configuring and producing for firm's products and technologies ( when they are ready ) are also the ones that often take leadership stakes in said radical efforts before a firm approves them. Oddly where this does not seem to be year or country dependent, and as detailed in Competencies, Innovation And Profitability Of Firms we can see how the exact definition of Innovation ( a radical breaking from an accepted inter or intra firm / organizational norm ) can directly impact firm performance if an only if firms are prepared from a sub-culture perspective to allow such radical growth to take place.

We see this even more specifically where the effectiveness of process innovators is more affected by a firms / organizations influence ( the more influence that a firm or organization has the more easily they find value in new and interesting ways of working with their clients and the design process ) where physical ( and in some cases digital ) product intensive innovation means that research skills ( including end user focused feedback and iterative manufacturing efforts ) are key factors to the optimization necessary for sustained radical efforts. This is further seen in the most recent Ai enabled design re-imagination capabilities where new functionalities and empowerment do not mean instant innovation ( and by the exact definition of innovation ) however where new Ai capabilities ( and especially due to the nature of physical iterative manufacturing ) means the digital realm continues to be turbocharged thus the nature of making radical breakthroughs is happening at an even more accelerated pace.

 

                    

   

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