Breakthroughs Means Clustering in Innovation, Commerce, Engineering & Design

Breakthroughs are not enough to engineer solutions and it's where " it takes a village " is equally a part of NPD success. Here's how.

With ever shortening cycles of differentiating how breakthroughs can be leveraged this increases value to international firms and world governments which we see in Creative industries & Innovation Policy how " knowledge transfer through embedded creatives mean that the creative industries are potentially more involved in the innovation systems than has previously been recognized.’ and where this is well known by the ideas of an " innovation trajectory – which consists of the origination, adoption and retention of a new idea or technology " and which can be easily also understood as a " it takes a village " mindset.

More we see in via Mitchell ; Postrell ; Andersson & Andersson where production of the same material output, but with greater cultural content or meaning, and thus economic value is one of the key contributors to increasing the capability of what cannot be done in the past, can in fact contribute to specific usage and more importantly wider understanding of the disconnect of sometimes noticeable bias in which innovation is viewed as an exclusive outcome of advances in science, engineering and technology when there are also many other factors that can directly effect how breakthrough R&D efforts can lead to unforeseen successes with advanced labs based research work.

To further this is where the capability for R&D to be central to innovation policy inside corporations, governments and universities is crucial because such efforts are the critical investment in the origination phase of the innovation process that allows unconventional science to be performed and where in almost every country not only are there specific tax credits that allow for advanced R&D to directly effect firms capability to bring specific previously unknown directions into reality. When specific corporate, government, militarily and university policy changes allow for expert investigations into previously uncharted industrial process and products to take shape, and separate from " creative innovation with other enabling social technologies like law, science and markets " coming in second and behind the raw science and lab bases research to get to this step. It seems then that in many aspects the social nature of innovation is possibly equal but certainly separate from the raw science of advanced R&D itself.

                    

   

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Abstraction Biology Creates Design Breakthroughs

When past associative correlation can drive the unknown to the known are there other paths? Yes.

Edge constraints, back propagation, and many more " action " oriented systems are routinely created to navigate an environment to be interacted with and or to change it, and where designers, engineers, managing directors, finance professionals and any number of specific discipline users are part of any such system.

But what is the effect of only looking at problems / opportunities from the standpoint of the discipline that you are trying to see past. Well it ends up that one set of principals, biological thinking ( an abstraction / alternative window to say design thinking, systems thinking, etc,. ) is where we see two very specific ways that groups begin to look at environmental scenarios of interest. And biological thinking is one of many such alternative viewing systems.

Surprisingly when determining the type of outlook that a person, team, group, organization, etc., begins to have when completely different vocabulary is introduced and shared, is where a new way of looking an an entire environment can occur but how? Can Abstraction Help Ideation? Clearly bridging this gap of alternative models to biologically inspired design allows non-interlinear jumps, and all by looking back is where possible pattern matching can also be first taking place but is it specifically required? Surprisingly even more it becomes that Rules + Constraints & History + Abstractions are methods and where there are countless others and All part of a abstraction biology lens or any number of other lenses for underlying patterns to emerge. In the world of R&D this is the every day capability and mindset. Where can the next leap be made from ?

In more than one case the well known alternative combination and recombination of what can be considered rectilinear pieces to a puzzle is where the repurposing occurs and which is directly supplanted by data systems, storage systems, processing systems and interaction systems that come into being with new technological change. In this it is the completely different ( almost artistic and certainly evolutionary: seeking completely unexpected alternatives ) ways of thinking that allow for even more unique and valuable outcomes to emerge from in some cases naturally occurring patterns that have the ability to create looking back and forward all at the same time.

 

                    

   

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Breakthroughs Mean Breaking Literally Through - Silicon

Mapping biological neuronal superpower onto ( for now ) physical chips means completely ignoring accepted silicon architectures and Memristor innovations are  likely to make it happen. But how?

Even with the juggernaut industrial shift to GPU based architectures and that has allowed Ai to ( almost ) literally pop into reality, it's power consumption that is always the killer issue even more so than error correcting and which for Memristors ( that have incredibly low energy consumption ) one of the most important factors in the architectural shift coming means we are edging closer to ( possibly ) the greatest change possible in computation: non-biological ( for now ) neuronal computing. In the past ( and even with original silicon computing and onto quantum error correction system making breakthroughs today ) as we get closer with neuromorphic computing ( and a key component of that, is Memristor technology ) with one of it's the last stages to adoption being that error correcting being locked down.

In a most recent breakthrough and as detailed in ...Computing Platform[s] Based On A Selector-less Memristor Array... the jump to domain non-specific architecture possibilities can be seen and with them any number of platforms are possible that may vastly rival current Ai based systems. These new architectures will allow for new and radical, incredibly low power consumption never been seen before ways to tackle some of the largest and most difficult design problems but more, engineering specific situations that require infinitely powerful computation with little or no power consumption and as detailed more in Mem-Systems And Neuromorphic Applications

Where the true breakthrough lay, will be in the shifts ( both in neuromorphic programing languages and applications ) and via the capabilities for Neuromorphic System with Online Learning at the forefront of usage most likely as the capability for such systems to rapidly and with very little power be able to adapt and morph into situational functionality will allow businesses, engineering and design capabilities in computation of achieve never before levels of sporadic and generative functionality and which is right around the corner.

 

                    

   

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Cultivating ( Ai ) Creativity Also Means Creativity Concatenation ?

When ( echo ) chambers of creativity happen, ateliers of divergence get smaller yet is this an Ai issue ? Or maybe this is a time to ignore there is a room all together issue? Absolutely.

Ai ( and especially LLM's + generative based system designs associated with higher dimensional gradient descent neuronal architectures ) and even when configurations such as Feedforward ; Perceptron + Multilayer Perceptron ; Radial Basis Function ; Recurrent ; and Modular back propagation system designs are examined, we many examples of configurations localizing on the same situation in the end: pushing the true stimulation of divergent thinking, concept blending, and creative honing theory together plus design solution sets which are all bound squarely in the realm of constraint based training due to design, engineering and financial requirements and which are the bounds we have to deal with when delving deep into Ai effectiveness.

In this " edge " & limitation based typological thought exercise Ai systems are able to combine known experience and accepted creativity output to expand possible design solutions yet where " edge " parameters are the exact limitations that these Ai systems face. Edge garbage in is still edge garbage out. But then what ?

In Effects Of Design Thinking On Artificial Intelligence Learning And Creativity we see the capability of Ai systems ( as a whole and where generative Ai takes center stage ) to directly effect design thinking outcomes which have significant effects on relational classes of AI concepts & output: design thinking in the end drives edge boundaries that Ai systems employ to bring further and more extreme solution sets into being. Ai systems designed to push edge boundaries are in the end only reinforcing said boundaries.

We then see how Design thinking mentalities and their models often a set of divergent investigation can positively affected learning attitudes toward AI: this is where Ai often shines the brightest, providing quick and dirty investigations into alternative solution sets in very short time and especially in the visual realm. Where it would have taken days or weeks to go from conceptual directions to final " possible " visual and even functional prototyping, this now takes seconds with the aid of Ai systems but this does not mean a further pushing of the edge boundaries, only the center mass. More importantly we see how from many research sources and in-depth user-centered thinking and design thinking systems that thematic, project-based, and daily life contextual design thinking can be a way to further provide instructional design data sets for Ai and ML learning & training but again this is moving toward central mass mentalities and again, pointing inward based on edge constraints.

However what is particularly interesting is how design thinking's effect on the perceived creativity of works associated with Ai and further elaboration where novelty of ideas and product creativity as connected to ideation models such as many of the currently accepted systems like the SCAMPER (substitute, combine, adapt, modify, put to another use, eliminate, reverse) technique ; brainstorming ; the Six Thinking Hats model; and attribute listing clearly allow for ( in the ideation stage and when combined with " learning " AI systems ) we see improved creative expression but to what extent ? Where this may seem tautological ( generative Ai systems literally are creative and interpolative engines )  what is not easily accepted is how Ai generative capabilities allow for truly unexpected ways of arriving at alternative strategies and thus output that can and does define valuable and defendable avenues for new product development efforts. The further afield that ideas come from the more alternative and valuable NPD efforts become if and only if these possibilities are not rooted too far from existing norms and their respective system implications.

Where novelity and expanding creativity efforts for enhanced solution set delivery are important factors in the development of new and unexpected directions for possible innovation activities and NPD goals with Ai, the question of is Ai ( generative, LLM based and other ) a true driver for alternative and thus important divergent directions vs. just amplifying the edges of exiting design, engineering and finance topological constraints ? In many cases the reality is that it is doing both at the same time but where the norm is to say that Ai systems are bound themselves by the notion of how much and how " alternative " they can be expected to evaluate and act upon particularly when direct contradictions to outcomes occur in such " both " scenarios and where powerful input by those driving such creative and tactical efforts come to bear.

 

                    

   

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Ai Perceptions In The Design Process: Probable? Possibly.

With the ever increasing subtlety of the influence of Ai, how will human and non human interfaces change the landscape of design efforts. Chances you will never even know. But you can & here’s how.

 In the design process, and with experienced iterators who are able to synthesize complex system thinking, engineering functionality and finance vocabulary ( the art of using time as a cross functional metric for value ) we see that in the findings of The Impact of Design Facilitator Identity on Human Designers where more often than not when the knowledge of the system ( Ai specifically ) takes place it’s the anthropomorphic traits
employed in prior studies (e.g., human-like appearance and verbal communication) cause the knowledge that a system is in place ( Ai or other ) that may not be able to benefit human designers directly. The reality of the system itself can become the impediment.

Where this becomes even more of a powerful factor is in the reality check when an environment of enhanced trust is established with design facilitators ( in person or Ai based ) is where high proficiency participants with unconsciously may rely on that human persona and where less motivation to keep creating better alternative models / design typologies emerges even when the advantage high proficiency participants with “human” facilitator may be offset by their own uncertainty and reliance on a human design facilitator therefore there is not as much significant impact on the average performance of high proficiency human designers vs non-high proficiency individuals. Can it be said that when there is not the interference of the human emotions connected to feedback and motivation that somehow there is a freer more open aspect of the creative process ? Possibly.

We see this even more in corporate, military and government offices where the need for confirmation based approval processes is known and often facilitated from the initial aspects of a particular work flow and where creativity operates within narrow bands of expected output so as to be able to deal with parameters such as, specifically, patent and trademark law which is often the key driver in such efforts.

 

                    

   

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