Can Ai Convergence on Divergence Be Significant? Here's How

In the race for ever more powerful ways to take what isn't and make it into an ' is 'can divergence itself be significant ? Should it? Can It ? Here's how. 

With the dominance and sheer instant creativity of Ai, diffusion technologies, motion and sound generation + editing, code manipulation & generation, along with molecular and materials modeling  ( just to name a few ) and where with all of the above manipulation taking place, and at a pace that is not even close to astounding, it's almost unfathomable, we are in one of the greatest industrial shifts of mankind's history taking place. With this was the idea that constant divergence ( and as a mechanism of creativity ) is enhanced by the use of Ai and even more that Ai itself can evolve and morph faster and more accurately than human creative endeavors. But can it ?

What we see in several studies and more importantly in industry engagement is where ( like in similar studies ) such as Generative Design Reasoning and Students’ Divergent and Convergent Thinking is where " research has pointed to generative design as constraint driven design, leading to a different type of design process creativity compared to traditional design [9]. " and where this becomes even more important is where " experienced designers use criteria beyond performance objectives in their design decision, considering many factors when selecting from the results created by the generative design tool ” and where this leads to the natural conclusion that with the idea that what can't be done can be done ( itself a form of divergent thinking ) the possibility for unexpected breakthroughs can occur.

But what does this mean for the future of Ai not only connected to digital manufacturing but in areas such as new product development and more importantly when generative design systems begin to embrace / are trained on constraint based design, cultural, material selection, machining capabilities, demand, etc,. will Ai be able to ( on behalf of a patron / production financier ) be able to determine what can and will be accepted have the largest possible uptake for purchase ? The numbers are sill in their infancy, but what becomes even more startling is the capability to use other forms of input and as described in Can Abstraction Help Ideation - A Case Study On Biologically Inspired Design where we can see further examples of how industrial use of edge based use cases allow for Ai systems to ( through a multitude of reinforcement learning and even with the emergence of SSN and NeuroMorphic computing architectures ) see into, around and through design and engineering problems given a set of parameters as defined above. With international firms using a multitude of manufacturing and high rate production prediction systems, there is an ever increasing need for alternative to expected divergence models to be created. It is these end to end " creativity " solution systems that give rise to the " breakthroughs " that firms both small and large are looking to in order to create the next markets that have yet to even be cracked or explored.

 

                    

   

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IP Insures Innovation Interdependence

When inventors, designers & engineers usage fate depends on IP to protect value can IP law itself be protected? Yes. Here's how.

In the race to the base ( of the brain stem ) which is a commonly used rubric for the fastest way to make users stick on any platform long enough to be served as many ads as possible ( in an advertising centric model of social media platform monetization - or just about any platform these days ) the idea that IP ( and especially in the case of Ai generated text ) is a sticking point almost beyond compare as we enter into the age of non-stop content and at the speed of light by non-human actors.

This can be seen even more clearly in our efforts to uncover for our clients and as detailed in When Why Not Becomes Here’s How, Breakthroughs Happen where we see how even when " Non-human " is in use as in Ai systems registered to either real or 100% made up people when 20, 50, 100 year known efforts are confused with unrelated registrations of IP across the globe, it is those that have created and been recognized for the long standing efforts that pay the price.

Thus, does it matter then how content and registered R&D, design, and engineering intellectual property is used ? Not any more it seems. Until now.

When the output of scientists, engineers, medical doctors, and anyone with specific industry certifications required to or given any extra credence in the race to the base when it comes to content on the web ? And, On social media platforms etc., ? No. Because in 2025, and in the past, The model is, as it has always been, constant content destination ( validated or not - in whatever way there is, blue check mark, etc,. ) is king and with that firms, brands, trademarked entities, government offices, etc.,, all pale in value to likes, views, shares and comments. But why? And, what can be done about it? This is where the team at iGNITIATE goes even deeper in Innovation IP = Advanced Awareness – Roadblock Resistance
getting into the depths of Generative Design systems and how they pertain to the famed Nature Magazine article The Patenting Versus Publishing Dilemma, yet, how can well established companies, world wide brands, 10, 20, 50, +100yr old institutions continue to own what was and is always theirs concerted and well honed social value without confusion from others: where their own identity so as to be able to continue to build and grow must be protected ?

When the worlds best known brands, longest running active trademarks, active patents, etc., need to be preserved and protected globally when international search engines output and now Ai systems ( that are supplanting any search engine output by order of magnitude of usage ) also cannot be a factor for providence what then. When It seems that with intent registering, using, or outright co-opting trademark, patent and copyright ( the foundations for IP protection and thus innovation ) is directly threatened by the infinitely " automatic " linking of the tokenization or LLM " intelligence " building that demands that the intake of knowledge ( and thus the output of Ai systems ) does not require ( without checking, who to know who owns what / what can be used and what cannot ) this means that in any public display of any word it seems ( and it's words that drive LLM's and thus derive and drive Ai ) either must be 100% free, or Ai can't function and so says the biggest firms in the world who use just about any Ai system at all. This is never so clearly seen in Musk and Dorsey’s Call to ‘Delete All IP Law’ Ignores Reality and yet, as LLM's are the path to so much growth in Ai are there other ways? Yes.

The reality and of course ownership need to ( in the case of Musk ) where he " directed " apparently the “nicest employee” of Tesla to camp out on the lawn of the person who owned it and apparently for days at a time so as to secure the trademark rights to the “Tesla Motors” which was so vital the development of the brand, is where ( and within one country but not internationally in many cases ) trademark in particular but not so much when it comes to Ai copyright and certainly not in patent related issues ( where the smallest change means a new product in many cases ) means that the primary way to push innovation forward and from the standpoint of the original creators of NPD, R&D and trademark registration efforts, the THE crux of the way innovation moves forward: without confusion from inter-country efforts and certainly intra-country efforts of those not connected to in any material way, those that are the owners of said IP.

 

                    

   

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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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