AI Design Means Collaboration Convergence

When Ai design tools for physical and digital objects focus on the right phase of R&D&D - Research, Design and Development is where Ai gives it's greatest boost but how? Here's how.

In areas of investigating the influence of the design process in the shaping of not only initial conditions but edge constraints used in a new product development initiative, we see how there are specific Implications for Human-AI Design Collaboration that show more valuable and specific artifacts coming from certain phases in the design and new product development process especially when the widening of the lens takes place via the widening of the definition of the NPD ( New Product Development ) parameters to something many firms now embrace which is a NP&PD ( New Product & Process Development ) system where Ai shows considerable promise.

In the case of quickly being able to execute on code base deployment for rapid application development initiatives ( and where this is increasing in some cases means jumping to fully working incredibly complex code repositories in real time ) where AI Design Means Collaboration Convergence scenarios, we see internal to a NP&PD situation the cycle time of experimentation directly effected and again considerably cut and even when these environments are physical goods oriented and the multitude of additional complexities such undertakings entail.

More specifically is where we see that in cases of engineering design and it's convergent solution methodologies when combined with designers with design problem outlook exploration as divergent possibilities can be bridged by Ai systems that are translating the differences in language between the two groups and their basic archetypal underpinnings allowing for a stronger exchange of interpretations as to what are within bounds extrapolations of the initial data sets that are presented in Ai design and development systems. This also assumes that any and all collaborative efforts will require that Ai systems in each of the distinct NP&PN processes has access to the same data sets so that when further questions, experimentation, suggestions, etc., are queried by the Ai design system in this example, means that all quantitative data that is used is part of all future branches from the initial design and development start.

But when clients teams are everywhere, when time zones and check-ins are all over the map, when an originally 8hr work cycles meant ( possibly ) two shifts in global NP&PD efforts, now in moving to a 3 phase model of 3 shifts of 8hrs and thus 24 hour NP&PD efforts, even more, we see that the strongest part of the Ai assisted design and development cycle is in the engineering areas but where the further develop of Ai Discover and Define sections of the process require the highest sense of divergent thinking that when further along in the development process previously could not have occurred as far down the process as now with Ai allowing for redefining and in some cases re-sequencing previously almost as permanent parameters in a design, means more flexibility up until the last moments of production. In that, the further creativity needed to wrangle last minute challenges that in pre Ai-Design and Development environments might have been the death nail to projects.

In that explicit way, Ai NP&PD enhanced environments ( and even before and within the design and prototyping phases of said efforts ) is as the possibility might become, allow for long hierarchical divergent development paths to take place and all where one change to specific directional changes allows for all aspects of the chain to take place and specifically in a visual way further cultivating not yet explored design directions that can lead to specific NP&PD ( New Product & Process Development ) further embedding R&D&D ( Research & Design & Development ) Ai tools as primary protagonists between the 7 distinct phases of experimental efforts R&D&D = NP&PD where the axis of influence ( and explicitly within the development portion ) is ' D ' or development activities.

 

                    

   

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Magic When Manufacturing Meets Micro(interventions )

In field theory it's not just field strength that determines particle movement, it's perturbations in the force. Literally. In R&D and NP&PD it's very much the same thing.

IBM had the wild ducks program ( later renamed the more socially acceptable IBM Fellows ) and which still exists today, and which was created to support and recognize individuals who break rules ( sometimes at their own career peril in more recent variations so as to show complete commitment to a specific course of change ) and so to achieve breakthroughs where others would not. In these cases, and with these individuals this is where significant contributions, including the Selectric typewriter, the Watson supercomputer, and the Fortran programming language just to name a few emerged. Where this breaks down ( or can break down ) is in the assumption that NPD ( New Product Development ) is bereft from ( even in the case of ' new now ' or ready for use now yet still not something that people are familiar with ) the peril of shifting from NPD to a more apt NP&PD model or New Product & Process Development system ( often ignored and rightfully so in the case of breakthrough and necessity situations ) and which assumes that processes, well established processed will be impacted. Which it always is.

Simply, NPD is not just step 2 of R&D where R&D is the precursor to NPD but where NPD or better yet NP&PD ( in it's most successful efforts ) works at the same tempo as R&D efforts. Where we see this so carefully detailed is in The Last Stage of Product Development where we come to understand that where people from NPD efforts are solely focused on the product itself and less on the actual changes that have to take place in Operations which often comes directly after the ability of R&D to show that products are even possible, we learn that this is due exactly to the idea that although internal implementation is the first ‘proof of the pudding’ this is best left to larger organizations who must validate amongst peers who depend on existing XYZ and from their entire departments and firms survival rather than in mavericks in the outside world situations.

Where this becomes more curious is how in many cases and as detailed in Complex Thinking & Transition Design where we see how in all phases of R&D and NP&PD those involved in explicit ideation and further down stream NP&PD efforts report the perception of the level of mastery of their complex thinking competency heightened with Ai based systems, and possibly, due to simply the conversational nature of such systems and processes. Is then just the idea of speaking smoothly and in the language and tone of a specific persons expertise enough to increase innovation acceptance? This seems to be highly correlated via a specific articulation and measurement of critical, innovative, scientific, and systemic thinking analysis, then testing and reporting on individuals " success " as part of the adaption of NP&PD efforts clearly coming from advanced R&D efforts when micro-interventions take place and which are systemically embedded in R&D and NP&PD efforts.

Where there are complexities in the idea of using individual response / awareness tools and balancing this with someones external competence with rapidly changing NP&PD situations based on design challenges / oriented goal sets expected and where personal responses / awareness is buffered by the implementation context itself ( of users of new products, firms stability, and a markets willingness to try new things etc,. ) we see where specific R&D is being leveraged to push defined use cases via out of the nor use cases, it seems then to be where mediation experiences via enhanced Ai systems and non linear R&D + NP&PD thinking and processes are able to move with much more fluidity and as detailed in Complex Thinking & Transition Design where we see Ai-based interventions via something as simple as awareness assessments breaking away from standard linear R&D + NP&PD effectiveness.

Where we see the idea of transition design as one method to deal with " wicked problems " where large groups of diversified stakeholders and their concerns at all numbers of layers of existing systems required multi-disciplinary and longitudinal interventions, alternative use case processes and a combination of non linear R&D + NP&PD efforts to effect change seem to allow greater breakthroughs to happen quicker and with more rapidity thus it's still a wild wild west ( awareness ) capability that essentially makes breakthroughs happen.

 

                    

   

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Can Generative Ai Defy The General ? Yes. Here's How.

When focusing on Expected Quality, Domain Knowledge and Functional Specificity Ai is anything but "general" and via sample sizes of ~800 we see it's all in the data ? More often it's in the emotions, surprisingly.

The ability for co-creation to be a viable function of collaborative group work is the assumption that not only is there a ( usually ) a physical place ( and in today's heavily digital world, a place in the clouds ) that is the nexus for efforts that take place to move work from ideas to innovations and where with the now almost ubiquitous inserting of Ai systems into that process where in fact that Ai keeps track of all aspects of data sharing, ideation, etc,. and in some cases experimentation. What has become more unexpected is that positive self reported emotional responses among co-creation participants and in one particular case study Generative AI Reshaping Teamwork and Expertise allowed a seemingly unexpected aspect of generative Ai to be shown where Ai systems often fulfill a large part of social and motivational roles more than human teammates often provided.

Where this becomes curious ( and not that far from the norm of typical daily team dynamics ) is where fewer negative emotions were reported when engaging with generative Ai systems mainly due to the tacit understanding that ( as the data showed ) interplay curtails blind spots, Ai encourages scrutiny of multiple viewpoints, and fosters collaborative creativity. And that does not mean it has to come from people - it can ( and soon the future ) if not now, where this will come from The 3rd Self and why we created the term in 2001 where we began exploring the idea of Can A Machine Design driving the groundbreaking TEDx Rome series of the same name and where we pushed on exploring The Third Self: Design Innovations' Next Muse in later efforts.

In particular the realization that ( like in A Field Experiment on Generative Ai ) specialized experts and interdisciplinary collaboration can drive innovation but does it and can it enable it via the poetic nature of design that is often the formal antithesis to belonging, collective commitment, and reciprocal support necessary in large organizations and where the often " softening " or in the case of marble sculpting non-pixelazation takes place. Where the idea of pixelated ideas or typologies that have unique and distinct form and languages are often the direct artifacts of formation of a topological style, in team work the idea of the product master is the antithesis of " good design " and where collective ( and in many cases ) generative Ai shines: taking multiple styles and coellessign them into an XYZ that can be accepted by a co-creation effort. Where a symphony or a masterpiece is attributed to individuality ( yet with many players ) we see co-creation ( with or without the advent of Ai ) is where generative systems ( even when these are human based ) takes it's biggest influence. Thus is Generative Ai co-creative or Coalessive?

Surprisingly the ability for non-core-job employees working alone is where they showed achieved performance levels comparable to teams with at least one core-job employee and where the idea of ideation to innovation has the capability thorough " expert access " to to cut through boundaries that unsurprisingly normally needed supervision. An example is where Ai becomes the local instantly queryable expert. Again, not surprisingly expanded problem-solving horizons via AI’s holistic and interdisciplinary thinking efficacy means, like in quantum mechanics, shall we call it spooky ( effectiveness ) action at a distance takes place.

Where the ability of language models ( and also within the realm of datasets connected to scientific and topologically specific knowledge domains ) will still often cause Ai output to be ( in many cases ) echo chamber oriented, with the advances in Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Transformers and State Space Models mean real time design environments are evolving to even more effectively encourage and interface directly with engineers and logistics for even faster than ever new product development analysis and viability scenarios and environments to evolve quickly and economically. The sky is essentially the limit. And, interestingly, the destination.

 

                    

   

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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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