Innovation Sustainability: R&D 300 ≠250 Ai

Innovation Sustainability is almost an oxymoron as whether it's Ai Vector databases, catalogued MetaData, or RAG pipelines, etc., today, 250 training repetitions can ≠ 300 Spartans at Thermopylae as R&D is still front and center for NPD success. How? Here’s How.

 When the underlying foundation of R&D, science, engineering and experimentation based analysis is the consistent convergence of large sample size and affiliated scientific verification ( and not only detailed in  Persistent Pre-Training of LLMs but in Large Language Models Using Semantic Entropy and many more ) we see how any number of industry standard Ai training sets ( and even with the emergence of real-time Ai system " last straw effect " 250 data point inputs ( and from any location, and in data format on the internet ) means persistence across any inputs can cause butterfly effects in final Ai outputs. More, it's  where these inputs can not only radically alter the ability of Ai systems to produce output that is not directly effected by " last minute " modalities ( like the last mile problem in logistics systems ) we also see how with a 300 ≠250 footprint, the length of the journey to the last inches can have severely unintended effects. Last second edge constraint changes are not conducive to sustained scientific, data driven artifacts in Ai systems. Where any number of minute changes and especially in last mile data scenarios can change whole gradient descent Ai system outputs even when unconstrained mathematical optimization models in first-order iterative algorithms used to minimize differentiable multivariate functions occur in any number of iterative methods for optimization be it search engines or Ai video or image output efforts, this is in fact happening in some of today's Ai system architecture. 250 is 300 but not for long.

When companies, organizations, group and even individuals move beyond the expected norms of scientific validation of empirical evidence the question becomes at what point do Ai systems begin to alter their output regardless of expert and industry validated guidance and more when sentiment and meta data a bearing that it should not. It is in these cases, and as expertly described in Medical Multi-Agent Systems we see how even in the most high " accurate " Ai systems alone are not a sufficient measure of clinical accuracy and when it comes to effected accuracy, and there is none of higher importance than medical next steps suggestion systems.

Further detailed in Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning we see how not only are the medical areas of discipline some of the 1st bastions of 300 ≠250 but also areas such as geological systems, nano-materials, optical chip fabrication, and environmental prediction systems ( areas with extremely high signal to noise ratio sensitivity ) and where Ai systems where interference from outside untested influences means edge constraints have larger chances of directly and negatively effecting tested and operational capabilities, this is the area for the largest concern. And, this is where specific new Ai system architectures are evolving to adjust for such discrepancies before output intended for next step actions is utilized and made available as usable and actionable.

 

                    

   

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When Systems Thinking Means A Thinking System Then What ?

With the proliferation of seemingly thinking ( Ai ) systems how far can Ai system thinking + design go ? Far !

Past the discovery of of transformer models, CNN's, Hybrid Neuro-Symbolic Systems ( LLMs + Symbolic Solvers ) used in reasoning in critical thinking applications, and onto Modular Multi-Modal + RAG designs, is where not only are specialized functional capabilities being stitched together in specific Ai models and to be able to seamlessly allow for intra-speciality spontaneity to emerge, it is also where evolutionary ( system design ) artifacts may emerge from exactly the same Ai systems. Is this recursive learning ? Does this mean systems that design themselves? Not in the way that is traditionally thought.

Where now, when Ai mavericks seemingly place " simple " processing ( as contrasted with say Ai drug discovery architectures to task ) what we see is, as an example in augmenting 3D design and modeling typologies with Ai, 3D tools like object multipliers, surface cloning, etc,. can even be cross purposed with standard innovation models of TRIZ and SCAMPER used for new product development efforts. It's then where almost real time design, training and testing environments allow the mapping of unrelated functional tools. And, where this is no better described in Design Creativity in A

When substitution, combination, adaptation, modification, repurposing, elimination, reversal; etc., efforts enhance ideation the amount of human validation necessary to move through the process of full say Ai system design, training and usage, dynamically, is slowly coming into an almost real-time environment. An Ai Vibe Architecture Interface.

This seemingly almost flies in the face of an evaluating criteria for " value " from investigations and where not only are there many such evaluating criteria for assessing creativity and alternative use cases, according to the López-Forniés system where Novelty, Usefulness, and Technical Feasibility may enumerate output, what are the ramifications of say using ( for example ) completely non visible spectrum analysis in say, auditory analysis is not something that can be quickly be created, tested and deployed in a rapid manner unless dynamic Ai system architectures can be used. And they are.

 

                    

   

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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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