Why Are You Doing ? The ( Design Thinking ) Task Ai Is Ready For ?

In Ai training and it's dependency on real world data for replication by repetition what happens when why an X is being done, radically changes and isn't known any more? Design thinking to the rescue ? Possibly.

Where the argument has always been that ( via one of the cornerstones of the design thinking toolset ) the black diamond method ( Discover, Define, Develop, Deliver ) is where abductive reasoning mentalities via contextual and holistic investigation strategies is the basis for articulating curiosity. This is the understanding that a " just the way it is " analysis process, is separate from engaging an engineering and science based thinking which is associated with deductive reasoning. Where in the past, the idea of curiosity being the differentiator for understanding what potential and yet unknown customers do and experience in their use of tools / completion of work, this can be seen ( as due to the over whelming adoption of Ai ) possibly a fundamental flaw in a new world and environment where software development and deployment plus knowledge work itself is being eroded further.

Throughout the uncoupling of the creative process: Novelty, Functionality, and Elaboration from the artistic form itself and as described in Effects Of Design Thinking On Artificial Intelligence Learning And Creativity we see the stark capability of " the state of the art " not in terms of creativity for creativity sake and thus when applied to unrestricted innovation efforts allows for a future training data set that could allow Ai systems to act in a way that anything is for the taking. This rather stark attitude that Ai cannot ascertain what can be done from what cannot be done, it is where international firms that are able to leverage Ai for suggesting and analyzing alternatives not only previously assumed generative ( as in next word suggestion and next act descriptions ) become almost artistic and therefore more valuable than ever.

Where the idea of " uncovering " via an iterative process to deliver user and customer-oriented results ( and where that process is based on rules that have already been defined ) is where active learning, constructivist learning, and where some firms define this as creativity is where the idea of iterative perspectives, problem-solving abilities, multidisciplinary learning, and complex background scanning can drive new product development. Firms that understand where what could be is as simple as what is to be invested in then realize Ai can sometimes ( in non-scientific domains ) at the least drive not only the uptake of some new product development efforts.

Where firms that have previously used design thinking models that include: inspiration, ideation, and implementation ( essentially a clear nod to the artistic mentality ) and the more recently business suited: empathy, define, ideate, prototype, and test is where these all require a consistent attitude that to understand, explore, prototype, and evaluate as well as empathy, ideation, and experimentation mindset can easily be seen an " as it is " and slightly non-visionary process. And when Ai datasets that use these types of input can only see so far that has not been seen. But as technology progresses, and more importantly the use of swarm processing and swarm datasets can Ai further expand into " why is someone doing " and where creativity is coming closer to reality. This is where the tables can truly be changed. And it's coming faster than even the most cutting edge firms can expect.

 

                    

   

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Material Experimentation Material Innovation

Cases in experimentation driving creativity producing innovations is nothing new until material experimentation becomes frictionless. Then what ?

With the nascent world of EAAS - Experimentation As A Service software as well as the physical systems connected to EAAS environments to accomplish tabula rosa experimental directions, a new horizon has opened as to how rapid iteration, the past limiting factor of discovery, becomes not the norm, but an entirely new avenue of creativity. In examples such as firms experimenting and exploring the cutting edge of nano-technology ( as in the case of Liquid-Metal Artificial Eye[s in ] Machine Vision ) we see entire platforms dedicated to alternative material construction methods, and in a way similar to a chef in a kitchen, where physical nano-scale manufacturing solutions can be rapidly reconfigured and then manufactured with the accompanying support technology to interact with what is manufactured, included. This means the physical and the digital world converging due to readily reconfigurable environments.

The ability to rapidly change the necessary control structures and mechanics necessary for massive object manufacturing at the 1 meter level and where we all live and breathe today, the idea experimentation we have in our current layer of physical being means resource constraint is the time limiting factor to advances in unexpected physical materials development. When material experimentation moved into an almost 10 orders of magnitude smaller this means material experimentation resources ( the machines necessary to do said experimentation ) become ( initially ) harder to produce but when controllable, allows for almost instant experimentation and outcome resolution because of the almost frictionless experimentation avenues allotted to not having to produce incredibly resource intensive manufacturing systems - once the systems are in place to make the machines that do the manufacturing, experimentation becomes almost instantaneous.

When the process of manufacturing the physical machines that themselves are able to enable rapid experimentation becomes, not less complex but less costly, the capability of creativity increases due to an unlimited supply of building materials. In this case at the nano level. Further described in The Role Of AI In Experimental Materials Science and where the same principals of Zero Marginal Cost and Infinite Iteration take effect, we see environments where rapid end choice structures for innovation begin to take hold in the after steps. This can be via highly restrictive patenting behaviors by individuals and organizations capable of engaging in such efforts, or by automatic systems that can prioritize and rank ablative defense stratifies for new product development or any number of new and novel ways to corral what has been experimented on. Scale literally is almost a button push away.

Firms, organizations and individuals adopting such systems and strategies create a natural moats around areas of experimentation that they are focusing on, much in the same way that matrix organizations are able to compartmentalize business unit around specific areas of profit and loss sometimes necessary to deliver new product development efforts. The interactions between these areas of expertise are able to interact with each other in an almost API - Advance Program Interface - manner and thus, interoperability also becomes almost frictionless.

Where previously the resource constraints of physical and electrical systems ( and in the case of electrical power grids to deliver electricity or in an always on environment ) severely again limited in the range of experimentations that can take place, in the example of materials development ( and particularly at the nano-level ) we see the rapid development of molecular and atomic level of experimentation with self feeding raw material environments. Any and all of the materials necessary for experimentation and production all in one place at one time.

This narrowing of an almost universal system for manufacturing is possible due to the scalar nature of the machines and systems that are emerging, and in the cases above, due to infinite work via Ai software development tools as well as at the nano-manufacturing level, infinite production, again, allows for the expansion of the field of alternative use cases for new materials development. Where there is new materials development there is instant new materials manufacturing capabilities and the software necessary to accomplish said manufacturing.

We see this further in the case of today, non stop content creation the help of AI where the previous limitations to usable and readable text, ideas, images, etc., was limited by the amount of highly trained people with the capability to communicate complex topics into easily digestible formats and produce images that correspond with this and or raw creative output that is then charged to and paid for by end users. The same then ( and in an case of nano-manufacturing ) happens to experimentation in manufacturing materials when the cost to develop nano-machinery almost also falls to zero. Firms pursuing this include 3D metal manufacturing firms at the standard 1 meter every day level however in the field of medical device manufacturing at the nano and molecular level. The new frontier for things like artificial biological systems that produce image processing and capture systems infinitely easier to produce than the time consuming and raw materials necessary image processing components of today.

Firms and organizations that are able to leverage the power of strategies of infinite material experimentation in terms of digital output and certainly in the case of digital and code focused environments have already see the almost instantaneous benefits that come from said material innovation and now with EAAS - Experimentation As A Service physical laboratories coming online the same has moved into the physical world. Organizations that are rapidly reconfiguring themselves to capture this type of value are also evolving at a much more rapid pace than ever before and squarely due to the idea that with infinite reconfigurable building blocks the idea that there is more than one way to skin a cat ( as the saying goes ) itself becomes moot with almost infinite cats that can be produced and while preserving the capability of intellectual property ownership protections.

                    

   

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Ai Twins Can Drive Collective Abstract Ai Reasoning? Yes.

Does ( Ai ) creativity demand new forms of reasoning ? Always - it is the nature of creativity itself to birth from what is known and unknown a corpus of the impossible in order to bring about useful innovation that can ( in some cases ) be realized as new product development and today where Collective Ai Twins may improve prove that path but how?

Recently with the further development and evolution of the the Abstraction and Reasoning Corpus ARC-AGI ( a benchmark designed to measure artificial general intelligence and fluid intelligence by testing a system's ability to learn and solve brand-new, unfamiliar puzzles ) we see the 1st inklings of Ai mechanisms that can with a very small sample set, sometimes even just two images, solve visual problems at the same level of humans. In the ARC-AGI mechanism and initially used as problem solving system, a path to alternative creative outputs can also be realized and as described in The Surprising Effectiveness of Test-Time Training for Abstract Reasoning.

Where this becomes particularly interesting is when Test-Time Training ( TTT ) data with digital ( human ) twins and within the context of either corporate work based employee Ai data-stores ( think, your work Ai, just like your work cell phone ) or on personal Third Self data-stores is where, new unexpected levels of creativity and innovation can be achieved. This is possible in the case of combining TTT systems with program-synthesis methods where a 61.9% accuracy is achieved, and which matches the average human score on ARC tests. And in this case, this is ONLY with incredibly small amounts of training data sometimes referred to as " one shot " training. Imagine with relatively unlimited data from just one Third Self or incredibly small amounts of data from large groups of Third Self data stores.

In the cases where we can look at simple problem solving examples where TTT and ARC-AGI systems shows the model variations of a puzzle using rotations, flips, and permuted orders and where puzzles consist of small grid-based visual patterns (ranging from 1x1 to 30x30 squares) where the solver must look at a few input-output demonstration pairs, deduce the hidden transformation rule, and apply it to a new input grid, this is just the most basic example of proving the validity of the TTT and ARC-AGI functionality and where this idea was originally created in 2019 by AI researcher François Chollet and primarily due to the need to reduce compute / token cycles to it's smallest amount as at that time GPU and even cloud based GPU systems were incredibly expensive ( in processor time and thus cost to end user ) and Ai model optimization was ( and in many cases is ) the focus of Ai research - reduce compute time while increasing effectiveness as the Abstraction and Reasoning Corpus (ARC) is a unique benchmark designed to measure AI skill acquisition and track progress towards achieving human-level AI. But this is not creativity or innovation. Or is it ?

With advances in large cloud computing GPU and TPU systems ( and soon to be internet everywhere accessible local models ) a new class of queryable digital twins is now emerging: individual Ai personas / digital twins based on in office and online activity of a firm's employees and as 1st detailed in 2001 as The Third Self but today realized in firms such as Viven, Eudia, Twin1 each having received 10's of millions in funding from Silicon Valley powerhouses such as Foundation Capital,  Khosla Ventures, General Catalyst Partners, Bessemer Venture Partners, Tribeca Venture Partners, and Aramco Ventures. Where these Ai Digital Twins previously were the domain of electrical systems for building management and physical product mechanical engineering devices, Ai and Neuromorphic computing has further pushed the boundaries in this area.

Combined with the latest breakthrough at the National University of Singapore (NUS), led by Associate Professor Mario Lanza from the Department of Materials Science and Engineering at the College of Design and Engineering and where it has been possible to ( with standard silicon based transistors ) a the silicon transistor can mimic the firing behavior of biological neurons and synapses when a bulk terminal connection is left unhooked or operated in an unconventional way, creating massive amounts of internal charge trapping and punch-through impact ionization. The startup that has emerged from this NewMorphic ( and based on some of the original Neuromorphic computing architectures ) has the potential to change the way Ai systems can be run on incredibly small processors. Now your smart watch is your Third Self on your wrist. For addressable and always on creativity systems, this has the innovative power possibly beyond our wildest ( current ) dreams.

 

                    

   

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Design Discovery Isn't Science Discovery But Ai Is Closing The Gap

When sketches jump almost straight to drive sales & manufactured output, Ai promises similar Science breakthroughs are here. Can they be ? Here's How.

Where current models of Ai systems ( and many based on text / language based processing: LLMs ) and where we have constantly seen great promise in a many design ( and sometimes ) scientific " discovery " like efforts, the idea of guided exploration ( which can sometimes be considered ) serendipity discovery or " ah ha " explorations ( and certainly in examples such as the robust development of Ai products such as Vizcom for the industrial design world, we see that the expectation to the same level of scientific jumps that could ( faster than light travel ) push us into a new world of scientific breakthroughs is where Ai systems are not the same for non qualitative outputs eg Physics, Biology, Materials, Chemistry domains.

With this in Large Language Models in Scientific Discovery  an incredibly robust effort focusing on project-level performance, and where the default to easily measurable validity of output where Ai models can and must propose testable hypotheses, design simulations or experiments, and then via interpreting results, is where we see state of the art frontier LLMs being directly effected from pre-training data and objectives rather than from their distinctive architecture and implementation details but more specifically the ability to NOT to be able to make jumps that currently are in the " human " and " ah ha " vector of desirable output. This is better described as AGI / human thinking and where next predictable statistical Ai systems struggle with such an objective. Scientific discovery after all advances through iterative cycles of hypothesis proposal, testing, interpretation, and refinement and fully connected to accidentalism which some have defined as a serendipitous factor. We use the word synchronistic.

In creating environments where beyond per-question Ai accuracy synchronistic ah ha situations can occur frameworks that enable project-level assessment and specifically in context of the system devised to require models to formulate testable hypotheses, execute analyses or simulations, and interpret outcomes to approximate an end-to-end discovery workflow this is where a new form of Ai systems, Serendipity LLM driven optimizations Projects, and specifically characterized by abundant, well-structured open-source data and codified knowledge, such as protein design, transition metal complex (TMC) optimizations, organic molecule optimization, crystal design, and symbolic regression, just to name a few exhibit the most significant gains from LLM integration to form " ah ha machines " as we have defined them. Further examples of this are firms such as Ginkgo Bioworks and Atinary both having partnered with leading US based Ai Labs use robotic hardware to Achieved major cost and yield breakthroughs in biological design loops but where this is still not " jumps " that synchronistic Ai is chasing.

Where this takes thins a step further is in the creation of SDE ( Scientific Discovery and Experimentation ) frameworks pushing project-level evaluations through well-defined research scenarios, enabling direct analysis of error propagation from Q&A to downstream discovery and where this may sound similar to image based " investigation " / generative diffusion based Ai systems. It is here also where the capacity to discern optimization directions and facilitate serendipitous exploration appears more critical even though top-performing LLMs score highly on questions regarding retrosynthesis, reaction mechanisms, and forward reaction prediction ( statistically possible due to limited edge constraint based possible outcomes ) it is here where these systems also struggle to generate valid multi-step synthesis routes due to pre-training data creating less decisive in discovery projects than in static question-level evaluation.

Where is the design world where there are almost no boundaries other than manufacturability thus effectively orchestrate the loop of " design " scientific discovery, future Ai developments that prioritize balanced knowledge and learning capabilities across diverse scenarios over narrow specialization is desired and the goal of the widest possible way to see the unseen and where such divergence capabilities underscores that proficiency in standard scientific examinations does not guarantee mastery of the nuanced, context-dependent reasoning required for scientific discovery, yet certainly within in say, the capability of furniture and object design.

Project-level evaluation indicates that question-level
patterns only partially predict discovery performance and that a model’s capacity to drive a research project relies on factors more complex than a simple linear correlation with its Q&A accuracy. This implies that precise knowledge of structure-property relationships may be less critical than the ability to navigate a hypothesis space effectively

In the raw scientific domains and where training and evaluation paradigms must expand beyond textual accuracy ( LLM systems ) to prioritize executable actions and where invoking tools, debugging execution failures, and iteratively
refine protocols in response to noisy feedback this is well within the reach of next generation, domain specific systems. A fully integrated with all of humanity's knowledge systems ( scientific of other ) is still not within reach and where in using scientific discovery loops and LLMs as proposals over a hypothesis space (e.g., the space of all possible molecular structures, symbolic equations) not only can LLMs generate offspring based on parent hypotheses sampled from the pool ( eg Discovering the best spin configurations that minimize the Ising model energy presents significant challenges due to vast combinatorial configuration spaces ) these are isolated and domain specific applications. Applying this to say a way to increase wood bendability for unconventional furniture design and within the context of " what if " design sessions all the way down to the financial implications of the demand necessary to build the machines necessary for specific new designs to be realized are, like scientific systems, still some way off.

 

                    

   

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Breakthrough ( Ai ) Innovation = API Incubation

Independent of scale, structures, molecular or architectural, are now possible, can API's keep up? Here's how.

Within the discipline of the myriad of technological systems, be it software or hardware ( and similar to Ai physical chemical labs now coming online ) we see the capability ( and due to the incredible amount of desperate interfaces needed to have the sheer number of processes controlled for Ai physical chemical labs ) is where we see some of the alternative use-cases for technological lateral thinking. And all, possibly now, being fully influenced by said technological and artistic intermixing.

When the capabilities of instant scientific and artistic functional output can be combined at a software and hardware level ( and as in the example of 3D Printing Of Pediatric Drug Delivery Systems natural extensions of a new way of recombination ( painting ) at a molecular level becomes possible.

This of course extends down to almost all levels of operational as well as organizational functional activity takes place and where it can be said, this is only a matter of time as to: doors open fee X ; doors semi-closed fee Y ; etc., from molecular modeling to material ready for delivery, and instant and possibly automatic product production. And, it's not even quantum, rather simple silicon chip systems. And, that's just today.

 

                    

   

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