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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Neuromorphic Computing Means Design's Innovation Convergence

When breakthroughs mean ( and via the moniker that ALL design is a remix ) pushing past the current Ai models the question becomes can creative convergence, diverge? Here's how.

When tools are constantly ( and in the current initial rollout phases of Ai, and in it's first forms ) wed to " style " or alternative use cases being based on training data only - and non experimental actions and feedback loops ( say in soccer playing robots ) when the jump to minimal compute chip level architectures that more closely resemble the human brain ( in terms of raw electricity use ) ala neuromorphic architectures is where we see ( in one possible path ) and as detailed in " Multifunctional Devices ... Neuromorphic Applications " some of the 1st fully articulated architectures that might have a chance at non-remix models of " thought " or as one might say, imaginative divergence.

Currently ( and as commonly understood in binary Von Neumann systems ) and as articulated in the latest nVidia chip architecture / training data computation action and reaction system design, we see how sensor data ( what is happening now ) and what could happen ( eg. standard video analysis of soccer games and the way say robots can read this and then be trained directly ) are still a clear call and response based design. It is effectively  and more importantly, non-creative. However where truly artistic breakthroughs must ( supposedly ) happening in an experimental environment and where current systems design ( even devoid of Ai ) can do this through machine learning with essentially learning computers it's the " now " sensor based and conversational architectures ( where suggestion and evaluation of what is being considered ) can be engineered to break the ideas of remixing ( preventing unknown and unexpected new directions to be investigated and immediately put into use ) mean a whole new computational architectural when chip level architectures are created. Neuromorphic computing can do this IF it is free of ALL constraints. That in itself is a tall order but well on the way.

More aptly in " The Road To ... Neuromorphic Technologies " the confusion becomes the standard better ( design ) faster ( compute ) and cheaper ( energy use ) tradeoffs again, not focusing on the divergence and convergence of external data that ( through specific behavioral lenses ) can transform what has not been expressed via possibly bread-crumbing, synchronistic, and  cut-up ( just to name a few ) mechanisms for unexpected connectivity. In this case, directions for investigations that can produce breakthroughs comes from known logical connections. Where the leaps begin to show their direction the clearest is in the not-knowing of directions. Similar to Johari Window introspection and extraspection oriented wanderings.

Where it is then, the idea of wanderings ( past just experimentation ) that leaves the specific, almost detective level decision tree analysis incomplete means chip level and application layer combinatoric logic systems under current development almost obsolete when neuromorphic computing architectures can be combined with non-local sensor based input and fully separate from training data models of input. In the oil and gas industries as well as in the photonics industry, these are just but a few of the ways vertical industry specific operational functionality ( is being scooped up by training data intake firms ) as a way, possibly, to pre-train in any and all industries that can be soaked up. But, this is still not divergent and synchronisitc in it's design. Neuromoripic systems are expaning that horizon and thus a way for breakthroughs to occur once learning computers can be miniaturized and made infinitely affordable - in a hand held way.

 

                    

   

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Ai Makes Innovation Happen, Until It Corrupts It. And Microsoft Proved It.

When Ai enters the remixing game, be it scientific of business, agents take a turn at a point where no review can return calculations to previous states. But this can be fixed. Here's how.

Like any organization of individuals the idea that the number of personas that are involved in highly complex problems is a function of the concern that errors can directly effect the outcome of precision output necessary for evaluating all steps involved in a process, the crux of this assumption is that by looking for any series of errors and rolling back steps until approval from the organization of individuals involved in said problem have all reviewed and approved the process, allows for a back and forth trapping errors before they happen. In the case of Ai, utilizing the same techniques.

In a recent study by Microsoft Ai Labs in LLMs Corrupt Your Documents When You Delegate not only do we see reflexive pattern recognition—discerning structures from past non-linear experiences— being used and where these systems can be associated with purely reflective or critical approaches in NPD and R&D when integrated with deliberate checkpoints, in many cases AI delegation bypasses this. Multi-agent simultaneous operations accelerate corruption because each agent remixes outputs without anchored reversion to verified base states and where when no natural organizational back-and-forth occurs; drift becomes irreversible once propagation exceeds review windows.

Delving further into possible solutions and from cross functional and adjacent industrial applications here we see how:

• Immutable base anchoring with versioned checkpoints.
  Where maintaining a human-orchestrated or cryptographically signed base document/state allows all agent actions to operate on temporary forks and then, post-task, apply delta validation against the anchor using rule-based or symbolic difference engine checks before merging thus rejecting rollback on detected deviation beyond predefined thresholds.
• Reflexive-critical gating layers.
  Where inserting mandatory reflective review nodes between agent handoffs means these nodes enforce critical fact-checking ( XYZ element swapping ) against external verified sources or human-approved knowledge bases, not agent memory and with this where reflexive pattern matching flags anomalies by comparing against historical successful patterns from prior non-corrupted runs.
• Non-linear but orchestrated rollback scaffolding.
  Allows design workflows with explicit non-linear opportunity points that include forced serialization at complexity thresholds to use design-thinking toolsets for error-trapping primitives: paper-analog logging of decision trees or AI-simulated but human-vetted micro-approvals. This reduces internal friction while preventing negligent leap-frog into corrupted states.
• Hybrid human-AI persona scaling.
  Where the "organization of individuals" assigns distinct agent personas with bounded responsibilities and mutual audit rights that limit simultaneous parallel edits. Enforcing consensus protocols requiring multi-agent cross-verification plus one reflexive human or symbolic verifier before state advancement means data from ( for example) DELEGATE-52 models can confirm degradation worsening within an interaction horizon and where constrains are placed on said horizons explicitly.
• Quantified monitoring and degradation thresholds.
  Means instrument workflows with continuous integrity metrics (e.g., semantic consistency scores, factual drift measurement) can set hard stops at <5% deviation from past step and in that equivalent internal benchmarks are able to track readiness before scaling to high-stakes scientific or business computation / interactions

And where not only to these mechanisms restore the back-and-forth error-trapping inherent in human organizations but also allow ( possibly ) for modified agent behaviors to be able to not only review in real time but in some cases a non-linear fashion similar to design, design thinking but also distuptive design while still maintaining scientific standards and international financial rule sets for not only NPD efforts but aggressive ( IP compliant ) breakthrough capabilities.

When Ai systems are not only integrated in real time with what can considered to be human interaction " checks and balances " against past NPD and Design outputs this drives the possibility for not only divergent design and scientific experimentation to take place, but also to allow for minimizing corruption by hand checking to take place, and which of course, helps to remove error artifacts in extremely long computational processed ensuring unassisted Ai workflows to complete without computational corruption taking place. Possibly. And Microsoft seems to agree.

 

                    

   

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