Deep Dive - Frontier AI with Dr. Jerry A. Smith

Dr. Jerry A. Smith

In-Depth Explorations of Neuroscience-Inspired Architectures Revolutionizing AI.

  1. What Holds an AI Together?

    03/12/2025

    What Holds an AI Together?

    Medium Article: https://medium.com/@jsmith0475/what-holds-an-ai-together-063fcb26c876 "What Holds an AI Together? The case for vertical causality in machine intelligence," by Dr. Jerry A. Smith, argues that contemporary artificial intelligence systems are fundamentally incomplete because they rely solely on horizontal causality, which governs the sequential flow of actions and feedback across time. This reliance on the temporal axis results in systems that are locally competent but lack global coherence, leading the author to introduce the concept of vertical causality. Vertical causality describes simultaneous, structural dependencies—such as the underlying architecture, goal representations, and identity models—that sustain the system and ground its purpose at every moment action occurs. The author explains that achieving genuine artificial agency requires integrating both dimensions in a "duplex ecosystem," where vertical structures define the space of possible behaviors while horizontal processes explore it. Consequently, robust AI alignment should focus not just on sequential checks but on the architecture itself, ensuring that essential commitments are structurally operative rather than merely procedural outcomes.

    14 min
  2. When All Your AI Agents Are Wrong Together

    24/11/2025

    When All Your AI Agents Are Wrong Together

    Medium Article: https://medium.com/@jsmith0475/when-all-your-ai-agents-are-wrong-together-c719ca9a7f74?postPublishedType=initial "When All Your AI Agents Are Wrong Together," by Dr. Jerry A. Smith, discusses advanced architectures for achieving million-step reliability in Large Language Model (LLM) agents, building upon the foundational success of the existing MAKER system. Although MAKER demonstrates long-horizon stability using probabilistic voting, which relies on logarithmic cost scaling against exponential reliability, the article identifies three major flaws: vulnerability to correlated errors, the requirement for a fully explicit state representation, and high per-step costs. To address these limitations, the author proposes a new structure called TAC-HAVA-K, which incorporates adversarial reasoning (Thesis, Antithesis, Consolidator), hierarchical verification (Belief States, World Model, Verifier), and K-fold parallelism to create a more robust, cost-efficient, and generalizable system capable of operating in ambiguous, partially observed environments. Ultimately, the new architecture aims to achieve reliability through structural diversity of verification rather than relying solely on statistical independence.

    16 min
  3. The Devil’s Advocate Architecture-How Multi-Agent AI Systems Mirror Human Decision-Making Psychology

    21/11/2025

    The Devil’s Advocate Architecture-How Multi-Agent AI Systems Mirror Human Decision-Making Psychology

    Medium Article: "The Devil’s Advocate Architecture," by Dr. Jerry A. Smith, introduces a novel framework for designing highly reliable artificial intelligence (AI) by mirroring principles of human psychological and organizational decision-making. The core argument is that modern AI fails due to overconfidence and a lack of doubt, which the proposed multi-agent system counters through structured conflict and debate. This architecture employs three distinct roles—the Worker (proposing a solution), the Devil’s Advocate (critiquing risks), and the Reviewer (synthesizing the final decision)—to overcome cognitive biases like groupthink. Crucially, suppose the Reviewer’s confidence falls below a set threshold. In that case, the system initiates a Genetic Mutation Loop, forcing agents to fundamentally evolve their strategies in response to the identified failure mode, leading to antifragile, battle-hardened solutions. A case study of IT incident resolution demonstrates how this dialectical process and targeted evolution yield comprehensive, contingent plans, making the approach applicable to high-stakes fields such as medical diagnosis and financial planning.

    13 min
  4. The Math That Kills Growth - Solving Supply Chain Growth Problems

    20/11/2025

    The Math That Kills Growth - Solving Supply Chain Growth Problems

    Medium Article: https://medium.com/@jsmith0475/the-math-that-kills-growth-how-we-built-an-ai-system-that-solves-manufacturings-50m-dcdf6e352124 "The Math That Kills Growth" describes a framework called VALORE, a multi-agent AI system designed to solve the Coordination Paradox that stifles growth in scaling manufacturing companies. The author, Dr. Jerry A. Smith, explains that as manufacturing operations grow, coordination complexity increases exponentially, leading to a collapse in decision velocity and massive financial losses, often totaling $15M to $50M annually. The VALORE system replaces manual, email-dependent coordination with specialized AI agents—such as the Scout, Analyst, and Strategist—that communicate and negotiate in real-time to manage disruptions like supply chain shocks or quality failures within seconds. The implementation of this agentic AI is proposed through a three-phase roadmap—Visibility, Assistance, and eventual Autonomy—to build trust in the regulated manufacturing environment gradually. Ultimately, the system aims to transform coordination from a growth-capping liability into a competitive advantage by achieving significant reductions in operational friction and cost.

    17 min
  5. When Machines Learn to Negotiate: Reimagining Manufacturing Coordination Through Multi-Agent AI

    18/11/2025

    When Machines Learn to Negotiate: Reimagining Manufacturing Coordination Through Multi-Agent AI

    Medium Article: https://medium.com/@jsmith0475/when-machines-learn-to-negotiate-reimagining-manufacturing-coordination-through-multi-agent-ai-fb6a60fc17eb WebApp: https://main.d2oyp76axtxaek.amplifyapp.com This article, by Dr. Jerry A. Smith, outlines a theoretical framework for revolutionizing complex manufacturing coordination, specifically within the regulated medical device sector, by using Multi-Agent AI (Artificial Intelligence). The core problem addressed is the exponential coordination complexity of running multiple facilities that current centralized enterprise software cannot handle effectively. Dr. Smith proposes a system where specialized AI agents—each an expert in an area like inventory or compliance—would negotiate and coordinate decisions within seconds, significantly improving speed and accuracy over current human-centered, email-based processes. Crucially, this framework advocates for a hybrid intelligence approach combining transparent mathematical formulas with Large Language Models (LLMs) to ensure both adaptability and the explainability required for regulatory audits. While the concept is compelling, the author acknowledges significant practical hurdles including expensive data integration, the need for robust safety mechanisms, and organizational resistance to such a dramatic shift.

    15 min
  6. Your AI Isn’t Intelligent — It’s Just Really Good at Pretending

    17/11/2025

    Your AI Isn’t Intelligent — It’s Just Really Good at Pretending

    Medium Article: https://medium.com/@jsmith0475/your-ai-isnt-intelligent-it-s-just-really-good-at-pretending-ac2fe872e838?postPublishedType=initial The source, an excerpt titled "From AI Simulation to Synthetic Intelligence," argues that current Artificial Intelligence (AI) models, such as Large Language Models (LLMs), are fundamentally limited because they operate as sophisticated simulations based on probabilistic pattern matching rather than genuine cognition. Authored by Dr. Jerry A. Smith, the text identifies several critical architectural flaws in today’s AI, including catastrophic forgetting (the inability to continuously learn new information without overwriting old knowledge) and a reliance on correlation instead of causal reasoning, which leads to unpredictable failures in novel scenarios. Smith posits that the solution is a transition to Synthetic Intelligence (SI), a new paradigm designed for genuine, non-imitative cognition based on three pillars: Material-Based Intelligence (integrating memory and processing), Nested Learning architectures (allowing continuous learning), and the integration of causal reasoning to enable true adaptability and understanding. This shift is presented as necessary to overcome the scaling wall, economic costs, and reliability issues inherent in current, simulation-based AI systems.

    11 min
  7. We Built a 10-Agent AI System That Monitors Our $225K Project in Real-Time - Here's What We Learned

    10/11/2025

    We Built a 10-Agent AI System That Monitors Our $225K Project in Real-Time - Here's What We Learned

    Medium: https://medium.com/@jsmith0475/we-built-a-10-agent-ai-system-that-monitors-our-225k-project-in-real-time-heres-what-we-learned-1f0de27ca852 "We Built a 10-Agent AI System That Monitors Our $225K Project in Real-Time - Here's What We Learned," written by Dr. Jerry A. Smith, detailing the development and performance of a specialized AI system. This system utilizes ten distinct, collaborating AI agents to continuously monitor a $225,000 consulting project by synthesizing data from multiple sources like email, calendar, budget, and task trackers. The core achievement of ForeSight is its ability to detect complex project risks 7 days earlier than human managers could and dramatically reduce status reporting time from hours to just 4.2 minutes. The author argues that this multi-agent architecture, which relies on parallel execution and inter-agent communication via a Redis message queue, shifts project management from reactive data compilation to proactive strategic decision-making. The article concludes by emphasizing that the collaborative intelligence of specialized agents offers a massive return on investment by saving hundreds of thousands of dollars in manual labor and preventing costly delays or budget overruns.

    15 min
  8. Your AI Might Be Thinking in 17 Dimensions. You’re Only Using 2.

    07/11/2025

    Your AI Might Be Thinking in 17 Dimensions. You’re Only Using 2.

    Medium: https://medium.com/@jsmith0475/your-ai-might-be-thinking-in-17-dimensions-youre-only-using-2-1a2a56131a1b "Your AI Might Be Thinking in 17 Dimensions. You’re Only Using 2." presents a conceptual framework and research agenda by Dr. Jerry A. Smith, proposing that the popular chain-of-thought prompting method, which forces AI to "think step-by-step," severely limits the system's native capabilities. The author argues that AI models operate in high-dimensional embedding spaces, handling numerous constraints simultaneously, and forcing linear reasoning is akin to flattening a complex sculpture onto a single line of text. The proposed solution is Higher-Dimensional Collaboration, where users specify constraints and objectives across multiple dimensions, allowing the AI to explore the full solution landscape rather than following a human-mimicking sequential path. While acknowledging that step-by-step reasoning is necessary for interpretability and regulation, the article advocates for prioritizing the computational efficiency of exploration for complex, multi-objective problems. Ultimately, the text calls for researchers and practitioners to rethink how they collaborate with AI to leverage its parallel, multi-dimensional processing strengths.

    19 min
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In-Depth Explorations of Neuroscience-Inspired Architectures Revolutionizing AI.

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