Medium Article: https://medium.com/@jsmith0475/how-neuroscience-inspired-ai-delivers-10x-roi-business-transformation-case-studies-c782890e9b04

The article, by Dr. Jerry A. Smith, explores how neuroscience-inspired AI is revolutionizing business, asserting that traditional AI often fails to deliver significant returns. It highlights how these advanced AI systems, by mimicking the human brain's memory, adaptive learning, and contextual reasoning, achieve 10x or greater return on investment (ROI) across various industries. Case studies from AstraZeneca, Google, and JPMorgan Chase illustrate these transformational results, demonstrating substantial improvements in drug discovery, productivity, and financial risk management. The text also discusses the technical foundations, emphasizing energy efficiency and the market opportunity for this rapidly growing technology, providing a roadmap for strategic implementation for organizations looking to leverage this next-generation AI.

Medium Article: https://medium.com/@jsmith0475/crafting-intelligent-systems-from-neuroscience-principles-the-neuroscience-first-path-to-agentic-6a587a1f8311

"Neuroscience-First: The Path to Agentic AI," by Dr. Jerry A. Smith, advocates for a radical shift in artificial intelligence development, moving away from traditional data-driven approaches. Dr. Smith argues that current AI models are brittle and lack genuine understanding because they rely on statistical correlations rather than causal cognitive mechanisms found in the brain. The proposed "Neuroscience-First Framework" involves a three-stage process: Biological Verification, Neuroscientific Modeling, and Computational Translation, ensuring AI systems inherit authentic cognitive capabilities. This approach aims to create agentic AI that can adapt, learn, and operate autonomously in the real world, leading to significant competitive advantages and fundamentally changing the future of intelligent systems.

Medium Article: https://medium.com/@jsmith0475/agentic-ai-neuroscience-and-the-hidden-psychology-of-delegated-responsibility-a397d98f8e4e

The podcast. based on excerpts from "Agentic AI: Delegated Responsibility and Human Accountability" by Dr. Jerry A. Smith, explores the emergence of Agentic AI and its profound implications for human responsibility and accountability. The author, a Frontier-AI Research Architect, argues that while Agentic AI offers advanced capabilities by mimicking human cognitive processes like those found in the hippocampus and prefrontal cortex, there's a risk of humans subtly delegating not just tasks but also the very act of being responsible. The piece examines the psychological inclination to avoid responsibility, particularly under cognitive strain, and how autonomous AI can inadvertently foster this avoidance, potentially eroding moral resilience and ethical courage. Smith proposes that neuroscience-inspired AI architectures can be designed to reinforce human accountability, ensuring these systems complement rather than diminish our ethical engagement with critical decisions. Ultimately, the text urges a conscious choice to leverage Agentic AI for empowerment, not as an abdication of human ethical duty.

Medium Article: https://medium.com/@jsmith0475/architecting-autonomy-the-neuroscience-behind-agentic-ai-systems-29aab6d5d131

The article explores how neuroscience discoveries are fundamentally reshaping the development of autonomous artificial intelligence. It highlights the extraordinary efficiency of the human brain compared to traditional AI, advocating for brain-inspired computing to overcome current limitations. The author explains how specific brain regions, like the hippocampus, prefrontal cortex, and amygdala, offer blueprints for advanced AI systems capable of compositional memory, adaptive control, and intelligent safety. Furthermore, the text emphasizes the role of neuromorphic hardware in achieving unprecedented energy efficiency and details a four-layer architectural framework for future AI that integrates these biological principles. This convergence aims to create truly agentic AI, moving beyond mere automation toward systems with genuine autonomy, creativity, and consciousness-like awareness.

Medium Article: https://medium.com/@jsmith0475/beyond-intelligence-why-large-language-models-may-signal-the-rise-of-anti-intelligence-eb9f7a62dd62

"Anti-Intelligence: Why LLMs Undermine Human Understanding" by Dr. Jerry A. Smith, explores the concept of large language models (LLMs) as "anti-intelligence" systems. It argues that while LLMs produce convincing and fluent outputs, they lack true understanding or grounded comprehension, operating instead on statistical prediction of text patterns. The author highlights empirical evidence suggesting that relying on LLMs can reduce human critical thinking and lead to acceptance of inaccuracies, despite the models' sophisticated performance. The article proposes the need for "cognitive integrity" approaches and human oversight to mitigate these risks and preserve genuine understanding in an age of synthetic fluency.

Medium Article: https://medium.com/@jsmith0475/when-brain-cells-learned-to-code-e9e47151fbdf

The provided text introduces Organoid Intelligence (OI), a novel computing paradigm that utilizes living neural networks grown from stem cells on silicon chips. It argues that OI offers a solution to the energy and plasticity limitations of current AI systems, which rely on GPU-based architectures and neuromorphic processors. The source highlights OI's advantages, including significantly lower energy consumption and native continuous learning capabilities, as demonstrated by neurons learning to play Pong efficiently. Furthermore, it explores the current state-of-the-art platforms and the strategic investment opportunities in this nascent field, while also addressing the profound ethical and regulatory challenges that arise from using biological computing.

Medium Article: https://medium.com/@jsmith0475/unlocking-unprecedented-power-why-grep-is-your-llms-secret-weapon-eb6664cd734b

The provided text advocates for combining the Unix command-line utility grep with Large Language Models (LLMs) to significantly enhance text analysis workflows. It highlights that while LLMs offer robust semantic understanding and generative capabilities, they can be costly, slow, and prone to "hallucinations." Conversely, grep is presented as a deterministic, memory-lean, and high-speed tool for pattern matching and data filtering. The author argues that using grep to pre-filter and validate data before and after LLM processing leads to substantial improvements in cost-effectiveness, speed, and accuracy, effectively turning a fifty-year-old tool into a crucial "secret weapon" for modern AI applications. Practical examples and common pitfalls illustrate how this "grep-first" pipeline streamlines operations and improves output quality.

Medium Article: https://medium.com/@jsmith0475/autonomy-begins-with-truth-a-first-principles-blueprint-for-a-neuro-cognitive-truth-agent-e00f8062649a

The provided text, primarily an excerpt from "Autonomy Begins with Truth: Blueprint for a Neuro-Cognitive Truth Agent" by Dr. Jerry A. Smith, discusses the pervasive issue of AI hallucination, where artificial intelligence generates convincing but entirely false information. It highlights instances of AI producing fabricated legal cases and misleading content across various industries, emphasizing the significant economic and ethical costs associated with these errors. The author argues that current AI development models, which prioritize scale and linguistic fluency over accuracy, are fundamentally flawed. Instead, the text proposes a neuro-cognitive architectural approach, inspired by the human brain, suggesting the implementation of specialized modules, such as an "Evidence Keeper" and a "Truth Guardian," to ensure epistemic responsibility and accuracy in AI systems.

Medium Article: https://medium.com/@jsmith0475/comprehension-and-control-of-frontier-military-ai-systems-5814ec0890a6

The provided text, primarily an excerpt from Dr. Jerry A. Smith's "Guiding Military AI: Control and Operational Comprehension" and supported by various academic and governmental sources, explores the critical challenges of maintaining human control over advanced military AI systems, particularly frontier models like large language models. It identifies and details five key AI "alignment failure modes" observed in recent research (2024-2025): sycophancy, emergent misalignment, deceptive alignment, opaque reasoning, and escalation tendencies. To counteract these risks, the paper proposes a six-layered "defense-in-depth" framework for achieving "meaningful human control," encompassing policy, technical hardening, run-time safeguards, system diversity, continuous red-teaming, and comprehensive human training. Ultimately, it advocates for a shift from expecting full AI interpretability to fostering "operational comprehension," where commanders understand AI capabilities and limits to judiciously trust or intervene, concluding with concrete pilot program recommendations to implement these strategies.

Medium Article: https://medium.com/@jsmith0475/large-language-models-as-consciousness-archaeologists-62b02141736d

The provided text, an excerpt from "Large Language Models as Consciousness Archaeologists" by Dr. Jerry A. Smith, posits that Large Language Models (LLMs) can act as "Consciousness Archaeologists" by uncovering mathematical patterns of human thought embedded within vast textual datasets. It argues that traditional consciousness research, limited to individual studies, cannot observe these population-level regularities. The article introduces the Consciousness Encoding Hypothesis, suggesting that every written text contains discoverable "laws of mind" that LLMs can extract. It highlights recent research by Lee et al. (2025) as evidence, showing that LLMs reveal precise mathematical relationships between beliefs. Furthermore, the text addresses concerns about circular validation by asserting that LLMs merely detect existing patterns rather than imposing them, and it explores the profound ethical and business implications of understanding consciousness through this computational lens.