I put your AI written-in-Markdown article into AI and asked it to respond critically. TLDR; LLMs cannot do science.
The concept of "Computational Thermoepistemics" ambitiously seeks to unify computation, thermodynamics, and information theory into a single theoretical framework claiming to reveal fundamental energy limits of AI and intelligence. While its interdisciplinary ambition is commendable, the framework overreaches by treating computational processes too literally as thermodynamic microstates with physical quantities like pressure and chemical potential, which lack rigorous empirical grounding in current computing technologies. The core claims about logical depth and undecidability bounding thermodynamic efficiency conflate abstract computability theory with physical energy costs in a way that oversimplifies highly distinct domains. Moreover, practical quantum advantages are currently limited by error correction and decoherence costs rather than fundamental thermodynamic laws, so the paper’s distinctions are mostly theoretical, not yet substantiated by experimental evidence. Biological efficiency comparisons often ignore the vastly different operational scales and purposes between brains and silicon AI. Finally, while energy-aware algorithm-hardware co-design and reversible computing are promising research paths, the lofty promises that energy efficiency gains could approach biological levels or exploit deep thermodynamic parallels remain speculative. Overall, the framework presents an intriguing synthesis but lacks conclusive validation, so it should be seen as a stimulating hypothesis rather than an established theory reshaping AI energy paradigms.
boredthoughts · 15h ago
Thanks, I appreciate that, and definitely agree about speculative and hypothetical nature of the ideas, and did not intend to portray otherwise. I should add that human caveat at the outset.
The concept of "Computational Thermoepistemics" ambitiously seeks to unify computation, thermodynamics, and information theory into a single theoretical framework claiming to reveal fundamental energy limits of AI and intelligence. While its interdisciplinary ambition is commendable, the framework overreaches by treating computational processes too literally as thermodynamic microstates with physical quantities like pressure and chemical potential, which lack rigorous empirical grounding in current computing technologies. The core claims about logical depth and undecidability bounding thermodynamic efficiency conflate abstract computability theory with physical energy costs in a way that oversimplifies highly distinct domains. Moreover, practical quantum advantages are currently limited by error correction and decoherence costs rather than fundamental thermodynamic laws, so the paper’s distinctions are mostly theoretical, not yet substantiated by experimental evidence. Biological efficiency comparisons often ignore the vastly different operational scales and purposes between brains and silicon AI. Finally, while energy-aware algorithm-hardware co-design and reversible computing are promising research paths, the lofty promises that energy efficiency gains could approach biological levels or exploit deep thermodynamic parallels remain speculative. Overall, the framework presents an intriguing synthesis but lacks conclusive validation, so it should be seen as a stimulating hypothesis rather than an established theory reshaping AI energy paradigms.