2024/02/12 by Alex Ushveridze, Ushveridze, Alex
Computer Science · #Artificial Intelligence (cs.AI) #Classical Physics (physics.class-ph) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.2407.04700
openalex publication_date 2024/02/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The fact that accurately predicted information can serve as an energy source paves the way for new approaches to autonomous learning. The energy derived from a sequence of successful predictions can be recycled as an immediate incentive and resource, driving the enhancement of predictive capabilities in AI agents. We propose that, through a series of straightforward meta-architectural adjustments, any unsupervised learning apparatus could achieve complete independence from external energy sources, evolving into a self-sustaining physical system with a strong intrinsic 'drive' for continual learning. This concept, while still purely theoretical, is exemplified through the autoencoder, a quintessential model for unsupervised efficient coding. We use this model to demonstrate how progressive paradigm shifts can profoundly alter our comprehension of learning and intelligence. By reconceptualizing learning as an energy-seeking process, we highlight the potential for achieving true autonomy in learning systems, thereby bridging the gap between algorithmic concepts and physical models of intelligence.