
TL;DR
This paper proposes that intelligence, both human and artificial, fundamentally involves entropy reduction through functional relationships between datasets, offering a mathematical framework that links neuroscience, physics, and AI.
Contribution
It introduces a novel entropy-based mathematical model of intelligence, connecting physical, informational, and neural processes to explain and predict intelligent behavior.
Findings
Intelligence minimizes system entropy via functional relationships.
Mathematical models of language, consciousness, and unconsciousness are established.
Intelligence counters universe entropy by connecting datasets across space and time.
Abstract
The human brain is the substrate for human intelligence. By simulating the human brain, artificial intelligence builds computational models that have learning capabilities and perform intelligent tasks approaching the human level. Deep neural networks consist of multiple computation layers to learn representations of data and improve the state-of-the-art in many recognition domains. However, the essence of intelligence commonly represented by both humans and AI is unknown. Here, we show that the nature of intelligence is a series of mathematically functional processes that minimize system entropy by establishing functional relationships between datasets over the space and time. Humans and AI have achieved intelligence by implementing these entropy-reducing processes in a reinforced manner that consumes energy. With this hypothesis, we establish mathematical models of language,…
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Taxonomy
TopicsFractal and DNA sequence analysis · Computability, Logic, AI Algorithms
