Nature of Intelligence

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 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, unconsciousness and consciousness, predicting the evidence to be found by neuroscience and achieved by AI engineering. Furthermore, a conclusion is made that the total entropy of the universe is conservative, and intelligence counters the spontaneous processes to decrease entropy by physically or informationally connecting datasets that originally exist in the universe but are separated across space and time. This essay should be a starting point for a deeper understanding of the universe and us as human beings and for achieving sophisticated AI models that are tantamount to human intelligence or even superior. Furthermore, this essay argues that more advanced intelligence than humans should exist if only it reduces entropy in a more efficient energy-consuming way.