Stephen Wolfram, founder of Wolfram Research, reflects on his decades-long journey to understand machine learning and neural networks. In the 1980s, he explored symbolic computation systems and wondered if they could be generalized to "fuzzy pattern matching" similar to human thinking. He later developed cellular automata, simple rules-based systems that exhibit complex behavior. Wolfram's curiosity about neural nets led him to simulate idealized models using statistical mechanics, but his early attempts were unsuccessful.
Years later, after the breakthroughs in deep learning, Wolfram revisited his ideas and began to think of neural nets as sampling programs from across the computational universe. He also wondered if simple systems could learn through "bashing" them hard enough. Recently, he conducted a "throwaway experiment" that surprisingly captured the essence of biological evolution. Building on this success, Wolfram has made progress in understanding machine learning, leveraging his Wolfram Language tools. His work may finally uncover why and when machine learning works its "magic."
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