Rob Freeman

Rob is from New Zealand where he studied physics. But a passion to understand thought, in the first place as personally experienced by speaking diverse languages,
led him to spend most of the last 30 years in Asia. There he combined his background in physics with his passion to understand thought by working on machine translation in Japan, and the computational analysis of grammar in Hong Kong.

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For most of the last 20 years he has been pushing a somewhat different quantum inspired perspective on machine learning, emphasizing aspects of complexity theory, even chaos. Which, quantum included, he sees as potentially being manifestations of properties of distributed representations. And that as a consequence of this, the immediate solution to continuing puzzles of AI may be as simple as turning the "learning" problem upside down. So that, instead of thinking of AI as a process of compressing or "learning" structure, we think of it as being an expansion or generation of structure, which it turns out is more powerful and entangled than we suspected.

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He believes this offers the key to understanding firstly perception, but also the big questions: creativity, freewill, consciousness. And even suggests a quantum like character and solution for contemporary political fragmentation and social conflict!