It stands to reason that as the only human-level intelligence is in humans, brain science should lead the way to create artificial intelligence (AI). Patom theory was first described in the 1990s and since then the theory has been effective at solving problems in natural language understanding (NLU). Its terse description is that with Patom theory (PT), all a brain does is store, match and use hierarchical, bidirectional linkset patterns[i]. That is, sets and lists are sufficient to explain all that a human brain is capable of. And by dealing with the specific patterns that we experience, the decomposition of those patterns into their atomic parts enables general patterns to be recognized not by processing, but because they are the same.
Patom theory answers the question:
“how does our relatively slow brain outperform super computers at tasks like language, vision and motor control?”
It is theoretical neuroscience in that it models the function of the human brain without regard to any particular brain[ii]. A Pattern-ATOM (Patom) answers the question about why someone experiences particular things in reaction to brain stimulation (such as during brain surgery). The Patom is the thing stimulated — a small brain region — and it represents a collection of patterns through forward and backwards projections and, if the material is used at…
