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November 13, 2018 12:58 pm PST

Common sense: the Chomsky/Piaget debates come to AI

In 1975, Noam Chomsky and Jean Paiget held a historic debate about the nature of human cognition; Chomsky held that babies are born with a bunch of in-built rules and instincts that help them build up the knowledge that they need to navigate the world; Piaget argued that babies are effectively blank slates that acquire knowledge from experiencing the world (including the knowledge that there is a thing called "experience" and "the world").

For most of AI's history, Chomsky's approach prevailed: computer scientists painstakingly tried to equip computers with a baseline of knowledge about the relationships between things in the world, hoping that computers would some day build up from this base to construct complex, powerful reasoning systems.

The current machine learning revolution can be traced to a jettisoning of this approach in favor of a Piaget-style blank slate, where layers of neural nets are trained on massive corpuses of data (sometimes labeled by hand, but often completely blank) and use equally massive computation to make sense of the data, creating their own understanding of the world.

Piaget-style deep learning has taken AI a long way in a short time, but it's hitting a wall. It's not just the weird and vastly entertaining local optima that these systems get stuck in: it's the huge corpuses of data needed to train them and the inability of machine learning to generalize one model to bootstrap another and another.

The fall-off the rate of progress in machine learning, combined with the excitement that ML's recent gains provoked, has breathed new life into the Chomskyian approach to ML, and computer scientists all over the world are trying to create "common sense" corpuses of knowledge that they can imbue machine learning systems with before they are exposed to training data. Read the rest


Original Link: http://feeds.boingboing.net/~r/boingboing/iBag/~3/cR2hyjXcPg4/naive-learning.html

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