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Emilio Bizzi, one of the founding members of MIT’s McGovern Institute of Brain Research, agreed that researchers should focus on important elements of human intellect, such as the ability to generalize learning experiences, or fluidly plan movements to avoid obstacles to achieve a specific goal such as grasping a pair of glasses. “I am optimistic that in the next few years, we will make a lot of progress, and the reason is that there are many laboratories scattered in various parts of the world that are pursuing humanoid robotics.”

The two linguists on the panel, Noam Chomsky and Barbara Partee, both made seminal contributions to our understanding of language by considering it as a computational, rather than purely cultural, phenomenon. Both also felt that understanding human language was the key to creating genuinely thinking machines. “Really knowing semantics is a prerequisite for anything to be called intelligence,” said Partee.

Chomsky derided researchers in machine learning who use purely statistical methods to produce behavior that mimics something in the world, but who don’t try to understand the meaning of that behavior. Chomsky compared such researchers to scientists who might study the dance made by a bee returning to the hive, and who could produce a statistically based simulation of such a dance without attempting to understand why the bee behaved that way. “That’s a notion of [scientific] success that’s very novel. I don’t know of anything like it in the history of science,” said Chomsky.

Sydney Brenner, who deciphered the three-letter DNA code with Francis Crick and teased out the complete neural structure of the c. elegans worm on a cellular level, agreed that researchers in both artificial intelligence and neuroscience might be getting overwhelmed with surface details rather than seeking the bigger questions underneath. Looking at attempts to replicate his mapping of the c. elegans neural “wiring diagram” with more complex organisms, Brenner worried that neuro- and cognitive scientists were being “overzealous” in these attempts. He said they should refocus on higher level problems instead. He used the analogy of someone taking a picture with a smart phone: no one today would bother to give a transistor-level description of such an action: it’s much more useful to discuss the process in terms of higher level subsystems and software.

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Tagged: Computing, MIT, neuroscience, artificial intelligence, AI

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