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    AI's vast knowledge versus learning from a few examples

    MTSlive quotes a Stanford professor arguing that impressive AI capabilities can obscure a weakness: learning from just a couple of examples.

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    1 Source, 21d ago, first seen 21d ago

    TLDR

    In remarks shared by MTSlive, a Stanford professor distinguishes drawing on 2,000-plus years of human knowledge from discovering knowledge as humans do. He contrasts people learning from a couple of examples with current AI being given 10,000, arguing that machine learning remains poor compared with human learning. He calls learning speed “the secret of human intelligence.”

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    1 Source, first seen 21d ago

    Combined views

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    1 Source, first seen 21d ago

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    1 Source

    MTS@MTSliveStanford Prof. @chrmanning says today’s AI can absorb 2,000 years of human knowledge and still be far worse than humans at learning from just a few examples: "No matter how fantastic these systems are, there's still a lot we can't do. A lot of it looks better than it is because these huge models have been able to slurp up 2,000-plus years of human learning across all domains and stick it all in one huge neural net." "That's amazing because it knows everything to a first approximation. But really that's exploiting hard-won human knowledge, and that's different to the large language model discovering its own knowledge the same way human beings have been for 2,000 years." "At the end of the day, the secret of human intelligence is actually the speed at which we can learn. We only need to see a couple of examples of something, and we can learn something and intuit it." "Our current AI is nothing like that. We're substituting, here are 10,000 examples of this thing, because really the machine learning is very poor compared to human learning." @stanfordnlp @Stanford21d

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    1 Source

    MTS@MTSliveStanford Prof. @chrmanning says today’s AI can absorb 2,000 years of human knowledge and still be far worse than humans at learning from just a few examples: "No matter how fantastic these systems are, there's still a lot we can't do. A lot of it looks better than it is because these huge models have been able to slurp up 2,000-plus years of human learning across all domains and stick it all in one huge neural net." "That's amazing because it knows everything to a first approximation. But really that's exploiting hard-won human knowledge, and that's different to the large language model discovering its own knowledge the same way human beings have been for 2,000 years." "At the end of the day, the secret of human intelligence is actually the speed at which we can learn. We only need to see a couple of examples of something, and we can learn something and intuit it." "Our current AI is nothing like that. We're substituting, here are 10,000 examples of this thing, because really the machine learning is very poor compared to human learning." @stanfordnlp @Stanford21d