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Thomas G. Dietterich 27. lis
I just published What does it mean for a machine to ‘understand’? 1/
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Thomas G. Dietterich 27. lis
Odgovor korisniku/ci @tdietterich
As an engineer, I advocate a functionalist approach to assessing "understanding" in which "understanding" is measured in degrees and capabilities, rather than as a binary predicate. 2/
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Thomas G. Dietterich 27. lis
Odgovor korisniku/ci @tdietterich
Today's AI systems exhibit some understanding. For example, when I tell Siri “Call Carol” and it dials the correct number, you will have a hard time convincing me that Siri did not understand my request. 3/
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Thomas G. Dietterich 27. lis
Odgovor korisniku/ci @tdietterich
But as many folks have pointed out, today's AI systems lack many forms of understanding. Such critics often claim that Siri's understanding is not "true", "real" or "genuine". But I argue that it is "real", and it can be measured functionally. 4/
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Thomas G. Dietterich 27. lis
Odgovor korisniku/ci @tdietterich
Why does this matter? Because the CS community has a history of dismissing advances in AI as not being "real" and declaring the entire AI enterprise to be a failure. Funding dries up, and research progress slows. 5/
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Thomas G. Dietterich 27. lis
Odgovor korisniku/ci @tdietterich
Today, despite the critics, AI R&D continues to deliver useful functionality to users. So let’s stop dismissing our successes as “fake” and not “genuine”, and let’s continue to move forward with honesty and productive self-criticism. 6/
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Thomas G. Dietterich
Speaking of productive self-criticism. When we demonstrate a new capability, let's also demonstrate its limits by showing instances of functional failures. Much of the criticism is a reaction to the hype generated by researchers and the organizations they work for. 7/
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Thomas G. Dietterich 27. lis
Odgovor korisniku/ci @tdietterich
It is important, both for intellectual honesty and for continued funding, that AI researchers claim credit for our successes and take ownership of our shortcomings. end/
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Mardochee Reveil, PhD 28. lis
Odgovor korisniku/ci @tdietterich
Couldn't agree more! self-reporting known limitations of proposed methods and algorithms can only help though reviewers tend to prey on those as though they negate merit
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