ClaimsYann LeCun

“In the time-honored approach to computer vision (and to pattern recognition in general), the question is avoided: internal representations are produced by a hand-crafted feature extractor, whose output is fed to a trainable classifier.”

Yann LeCun, co-author of paper 2010 Convolutional Networks and Applications in Vision (yann.lecun.com) archived copy

Factual AI capabilities

In traditional computer vision and pattern recognition, internal representations are produced by a hand-crafted feature extractor whose output is fed to a trainable classifier.

Not researched Checkable, but outside the sample that was researched.

Factual: Statements about the present or the past, checked against the record: did they hold up?

Cite this claim

Yann LeCun, “In traditional computer vision and pattern recognition, internal representations are produced by a hand-crafted feature extractor whose output is fed to a trainable classifier.” (said 2010). Verdict: not researched. Reliability API, claim c_b3dcf7a94635, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_b3dcf7a94635

This claim is in dataset release v1.1.2, with the same verdict.

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More factual claims by Yann LeCun

  1. Mostly true Factual 18 March 2024
    “For starters, transformers are equivariant to permutations.”

    Transformers are equivariant to permutations, as ConvNets are equivariant to translations.

  2. Mostly true Factual 14 March 2024
    “If it were the case, we would have AI systems that could teach themselves to drive a car in 20 hours of practice, like any 17 year-old.”

    There are currently no AI systems that can teach themselves to drive a car in about 20 hours of practice, as a 17-year-old can.

  3. Mostly true Factual 8 March 2024
    “The architecture is good, the architecture of the encoder is good, but the fact that you train the system to reconstruct images does not lead it to produce to long, good generic features of images.”

    Training an encoder to reconstruct images does not lead it to produce good generic image features, though the encoder architecture is good.

  4. Mostly true Factual 8 March 2024
    “LLMs can do none of those or they can only do them in a very primitive way and they don’t really understand the physical world.”

    Current LLMs can do none of understanding the physical world, persistent memory, reasoning and planning, or only in a very primitive way.

  5. Mostly true Factual 8 March 2024
    “No, there’s one thing that autoregressive LLMs or that LLMs in general, not just the autoregressive one, but including the bird style bidirectional ones, are exploiting and its self supervised…”

    LLMs, including BERT-style bidirectional ones, exploit self-supervised learning, and LeCun has been a very strong advocate of self-supervised learning for many years.

All of them

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