ClaimsYann LeCun

“This architecture, trained with regularized methods, may be a starting point for designing predictive world”

Anna Dawid and Yann LeCun, lecture notes authors June 2023 Introduction to Latent Variable Energy-Based Models (Les Houches lecture notes) (arXiv) archived copy

Prediction AI timelines and forecasting

H-JEPA trained with regularized methods may be a starting point for predictive world models capable of hierarchical planning under uncertainty, a breakthrough for autonomous AI.

Unresolvable The public record could not settle it.

Prediction: Statements about events outside the person's control, checked once they fell due: did they come true?

Reasoning

Hedged 'may be a starting point' prediction with no horizon and vague 'breakthrough' terms.

Cite this claim

Yann LeCun, “H-JEPA trained with regularized methods may be a starting point for predictive world models capable of hierarchical planning under uncertainty, a breakthrough for autonomous AI.” (said June 2023). Verdict: unresolvable. Reliability API, claim c_1d5de231f930, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_1d5de231f930

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

Reply to this claim

For Yann LeCun, or someone speaking for them: a correction or counter-evidence. Replies are read before anything is shown; an approved reply shows your name, role, date, statement and link. Your email address is never shown and is deleted 30 days after the decision (privacy).

More prediction claims by Yann LeCun

  1. Mostly true Prediction 18 February 2015
    “The next frontier for Deep Learning is natural language understanding.”

    Natural language understanding will be the next frontier for Deep Learning.

  2. Mostly true Prediction 8 March 2024
    “Increasingly, the search engines that we’re going to use are not going to be search engines, they’re going to be dialogue systems”

    Search engines will increasingly be replaced by dialogue systems that answer questions and perhaps point to appropriate references.

  3. Mostly true Prediction 2019
    “We foresee an increase in the usage of such networks for a wide variety of applications, which are likely to violate the assumptions of current DL hardware.”

    Usage of graph neural networks will increase for a wide variety of applications likely to violate the assumptions of current deep learning hardware.

  4. Mostly true Prediction 2019
    “As DL systems are increasingly used for high-level cognitive tasks, such memory modules will become commonplace and very large, requiring hardware support.”

    As DL is used for high-level cognitive tasks, memory modules will become commonplace and very large, requiring hardware support.

  5. Mostly true Prediction 2010
    “one of the major challenges of computer vision, machine learning, and AI in general in the next decade will be to devise methods that can automatically learn good features hierarchies from unlabeled…”

    In the next decade, a major challenge of computer vision, machine learning and AI will be devising methods that learn good feature hierarchies from unlabeled and labeled data in an integrated fashion.

All of them

Verdicts are made automatically; how verdicts are made.