ClaimsPeople

Yann LeCun

Yann LeCun is a computer scientist who shared the 2018 ACM Turing Award, is a Jacob T. Schwartz Professor at NYU's Courant Institute, was Meta's Chief AI Scientist and founding director of FAIR, and is Executive Chairman of AMI Labs.

1,175 claims collected, 197 researched, 84 resolved. Report of 4 November 2026, method 0.1.0+c876030c.

Scores by kind of claim

Each kind of claim is scored on its own, out of 100: the share that held up, with the range it likely falls in. Fewer resolved claims give a wider range.

  • Factual claims

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

    78 72 83

    High confidence, 79 factual claims resolved. From 16 documents, 2006 to 2026; 19 documents sampled from 33 candidates found.

    Not scored: 43 unresolvable.

    See the factual claims

  • Predictions

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

    64 43 83

    Low confidence, 5 predictions resolved. From 4 documents, 2010 to 2024; 19 documents sampled from 33 candidates found.

    Not scored: 66 unresolvable. 3 more fall due later.

    Does not yet allow for how likely each prediction was when made.

    See the predictions

  • Promises

    Commitments to the person's or organisation's own future action, with something to deliver: was it delivered?

    Insufficient record: 0 promises resolved, 5 needed for a score.

    Not scored: 1 unresolvable.

    See the promises

All types combined: 77 (likely 71 to 82, high confidence), from 84 claims: 79 factual claims, 5 predictions. It mixes kinds of claim in different proportions for different people, so compare people kind by kind.

Yann LeCun's claims

55 claims

55 claims, sorted by newest said. Choose a claim to read it in full.
VerdictKindSaidClaimTopic
Mostly true Factual 29 Mar 2024 Yann LeCun gave a lecture hosted by the Center of Mathematical Sciences and Applications at Harvard on 2024-03-28 to a full house. Factual · 29 Mar 2024 Their own work or organisation
Mostly true Factual 18 Mar 2024 People can still think even if their language abilities are destroyed by a stroke. Factual · 18 Mar 2024 Philosophy, ethics and society
Mostly true Factual 18 Mar 2024 Transformers are equivariant to permutations, as ConvNets are equivariant to translations. Factual · 18 Mar 2024 AI capabilities
Mostly true Factual 14 Mar 2024 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. Factual · 14 Mar 2024 AI capabilities
Mostly true Factual 10 Mar 2024 The white-noise-sounding flat-spectrum signal is the pseudo-random bit sequence used to pre-train an adaptive equalizer that cleans up an analog signal into clean bits. Factual · 10 Mar 2024 Technology
Mostly true Factual 8 Mar 2024 Training an encoder to reconstruct images does not lead it to produce good generic image features, though the encoder architecture is good. Factual · 8 Mar 2024 AI capabilities
Mostly true Prediction 8 Mar 2024 Search engines will increasingly be replaced by dialogue systems that answer questions and perhaps point to appropriate references. Prediction · 8 Mar 2024 AI capabilities
Mostly true Factual 8 Mar 2024 Rocket trajectories have been planned using model-based optimization of command sequences since computers existed, essentially since the early 1960s. Factual · 8 Mar 2024 Other
Mostly true Factual 8 Mar 2024 Current LLMs can do none of understanding the physical world, persistent memory, reasoning and planning, or only in a very primitive way. Factual · 8 Mar 2024 AI capabilities
Mostly true Factual 8 Mar 2024 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. Factual · 8 Mar 2024 AI capabilities
Mostly true Factual 8 Mar 2024 Training a base LLM is currently very expensive and difficult, so only a few companies can do it properly. Factual · 8 Mar 2024 Economics and markets
Mostly true Factual 28 Jun 2023 The US is a clear outlier among developed countries in healthcare cost and outcomes. Factual · 28 Jun 2023 Other
Mostly true Factual 28 Jun 2023 The audience YES/NO split at the Munk Debate was 67/33 before the debate and 63/37 after. Factual · 28 Jun 2023 AI safety and alignment
Mostly true Factual 28 Jun 2023 In the official result of the Munk Debate on AI existential threat, the NO side (Mitchell and LeCun) won by 4% over the YES side (Tegmark and Bengio). Factual · 28 Jun 2023 AI safety and alignment
Mostly true Factual Jun 2023 Masked autoencoders are less successful with images and other continuous domains than with discrete domains such as language. Factual · Jun 2023 AI capabilities
Mostly true Factual Jun 2023 Probabilistic models require normalization and may become intractable for high-dimensional data. Factual · Jun 2023 AI capabilities
Mostly true Factual Jun 2023 With probabilistic models, training is confined to loss functions derived from the negative log-likelihood, such as cross entropy. Factual · Jun 2023 AI capabilities
Mostly true Factual Jun 2023 All modules of the proposed architecture are fully differentiable. Factual · Jun 2023 AI capabilities
Mostly true Factual 4 Mar 2021 Facebook has deployed XLM-R, a model based on its RoBERTa architecture, to improve hate speech classifiers in multiple languages across Facebook and Instagram. Factual · 4 Mar 2021 AI capabilities
Mostly true Factual 4 Mar 2021 LeCun said Facebook AI had created and open sourced SEER, a billion-parameter self-supervised computer vision model that he described as proven to work efficiently with complex, high-dimensional image data. Factual · 4 Mar 2021 AI capabilities
Mostly true Factual 4 Mar 2021 As of March 2021, self-supervised learning had not yet brought computer vision the same improvements it has brought to natural language processing. Factual · 4 Mar 2021 AI capabilities
Mostly true Factual 4 Mar 2021 The Barlow Twins method tries to minimize redundancy between the individual components of the embedding vectors. Factual · 4 Mar 2021 AI capabilities
Mostly true Factual 2021 A cat has fewer than 1 billion neurons, while a dog has 2 billion. Factual · 2021 Other
Mostly true Factual 2021 Since the 1990s, algorithms exist that learn oriented edge detectors fully unsupervised with the equivalent of a few minutes of real time. Factual · 2021 AI capabilities
Mostly true Factual 2021 Self-supervised training of visual representations from video has not yet been successful. Factual · 2021 AI capabilities
Mostly true Factual 2021 In reinforcement learning the machine receives only a single scalar feedback, and only occasionally. Factual · 2021 AI capabilities
Mostly true Factual 2021 The amount of image training data available for self-supervised learning is essentially unlimited, with billions of new images per day. Factual · 2021 AI compute, hardware and data
Mostly true Factual 28 Jun 2019 Jumping spiders can count to three, with categories for one prey, two preys and many preys. Factual · 28 Jun 2019 Other
Mostly true Factual 25 Jun 2019 Nick Clegg, Facebook's VP of global affairs and communications, wrote an op-ed about the future of the Internet. Factual · 25 Jun 2019 AI policy and governance
Mostly true Factual 3 Jun 2019 The Beijing Academy of Artificial Intelligence's published AI ethics guidelines mention the protection of individual privacy. Factual · 3 Jun 2019 AI policy and governance
Mostly true Factual 2019 The ConvNet architecture was loosely inspired by the ventral pathway in the visual cortex. Factual · 2019 AI capabilities
Mostly true Factual 2019 ReLU nonlinearities and dropout regularization enabled purely supervised backprop training of very deep networks with a dozen layers. Factual · 2019 AI capabilities
Mostly true Prediction 2019 Usage of graph neural networks will increase for a wide variety of applications likely to violate the assumptions of current deep learning hardware. Prediction · 2019 AI compute, hardware and data
Mostly true Prediction 2019 As DL is used for high-level cognitive tasks, memory modules will become commonplace and very large, requiring hardware support. Prediction · 2019 AI compute, hardware and data
Mostly true Factual 28 Jul 2016 The check reading system LeCun and a colleague built at Bell Labs in the early 1990s used structured prediction on top of convolutional nets, called Graph Transformer Networks. Factual · 28 Jul 2016 AI capabilities
Mostly true Factual 27 May 2015 Deep learning uses the backpropagation algorithm to indicate how a machine should change the internal parameters that compute each layer's representation from the previous layer's. Factual · 27 May 2015 AI capabilities
Mostly true Factual 27 May 2015 The acknowledgements, dated 2015, state that the authors Y.L. and Y.B. are fellows of the Canadian Institute For Advanced Research (CIFAR). Factual · 27 May 2015 Field building and careers
Mostly true Factual 18 Feb 2015 Deep Learning systems augmented with a memory module are starting to show impressive results in natural language processing. Factual · 18 Feb 2015 AI capabilities
Mostly true Factual 18 Feb 2015 The brain is much better than current artificial models at unsupervised learning. Factual · 18 Feb 2015 AI capabilities
Mostly true Factual 18 Feb 2015 Shallow learning systems have one or two layers, while deep learning systems typically have five to 20 layers. Factual · 18 Feb 2015 AI capabilities
Mostly true Factual 18 Feb 2015 The system would probably not be more accurate, but it can potentially recognize people among hundreds of millions. Factual · 18 Feb 2015 AI capabilities
Mostly true Factual 18 Feb 2015 A typical Deep Learning system is like a box with 500 million knobs, 1,000 light bulbs and 10 million training images. Factual · 18 Feb 2015 AI compute, hardware and data
Mostly true Prediction 18 Feb 2015 Natural language understanding will be the next frontier for Deep Learning. Prediction · 18 Feb 2015 AI timelines and forecasting
Mostly true Factual 18 Feb 2015 A trained classifier may also correctly classify cars and dogs it has never seen before. Factual · 18 Feb 2015 AI capabilities
Mostly true Factual 15 May 2014 The notions of partition functions, entropy, free energy and variational methods prevalent in the graphical models literature come from statistical physics. Factual · 15 May 2014 Science and research methods
Mostly true Factual 15 May 2014 The first commercially viable check reading system, deployed by AT&T/NCR in 1996, used a ConvNet rather than an SVM. Factual · 15 May 2014 AI capabilities
Mostly true Factual 15 May 2014 No one uses generalization bounds to do model selection; practitioners use cross-validation. Factual · 15 May 2014 Science and research methods
Mostly true Factual 15 May 2014 Speech is a domain with access to ridiculously large amounts of data and a very large number of categories. Factual · 15 May 2014 AI capabilities
Mostly true Factual 15 May 2014 Facebook AI Research uses Torch7 for many projects, as do Deep Mind and several groups at Google. Factual · 15 May 2014 Technology
Mostly true Prediction 2010 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. Prediction · 2010 AI capabilities
Mostly true Factual 2010 A face detection application on the CNP system was demonstrated achieving 10 fps on VGA images. Factual · 2010 AI compute, hardware and data
Mostly true Factual 2010 Convolutional Networks are a biologically inspired trainable architecture that can learn invariant features. Factual · 2010 AI capabilities
Mostly true Factual 2006 The hinge loss with G_W(X)=W^T X and a ||W||^2 regularizer gives the linear support vector machine. Factual · 2006 AI capabilities
Mostly true Factual 2006 The hinge loss systematically chooses the most offending incorrect answer as the one whose energy is pulled up. Factual · 2006 AI capabilities
Mostly true Factual 2006 Support vector Markov models, max-margin Markov networks and conditional random fields are all sequence modeling systems that use linearly parameterized energy factors. Factual · 2006 AI capabilities
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Yann LeCun

Open its page

Factual Their own work or organisation

“Full house for my lecture hosted by the Center of Mathematical Sciences and Applications at Harvard yesterday.”

Yann LeCun, on X 29 March 2024 Post by @ylecun on X, 2024-03-29 (X)

Yann LeCun gave a lecture hosted by the Center of Mathematical Sciences and Applications at Harvard on 2024-03-28 to a full house.

Mostly true True in substance, wrong in a detail.

The second judge would not resolve this claim; the verdict shown is the first judge's.

What would count as true or false

True if records show LeCun gave a lecture hosted by Harvard's Center of Mathematical Sciences and Applications (CMSA) on 2024-03-28 and that the venue was full or close to full. Mostly true if the CMSA-hosted lecture took place on that date but attendance was large rather than clearly full, or the date was off by a day. Mixed if the lecture happened but was hosted by a different Harvard unit, or the date differs materially. Mostly false if the event happened at a materially different time or in a different format (for example online only). False if no such CMSA lecture by LeCun took place around then.

Evidence

  1. 2024 Ding Shum Lecture: Yann LeCun: Objective-Driven AI: Towards AI systems that can learn, remember, reason, and plan - CMSA Center of Mathematical Sciences and Applications (CMSA), Harvard University · 10 May 2024
    “On March 28, 2024, the CMSA hosted the fifth annual Ding Shum Lecture, given by Yann LeCun.”
  2. 2024 Ding Shum Lecture: Yann LeCun: Objective-Driven AI: Towards AI systems that can learn, remember, reason, and plan - CMSA Center of Mathematical Sciences and Applications (CMSA), Harvard University · 10 May 2024
    “Location: Harvard Science Center Hall A & via Zoom Webinar”

Reasoning

Harvard CMSA's own news page (E1) says the CMSA hosted the fifth annual Ding Shum Lecture, given by Yann LeCun, on March 28, 2024, matching the claimed host and date. E2 shows the lecture was held in person in Harvard Science Center Hall A and also streamed on Zoom, so it was not an online-only event. Neither source mentions attendance, so the 'full house' part cannot be confirmed. The core event is confirmed but the full venue is not, which fits the criteria for mostly true rather than true.

Cite this claim

Yann LeCun, “Yann LeCun gave a lecture hosted by the Center of Mathematical Sciences and Applications at Harvard on 2024-03-28 to a full house.” (said 29 March 2024). Verdict: mostly true. Reliability API, claim c_63cd3538be04, method 0.1.0+c876030c. https://epistacklabs.com/claims/c_63cd3538be04

Claims without a verdict

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