From Machine Learning to Autonomous Intelligence – AI-Talk by Prof. Dr. Yann LeCun

Ludwig-Maximilians-Universität München
Ludwig-Maximilians-Universität München
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Published 2023-09-29
Livestream powered by baiosphere – the bavarian ai network

How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons?

Prof. Dr. Yann LeCun, Chief AI Scientist for Meta AI Research and Silver Professor at the Courant Institute of Mathematical Sciences at New York University will propose a possible path towards autonomous intelligent agents, based on a new modular cognitive architecture and a somewhat new self-supervised training paradigm. The centerpiece of the proposed architecture is a configurable predictive world model that allows the agent to plan. Behavior and learning are driven by a set of differentiable intrinsic cost functions. The world model uses a new type of energy-based model architecture called H-JEPA (Hierarchical Joint Embedding Predictive Architecture). H-JEPA learns hierarchical abstract representations of the world that are simultaneously maximally informative and maximally predictable.

The event is organized by Prof. Dr. Gitta Kutyniok, Bavarian AI-Chair for Mathematical Foundations of Artificial Intelligence at the Ludwig-Maximilians-Universität München, the professorship is funded by the Hightech Agenda Bayern. She is spokesperson of the CAS Research Focus "Next Generation AI" at the Center for Advanced Studies (CAS) at LMU and LMU-Director of the Konrad Zuse School of Excellence for Reliable AI (relAI). The following ecosystem-partners support the event: Center for Advanced Studies (CAS) at LMU, baiosphere – the bavarian ai network, BAdW – Bayerische Akademie der Wissenschaften, bidt – Bayerisches Forschungsinstitut für Digitale Transformation, MCML – Munich Center for Machine Learning, Konrad Zuse School of Excellence in Reliable AI (relAI).
All Comments (21)
  • @user-cq8oc9du3p
    Prof LeCun is the most objective and balanced expert in the field of AI.
  • @2pers
    This is one of the best talks I have seen in AI. Very insightful. I hope Yann has been able to taste bavarian beer with his glass 🙂
  • @inigopikabea3511
    Anyone knows where is the V-JEPA Yann talked in 1:11:25 ? He said the paper is currently released in I don't know where.
  • @richnewman4780
    He should have called this talk 'LLMs suck and what to do about it'
  • @loveplay1983
    Where can I get the slide? It seems to contain a great many critical information therein.
  • @MrShoorf
    55:30 "There is absolutely no question, that machines will surpass human intelligence. It's going to happen during the lifetime of most people here.. maybe not me *not-my-problem-laughter*" 1:23:22 "We don't know how to make those things safe" Sickening.
  • @paulm3969
    It's ironic because the more capable AI systems we get, the less predictable the future will become, so I wonder how the AIs themselves will deal with this? 1000 years ago, the world was far more predictable, but the more the world changes due to technological progress, the harder that world is to predict....interesting problem.
  • @andreasholzinger7056
    I tried to verify the prompt on the rap album of Yann LeCun (at 50:27), but my answer I got was: There is no verifiable information or credible sources indicating that Yann LeCun, a prominent figure in the field of artificial intelligence, released a rap album last year. This claim appears to be unfounded or a misunderstanding. Yann LeCun is primarily known for his contributions to AI, particularly in neural networks and deep learning, rather than musical endeavors - so quite ok!
  • @tcpip9999
    Not really agreeing with Derrida here, but it might be useful to take seriously for now 'Il n’y a pas de hors-texte' - push that particular school of philosophy to its limit in the context of the new AI.
  • @amandamorriss3658
    that you, prof LeCun, for sharing this wide, clear, balanced view on an often misrepresented topic.
  • @phpn99
    Even then, machines would still be deterministic agents because they have no sense of self and no need to live ; therefore their entire scheme of activity is not autonomous in the biological sense (even if in biology the individual doesn't exist without the environment and vice versa). Therefore it's the concept of autonomy that we have to be more specific about. Machines will attain a very high level of intelligence, surpassing ours, but they will still be inanimate objects and essentially tools in our hands, like a knife. The ethical and practical question that will need to be asked about the activity of a machine in the future as it is today, is 'in whose name is this machine acting for ?"
  • @richardnunziata3221
    It seems that hierarchical machine learning is another way to say utility of abstraction. We need to predict under constraint (goal) utility that can cross domains. Cross domain reasoning (analogy) .
  • @carvalhoribeiro
    He is undoubtedly one of the great minds of our age. I feel very privileged to be able to watch and learn from him. Thank your very much.