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Markus J. Buehler
6,724 posts
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Markus J. Buehler
@ProfBuehlerMIT
McAfee Professor of Engineering @MIT; Co-Founder & CTO at Unreasonable Labs; AI-Driven Scientific Discovery
Cambridge, MA
meche.mit.edu/people/faculty…
Датум придруживања: December 2014
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  • Закачено
    user avatar
    Markus J. Buehler
    @ProfBuehlerMIT
    12h
    Can we compile matter - for instance, a pine cone - and derive new active materials, end-to-end from observation to manufacturing? If physical systems can be formalized as composable mathematics, we can point AI that has been shown to resolve long-open mathematical problems at
    00:00
    27 хиљ.
  • user avatar
    Markus J. Buehler
    @ProfBuehlerMIT
    16h
    This feels like a real inflection point: The momentum toward AI that expand knowledge is impossible to ignore...moving beyond solving problems with known answers to settling long-open questions (with Lean certificates attached) across group theory, operator algebras,
    user avatar
    Sebastien Bubeck
    @SebastienBubeck
    21h
    yes, nonsofic groups exist: this statement is one of many new beautiful results proved by Astra, our next major model. We're releasing 10 such Astra proofs, complete with lean certificates and CoT walkthroughs for each of them. The results are wide-ranging, from von Neumann
    6 хиљ.
  • user avatar
    Markus J. Buehler
    @ProfBuehlerMIT
    31. јул
    I just completed teaching a four-day course on Applied AI for Materials Discovery at MIT - for an amazing group of industry professionals and researchers from nearly every continent - to discuss how modern AI really works under the hood, and share deep insights into how AI for
    00:00
    4 хиљ.
  • user avatar
    Markus J. Buehler
    @ProfBuehlerMIT
    30. јул
    Mechanics that Shape Matter and Life…. Just returned from the Gordon Research Conference on Multiscale Mechanochemistry and Mechanobiology, where I gave the opening keynote on Biological Material Intelligence Across Scales. My key message was that AI helps us design biological
    1 хиљ.
  • user avatar
    Markus J. Buehler
    @ProfBuehlerMIT
    25. јул
    Impressive native capability by Opus 5 - the model compresses the problem into an equation - a program - then generalizes and solves from it. The pattern is: 1) perceive, 2) formalize, 3) generalize. This is extremely relevant for open-ended discovery tasks in AI for science.
    user avatar
    ARC Prize
    @arcprize
    24. јул
    Одговараш кориснику @arcprize
    During our analysis of Opus 5, we observed a new capability previously unseen from frontier models Opus 5 used advanced logical reasoning to turn ARC-AGI-3 layouts into algebraic notation. On action 23 it described the scene as "4_center = 2×axis − 5_center" This is the first
    GIF
    3 хиљ.