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Computer Science > Artificial Intelligence

arXiv:2403.04571 (cs)
[Submitted on 7 Mar 2024]

Title:Machine learning and information theory concepts towards an AI
Mathematician

Authors:Yoshua Bengio, Nikolay Malkin
View a PDF of the paper titled Machine learning and information
theory concepts towards an AI Mathematician, by Yoshua Bengio and 1
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    Abstract:The current state-of-the-art in artificial intelligence
    is impressive, especially in terms of mastery of language, but
    not so much in terms of mathematical reasoning. What could be
    missing? Can we learn something useful about that gap from how
    the brains of mathematicians go about their craft? This essay
    builds on the idea that current deep learning mostly succeeds at
    system 1 abilities -- which correspond to our intuition and
    habitual behaviors -- but still lacks something important
    regarding system 2 abilities -- which include reasoning and
    robust uncertainty estimation. It takes an
    information-theoretical posture to ask questions about what
    constitutes an interesting mathematical statement, which could
    guide future work in crafting an AI mathematician. The focus is
    not on proving a given theorem but on discovering new and
    interesting conjectures. The central hypothesis is that a
    desirable body of theorems better summarizes the set of all
    provable statements, for example by having a small description
    length while at the same time being close (in terms of number of
    derivation steps) to many provable statements.

Comments: To appear in the Bulletin of the AMS, 2024
Subjects: Artificial Intelligence (cs.AI)
Cite as:  arXiv:2403.04571 [cs.AI]
          (or arXiv:2403.04571v1 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2403.04571
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Nikolay Malkin [view email]
[v1] Thu, 7 Mar 2024 15:12:06 UTC (33 KB)
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    1 other authors
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