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A Moonshot for AI Oracles in the Sciences

arXiv 2406.17836 ↗First posted 25 Jun 2024cs.AI cs.CY math.HOcited by 0

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Abstract (arXiv)

Nobel laureate Philip Anderson and Elihu Abrahams once stated that, "even if machines did contribute to normal science, we see no mechanism by which they could create a Kuhnian revolution and thereby establish a new physical law." In this Perspective, we draw upon insights from the philosophies of science and artificial intelligence (AI) to propose necessary conditions of precisely such a mechanism for generating revolutionary mathematical theories. Recent advancements in AI suggest that satisfying the proposed necessary conditions by machines may be plausible; thus, our proposed necessary conditions also define a moonshot challenge. We also propose a heuristic definition of the intelligibility of mathematical theories to accelerate the development of machine theorists.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 115 references that are in the map. First posted on arXiv 25 Jun 2024.

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References · 29

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2020Learning to summarize from human feedbackcited by 2,562
  3. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  4. 2022Galactica: A Large Language Model for Sciencecited by 736
  5. 2016The Mythos of Model Interpretabilitycited by 716
  6. 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 536
  7. 2019The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervisioncited by 369
  8. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
  9. 2023Mathematical Capabilities of ChatGPTcited by 263
  10. 2018Deep Learning: A Critical Appraisalcited by 214
  11. 2022Talking About Large Language Modelscited by 174
  12. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 165
17 more
  1. 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
  2. 2020Making deep neural networks right for the right scientific reasons by interacting with their explanationscited by 141
  3. 2022From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagationcited by 141
  4. 2020Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challengescited by 120
  5. 2018Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontierscited by 117
  6. 2020Principles and Practice of Explainable Machine Learningcited by 99
  7. 2022On scientific understanding with artificial intelligencecited by 68
  8. 2023When Brain-inspired AI Meets AGIcited by 54
  9. 2021Why AI is Harder Than We Thinkcited by 40
  10. 2022Machine Learning Model Sizes and the Parameter Gapcited by 39
  11. 2021Symbolic Behaviour in Artificial Intelligencecited by 24
  12. 2019The Diversity-Innovation Paradox in Sciencecited by 21
  13. 2020Artificial Intelligence is stupid and causal reasoning won't fix itcited by 20
  14. 2021Bridging observation, theory and numerical simulation of the ocean using Machine Learningcited by 15
  15. 2017Learning Functional Causal Models with Generative Neural Networkscited by 14
  16. 2023Tree-Based Representation and Generation of Natural and Mathematical Languagecited by 12
  17. 2021Is the Number of Trainable Parameters All That Actually Matters?cited by 3

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