This research paper investigates the fusion of cognitive science and machine learning in the development of superhuman mathematical systems. It emphasizes the importance of collaboration between cognitive scientists, AI researchers, and mathematicians to advance mathematical AI systems. The study highlights the need for adaptable and innovative automated mathematicians that push the frontiers of mathematics. It also calls for improved collaboration tools and convening opportunities to foster cross-disciplinary efforts. The paper acknowledges the challenges and recognizes the contributions of various individuals and groups in achieving human-level math performance with large language models.
This AI Paper Explores the Fusion of Cognitive Science and Machine Learning in Pursuit of Superhuman Mathematical Systems
Researchers from MIT BCS, the University of Cambridge, and the Alan Turing Institute have conducted a study on the historical pursuit of automated mathematicians in artificial intelligence (AI). The study emphasizes the recent impact of large language models (LLMs) and advocates for a cognitive science perspective. It highlights the importance of collaboration between cognitive scientists, AI researchers, and mathematicians to advance mathematical AI systems.
The study recognizes the challenges in achieving human-level math performance with LLMs and addresses the goal of achieving human-level proficiency in mathematics through computational systems in AI. It proposes a holistic approach to develop automated mathematicians that surpass static benchmarks, incorporating intuitions, judgments, reasoning, and problem-solving tactics.
The research emphasizes the importance of insights from cognitive science and AI in creating advanced mathematical systems. It investigates problem-solving, computational insights, and the role of prior knowledge. The study calls for improved collaboration tools and more opportunities for convening, highlighting the pivotal role of joint efforts across diverse fields.
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