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Update AI and the Future of Science
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@@ -186,37 +186,6 @@ <h4>[DATE Y/M/D]</h4>
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--><!-- END TALK TEMPLATE -->
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<h4>2026/03/03</h4>
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<b><a href="[PAPER LINK]">AI and the Future of Science</a></b>
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<br>
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Presenter: <u><a href="https://martincsweiss.com/" target="_blank" rel="noopener noreferrer">Martin Weiss</a></u>, <a href="https://tiptreesystems.com/">Tiptree Systems</a>
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<a class="btn btn-info btn-xs" data-toggle="collapse" href="#20260303-bio" role="button" aria-expanded="false">
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Speaker Bio
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Martin Weiss is Co-Founder of Tiptree Systems, a startup building AI agents that help ML researchers find, create, and share knowledge more efficiently. Tiptree is deployed to researchers across many top-tier institutes including Mila, ELLIS, MIT, and many more. Martin holds a PhD in AI from Mila, where he studied under Hugo Larochelle and Chris Pal. Before his PhD, he was an early employee at YesGraph, a social graph startup acquired by Lyft.
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<!-- <a href="[RECORDING LINK - ADD AFTER TALK]"><img src="https://img.shields.io/badge/Youtube-Recording-orange"></a> -->
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<!-- <a href="[PAPER LINK]"><img src="https://img.shields.io/badge/Paper-link-important"></a> -->
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<!-- <a href="[GITHUB_LINK]"><img src="https://img.shields.io/badge/Github-link-lightgrey"></a> -->
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<!-- <a href="[SLIDES_LINK]"><img src="https://img.shields.io/badge/Talk-Slides-blue"></a> -->
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<a class="btn btn-primary btn-xs" data-toggle="collapse" href="#20260303-abstract" role="button" aria-expanded="false">
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Abstract
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This talk examines three converging crises. First, the decoupling of control from comprehension — we can increasingly predict and manipulate systems without understanding why they work. Second, the collapse of the generator-verifier gap — AI makes it trivial to produce the aesthetics of deep thought. This makes peer review more difficult because we can no longer rely on easy-to-verify signals of work quality. Third, the credit assignment gap — our academic reward systems optimize for publication metrics, not the increase in understanding that a new paper produces.
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<h4>2026/03/10</h4>
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<b><a href="[PAPER LINK]">Testing and Improving Multi-Agent LLM Cooperation</a></b>
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<h4>2026/03/03</h4>
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<b><a href="/">AI and the Future of Science</a></b>
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<br>
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Presenter: <u><a href="https://martincsweiss.com/" target="_blank" rel="noopener noreferrer">Martin Weiss</a></u>, <a href="https://tiptreesystems.com/">Tiptree Systems</a>
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<a class="btn btn-info btn-xs" data-toggle="collapse" href="#20260303-bio" role="button" aria-expanded="false">
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Speaker Bio
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</a>
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<div class="collapse" id="20260303-bio">
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<div class="card card-body">
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Martin Weiss is Co-Founder of Tiptree Systems, a startup building AI agents that help ML researchers find, create, and share knowledge more efficiently. Tiptree is deployed to researchers across many top-tier institutes including Mila, ELLIS, MIT, and many more. Martin holds a PhD in AI from Mila, where he studied under Hugo Larochelle and Chris Pal. Before his PhD, he was an early employee at YesGraph, a social graph startup acquired by Lyft.
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<a href="https://youtu.be/vwbpX-585qI"><img src="https://img.shields.io/badge/Youtube-Recording-orange"></a>
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<!-- <a href="[PAPER LINK]"><img src="https://img.shields.io/badge/Paper-link-important"></a> -->
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<!-- <a href="[GITHUB_LINK]"><img src="https://img.shields.io/badge/Github-link-lightgrey"></a> -->
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<!-- <a href="[SLIDES_LINK]"><img src="https://img.shields.io/badge/Talk-Slides-blue"></a> -->
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<a class="btn btn-primary btn-xs" data-toggle="collapse" href="#20260303-abstract" role="button" aria-expanded="false">
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Abstract
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<div class="card card-body">
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This talk examines three converging crises. First, the decoupling of control from comprehension — we can increasingly predict and manipulate systems without understanding why they work. Second, the collapse of the generator-verifier gap — AI makes it trivial to produce the aesthetics of deep thought. This makes peer review more difficult because we can no longer rely on easy-to-verify signals of work quality. Third, the credit assignment gap — our academic reward systems optimize for publication metrics, not the increase in understanding that a new paper produces.
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