Talk by Pranjal Awasthi (Google) (SIMONS-TCS-ML)
38:15Talk by Katrina Ligett (Hebrew University of Jerusalem) (SIMONS-TCS-ML)
36:41Efficient Algorithms for Reliable Machine Learning (SIMONS-TCS-ML)
37:26Computational Thinking on Learning Models (SIMONS-TCS-ML)
38:30On the Komlós Conjecture (SIMONS-TCS-ML)
33:41Multi-objective Learning: An Algorithmic Toolbox for Optimal Predictions Anytime Anywhere! (SIMONS-TCS-ML)
37:40Approximately Packing Dijoins Via Nowhere-Zero Flows (SIMONS-TCS-ML)
33:46Lightning Talks! (SIMONS-TCS-ML)
56:01Venturing out into the world with AI (SIMONS-TCS-ML)
44:41Unfamiliar Terrain (SIMONS-TCS-ML)
29:51Astral Space: Convex Analysis at Infinity (SIMONS-TCS-ML)
44:01AI and Safety (SIMONS-TCS-ML)
29:26The Role of TCS in Understanding (AI) Consciousness (SIMONS-TCS-ML)
43:21The Conscious Turing Machine (CTM), a formally defined Theoretical model of Consciousness (SIMONS-TCS-ML)
45:00Theory of Modern AI: Learning Theoretic, Game Theoretic, and Algorithmic Perspectives (SIMONS-TCS-ML)
40:41An Occam's Razor Principle for Transformers? (SIMONS-TCS-ML)
39:55Red Points and Blue Points (SIMONS-TCS-ML)
38:15Latent Variable models and Subset Smoothing (SIMONS-TCS-ML)
29:26Enhanced and Efficient Reasoning in Large Language Models (SIMONS-TCS-ML)
36:16Enhanced and Efficient Reasoning in Large Language Models (SIMONS-TCS-ML)
Les Valiant proposes a principled and efficient approach to enhance reasoning in Large Language Models, aiming to justify trust in content generation beyond fluent prose and improve reliability.
Les Valiant (Harvard University)
The Role of TCS in Modern Machine Learning
In current Large Language Models, we can trust the production of smoothly flowing prose on the basis of the principles of machine learning. However, there is no comparably principled basis to justify trust in the content of the text produced. It appears to be conventional wisdom that addressing this issue by adding more principled reasoning is not computationally affordable.