NeurIPS 2024: The co-evolution of AI and systems with Lidong Zhou
Just after his NeurIPS 2024 keynote on the co-evolution of systems and AI, Microsoft CVP Lidong Zhou joins the podcast to discuss how rapidly advancing AI impacts the systems supporting it and the opportunities to use AI to enhance systems engineering itself.Learn more:Verus: A Practical Foundation for Systems Verification | Publication, November 2024SuperBench: Improving Cloud AI Infrastructure Reliability with Proactive Validation | Publication, July 2024BitNet: Scaling 1-bit Transformers for Large Language Models | Publication, October 2023
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NeurIPS 2024: AI for Science with Chris Bishop
In this special edition of the podcast, Technical Fellow and Microsoft Research AI for Science Director Chris Bishop joins guest host Eliza Strickland in the Microsoft Booth at the 38th annual Conference on Neural Information Processing Systems (NeurIPS) in Vancouver, British Columbia, to talk about deep learning’s potential to improve the speed and scale at which scientific advancements can be made.
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Abstracts: NeurIPS 2024 with Jindong Wang and Steven Euijong Whang
Researcher Jindong Wang and Associate Professor Steven Euijong Whang explore the NeurIPS 2024 work ERBench. ERBench leverages relational databases to create LLM benchmarks that can verify model rationale via keywords in addition to checking answer correctness. Read the paperGet datasets and codes
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Abstracts: NeurIPS 2024 with Weizhu Chen
Next-token prediction trains a language model on all tokens in a sequence. VP Weizhu Chen discusses his team’s 2024 NeurIPS paper on how distinguishing between useful and “noisy” tokens in pretraining can improve token efficiency and model performance.Read the paperGet the code
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Abstracts: NeurIPS 2024 with Dylan Foster
Can existing algorithms designed for simple reinforcement learning problems be used to solve more complex RL problems? Researcher Dylan Foster discusses the modular approach he and his coauthors explored in their 2024 NeurIPS paper on RL under latent dynamics.Read the paper
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