Struggles with programming languages helped research manager Shan Lu find her calling as a bug hunter. She discusses one bug that really haunted her, the thousands she’s identified since, and how she’s turning to LLMs to help make software more reliable.

How do you generate and test materials that don’t exist yet? Researchers Tian Xie and Ziheng Lu share the story behind MatterGen and MatterSim, AI tools poised to transform materials discovery and help drive advances in energy, manufacturing, and sustainability.

As the “biggest election year in history” comes to an end, researchers Madeleine Daepp and Robert Osazuwa Ness and Democracy Forward GM Ginny Badanes discuss AI’s impact on democracy, including Daepp and Ness’s research into the tech’s use in Taiwan and India.

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 2024

SuperBench: Improving Cloud AI Infrastructure Reliability with Proactive Validation | Publication, July 2024

BitNet: Scaling 1-bit Transformers for Large Language Models | Publication, October 2023

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.

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.

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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.

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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.

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