Firehose

This episode features Bo Wang and Si Chu from Xaira Therapeutics, discussing their AI drug discovery platform, X-Cell, which uses high-throughput experimentation to generate causal data for predicting cellular responses to genetic perturbat…

This paper investigates how Diffusion Language Models (DLMs) internally represent time and how this representation can be used to modulate the model's behavior. Practitioners might care because understanding how DLMs process time could lead to more controllable and interpretable models.

This paper proposes a new way to improve the performance of diffusion models by aligning their inference process with the forward statistical structure of the model. This can lead to more realistic and diverse generated images, which is important for applications like image generation.

In this episode of Latent Space, Evan Feinberg and Sergey Edunov of Genesis Molecular AI discuss their pioneering work in applying diffusion models to protein-small molecule interactions for drug discovery. They explain how their foundation…

In this episode, Thomas Ahle discusses the development of thermodynamic computing chips and the challenges of chip design automation using AI agents. He explains how his team built an open-source Verilog simulator with AI collaboration to o…

This episode features John Jumper, co-creator of AlphaFold, discussing the breakthrough in protein structure prediction that won the 2024 Nobel Prize in Chemistry. Jumper explains the technical innovations behind AlphaFold, its impact on bi…

This episode features Logan Kilpatrick and Tulsee Doshi of Google DeepMind discussing Google's AI strategy and new launches at Google I/O, including Gemini 3.5 Flash, the Omni video generation model, and the Gemini Spark agentic product. Th…