This paper investigates how text conditioning affects visual generation and proposes ways to improve it, leading to better performance on various benchmarks. Practitioners might care about the findings to develop more effective text-to-image models.
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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…
🔬 The Coolest Diffusion Research Isn't in LLMs — Evan Feinberg & Sergey Edunov, Genesis Molecular AI
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…
The Model Eats the Scaffolding: DeepMind's Logan Kilpatrick & Tulsee Doshi on 3.5 Flash, Omni & More
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…