The emergence of foundation models has sparked interest in applications to single-cell biology, but when tested in zero-shot settings, they underperform compared to simpler methods. Alex Lu shares insights on why more research on AI models is needed in biological applications.
A new Nature paper explores Aurora, an AI model that redefines weather prediction with application to other environmental domains such as tropical cyclones. Hear from senior researchers Megan Stanley and Wessel Bruinsma as they share their groundbreaking work.
Silicon has long borne the burden of heat transfer in electronics, but in a post-Moore’s Law world, researchers like Hongxia Hao and Bing Lv are using AI to discover and design next-generation materials that exceed the limits of silicon’s thermal conductivity.
New AI models aren’t just changing the world of research; they’re also poised to impact society. Xing Xie talks about Societal AI, a white paper that explores the changing landscape with an eye to future research and improved communication across disciplines.
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.
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.
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.
Pranjal Chitale discusses the 2024 NeurIPS work CVQA. Spanning 31 languages and the cultures of 30 countries, this VQA benchmark was created with native speakers and cultural experts to evaluate model performance across diverse linguistic and cultural contexts.
The efficient simulation of molecules has the potential to change how the world understands biological systems and designs new drugs and biomaterials. Tong Wang discusses AI2BMD, an AI-based system designed to simulate large biomolecules with speed and accuracy.
Researchers Chris Hawblitzel and Jay Lorch share how progress in programming languages and verification approaches are bringing bug-free software within reach. Their work on the Rust verification tool Verus won the Distinguished Artifact Award at SOSP ’24.
In their 2024 SOSP paper, researchers explore a common—though often undertested—software system issue: retry bugs. Research manager Shan Lu and PhD candidate Bogdan Stoica share how they’re combining traditional program analysis and LLMs to address the challenge.
The personalizable object recognizer Find My Things was recently recognized for accessible design. Researcher Daniela Massiceti and software development engineer Martin Grayson talk about the research project’s origins and the tech advances making it possible.
