The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

And a plea to think seriously about climate change without getting distracted

NOAA is an agency that enriches life through science. Our reach goes from the surface of the sun to the depths of the ocean floor as we work to keep the public informed of the changing environment aro...

How the carbon stocks of the Arctic–Boreal Zone change with warming is not well understood. Here the authors show that wildfires and large regional differences in net carbon fluxes offset the overall ...

Vapour trails (contrails) from aircraft make a substantial contribution to aviation’s climate impact. Here we execute a per-flight contrail avoidance feasibility test through altitude adjustments base...

FrontierMath is a benchmark of hundreds of unpublished and extremely challenging math problems to help us to understand the limits of artificial intelligence.

Can Large Language Model Agents Simulate Human Trust Behavior?

YOLOv10: Real-Time End-to-End Object Detection [NeurIPS 2024] - GitHub - THU-MIG/yolov10: YOLOv10: Real-Time End-to-End Object Detection [NeurIPS 2024]

Surveys have recently gained popularity as a tool to study large language models. By comparing models’ survey responses to those of different human reference populations, researchers aim to infer...

In this work, the authors assess the hypothesis that classifying animals cropped from camera trap images using a species-agnostic detector yields better accuracy than classifying whole images. We fin...

Nature - Geological Net Zero and the need for disaggregated accounting for carbon sinks

Data from millions of smartphones are used to map the ionosphere in greater detail, leading to improved smartphone location accuracy, particularly in parts of the world with few monitoring stations.