Lawrence Berkeley Lab and four other Department of Energy facilities deployed Meta's SAM 3 and DINOv3 models as part of the Genesis Mission's SYNAPS-I project to automate image segmentation in scientific research. The system processes X-ray and neutron imaging data that previously required weeks of manual annotation, now delivering fully segmented 3D volumes in approximately 15 minutes on 300 A100 GPUs. This real-time analysis enables scientists to observe dynamic biological processes and material changes as experiments occur, rather than analyzing data months later.
Xaira Therapeutics developed X-Cell, a causal AI model for predicting how cells respond to genetic changes by training on X-Atlas, a large dataset of CRISPR experiments that directly measure gene expression dependencies rather than just observational correlations. The model scaled successfully after access to approximately 30 times more information-rich experimental data, overcoming previous scaling plateaus where test loss stalled at 3.1B parameters. This approach enables more accurate drug discovery predictions by learning actual causal relationships between genes rather than correlations, shifting the bottleneck from model architecture to data quality.
Sakana AI released The AI Scientist, a system that uses large language models to autonomously conduct scientific research, generating full papers from idea conception through peer review without human intervention. Each generated paper costs approximately $15 to produce, and the system has created papers in areas like diffusion modeling and language modeling that score at 'Weak Accept' level on top machine learning conference standards. The system raises safety and ethical concerns around paper quality, reviewer workload, potential misuse, and the need for transparency when AI substantially generates research submissions.
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