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Google released TimesFM-3, a 330-million-parameter model that forecasts multiple related time series simultaneously by processing them in a single forward pass. It outperforms competing foundation models on standard benchmarks by learning relationships between different series and incorporating known future events like promotions or weather.
- TimesFM-3 uses a 2D attention mechanism (causal temporal + full variate) that lets the model see patterns across time within a series and correlations between different series at the same time step, enabling it to capture how one series influences another.
- The model generates entire forecast horizons in one pass using "Contiguous Patch Masking" instead of iteratively predicting one step at a time, reducing latency and error accumulation.
- It ranked first on three major benchmarks (Gift-Eval, FEV-Bench, Time) for both point and probabilistic forecasting accuracy against recent competitors like Chronos-2 and Toto 2.0.
AWS introduced Amazon Bio Discovery, an AI-driven platform that lets researchers run complex drug-design workflows without coding. It provides a library of biological foundation models, an AI agent for workflow setup and analysis, and links to lab partners for synthesis and testing, cutting months of work down to weeks.
- AWS launched Amazon Bio Discovery, a no-code AI platform letting scientists run drug-design workflows using foundation models plus an AI agent, cutting months of research into weeks.
- In a Memorial Sloan Kettering/Twist Bioscience collaboration, nearly 300,000 AI-designed antibodies were narrowed to 100,000 for physical lab testing.
- Bayer, Broad Institute and Voyager Therapeutics are early adopters, and 19 of the top 20 global pharma companies already use AWS cloud services.
- AWS is separately partnering with Boston Consulting Group and Merck on an AI tool to improve clinical trial site selection.
SleepFM is a novel foundation model developed to analyze polysomnography (PSG) recordings, facilitating accurate predictions of various health conditions based on sleep data. Trained on over 585,000 hours of sleep recordings, it demonstrates strong performance in predicting diseases such as dementia and heart failure, while also supporting standard sleep analysis tasks.
- SleepFM was trained on over 585,000 hours of polysomnography recordings, an unusually massive dataset for this domain.
- The model predicts diverse downstream conditions like dementia and heart failure directly from raw sleep data, not just standard sleep-stage/apnea metrics.
- It performs well on conventional sleep analysis tasks while also generalizing to disease prediction, suggesting a single foundation model can replace many task-specific ones.