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Netholabs is building foundation models of living organisms from longitudinal, multimodal data spanning neural activity, physiology, behavior and environment. The thesis is that living systems contain learning rules and priors that current AI has not recovered from scale alone. Netholabs turns those signals into proprietary training data for frontier models today, while using the same data to train cross-species models of organisms. As those models become more predictive and individualized, they create a path toward new AI architectures, in-silico biology, and ultimately deeply individualized models of humans.
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