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Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) used a GPT-3 large language style model to extract important data from electronic health records to decipher clinical notes and enable better personalized care.
A machine-learning technique called SALIENT addresses key bottlenecks in computation with graph neural networks by optimizing usage of the hardware, particularly GPUs. This upgrade significantly reduces training and inference time on extensive datasets to keep pace with fast-moving and growing data in finance, social networks, and fraud detection in cryptocurrency.
Researchers from the MIT CSAIL have discovered a quicker way to solve an equation used in the algorithms for ‘liquid’ neural neurons.
MIT researchers solved a differential equation behind the interaction of two neurons through synapses to unlock a new type of speedy and efficient artificial intelligence algorithms.
A new algorithm makes headway in solving the problem of exploration vs. exploitation in reinforcement learning. Developed by MIT CSAIL researchers, it attempts to make decision-making more efficient.