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Using AI to Identify Brain Tumors with Federated Learning

Researchers at Intel Labs and the Perelman School of Medicine are using privacy-preserving technique called federated learning to train AI models that identify brain tumors. With federated learning, research institutions can collaborate on deep learning projects without sharing patient data. “AI shows great promise for the early detection of brain tumors, but it will require more data than any single medical center holds to reach its full potential,” said Jason Martin, principal engineer at Intel Labs.

SLIDE algorithm for training deep neural nets faster on CPUs than GPUs

Rice researchers created a cost-saving alternative to GPU, an algorithm called “sub-linear deep learning engine” (SLIDE) that uses general purpose central processing units (CPUs) without specialized acceleration hardware. “Our tests show that SLIDE is the first smart algorithmic implementation of deep learning on CPU that can outperform GPU hardware acceleration on industry-scale recommendation datasets with large fully connected architectures.”

Intel Labs Unveils Pohoiki Beach 64-Chip Neuromorphic System

At the DARPA ERI summit this week, Intel Labs director Rich Uhlig unveiled “Pohoiki Beach” – a 64-Loihi Chip Neuromorphic system capable of simulating eight million neurons. Now available to the broader research community, the Pohoiki Beach enables researchers to experiment with Intel’s brain-inspired research chip, Loihi, which applies the principles found in the biological brains to computer architectures.