New brain-like computer could bring self-learning AI to devices

Engineers at The University of Texas at Dallas, led by Dr. Joseph S. Friedman, have developed a small-scale brain-inspired computer prototype that learns and processes information more like the human brain. Unlike traditional AI systems, which separate memory and processing and require extensive training with large labeled datasets, this neuromorphic hardware integrates memory and computation, enabling it to recognize patterns and make predictions with significantly fewer training computations and lower energy consumption. The design is based on Hebb’s law, where connections between artificial neurons strengthen when they activate together, allowing continuous self-learning. The prototype uses magnetic tunnel junctions (MTJs)—nanoscale devices with two magnetic layers separated by an insulator—that adjust their connectivity dynamically as signals pass through, mimicking synaptic changes in the brain. MTJs also provide reliable binary data storage, overcoming limitations seen in other neuromorphic approaches. Dr. Friedman aims to scale up this technology to handle more complex tasks, potentially enabling smart devices like phones and wearables to run