1h later...new massive computing breakthrough
New brain inspired chip learns on its own, no massive AI training needed
A team led by Dr. Joseph S. Friedman at The University of Texas at Dallas has developed a neuromorphic computing prototype, a brain inspired computer that learns and makes predictions using far fewer computations and much less energy than traditional AI systems.
Unlike conventional computers that separate memory and processing, neuromorphic hardware integrates both, mimicking how neurons and synapses in the brain adapt and learn.
The team used magnetic tunnel junctions (MTJs), nanoscale magnetic devices that strengthen connections between artificial neurons based on activity, following Hebb’s law ("neurons that fire together, wire together").
This breakthrough, published in Communications Engineering, is a big step toward energy efficient, self learning computers that can bring powerful AI capabilities to mobile devices without massive training costs.
Another massive computing/AI breakthrough
Engineers create artificial neurons that think like real brain cells, big leap toward true AGI
Researchers at USC Viterbi School of Engineering have built artificial neurons that physically replicate how real brain cells process electrical and chemical signals, a historic step toward brain like computing and potentially AGI.
Powered by a breakthrough device called a diffusive memristor, these neurons use ions instead of electrons to compute, just like the human brain, enabling chips that are orders of magnitude smaller and more energy efficient than today’s silicon processors.
The new design, published in Nature Electronics, could revolutionize neuromorphic computing, making AI hardware that doesn’t just simulate thought but actually works like the human brain.