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Category: robotics/AI – Page 33

Memristor chip breaks the capacity limit of brain-inspired associative memory

Researchers in the Department of Electrical and Computer Engineering of the Faculty of Engineering and the Centre for Advanced Semiconductors and Integrated Circuits (CASIC) at the University of Hong Kong (HKU) have made a breakthrough in brain-inspired computing. In collaboration with Hewlett Packard Labs, the team has developed a memristor chip that overcomes a long-standing limit on the capacity of “associative memory,” the brain-like ability to recall complete information from a partial cue, while keeping it reliable even when a large fraction of the hardware fails.

Associative memory is something the brain does effortlessly: A few notes bring a whole song to mind, and a glimpse of a face identifies a person. Unlike the RAM in a computer, which must be told exactly where information is stored, associative memory retrieves it by content—the very capability that pattern completion, error correction and recognition depend on.

Princeton AI Tames Fusion Plasma Hotter Than the Sun

Princeton’s PACMAN AI can control fusion plasma in milliseconds and predict dangerous instabilities before they start.

In some fusion energy systems, particles can reach temperatures hotter than the center of the Sun. The challenge is keeping that extreme plasma under control, because disturbances can develop within just a few thousandths of a second, much faster than a person could respond.

Researchers at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University have developed a new software framework that uses artificial intelligence (AI) to make those rapid control decisions. The system is designed to respond at machine speed while maintaining strict safety protections and leaving the overall goals in human hands.

On the Navier–Stokes Millennium Prize Problem

We’re sharing a solution to the Navier–Stokes existence and smoothness problem, one of the Millennium Prize Problems. This proof, produced by an internal OpenAI system, shows that the dynamics of the Navier-Stokes equations for fluid motion can develop a singularity in finite time. We’re sharing both a writeup of the proof and a formalization in Lean.

The Millennium Prize Problems ⁠ (opens in a new window) represent some of the deepest questions at the frontier of mathematics. The question of whether smooth three-dimensional fluid motion can break down has remained unresolved for roughly 90 years.

Folding Giant AI Brains Into Your Pocket

A team of researchers proved a simple mathematical law — the “linearity theorem” — that lets engineers predict, in advance and with precision, exactly how compressing an AI model will affect its intelligence. No more guess-and-check. No more crossing your fingers and hoping a 70-billion-parameter model doesn’t collapse into gibberish after compression.

A global research team just proved a simple mathematical law that lets engineers shrink massive AI models onto ordinary laptops and phones — without guessing, and without dumbing them down.

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