Aug 05, 2020

A prosthetic leg that learns from the user’s motion could help amputees walk more naturally. Researchers from the University of Utah designed a robotic leg that uses machine learning to generate a human-like stride.

Language models like Bert, Ernie, and Elmo have achieved spectacular results based on clever pre-training approaches. New research applies some of those Sesame Street lessons into image processing.

Chips specially designed for AI are becoming much faster at training neural networks, judging from recent trials. MLPerf, an organization that’s developing standards for hardware performance in machine learning tasks, released results from its third benchmark competition.

A high-profile semiconductor startup made a bid for the future of AI computation. UK startup Graphcore released the Colossus Mk2, a processor intended to perform the matrix math calculations at the heart of deep learning more efficiently than other specialized processors.

I spoke last week at the National Intergovernmental Audit Forum, a meeting attended by U.S. federal, state, and local government auditors. (Apparently some of the organizers had taken AI for Everyone.)

I spoke last week at the National Intergovernmental Audit Forum, a meeting attended by U.S. federal, state, and local government auditors. (Apparently some of the organizers had taken AI for Everyone.)

Neural networks are famously bad at interpreting input that falls outside the training set’s distribution, so it’s not surprising that some models are certain that cat pictures show symptoms of Covid-19. A new approach won’t mistakenly condemn your feline to a quarantine.