Apr 29, 2020

In an earlier letter, I wrote about the challenge of robustness: A learning algorithm that performs well on test data often doesn’t work well in a practical production environment because the real world turns out to be different than the test set.

Recognizing stop signs, with their bold color scheme and distinctive shape, ought to be easy for computer vision — but it turns out to be a tricky problem. Tesla pulled back the curtain on what it takes to train its self-driving software to perform this task and others.

Your body disturbs Wi-Fi signals as you move through them. New research takes advantage of the effect to recognize the presence of people. Yang Liu and colleagues at Syracuse University detected people in a room with a Wi-Fi router by analyzing the signal.

Deep learning promises to help emergency responders find their way through disaster zones. MIT researchers developed a tool that maps where hurricanes and other calamities have wiped out roads, helping to show aid workers the fastest ways to get to people in need.

A new algorithm can triage programming bugs, highlighting dangerous flaws. Microsoft researchers developed a machine learning model that reads the titles of bug reports and recognizes those describing flaws that compromise security.

Robot vacuum cleaners are pretty good at navigating rooms, but they still get stuck in tight spaces. New work takes a step toward giving them the smarts they’ll need to escape the bathroom.

A consortium of top AI experts proposed concrete steps to help machine learning engineers secure the public’s trust. Dozens of researchers and technologists recommended actions to counter public skepticism toward artificial intelligence, fueled by issues like data privacy.

In an earlier letter, I wrote about the challenge of robustness: A learning algorithm that performs well on test data often doesn’t work well in a practical production environment because the real world turns out to be different than the test set.