An algorithm is helping cities locate pipes that could release highly toxic lead into drinking water. BlueConduit, a startup that focuses on water safety, is working with dozens of North American municipal governments to locate lead water lines so they can be replaced.

Computer vision is alerting authorities the moment someone draws a gun. Several companies offer deep learning systems that enable surveillance cameras to spot firearms and quickly notify security guards or police.

An AI-powered collar may help protect wild elephants from poachers, hunters, and other hostile humans. Ten ElephantEdge wireless tracking collars will be fitted onto African elephants next year, TechCrunch reported.

The past year was the first in which general-purpose models became economically useful. GPT-3, in particular, demonstrated that large language models have surprising linguistic competence and the ability to perform a wide variety of useful tasks.

As AI engineers, we have tools to design and build any technology-based solution we can dream of. But many AI developers don’t consider it their responsibility to address potential negative consequences as a part of this work.

The worldwide pandemic and a contentious U.S. election whipped up a storm of automated disinformation, and some big AI companies reaped the whirlwind.

A new database tracks failures of automated systems including machine learning models. The Partnership on AI, a nonprofit consortium of businesses and institutions, launched the AI Incident Database, a searchable collection of reports on the technology’s missteps.

In Argentina, a municipal face recognition system could misidentify children as suspected lawbreakers. Authorities in Buenos Aires are scanning subway riders’ faces to find offenders in a database of suspects but the system mixes criminal records with personal information about minors.

Will AI that discriminates based on race, gender, or economic status undermine the public’s confidence in the technology? Seduced by the promise of cost savings and data-driven decision making, organizations will deploy biased systems that end up doing real-world damage.

Will we ever understand what goes on inside the mind of a neural network?The fear: When AI systems go wrong, no one will be able to explain the reasoning behind their decisions.

Facing a tsunami of user-generated disinformation, YouTube is scrambling to stop its recommendation algorithm from promoting videos that spread potentially dangerous falsehoods.

Three of the biggest AI vendors pledged to stop providing face recognition services to police — but other companies continue to serve the law-enforcement market.

Facebook uses automated systems to block hate speech, but hateful posts can slip through when seemingly benign words and pictures combine to create a nasty message. The social network is tackling this problem by enhancing AI’s ability to recognize context.

Facebook’s leadership has thwarted changes in its algorithms aimed at making the site less polarizing, according to the Wall Street Journal. The social network’s own researchers determined that its AI software promotes divisive content.

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.