A solar-powered autonomous drone scans for forest fires. A surgeon first operates on a digital heart before she picks up a scalpel. A global community bands together to print personal protection equipment to fight a pandemic. “The future is now,” says Frédéric Vacher, head of innovation at Dassault Systèmes. And all of this is possible with…
In the second of two exclusive interviews, Technology Review’s Editor-in-Chief Gideon Lichfield sat down with Parag Agrawal, Twitter’s Chief Technology officer to discuss the rise of misinformation on the social media platform. Agrawal discusses some of the measures the company has taken to fight back, while admitting Twitter is trying to thread a needle of…
Misinformation and social media have become inseparable from one another; as platforms like Twitter and Facebook have grown to globe-spanning size, so too has the threat posed by the spread of false content. In the midst of a volatile election season in the US and a raging global pandemic, the power of information to alter…
Defining what is, or isn’t artificial intelligence can be tricky (or tough). So much so, even the experts get it wrong sometimes. That’s why MIT Technology Review’s Senior AI Reporter Karen Hao created a flowchart to explain it all. In this bonus content our Host Jennifer Strong and her team reimagine Hao’s reporting, gamifying it…
The news: A new AI model for summarizing scientific literature can now assist researchers in wading through and identifying the latest cutting-edge papers they want to read. On November 16, the Allen Institute for Artificial Intelligence (AI2) rolled out the model onto its flagship product, Semantic Scholar, an AI-powered scientific paper search engine. It provides a one-sentence tl;dr (too long;...
Moves have been made to restrict the use of facial recognition across the globe. In part one of this series on Face ID, Jennifer Strong and the team at MIT Technology Review explore the unexpected ways the technology is being used, including how technology is being turned on police. We meet: Christopher Howell, data scientist…
It’s no secret that machine-learning models tuned and tweaked to near-perfect performance in the lab often fail in real settings. This is typically put down to a mismatch between the data the AI was trained and tested on and the data it encounters in the world, a problem known as data shift. For example, an AI trained to spot signs of disease in high-quality medical images will struggle with...