There was a moment when logistic regression was used to classify just one thing: If you drink a vial of poison, are you likely to be labeled “living” or “deceased”? Times have changed.

A wearable device may help warehouse workers avoid injuries. Modjoul, maker of a system that evaluates risks to people engaged in physical labor, received an undisclosed sum from Amazon as part of a $1 billion investment in technologies that might enhance the retailer giant’s operations.

Amid rising social tension in the United States over reproductive freedom, a company that analyzes location data on abortion clinics stopped distributing its findings after a critical press report.

It’s surprisingly easy to turn a well-intended machine learning model to the dark side. In an experiment, Fabio Urbina and colleagues at Collaborations Pharmaceuticals, who had built a drug-discovery model to design useful compounds and avoid toxic ones, retrained it to generate poisons.

Robocalls slip through smartphone spam filters, but a new generation of deep learning tools promises to tighten the net. Research proposed fresh approaches to thwarting robocalls. Such innovations soon could be deployed in apps.

OpenAI unveiled a more reliable successor to its GPT-3 natural language model. InstructGPT is a version of GPT-3 fine-tuned to minimize harmful, untruthful, and biased output. It's available via an application programming interface.

The U.S. Department of Defense issued new ethical guidelines for contractors who develop its AI systems. The Pentagon’s Defense Innovation Unit, which issues contracts for AI and other high-tech systems, issued guidelines that contractors must follow.

There’s a reason why artificial intelligence is sometimes referred to as “software 2.0”: It represents the most significant technological advance in decades. Like any groundbreaking invention, it raises concerns about the future, and much of the media focus is on the threats it brings.

I believe that natural language processing in 2022 will re-embrace symbolic reasoning, harmonizing it with the statistical operation of modern neural networks. Let me explain what I mean by this.

While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive

Legislators worldwide wrote new laws — some proposed, some enacted — to rein in societal impacts of automation.What happened: Authorities at all levels ratcheted up regulatory pressure as

DeepMind released three papers that push the boundaries — and examine the issues — of large language models.

One year after her acrimonious exit from Google, ethics researcher Timnit Gebru launched an independent institute to study neglected issues in AI.

Face recognition algorithms have come under scrutiny for misidentifying individuals. A U.S. government agency tested over 1,000 of them to see which are the most reliable.

Microsoft is making GPT-3 available to selected customers through its Azure cloud service.