AI

An analysis of 160 deepfake websites reveals politicians in 22 countries appear on them. Nearly all of them are women.

Sam Altman and Elon Musk this weekend backed a call for restraint, after Dario Amodei called on Washington to slow AI development. Donald Trump, meanwhile, wants the US to maintain its lead over China.

While the publishing industry frets over how authors are using AI, many readers are taking things into their own hands.

MIT researchers have developed a new technique that helps generative artificial intelligence models find solutions to high-stakes problems. In these settings, a plausible answer is not enough: The output often must also satisfy nonnegotiable safety, physical, or task-specific requirements, known as hard constraints. The researchers developed a method that helps generative models meet these strict

The world needs more homes. The world also has too much plastic. Perhaps the only thing those two problems have in common is that they’re hard to solve. Atlas Building Composites, a spinout of MIT, is on a mission to address both problems with a single solution. The company has developed an AI-powered robotic manufacturing

Silicon Valley is shifting away from chatbot queries toward a future filled with resource-intensive agentic AI—and it’s driving the data center buildout.

Plus: The US disrupts the internet’s biggest black market, a Conti ransomware hacker gets prison time, Meta fails to stop AI-generated videos of child abuse.

The proposed class action alleges Meta illegally harvested people’s Facebook and Instagram photos to train its AI image-generation models and to build its unreleased “NameTag” face recognition feature.

Multi-agent systems in production experience issues in ways that traditional monitoring misses. For example, the agent can’t invoke its foundation model (FM) and returns an empty response. This could be because of a missing AWS Identity and Access Management (IAM) permission on an agent’s execution role that doesn’t throw a 500 error. A supervisor agent

Organizations building generative AI applications usually compare models the same way: dollars per million tokens. It’s the number on every pricing page, so it becomes the number in every spreadsheet. But production workloads don’t buy tokens. They buy outcomes: a resolved support ticket, a completed research brief, a correct financial summary. Between the pricing page