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The ruling holds that a company that designs, trains, operates, and manages an AI system must assume legal liability for any damages caused by the responses it generates.
“The government believes it has become aware of a method of bypassing, or ‘jailbreaking’ Fable 5,” the company said in a blog post.
“I’m not sure that this company supports a hackathon culture anymore,” one employee posted in a forum open to the entire staff.
Executives and employees alike are struggling with Meta's chaotic AI strategy, according to sources and internal discussions reviewed by WIRED.
Winning fight against AI data centers gives people a "taste of political power."
GOP lawmakers, tech investors, and even OpenAI have tied the anti-data center movement in the US to Chinese interference. Experts say it’s much more complicated than that.
Google has announced the Google Colab CLI, a command-line tool that allows developers and AI agents to interact with remote Colab runtimes directly from a local terminal. By Daniel Dominguez
The fraudsters allegedly targeted hundreds of thousands of people with Gemini-coded scams sites.
Pinecone has announced a new integration between its Nexus knowledge engine and Microsoft OneLake, aiming to fundamentally change how enterprise AI agents access and reason over corporate data. By Craig Risi
The repurposing of Pokémon Go data for AI training continues to draw scrutiny.
The company has set aside an unusually high number of shares for retail investors. Still, experts say, you’re just getting the crumbs.
David Stein shares how to rethink large-scale architectural migrations using AI. He discusses ServiceTitan's "assembly line" pattern, explaining how decomposing legacy codebase refactoring into standardized tasks can achieve massive parallelization. He highlights the critical role of programmatically rigid validation loops to eliminate LLM hallucinations and accelerate engineering agility. By David Stein
Today on Uncanny Valley, we take an early look at the SpaceX IPO and why you might find yourself among the investors without even realizing it.
Thibault Sottiaux helped make AI coding one of OpenAI’s fastest-growing businesses. Now he’s overseeing a sweeping overhaul of ChatGPT.
A WIRED investigation found dozens of “nudified” deepfake images and videos on Grok's website, including nonconsensual depictions of celebrities and at least one prominent US politician.
submitted by /u/Foreign-Swan4271 [link] [留言]
It feels like just a couple of years ago, using AI for everyday tasks still felt like something new or even a bit weird. Now it seems like a lot of people are using it without thinking twice, whether for writing, learning, brainstorming, or just quick answers. I’m curious how others see this shift. Do you think AI has become normalized quicker than most people predicted, or does it still feel like a big deal to a lot of users? submitted by /u/NoFilterGPT [link] [留言]
submitted by /u/aarshie [link] [留言]
I’ve been thinking about a support automation story I read recently. A team replaced a simple rules engine with an LLM classifier. The model was around 92% accurate. Sounds good. Until you realize that at 100 tickets a day, that’s roughly 8 mistakes every day. The interesting part wasn’t the accuracy though. It was what happened when the model was wrong. Nobody could explain why a ticket was classified a certain way. Nobody could point to a specific rule. Nobody could quickly fix the behavior. The team eventually started reviewing every classification manually. The automation was still running, but the trust was gone. That got me thinking. A lot of discussion around AI agents focuses on making decisions better. Better prompts. Better models. Better reasoning. But I rarely see people discussing what happens after the decision. How is the decision verified? How is it audited? How do you know an action should actually be executed? Maybe the biggest challenge for AI agents isn’t getting from 92% to 96%. Maybe it’s building systems that people can trust when things go wrong. Curious how others are thinking about this. submitted by /u/docybo [link] [留言]
submitted by /u/andix3 [link] [留言]