Tennessee minors sue Musk's xAI, alleging Grok generated sexual images of them Governments and regulators around the world have launched probes into xAI, imposed bans and demanded safeguards in a growing push to curb illegal and offensive material. Three Tennessee plaintiffs, including two minors, sued Elon Musk's xAI on Monday, alleging that it knowingly designed its Grok image generator to let people create sexually explicit content by using real photos of others. The lawsuit, filed in the San Jose, California federal court, is seeking class-action status for people in the United States who were reasonably identifiable in sexualized images or videos generated by Grok based on real images of themselves. The artificial intelligence company did not immediately respond to a request for comment. After an outcry over sexually explicit content generated by the chatbot, xAI said in January that it had blocked all users from editing images of real people in revealing clothing and from generating images of people in revealing clothing in jurisdictions where it's illegal. Governments and regulators around the world have also since launched probes, imposed bans and demanded safeguards in a growing push to curb illegal and offensive material.

AI transforms industries, but electricity demand surge could double American households' power bills as utilities build new infrastructure to support data centers.

We can't let them hide behind their models From Gaza to Iran, the pattern is the same: precision weapons, chosen blindness, and dead children. There is an Israeli military strategy called the "fog procedure". First used during the second intifada, it's an unofficial rule that requires soldiers guarding military posts in conditions of low visibility to shoot bursts of gunfire into the darkness, on the theory that an invisible threat might be lurking. It's violence licensed by blindness. Shoot into the darkness and call it deterrence. With the dawn of AI warfare, that same logic of chosen blindness has been refined, systematized, and handed off to a machine.

Our study of 22 mitigation techniques and five baselines reveals up to 12.6% fairness variance across identical training runs with identical seeds.

We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue. By dynamically interpolating between model rollouts of various horizon lengths for each individual example, STEVE ensures that the model is only utilized when doing so does not introduce significant errors.

Computing an approximately optimal policy with high probability in this case is known as PAC RL with a generative model.

Neural Information Processing Systems http://nips.cc/

See Corollary 2 in Appendixfordetail.

Influence functions estimate the effect of removing a training point on a model without theneedtoretrain. Theyarebasedonafirst-order Taylorapproximation thatisguaranteed tobeaccurate forsufficiently small changes tothemodel, and so are commonly used to study the effect of individual points in large datasets. However, we often want to study the effects of largegroups of training points, e.g., todiagnose batch effects orapportion credit between different data sources.

The city's narrow streets and brutal traffic will present a'pressure test' for the tech, its CEO said. US self-driving startup Nuro, which is backed by the likes of NVIDIA, Toyota and Uber, has started testing its autonomous vehicles on Tokyo's challenging streets, reported. The company, which plans to launch a robotaxi service with Uber and Lucid in San Francisco this year, will be testing a handful of vehicles in the city. Human safety drivers will be at the wheel, as is required by Japanese law. Tokyo presents a challenge for autonomous vehicles, given its narrow, crowded streets and left side of the road driving.