The Fight to Hold AI Companies Accountable for Children's Deaths After a series of suicides allegedly linked to AI chatbots, one lawyer is trying to hold companies like OpenAI accountable. Cedric Lacey relied on a camera to check on his kids while he was working as a commercial van driver going to and back from Alabama. Each morning, he would tune into the feed of his living room to make sure his teenage son, Amaurie, and his 14-year-old daughter were packing up their bags and getting ready to leave for school. But one morning last June, Lacey didn't see Amaurie up and about. Concerned, he called home, only to find out that his 17-year-old had hanged himself.

The Tokyo Metropolitan Government is developing a Generative AI Platform, which will allow government employees to create AI applications to assist with their work. The Tokyo Metropolitan Government and municipal governments throughout the Japanese capital are increasingly using generative artificial intelligence in their administrative operations. To support this trend, the metropolitan government is working with GovTech Tokyo, an affiliated organization that promotes digitalization in local governments, to develop a Generative AI Platform. The system will allow government employees to create generative AI applications tailored to their specific duties. By encouraging active use of the platform, Tokyo authorities aim to boost efficiency in public services and address growing concerns over labor shortages. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Recent advances in generative AI have led to increasingly realistic synthetic data, yet evaluation criteria remain focused on marginal distribution matching. While these diagnostics assess local realism, they provide limited insight into whether a generative model preserves the multivariate dependence structures governing downstream inference. We introduce covariance-level dependence fidelity as a practical criterion for evaluating whether a generative distribution preserves joint structure beyond univariate marginals. We establish three core results. First, distributions can match all univariate marginals exactly while exhibiting substantially different dependence structures, demonstrating marginal fidelity alone is insufficient. Second, dependence divergence induces quantitative instability in downstream inference, including sign reversals in regression coefficients despite identical marginal behavior. Third, explicit control of covariance-level dependence divergence ensures stable behavior for dependence-sensitive tasks such as principal component analysis. Synthetic constructions illustrate how dependence preservation failures lead to incorrect conclusions despite identical marginal distributions. These results highlight dependence fidelity as a useful diagnostic for evaluating generative models in dependence-sensitive downstream tasks, with implications for diffusion models and variational autoencoders. These guarantees apply specifically to procedures governed by covariance structure; tasks requiring higher-order dependence such as tail-event estimation require richer criteria.

After OpenAI's Instant Checkout feature fell short, Walmart is instead embedding its Sparky chatbot directly into ChatGPT and Google Gemini. Since November, Walmart has let some ChatGPT users order a limited selection of products without ever leaving OpenAI's chatbot interface. Sales have been disappointing, a Walmart executive vice president exclusively tells WIRED. The results suggest that a future where chatbots and AI agents take over ecommerce is still a way off, if it ever materializes. Last year, OpenAI made a bet that it could boost revenue by charging a commission on purchases made through ChatGPT.

The Download: The Pentagon's new AI plans, and next-gen nuclear reactors Plus: The OpenClaw frenzy has led to a new Nvidia product. The Pentagon plans to set up secure environments for generative AI companies to train military-specific versions of their models on classified data, MIT Technology Review has learned. AI models like Anthropic's Claude are already used to answer questions in classified settings, including for analyzing targets in Iran. But allowing them to train on and learn from classified data is a major new development that presents unique security risks. It would also bring AI firms closer to classified data than ever before. What do new nuclear reactors mean for waste?

The generative AI models used in classified environments can answer questions but don't currently learn from the data they see. The Pentagon is discussing plans to set up secure environments for generative AI companies to train military-specific versions of their models on classified data, has learned. AI models like Anthropic's Claude are already used to answer questions in classified settings; applications include analyzing targets in Iran. But allowing models to train on and learn from classified data would be a new development that presents unique security risks. It would mean sensitive intelligence like surveillance reports or battlefield assessments could become embedded into the models themselves, and it would bring AI firms into closer contact with classified data than before. Training versions of AI models on classified data is expected to make them more accurate and effective in certain tasks, according to a US defense official who spoke on background with .

Nvidia CEO Jensen Huang defends DLSS 5 against user backlash, calling critics "completely wrong" about the generative AI graphics technology's function. PCWorld notes the controversy stems from concerns that DLSS 5 applies an "AI skin" over game models rather than true enhancement. Huang clarifies DLSS 5 offers developers controllability at the geometry level, describing it as real-time neural rendering that infuses photorealism into pixels. In just a day, Nvidia's DLSS 5 technology has become the hot button for most of the PC and gaming world. Now Nvidia's chief executive has weighed in, claiming that everyone is "completely wrong" about the technology. At a question-and-answer session at Nvidia's own Game Technology Conference, Nvidia chief executive Jensen Huang said that "as I have explained very carefully, DLSS 5 fuses controllability of the of geometry and textures and everything about the game with generative AI," he said. Huang went on to say of the controversy: "They're completely wrong." Nvidia's DLSS 5 has sparked controversy because it essentially applies a generative AI filter to computer graphics. Nvidia describes DLSS 5 as a "real-time neural rendering model that infuses pixels with photoreal lighting and materials," and a "GPT moment for graphics -- blending hand-crafted rendering with generative AI".

GPT-5.4 mini brings some of the smarts of OpenAI's latest model to ChatGPT Free and Go users The new model offers performance improvements in reasoning, multimodal understanding and more. The ChatGPT icon, as seen on iPhone 12 running iOS. When OpenAI released GPT-5.4 at the start of March, the company said the new model was designed primarily for professional work like programming and data analysis. Now OpenAI is launching GPT-5.4 mini and nano, and while it is once again highlighting the usefulness of these new systems for tasks like coding, one of the new models is available to Free and Go users . What's more, that model, GPT-5.4 mini, even offers performance that approaches GPT-5.4 in a handful of areas.

Why can't language models write well? I n a certain, strange way, generative AI peaked with OpenAI's GPT-2 seven years ago. Little known to anyone outside of tech circles, GPT-2 excelled at producing unexpected answers. "You could be like, 'Continue this story:,' and GPT-2 would be like, ','" Katy Gero, a poet and computer scientist who has been experimenting with language models since 2017, told me. "The models won't do that anymore." AI leaders boast about their models' superhuman technical abilities.

Plus: China has approved the world's first commercial brain chip. Where OpenAI's technology could show up in Iran OpenAI has controversially agreed to give the Pentagon access to its AI. But where exactly could its tech show up, and which applications will its customers and employees tolerate? There's pressure to integrate it quickly with existing military tools. One defense official revealed it could even assist in selecting strike targets. OpenAI's partnership with Anduril, which makes drones and counter-drone technologies, adds another hint at what is to come.