While synthetic data hold great promise for privacy protection, their statistical analysis poses significant challenges that necessitate innovative solutions. The use of deep generative models (DGMs) for synthetic data generation is known to induce considerable bias and imprecision into synthetic data analyses, compromising their inferential utility as opposed to original data analyses. This bias and uncertainty can be substantial enough to impede statistical convergence rates, even in seemingly straightforward analyses like mean calculation.

The company is reportedly making a unified app to streamline the user experience. OpenAI is developing a "super app" for desktop that unifies ChatGPT, its browser and its Codex app, according to the and . A company spokesperson told the publications that OpenAI Chief of Applications Fidji Simo will lead the application revamp with assistance from OpenAI President Greg Brockman. Simo will also help the marketing team advertise the app when it comes out. OpenAI's leadership is apparently hoping that combining several products can help it streamline user experience and dedicate its resources to one project.

High-dimensional data commonly lies on low-dimensional submanifolds, and estimating the local intrinsic dimension (LID) of a datum -- i.e. the dimension of the submanifold it belongs to -- is a longstanding problem. LID can be understood as the number of local factors of variation: the more factors of variation a datum has, the more complex it tends to be. Estimating this quantity has proven useful in contexts ranging from generalization in neural networks to detection of out-of-distribution data, adversarial examples, and AI-generated text. The recent successes of deep generative models present an opportunity to leverage them for LID estimation, but current methods based on generative models produce inaccurate estimates, require more than a single pre-trained model, are computationally intensive, or do not exploit the best available deep generative models: diffusion models (DMs). In this work, we show that the Fokker-Planck equation associated with a DM can provide an LID estimator which addresses the aforementioned deficiencies.

PCWorld reports that Nvidia's DLSS 5 announcement caught major game developers from Ubisoft and Capcom off-guard, who were unaware their games would be featured in demonstrations. The generative AI technology faces significant backlash from gamers who criticize it as an "AI filter" that potentially devalues game aesthetics and may require two high-end GPUs. Despite being planned for fall 2026 release, DLSS 5 already raises concerns about artistic control and whether developers want this AI-enhanced visual processing in their games. Nvidia DLSS 5 is coming later this year, adding generative "AI" features to the performance-enhancing tech . Gamers are calling the tool an "Instagram yaas filter" and "AI slop," among other, less kind terms. The way that it adds detail to faces and seems to hijack -- or replace?

Instant to reduce annoying "if you want" and teaser-style phrasing that users found intrusive. This change addresses widespread user complaints about persistent, clickbait-like follow-up prompts that negatively impacted the AI interaction experience. The update aims to create more natural, direct conversations by making ChatGPT less chatty and eliminating the bothersome response teasers. It wasn't all that long ago that ChatGPT was a constant nag, persistently dropping "Would you like me to?"-style questions at the end of its responses. OpenAI eventually tweaked the phrasing, dropping the question marks and going for "if you want"-style teasers that invited users to extend their chat sessions. Now, OpenAI has acknowledged that it went too far with the clickbaity follow-ups, noting in a recent update for one of its newest models that it's now cutting back on the teasers. "We're rolling out an update to GPT-5.3 Instant that improves follow-up tone and reduces teaser-style phrasing," reads a recent ChatGPT release note, which adds that users should soon see fewer follow-ups like "if you want," "you'll never believe," and "I can tell you three things that " Those teasers are, of course, a way for ChatGPT to keep subscribers chatting, but users have been complaining that the persistent follow-ups are more annoying than they are intriguing. "I hated it with a passion and hope it's completely gone," wrote one user on Reddit .

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?