Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been empirically observed that these same classifiers are relatively robust to random noise. In this paper, we propose to study a semi-random noise regime that generalizes both the random and worst-case noise regimes. We propose the first quantitative analysis of the robustness of nonlinear classifiers in this general noise regime. We establish precise theoretical bounds on the robustness of classifiers in this general regime, which depend on the curvature of the classifier's decision boundary. Our bounds confirm and quantify the empirical observations that classifiers satisfying curvature constraints are robust to random noise. Moreover, we quantify the robustness of classifiers in terms of the subspace dimension in the semi-random noise regime, and show that our bounds remarkably interpolate between the worst-case and random noise regimes. We perform experiments and show that the derived bounds provide very accurate estimates when applied to various state-of-the-art deep neural networks and datasets. This result suggests bounds on the curvature of the classifiers' decision boundaries that we support experimentally, and more generally offers important insights onto the geometry of high dimensional classification problems.

Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can view them. Self-driving cars process camera and LiDAR data directly with neural networks.

The AI Race Is Pressuring Utilities to Squeeze More From Europe's Power Grids As data center developers queue up to connect to power grids across Europe, network operators are experimenting with novel ways of clearing room for them. European countries are racing to bring new data centers online as AI labs across the globe continue to demand more compute. The primary limiting factor is energy--and specifically, the ability to move it. Though Europe is on track to generate enough energy, utilities experts say, grid operators broadly lack the infrastructure needed to transport it to where it needs to go. That's throttling grid capacity and, by extension, the number of new power-hungry data centers that can connect without risking blackouts.

This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over the uncertainty in a latent path, like a state space model, we improve the state of the art results on the Blizzard and TIMIT speech modeling data sets by a large margin, while achieving comparable performances to competing methods on polyphonic music modeling.

Pearl Abyss, the game's developer, issued a lengthy apology on X and detailed its corrective actions. The developer behind the open-world RPG Crimson Desert has issued an official apology after players discovered several instances of AI-generated art in the game. Pearl Abyss posted on X that it released the game with some 2D visual props that were made with experimental AI generative tools and forgot to replace them before launch. We would like to address questions regarding the use of AI in Crimson Desert. During development, some 2D visual props were created as part of early-stage iteration using experimental AI generative tools.

Elon Musk-owned X's Grok AI chatbot stirred outrage earlier this year over it generating images of naked women and girls without their consent. Paris - French prosecutors said Saturday they had alerted U.S. authorities to a suspicion that tech tycoon Elon Musk had encouraged controversy over sexualized deepfakes on X to artificially increase the value of his company. The social media network's Grok AI chatbot stirred outrage earlier this year over it generating images of naked women and girls without their consent. The controversy sparked by sexually explicit deepfakes generated by Grok (X's AI) may have been deliberately generated in order to artificially boost the value of companies X and xAI, the Paris prosecutor's office said, confirming a report in Le Monde newspaper on Friday. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

The Department of Defense alleges the AI developer could manipulate models in the middle of war. Company executives argue that's impossible. Anthropic cannot manipulate its generative AI model Claude once the US military has it running, an executive wrote in a court filing on Friday. The statement was made in response to accusations from the Trump administration about the company potentially tampering with its AI tools during war . "Anthropic has never had the ability to cause Claude to stop working, alter its functionality, shut off access, or otherwise influence or imperil military operations," Thiyagu Ramasamy, Anthropic's head of public sector, wrote .

Nvidia's new AI upscaling gaming technology struck gamers as uncanny and off-putting. Developers don't seem to like it, either, but it could be "the default" in a few years. Nvidia announced a new version of its DLSS AI upscaling technology for its graphics cards earlier this week at its GPU Technology Conference (GTC), which it calls the Super Bowl of AI . But unlike previous versions of DLSS that used AI to improve frame rates in video games, DLSS 5 has a much more ambitious calling: using generative AI to make character faces in games look more realistic and detailed. The demonstration received sharp blowback on social media, with many finding the effect off-putting, reacting with outright disgust, and calling it yet another example of AI slop .

PCWorld reports that AI is evolving beyond simple chatbots to become autonomous agents that directly control PC functions and applications. Major tech companies are developing agentic AI systems, including Anthropic's Claude tools, OpenAI's upcoming superapp, and Google's Gemini Mac app with desktop intelligence features. This shift toward AI agents managing tasks like software development and data analysis represents a fundamental change in how users will interact with their computers. Remember when ChatGPT was just an AI chatbox that sat on your desktop? That was, like, so December.

OpenAI is developing a unified desktop superapp that will integrate ChatGPT, Codex, and Atlas into a single application, according to PCWorld's coverage of The Wall Street Journal report. This consolidation aims to reduce service fragmentation and improve overall quality for users accessing OpenAI's various AI tools. The superapp represents a significant shift toward streamlined AI services, potentially making OpenAI's offerings more accessible and efficient for desktop users. It seems you'll soon be able to access most of OpenAI's services in one place on your computer.