The underlyingmetricis,however,non-parametric.Wedevelopamaximumlikelihood algorithm to infer the distribution parameters that relies on a combination of gradient descent and Monte Carlo integration. We further extend the LAND to mixture models, andprovidethecorresponding EMalgorithm.

Multimodal Large Language Models (MLLMs) struggle with precise reasoning for structured visuals like charts and diagrams, as pixel-based perception lacks a mechanism for verification. To address this, we propose to leverage derendering -- the process of reverse-engineering visuals into executable code -- as a new modality for verifiable visual reasoning. Specifically, we propose RECODE, an agentic framework that first generates multiple candidate programs to reproduce the input image. It then uses a critic to select the most faithful reconstruction and iteratively refines the code. This process not only transforms an ambiguous perceptual task into a verifiable, symbolic problem, but also enables precise calculations and logical inferences later on. On various visual reasoning benchmarks such as CharXiv, ChartQA, and Geometry3K, RECODE significantly outperforms methods that do not leverage code or only use code for drawing auxiliary lines or cropping. Our work demonstrates that grounding visual perception in executable code provides a new path toward more accurate and verifiable multimodal reasoning.

Despite the progress in the development of generative models, their usefulness in creating synthetic data that improve prediction performance of classifiers has been put into question. Besides heuristic principles such as "synthetic data should be close to the real data distribution", it is actually not clear which specific properties affect the generalization error. Our paper addresses this question through the lens of high-dimensional regression. Theoretically, we show that, for linear models, the covariance shift between the target distribution and the distribution of the synthetic data affects the generalization error but, surprisingly, the mean shift does not. Furthermore we prove that, in some settings, matching the covariance of the target distribution is optimal. Remarkably, the theoretical insights from linear models carry over to deep neural networks and generative models. We empirically demonstrate that the covariance matching procedure (matching the covariance of the synthetic data with that of the data coming from the target distribution) performs well against several recent approaches for synthetic data selection, across training paradigms, architectures, datasets and generative models used for augmentation.

Though artificial intelligence is fueling a surge in synthetic child abuse images, it's also being tested as a way to stop harm to real victims. Generative AI has enabled the production of child sexual abuse images to skyrocket. Now the leading investigator of child exploitation in the US is experimenting with using AI to distinguish AI-generated images from material depicting real victims, according to a new government filing. The Department of Homeland Security's Cyber Crimes Center, which investigates child exploitation across international borders, has awarded a $150,000 contract to San Francisco-based Hive AI for its software, which can identify whether a piece of content was AI-generated. The filing, posted on September 19, is heavily redacted and Hive cofounder and CEO Kevin Guo told that he could not discuss the details of the contract, but confirmed it involves use of the company's AI detection algorithms for child sexual abuse material (CSAM). The filing quotes data from the National Center for Missing and Exploited Children that reported a 1,325% increase in incidents involving generative AI in 2024.

Pok\'emon Red, a classic Game Boy JRPG, presents significant challenges as a testbed for agents, including multi-tasking, long horizons of tens of thousands of steps, hard exploration, and a vast array of potential policies. We introduce a simplistic environment and a Deep Reinforcement Learning (DRL) training methodology, demonstrating a baseline agent that completes an initial segment of the game up to completing Cerulean City. Our experiments include various ablations that reveal vulnerabilities in reward shaping, where agents exploit specific reward signals. We also discuss limitations and argue that games like Pok\'emon hold strong potential for future research on Large Language Model agents, hierarchical training algorithms, and advanced exploration methods. Source Code: https://github.com/MarcoMeter/neroRL/tree/poke_red

My main concern is that using a flattened surrogate energy in this fashion is suitable for most sampling situations. The main reason is, by construction our iterates are not following the true distribution particularly closely; for example a plot of the samples obtained in the synthetic experiments (figs 2c--d) would look quite different from the original. While this does allow the algorithm to bounce out of local optima, the deviance from the true energy would make samples obtained after convergence to not be super useful. For point estimation situations, we might be able to get away with these samples for cases where the multiple modes of the real energy are sort of symmetric (as in the synthetic Gaussian experiments); it seems that even if we use a'flattened' energy (can be thought of as lower peaks with higher elevation between them), the original distribution's symmetry would be essentially preserved and the mean / other point estimates would be close enough. But flattening energies with skewed distribution of modes might not be as accurate, as the flattened version might have a mean closer to the'center' of the space, but the original would be closer to one of the modes near the periphery (am visualizing a simple 2-d space).

Artificial intelligence could lead to extinction and should be a global priority on the scale of nuclear war and pandemics, Center for AI Safety chief Dan Hendrycks said. An artificial intelligence arms race between countries and corporations to see who can develop the most powerful AI machines could create an existential threat to humanity, the co-founder of an AI safety nonprofit told Fox News. "AI could pose the risk of extinction, and part of the reason for this is because we're currently locked in an AI arms race," Center for AI Safety Executive Director Dan Hendrycks said. "We're building increasingly powerful technologies, and we don't know how to completely control them or understand them." Sam Altman, CEO of OpenAI, signed the Center for AI Safety's statement saying that AI poses an existential threat to humanity.

… and distributed SLAM, c) deep learning in safety and security, d) collaborative brain-machine intelligence, and e) robotic human augmentation.

According to the National Interagency Fire Center, wildfires have burned 2,990,255 acres this year (June 17). Adding to firefighters' challenges, using current resources, it often takes hours to map a growing wildfire's perimeter and heat spots. It sometimes takes days using fuel property data – often 3-5 years old – to help predict fire behavior. Time is not on their side and the situation on the ground is always changing. Lockheed Martin is using AI/ML to help get critical data to firefighters faster.

Will smart machines someday replace attorneys, physicians, computer programmers, and world leaders? Are we just wetware, natural computers doomed to obsolescence by tomorrow's ultra-powerful artificial intelligence? A pioneer in computing intelligence says "no way." Non-Computable You explains how humans are unique and why Artificial Intelligence will never replicate you. Robert J. Marks, II newest book, Non-Computable You: What You Do That Artificial Intelligence Never Will (Discovery Institute Press 2022) explains how humans are unique and why artificial intelligence will never replicate humans.