National security and military analyst Dr. Rebecca Grant joins'Fox & Friends First' to discuss President Donald Trump's historic business-focused trip to the Middle East and why a Trump-Putin meeting could be essential for peace in Ukraine. President Donald Trump secured 2 trillion worth of deals with Saudi Arabia, Qatar and the UAE during his trip to the Middle East last week in what some have argued is a move to counter China's influence in the region. While China has increasingly bolstered its commercial ties with top Middle Eastern nations who have remained steadfast in their refusal to pick sides amid growing geopolitical tension between Washington and Beijing, Trump may have taken steps to give the U.S. an edge over its chief competitor. But concern has mounted after Trump reversed a Biden-era policy – which banned the sale of AI-capable chips to the UAE and Saudi Arabia – that highly coveted U.S. technologies could potentially fall into the hands of Chinese companies, and in extension, the Chinese Communist Party (CCP). U.S. President Donald Trump walks with Saudi Crown Prince Mohammed Bin Salman during a welcoming ceremony in Riyadh, Saudi Arabia, May 13, 2025.

Fox News anchor Bret Baier has the latest on the Murdoch Children's Research Institute's partnership with the Gladstone Institutes for the "Decoding Broken Hearts" initiative on "Special Report." Imagine having a tireless guardian watching over you during your subway commute. New York City's subway system is testing artificial intelligence to boost security and reduce crime. Michael Kemper, a 33-year NYPD veteran and the chief security officer for the Metropolitan Transportation Authority (MTA), which is the largest transit agency in the United States, is leading the rollout of AI software designed to spot suspicious behavior as it happens. The MTA says this technology represents the future of subway surveillance and reassures riders that privacy concerns are being taken seriously.

Saliency methods have been widely used to highlight important input features in model predictions. Most existing methods use backpropagation on a modified gradient function to generate saliency maps. Thus, noisy gradients can result in unfaithful feature attributions.

OpenAI started as a non-profit dedicated to building safe A.I. Now, they're obsessed with building artificial general intelligence by any means necessary - even if they don't quite know what that is. Subscribe to Slate Plus to access ad-free listening to the whole What Next family and all your favorite Slate podcasts. Subscribe today on Apple Podcasts by clicking "Try Free" at the top of our show page. Sign up now at slate.com/whatnextplus to get access wherever you listen.

We introduce a "learning-based" algorithm for the low-rank decomposition problem: given an n d matrix A, and a parameter k, compute a rank-k matrix A

In this work, we observe that model trained on vast general images via masking strategy, has been naturally embedded with their distribution knowledge, thus spontaneously attains the underlying potential for strong image denoising. Based on this observation, we propose a novel zero-shot denoising paradigm, i.e., Masked Pre-train then Iterative fill (MPI). MPI first trains model via masking and then employs pre-trained weight for high-quality zero-shot image denoising on a single noisy image. Concretely, MPI comprises two key procedures: 1) Masked Pre-training involves training model to reconstruct massive natural images with random masking for generalizable representations, gathering the potential for valid zero-shot denoising on images with varying noise degradation and even in distinct image types.

This year's surprise hit Blue Prince is a proper video game wonder. It's an architectural puzzler in which you explore a transforming mansion left to you by an eccentric relative. The place is filled with secrets, and whenever you reach a door you get to pick the room on the other side from a handful of options. The whole game is a rumination on houses and how we live in them. Nostalgic and melancholic, it feels designed to make us look harder at what surrounds us. This Addams'-style Queen Anne with clapboard facades and dark windows is a classic haunted house, reportedly inspired by the Skywalker Ranch.

Polynomial inequalities lie at the heart of many mathematical disciplines. In this paper, we consider the fundamental computational task of automatically searching for proofs of polynomial inequalities. We adopt the framework of semi-algebraic proof systems that manipulate polynomial inequalities via elementary inference rules that infer new inequalities from the premises. These proof systems are known to be very powerful, but searching for proofs remains a major difficulty. In this work, we introduce a machine learning based method to search for a dynamic proof within these proof systems. We propose a deep reinforcement learning framework that learns an embedding of the polynomials and guides the choice of inference rules, taking the inherent symmetries of the problem as an inductive bias. We compare our approach with powerful and widely-studied linear programming hierarchies based on static proof systems, and show that our method reduces the size of the linear program by several orders of magnitude while also improving performance. These results hence pave the way towards augmenting powerful and well-studied semi-algebraic proof systems with machine learning guiding strategies for enhancing the expressivity of such proof systems.

A.1 Dataset Samples We show different kinds of perturbations in our benchmarks in Fig.5. Specifically, our benchmarks include 9 basic types of perturbations, including Gaussian blur, Gaussian noise, radial distortion, and RGB and HSV channels. Another type of datasets include multiple perturbations, where we create multiple random combinations of the basic perturbations. We also include 7 types of previously unseen perturbations (during training) from ImageNet-C [20], which are snow, fog, frost, motion blur, zoom blur, pixelate, and jpeg compression. For each type of perturbation, we generate 5 or 10 levels of varying intensity based on sensitivity analysis in the FID-MA space.

In this paper, we propose a max-min entropy framework for reinforcement learning (RL) to overcome the limitation of the soft actor-critic (SAC) algorithm implementing the maximum entropy RL in model-free sample-based learning. Whereas the maximum entropy RL guides learning for policies to reach states with high entropy in the future, the proposed max-min entropy framework aims to learn to visit states with low entropy and maximize the entropy of these low-entropy states to promote better exploration. For general Markov decision processes (MDPs), an efficient algorithm is constructed under the proposed max-min entropy framework based on disentanglement of exploration and exploitation. Numerical results show that the proposed algorithm yields drastic performance improvement over the current state-of-the-art RL algorithms.