Amid signs of a stagnating economy, the UK is going all-in on AI. On Monday, British Minister Keir Starmer announced a new AI Opportunities Action Plan. At the center of the initiative are "AI Growth Zones," which the government plans to establish in de-industrialized areas throughout the country. Starmer said the UK's first AI Growth Zone would be established in Culham, Oxfordshire, home to the country's Atomic Energy Authority. More zones will be announced in the summer.

The Biden administration has unveiled its " AI diffusion rule," which aims to restrict the export of GPUs that are most coveted for AI applications. Although it does not mention the nation by name, it's broadly viewed as a means to prevent China from outpacing the US in AI development. The rule proposes three licensing tiers. The first tier is unrestricted and includes the domestic market as well as 18 strategic allies. The majority of countries fall into a second tier, which will have caps on how much compute power they can import via top GPUs from the US.

OpenAI CEO Sam Altman sits down with Shannon Bream to discuss the positives and potential negatives of artificial intelligence and the importance of maintaining a lead in the A.I. industry over China. OpenAI is staking out a plethora of new artificial intelligence (AI) policy proposals that the research organization believes will help the United States maintain its lead over the Chinese Communist Party (CCP). On Monday, OpenAI revealed the details of its AI "Economic blueprint," which the company hopes will be adopted by the incoming Trump administration and Congress. The blueprint will serve as a "living document" for responsible AI building and deployment. Speaking with Fox News Digital, Open AI's Vice President of Global Affairs, Chris Lehane, said it is "absolutely imperative" that the U.S. stays in command of AI innovation and production.

The Biden administration is proposing a new framework for the exporting of the advanced computer chips used to develop artificial intelligence, an attempt to balance national security concerns about the technology with the economic interests of producers and other countries. But the framework proposed Monday also raised concerns of chip industry executives who say the rules would limit access to existing chips used for video games and restrict in 120 countries the chips used for data centers and AI products. Mexico, Portugal, Israel and Switzerland are among the nations that could have limited access. Commerce Secretary Gina Raimondo said on a call with reporters previewing the framework that it's "critical" to preserve America's leadership in AI and the development of AI-related computer chips. The fast-evolving AI technology enables computers to produce novels, make scientific research breakthroughs, automate driving and foster a range of other transformations that could reshape economies and warfare.

The Biden administration announced a bold and controversial new export control scheme today, designed to prevent the advanced chips and artificial intelligence models themselves from ending up in the hands of adversaries such as China. The administration's new "AI Diffusion rule" divides the world into nations that are allowed relatively unfettered access to America's most advanced AI silicon and algorithms, and those that will require special licenses to access the technology. The rule, which will be enforced by the Commerce Department's Bureau of Industry and Security, also seeks to restrict the movement of the most powerful AI models for the first time. "The US leads the world in AI now, both AI development and AI chip design, and it's critical that we keep it that way," the US Commerce Secretary Gina Raimondo said ahead of today's announcement. The list of trusted nations are the UK, Canada, Australia, Japan, France, Germany, Belgium, Denmark, Finland, Ireland, Italy, the Netherlands, New Zealand, Norway, Republic of Korea, Spain, Sweden and Taiwan.

Fox News correspondent William La Jeunesse joins'Fox News Sunday' to discuss the evolution of AI and the push lawmakers are making to regulate it. FIRST ON FOX: A select group of tech industry titans and venture capitalists will gather in Washington, D.C., this week to welcome the incoming Trump administration and celebrate new opportunities for global innovation in artificial intelligence and entrepreneurship. Presidents and CEOs from companies on the cutting edge of AI tech and their big financial backers, along with personnel from the incoming administration, will attend a dinner on Thursday organized by Outside the Box Ventures, a firm founded last year by journalist-turned-investment banker Katherine Tarbox, along with Laurent Bili, the French ambassador to the U.S. The list of those invited to Thursday's dinner includes "DOGE" chief Elon Musk, Silicon Valley investor and GOP mega-donor Peter Thiel, NVCA chief executive Bobby Franklin, incoming White House AI and crypto czar David Sacks, OpenAI's Sam Altman, investor Joe Lonsdale and Narya co-founder Colin Greenspon. "This gathering represents more than discussion. We hope it symbolizes a new chapter in public-private collaboration to harness technology's transformative power for the nation's future," a source close to the planning told Fox News Digital.

We describe two Monte Carlo algorithms for sampling from the integrated posterior distributions of a range of Bayesian mixture models. Both algorithms allow us to directly sample not only the assignment of observations to components but also the number of components, thereby fitting the model and performing model selection over the number of components in a single computation. The first algorithm is a traditional collapsed Gibbs sampler, albeit with an unusual move-set; the second builds on the first, adding rejection-free sampling from the prior over component assignments, to create an algorithm that has excellent mixing time in typical applications and outperforms current state-of-the-art methods, in some cases by a wide margin. We demonstrate our methods with a selection of applications to latent class analysis.

This paper introduces the Semantic Propagation Graph Neural Network (SProp GNN), a machine learning sentiment analysis (SA) architecture that relies exclusively on syntactic structures and word-level emotional cues to predict emotions in text. By semantically blinding the model to information about specific words, it is robust to social biases such as political or gender bias that have been plaguing previous machine learning-based SA systems. The SProp GNN shows performance superior to lexicon-based alternatives such as VADER (Valence Aware Dictionary and Sentiment Reasoner) and EmoAtlas on two different prediction tasks, and across two languages. Additionally, it approaches the accuracy of transformer-based models while significantly reducing bias in emotion prediction tasks. By offering improved explainability and reducing bias, the SProp GNN bridges the methodological gap between interpretable lexicon approaches and powerful, yet often opaque, deep learning models, offering a robust tool for fair and effective emotion analysis in understanding human behavior through text.

Distributed-memory implementations of numerical optimization algorithm, such as stochastic gradient descent (SGD), require interprocessor communication at every iteration of the algorithm. On modern distributed-memory clusters where communication is more expensive than computation, the scalability and performance of these algorithms are limited by communication cost. This work generalizes prior work on 1D $s$-step SGD and 1D Federated SGD with Averaging (FedAvg) to yield a 2D parallel SGD method (HybridSGD) which attains a continuous performance trade off between the two baseline algorithms. We present theoretical analysis which show the convergence, computation, communication, and memory trade offs between $s$-step SGD, FedAvg, 2D parallel SGD, and other parallel SGD variants. We implement all algorithms in C++ and MPI and evaluate their performance on a Cray EX supercomputing system. Our empirical results show that HybridSGD achieves better convergence than FedAvg at similar processor scales while attaining speedups of $5.3\times$ over $s$-step SGD and speedups up to $121\times$ over FedAvg when used to solve binary classification tasks using the convex, logistic regression model on datasets obtained from the LIBSVM repository.

In recent years, AI red teaming has emerged as a practice for probing the safety and security of generative AI systems. Due to the nascency of the field, there are many open questions about how red teaming operations should be conducted. Based on our experience red teaming over 100 generative AI products at Microsoft, we present our internal threat model ontology and eight main lessons we have learned: 1. Understand what the system can do and where it is applied 2. You don't have to compute gradients to break an AI system 3. AI red teaming is not safety benchmarking 4. Automation can help cover more of the risk landscape 5. The human element of AI red teaming is crucial 6. Responsible AI harms are pervasive but difficult to measure 7. LLMs amplify existing security risks and introduce new ones 8. The work of securing AI systems will never be complete By sharing these insights alongside case studies from our operations, we offer practical recommendations aimed at aligning red teaming efforts with real world risks. We also highlight aspects of AI red teaming that we believe are often misunderstood and discuss open questions for the field to consider.