Alex Shieh is a student at Brown University. He is making waves and facing charges for asking the school's non-faculty employees what they do all day. A sophomore at Brown University is facing the school's wrath after he sent a DOGE-like email to non-faculty employees asking them what they do all day to try to figure out why the elite school's tuition has gotten so expensive. "The inspiration for this is the rising cost of tuition," Alex Shieh told Fox News Digital in an interview. "Next year, it's set to be 93,064 to go to Brown," Shieh said of the Ivy League university.

The artificial intelligence (AI) boom is transforming industries and reshaping work. The reach and capabilities of AI models go far beyond what people have seen from tinkering around with ChatGPT, which, although a great tool for proofreading or debugging code, only gives a brief glimpse into what large language models (LLMs), the technology powering tools like the chatbot, can do. Also: What is an AI PC exactly? And should you buy one in 2025? For instance, HCLTech, a consulting firm, worked with one of the largest end-to-end healthcare providers in the US to help implement a user-friendly, compliant AI clinical advisor. The clinical advisor, trained using one of the world's largest clinical libraries, allows medical professionals to conversationally ask for the information they need to consult without wasting time digging for it.

Yes, tariffs are a tool of power. You will protect our industries, our jobs, our economy,

Look beyond the actions and theatre of the Trump White House to the macro trends of the 21st century. There is the migration of economic and political heft to the East. There is the migration of many, many people towards the West, digitally savvy about the relative riches here, climate change and conflict among the push factors for some too. There is the internet revolution upending business models and working patterns, inventing social media and concentrating vast wealth and influence among a clutch of global behemoths like Apple, Meta, Amazon and X. And there is the artificial intelligence revolution in the infancy of its influence.

A new AI weather prediction system, developed by a team of researchers from the University of Cambridge, can deliver accurate forecasts which use less computing power than current AI and physics-based forecasting systems. The system, Aardvark Weather, has been supported by the Alan Turing Institute, Microsoft Research and the European Centre for Medium Range Weather Forecasts. It provides a blueprint for a new approach to weather forecasting with the potential to improve current practices. The results are reported in the journal Nature. "Aardvark reimagines current weather prediction methods offering the potential to make weather forecasts faster, cheaper, more flexible and more accurate than ever before, helping to transform weather prediction in both developed and developing countries," said Professor Richard Turner from Cambridge's Department of Engineering, who led the research.

Tricia Helfer, who played a humanoid robot Cylon on "Battlestar Galactica," says the show's look at the conflict between humans and AI still resonates today. "We did warn against AI while we were shooting it," Helfer told Fox News Digital at the Beverly Hills Film Festival this week. She continued, "It was 20 years ago, and I've recently re-watched it and went, 'Oh my gosh, it's even more relevant now.' So I think we just really need to be careful. It's a slippery slope between using it to our advantage and having it maybe be able to control us a little bit." "I think we're a little bit far off from the humanoid Cylons yet and humanoid robots, but I don't know, they're coming," Helfer added.

A Russian ballistic missile strike killed at least four people and wounded 17 in the city of Kryvyi Rih, Ukrainian President Volodymyr Zelenskyy's hometown. The attack also sparked a fire in the city, said Oleksandr Vilkul, the head of Kryvyi Rih's military administration. Russian drone attacks overnight targeted the Ukrainian regions of Zaporizhia and Kharkiv, killing one person and injuring several others, officials said. Two people were killed and at least 32, including two children, were injured by a Russian drone attack which hit several multistorey apartment blocks in Kharkiv, the region's governor said. One person was also injured in a separate drone attack on Ruski Tyshky, a village outside Kharkiv.

Recent advancements in Large Language Models (LLMs) have shown that it is promising to utilize Process Reward Models (PRMs) as verifiers to enhance the performance of LLMs. However, current PRMs face three key challenges: (1) limited process supervision and generalization capabilities, (2) dependence on scalar value prediction without leveraging the generative abilities of LLMs, and (3) inability to scale the test-time compute of PRMs. In this work, we introduce GenPRM, a generative process reward model that performs explicit Chain-of-Thought (CoT) reasoning with code verification before providing judgment for each reasoning step. To obtain high-quality process supervision labels and rationale data, we propose Relative Progress Estimation (RPE) and a rationale synthesis framework that incorporates code verification. Experimental results on ProcessBench and several mathematical reasoning tasks show that GenPRM significantly outperforms prior PRMs with only 23K training data from MATH dataset. Through test-time scaling, a 1.5B GenPRM outperforms GPT-4o, and a 7B GenPRM surpasses Qwen2.5-Math-PRM-72B on ProcessBench. Additionally, GenPRM demonstrates strong abilities to serve as a critic model for policy model refinement. This work establishes a new paradigm for process supervision that bridges the gap between PRMs and critic models in LLMs. Our code, model, and data will be available in https://ryanliu112.github.io/GenPRM.

Bayesian Optimization (BO) is increasingly used to guide experimental optimization tasks. To elucidate BO behavior in noisy and high-dimensional settings typical for materials science applications, we perform batch BO of two six-dimensional test functions: an Ackley function representing a needle-in-a-haystack problem and a Hartmann function representing a problem with a false maximum with a value close to the global maximum. We show learning curves, performance metrics, and visualization to effectively track the evolution of optimization in high dimensions and evaluate how they are affected by noise, batch-picking method, choice of acquisition function,and its exploration hyperparameter values. We find that the effects of noise depend on the problem landscape; therefore, prior knowledge of the domain structure and noise level is needed when designing BO. The Ackley function optimization is significantly degraded by noise with a complete loss of ground truth resemblance when noise equals 10 % of the maximum objective value. For the Hartmann function, even in the absence of noise, a significant fraction of the initial samplings identify the false maximum instead of the ground truth maximum as the optimum of the function; with increasing noise, BO remains effective, albeit with increasing probability of landing on the false maximum. This study systematically highlights the critical issues when setting up BO and choosing synthetic data to test experimental design. The results and methodology will facilitate wider utilization of BO in guiding experiments, specifically in high-dimensional settings.

We introduce a data-driven approach to learn a generalized kinetic collision operator directly from molecular dynamics. Unlike the conventional (e.g., Landau) models, the present operator takes an anisotropic form that accounts for a second energy transfer arising from the collective interactions between the pair of collision particles and the environment. Numerical results show that preserving the broadly overlooked anisotropic nature of the collision energy transfer is crucial for predicting the plasma kinetics with non-negligible correlations, where the Landau model shows limitations.