The National Institute of Standards and Technology (NIST) has issued new instructions to scientists that partner with the US Artificial Intelligence Safety Institute (AISI) that eliminate mention of "AI safety," "responsible AI," and "AI fairness" in the skills it expects of members and introduces a request to prioritize "reducing ideological bias, to enable human flourishing and economic competitiveness." The information comes as part of an updated cooperative research and development agreement for AI Safety Institute consortium members, sent in early March. Previously, that agreement encouraged researchers to contribute technical work that could help identify and fix discriminatory model behavior related to gender, race, age, or wealth inequality. Such biases are hugely important because they can directly affect end users and disproportionately harm minorities and economically disadvantaged groups. The new agreement removes mention of developing tools "for authenticating content and tracking its provenance" as well as "labeling synthetic content," signaling less interest in tracking misinformation and deep fakes.

The Republican-led House Judiciary Committee is looking into whether the Biden administration tried to "censor" artificial intelligence. Representative Jim Jordan has sent subpoenas to sixteen different tech companies that work with AI in some capacity to ask for any and all communications from the previous administration about limiting "harmful bias" and "algorithmic discrimination." Subpoenas were sent to Adobe, Alphabet, Amazon, Anthropic, Apple, Cohere, International Business Machines Corp. (IBM), Inflection AI, Meta, Microsoft, Nvidia, Open AI, Palantir, Salesforce, Scale AI and Stability AI, and each requests an extensive amount of information, covering five years from January 1, 2020 to January 20, 2025. Essentially any and all documents and communications "referring or relating to the moderation, deletion, suppression, restriction, or reduced circulation of the content, input, or output of an AI model, training dataset, algorithm, system, or product," need to be included, whether between the companies and the previous administration, internal communications about those discussions or discussions with third-parties. Jordan and the committee are alleging that the former President's executive order calling for regulations on algorithmic discrimination and guidelines for how the federal government will use AI pressured private companies to censor speech.

In a blog post spotted by The Verge, the company this week published its response to President Trump's AI Action Plan. Announced at the end of February, the initiative saw the White House seek input from private industry, with the goal of eventually enacting policy that will work to "enhance America's position as an AI powerhouse" and enable innovation in the sector. "America's robust, balanced intellectual property system has long been key to our global leadership on innovation. In the same document, the company recommends the US maintain tight export controls on AI chips to China. It also says the US government should broadly adopt AI tools.

Lawmakers in New York have proposed a new bill that would make it illegal to mount weapons to robots or drones. That is of course, unless the people operating those weaponized machines happen to work with law enforcement or the military. If passed, the Responsible Robotics Act would make New York the first state to ban weaponized robots for civilians, at a time when government spending on autonomous and semi-autonomous police technology is on the rise. The bill specifically bans the sale, transfer, modification, operation, or equipping of robots or drones with mounted weapons. Prohibited weapons include firearms, stun guns, chemical agents, lasers, and explosives.

It is a question that scientists have been wrestling with since the dawn of computing in the 1950s, when Alan Turing asked: "Can machines think?" Now that large language models (LLMs) like ChatGPT have been unleashed on the world, finding an answer has never been more pressing. While their use has already become widespread, the social norms around these new AI tools are still rapidly evolving. Should students use them to write essays? Will they replace your therapist?

Artificial intelligence (AI) is producing hyperrealistic "digital twins" of politicians, celebrities, pornographic material, and more – leaving victims of deepfake technology struggling to determine legal recourse. Former CIA agent and cybersecurity expert Dr. Eric Cole told Fox News Digital that poor online privacy practices and people's willingness to post their information publicly on social media leaves them susceptible to AI deepfakes. "The cat's already out of the bag," he said. "They have our pictures, they know our kids, they know our family. They know where we live. And now, with AI, they're able to take all that data about who we are, what we look like, what we do, and how we act, and basically be able to create a digital twin," Cole continued.

High energy consumption remains a key challenge in high-performance computing (HPC) systems, which often feature hundreds or thousands of nodes drawing substantial power even in idle or standby modes. Although powering down unused nodes can improve energy efficiency, choosing the wrong time to do so can degrade quality of service by delaying job execution. Machine learning, in particular reinforcement learning (RL), has shown promise in determining optimal times to switch nodes on or off. In this study, we enhance the performance of a deep reinforcement learning (DRL) agent for HPC power management by integrating curriculum learning (CL), a training approach that introduces tasks with gradually increasing difficulty. Using the Batsim-py simulation framework, we compare the proposed CL-based agent to both a baseline DRL method (without CL) and the conventional fixed-time timeout strategy. Experimental results confirm that an easy-to-hard curriculum outperforms other training orders in terms of reducing wasted energy usage. The best agent achieves a 3.73% energy reduction over the baseline DRL method and a 4.66% improvement compared to the best timeout configuration (shutdown every 15 minutes of idle time). In addition, it reduces average job waiting time by 9.24% and maintains a higher job-filling rate, indicating more effective resource utilization. Sensitivity tests across various switch-on durations, power levels, and cluster sizes further reveal the agent's adaptability to changing system parameters without retraining. These findings demonstrate that curriculum learning can significantly improve DRL-based power management in HPC, balancing energy savings, quality of service, and robustness to diverse configurations.

Simulating the long-term dynamics of multi-scale and multi-physics systems poses a significant challenge in understanding complex phenomena across science and engineering. The complexity arises from the intricate interactions between scales and the interplay of diverse physical processes. Neural operators have emerged as promising models for predicting such dynamics due to their flexibility and computational efficiency. However, they often fail to effectively capture multi-scale interactions or quantify the uncertainties inherent in the predictions. These limitations lead to rapid error accumulation, particularly in long-term forecasting of systems characterized by complex and coupled dynamics. To address these challenges, we propose a spatio-temporal Fourier transformer (StFT), in which each transformer block is designed to learn dynamics at a specific scale. By leveraging a structured hierarchy of StFT blocks, the model explicitly captures dynamics across both macro- and micro- spatial scales. Furthermore, a generative residual correction mechanism is integrated to estimate and mitigate predictive uncertainties, enhancing both the accuracy and reliability of long-term forecasts. Evaluations conducted on three benchmark datasets (plasma, fluid, and atmospheric dynamics) demonstrate the advantages of our approach over state-of-the-art ML methods.

FastMap was first introduced in the Data Mining community for generating Euclidean embeddings of complex objects. In this dissertation, we first present FastMap to generate Euclidean embeddings of graphs in near-linear time: The pairwise Euclidean distances approximate a desired graph-based distance function on the vertices. We then apply the graph version of FastMap to efficiently solve various graph-theoretic problems of significant interest in AI: including facility location, top-K centrality computations, community detection and block modeling, and graph convex hull computations. We also present a novel learning framework, called FastMapSVM, by combining FastMap and Support Vector Machines. We then apply FastMapSVM to predict the satisfiability of Constraint Satisfaction Problems and to classify seismograms in Earthquake Science.

A timely and effective response is crucial to minimize damage and save lives during natural disasters like earthquakes. Microblogging platforms, particularly Twitter, have emerged as valuable real-time information sources for such events. This work explores the potential of leveraging Twitter data for earthquake response analysis. We develop a machine learning (ML) framework by incorporating natural language processing (NLP) techniques to extract and analyze relevant information from tweets posted during earthquake events. The approach primarily focuses on extracting location data from tweets to identify affected areas, generating severity maps, and utilizing WebGIS to display valuable information. The insights gained from this analysis can aid emergency responders, government agencies, humanitarian organizations, and NGOs in enhancing their disaster response strategies and facilitating more efficient resource allocation during earthquake events.