Rep. Marjorie Taylor Greene, R-Ga., joins'Sunday Morning Futures' to discuss whether the government should regulate artificial intelligence, and how AI ties into President Donald Trump's spending bill. As large tech companies continue to take the lead implementing artificial intelligence (AI) into their platforms and workplaces, the latest Fox News national survey finds that while positive reviews of AI have increased, many remain skeptical about its role in society. The survey, released Thursday, finds 43% view AI technology as a good thing for society, up 5 points from April 2023. Still, nearly half of voters, 47%, think AI is bad for society -- about where it was two years ago (46% bad in April 2023). Overall, urban voters (60%), nonwhite voters (56%), voters under age 45 (53%), and men (52%) are those most likely to say AI is a good thing, while rural voters (55%), White voters (51%), voters ages 45 and over (49%), and women (55%) are likely to say it's a bad thing.

The head of the US government's Intelligence Advanced Research Projects Activity (IARPA) is leaving the unit this month to take a job with a quantum computing company, WIRED has learned. Rick Muller's pending departure from IARPA comes amid broader efforts to downsize the United States intelligence community, including the Office of the Director of National Intelligence (ODNI), which oversees IARPA. A person familiar with Muller's plans confirmed to WIRED his departure from IARPA. Born during the aftermath of the September 11, 2001 terrorist attacks, IARPA is tasked with testing AI, quantum computing, and other emerging technologies that could aid the missions of spy agencies including the Central Intelligence Agency and National Security Agency. The Trump administration reportedly has been moving to cut the workforces of intelligence agencies as part of the president's broad efforts to dismantle diversity programs and streamline government operations.

An Israeli drone attack has killed at least one person and injured three near the Lebanese capital, Beirut, the Lebanese Ministry of Public Health says, the latest violation of the ceasefire between the two countries. The air raid on Thursday hit a vehicle on a busy motorway in the Khaldeh area, about 12km (8 miles) south of Beirut. The Israeli military said it targeted "military sites and weapons depots" in the area. Bombing an area near the Lebanese capital marks another escalation by Israel, which has been carrying out near-daily bombardment in Lebanon since it reached a truce with Hezbollah in November of last year. The identities of the victims of the attack have not been released.

The Department of Justice unveiled charges against 300 defendants, alleging they misled patients into paying for, and sometimes receiving, medical care they did not need in a 14.6 billion healthcare fraud scheme. The Department of Justice's unveiling this week of sweeping charges against more than 300 defendants who allegedly defrauded Medicare and other taxpayer-funded programs came as part of the department's annual "takedown" event. The healthcare fraud takedowns have been a practice at the DOJ for more than a decade, but officials touted this one as the largest on record. It stood out not only for its size but also because it focused on transnational criminals and broached artificial intelligence. "This takedown represents the largest healthcare fraud takedown in American history," DOJ Criminal Division head Matthew Galeotti said.

The Thirty-Seventh Annual Conference on Innovative Applications of Artificial Intelligence (IAAI 2025), which took place alongside AAAI 2025, serves as a showcase for successful applications and novel uses of AI. One such application is the Onboard Artificial Intelligence Research (OnAIR) platform, introduced by Evana Gizzi and colleagues in their paper OnAIR: Applications of The NASA On-Board Artificial Intelligence Research Platform. This open-source software pipeline and cognitive architecture tool has been designed to aid space research and missions. We spoke to Evana, Artificial Intelligence Research Lead at NASA Goddard Space Flight Center, about the OnAIR platform, some of the particular challenges of deploying AI-based solutions in space, and how the tool has been used so far. OnAIR is an open-source software pipeline and cognitive architecture tool.

Graph neural networks (GNNs) have achieved remarkable success in molecular property prediction. However, traditional graph representations struggle to effectively encode the inherent 3D spatial structures of molecules, as molecular orientations in 3D space introduce significant variability, severely limiting model generalization and robustness. Existing approaches primarily focus on rotation-invariant and rotation-equivariant methods. Invariant methods often rely heavily on prior knowledge and lack sufficient generalizability, while equivariant methods suffer from high computational costs. To address these limitations, this paper proposes a novel plug-and-play 3D encoding module leveraging rotational sampling. By computing the expectation over the SO(3) rotational group, the method naturally achieves approximate rotational invariance. Furthermore, by introducing a carefully designed post-alignment strategy, strict invariance can be achieved without compromising performance. Experimental evaluations on the QM9 and C10 Datasets demonstrate superior predictive accuracy, robustness, and generalization performance compared to existing methods. Moreover, the proposed approach maintains low computational complexity and enhanced interpretability, providing a promising direction for efficient and effective handling of 3D molecular information in drug discovery and material design.

The rapid growth of interest in large language models (LLMs) reflects their potential for flexibility and generalization, and attracted the attention of a diverse range of researchers. However, the advent of these techniques has also brought to light the lack of transparency and rigor with which development is pursued. In particular, the inability to determine the number of epochs and other hyperparameters in advance presents challenges in identifying the best model. To address this challenge, machine learning frameworks such as MLFlow can automate the collection of this type of information. However, these tools capture data using proprietary formats and pose little attention to lineage. This paper proposes yProv4ML, a framework to capture provenance information generated during machine learning processes in PROV-JSON format, with minimal code modifications.

Multi-agent collaborative driving promises improvements in traffic safety and efficiency through collective perception and decision making. However, existing communication media -- including raw sensor data, neural network features, and perception results -- suffer limitations in bandwidth efficiency, information completeness, and agent interoperability. Moreover, traditional approaches have largely ignored decision-level fusion, neglecting critical dimensions of collaborative driving. In this paper we argue that addressing these challenges requires a transition from purely perception-oriented data exchanges to explicit intent and reasoning communication using natural language. Natural language balances semantic density and communication bandwidth, adapts flexibly to real-time conditions, and bridges heterogeneous agent platforms. By enabling the direct communication of intentions, rationales, and decisions, it transforms collaborative driving from reactive perception-data sharing into proactive coordination, advancing safety, efficiency, and transparency in intelligent transportation systems.

Modern general-purpose artificial intelligence (AI) systems present an urgent risk management challenge, as their rapidly evolving capabilities and potential for catastrophic harm outpace our ability to reliably assess their risks. Current methods often rely on selective testing and undocumented assumptions about risk priorities, frequently failing to make a serious attempt at assessing the set of pathways through which AI systems pose direct or indirect risks to society and the biosphere. This paper introduces the probabilistic risk assessment (PRA) for AI framework, adapting established PRA techniques from high-reliability industries (e.g., nuclear power, aerospace) for the new challenges of advanced AI. The framework guides assessors in identifying potential risks, estimating likelihood and severity bands, and explicitly documenting evidence, underlying assumptions, and analyses at appropriate granularities. The framework's implementation tool synthesizes the results into a risk report card with aggregated risk estimates from all assessed risks. It introduces three methodological advances: (1) Aspect-oriented hazard analysis provides systematic hazard coverage guided by a first-principles taxonomy of AI system aspects (e.g. capabilities, domain knowledge, affordances); (2) Risk pathway modeling analyzes causal chains from system aspects to societal impacts using bidirectional analysis and incorporating prospective techniques; and (3) Uncertainty management employs scenario decomposition, reference scales, and explicit tracing protocols to structure credible projections with novelty or limited data. Additionally, the framework harmonizes diverse assessment methods by integrating evidence into comparable, quantified absolute risk estimates for lifecycle decisions. We have implemented this as a workbook tool for AI developers, evaluators, and regulators.

As modern computers evolve to have better performance and larger computation power, new modes of interaction emerge along with novel designs of devices. Augmented Reality (AR), which allows users to interact with common objects via virtual interfaces, establishes a new space of designs and development and hence poses a significant challenge to machines' spatial perception of the complex real-world environment. Pose estimation of 3D objects, as one of the most commonplace and important tasks in 3D perception, is still a challenging task for modern algorithms, especially in complex real environments with alternating scene properties and irregular object geometries. Our project is at the forefront of addressing critical challenges in human-robot interaction within dynamic mobile AR environments. We focus on exploring the potential ways of interacting with robots in space, especially in a non-intrusive manner. This interaction is made possible by integrating three key components: a non-intrusive head-mounted device serving as a user interface, voice control to enable astronauts to manipulate the interface and interact with robots using verbal commands, and a robot tracking algorithm that accurately localizes the robot's position in 3D space. Enabled by these technologies, we proposed URSA, an LLM-driven immersive AR user interface for robotics and space exploration, and participated in the 2023 NASA Spacesuit User Interface Technologies for Students (NASA SUITS) [1]. This project aims to develop solutions for future spaceflight needs, particularly for the Artemis missions, which seek to establish a sustained human presence on the Moon and Mars.