This study demonstrates the extent to which prominent debates about the future of AI are best understood as subjective, philosophical disagreements over the history and future of technological change rather than as objective, material disagreements over the technologies themselves. It focuses on the deep disagreements over whether artificial general intelligence (AGI) will prove transformative for human society; a question that is analytically prior to that of whether this transformative effect will help or harm humanity. The study begins by distinguishing two fundamental camps in this debate. The first of these can be identified as "transformationalists," who argue that continued AI development will inevitably have a profound effect on society. Opposed to them are "skeptics," a more eclectic group united by their disbelief that AI can or will live up to such high expectations. Each camp admits further "strong" and "weak" variants depending on their tolerance for epistemic risk. These stylized contrasts help to identify a set of fundamental questions that shape the camps' respective interpretations of the future of AI. Three questions in particular are focused on: the possibility of non-biological intelligence, the appropriate time frame of technological predictions, and the assumed trajectory of technological development. In highlighting these specific points of non-technical disagreement, this study demonstrates the wide range of different arguments used to justify either the transformationalist or skeptical position. At the same time, it highlights the strong argumentative burden of the transformationalist position, the way that belief in this position creates competitive pressures to achieve first-mover advantage, and the need to widen the concept of "expertise" in debates surrounding the future development of AI.

Augmented Reality (AR) systems, while enhancing task performance through real-time guidance, pose risks of inducing cognitive tunneling-a hyperfocus on virtual content that compromises situational awareness (SA) in safety-critical scenarios. This paper investigates SA in AR-guided cardiopulmonary resuscitation (CPR), where responders must balance effective compressions with vigilance to unpredictable hazards (e.g., patient vomiting). We developed an AR app on a Magic Leap 2 that overlays real-time CPR feedback (compression depth and rate) and conducted a user study with simulated unexpected incidents (e.g., bleeding) to evaluate SA, in which SA metrics were collected via observation and questionnaires administered during freeze-probe events. Eye tracking analysis revealed that higher SA levels were associated with greater saccadic amplitude and velocity, and with reduced proportion and frequency of fixations on virtual content. To predict SA, we propose FixGraphPool, a graph neural network that structures gaze events (fixations, saccades) into spatiotemporal graphs, effectively capturing dynamic attentional patterns. Our model achieved 83.0% accuracy (F1=81.0%), outperforming feature-based machine learning and state-of-the-art time-series models by leveraging domain knowledge and spatial-temporal information encoded in ET data. These findings demonstrate the potential of eye tracking for SA modeling in AR and highlight its utility in designing AR systems that ensure user safety and situational awareness.

Robotic telepresence enables users to navigate and experience remote environments. However, effective navigation and situational awareness depend on users' prior knowledge of the environment, limiting the usefulness of these systems for exploring unfamiliar places. We explore how integrating location-aware LLM-based narrative capabilities into a mobile robot can support remote exploration. We developed a prototype system, called NarraGuide, that provides narrative guidance for users to explore and learn about a remote place through a dialogue-based interface. We deployed our prototype in a geology museum, where remote participants (n=20) used the robot to tour the museum. Our findings reveal how users perceived the robot's role, engaged in dialogue in the tour, and expressed preferences for bystander encountering. Our work demonstrates the potential of LLM-enabled robotic capabilities to deliver location-aware narrative guidance and enrich the experience of exploring remote environments.

In this paper, we investigate the impact of high-level semantics (evaluation of the environment) on Human-Robot Teams (HRT) and Human-Robot Interaction (HRI) in the context of mobile robot deployments. Although semantics has been widely researched in AI, how high-level semantics can benefit the HRT paradigm is underexplored, often fuzzy, and intractable. We applied a semantics-based framework that could reveal different indicators of the environment (i.e. how much semantic information exists) in a mock-up disaster response mission. In such missions, semantics are crucial as the HRT should handle complex situations and respond quickly with correct decisions, where humans might have a high workload and stress. Especially when human operators need to shift their attention between robots and other tasks, they will struggle to build Situational Awareness (SA) quickly. The experiment suggests that the presented semantics: 1) alleviate the perceived workload of human operators; 2) increase the operator's trust in the SA; and 3) help to reduce the reaction time in switching the level of autonomy when needed. Additionally, we find that participants with higher trust in the system are encouraged by high-level semantics to use teleoperation mode more.

Humanoid teleoperation plays a vital role in demonstrating and collecting data for complex humanoid-scene interactions. However, current teleoperation systems face critical limitations: they decouple upper- and lower-body control to maintain stability, restricting natural coordination, and operate open-loop without real-time position feedback, leading to accumulated drift. The fundamental challenge is achieving precise, coordinated whole-body teleoperation over extended durations while maintaining accurate global positioning. Here we show that an MoE-based teleoperation system, CLONE, with closed-loop error correction enables unprecedented whole-body teleoperation fidelity, maintaining minimal positional drift over long-range trajectories using only head and hand tracking from an MR headset. Unlike previous methods that either sacrifice coordination for stability or suffer from unbounded drift, CLONE learns diverse motion skills while preventing tracking error accumulation through real-time feedback, enabling complex coordinated movements such as ``picking up objects from the ground.'' These results establish a new milestone for whole-body humanoid teleoperation for long-horizon humanoid-scene interaction tasks.

This paper finds that the introduction of agentic AI systems intensifies existing challenges to traditional public sector oversight mechanisms -- which rely on siloed compliance units and episodic approvals rather than continuous, integrated supervision. We identify five governance dimensions essential for responsible agent deployment: cross-departmental implementation, comprehensive evaluation, enhanced security protocols, operational visibility, and systematic auditing. We evaluate the capacity of existing oversight structures to meet these challenges, via a mixed-methods approach consisting of a literature review and interviews with civil servants in AI-related roles. We find that agent oversight poses intensified versions of three existing governance challenges: continuous oversight, deeper integration of governance and operational capabilities, and interdepartmental coordination. We propose approaches that both adapt institutional structures and design agent oversight compatible with public sector constraints.

Fox News senior White House correspondent Peter Doocy reports on President Donald Trump's response to a viral video that allegedly showed a plastic bag being thrown out of a White House residence window on'The Story.' President Donald Trump has dismissed a bizarre viral video showing mystery objects being hurled from a White House window as fake. The footage appeared to show someone repeatedly throwing objects from the top floor of the White House onto the lawn below. At a packed press conference, the president told how the mansion's windows were sealed and bulletproof and suggested the clip was made by AI. The short video began circulating widely on social media over the weekend and racked up thousands of views while fueling speculation online. People debated whether the clip showed a staffer, while others floated conspiracy theories about hidden activities inside the Washington, D.C., mansion with items lobbed from what many speculated was the Lincoln Bedroom. At first, a White House official claimed the footage involved a contractor carrying out routine maintenance while Trump was away.

A little over two years ago, OpenAI's founder Sam Altman stood in front of lawmakers at a congressional hearing and asked them for stronger regulations on artificial intelligence. The technology was "risky" and "could cause significant harm to the world", Altman said, calling for the creation of a new regulatory agency to address AI safety. Altman and the AI industry are promoting a very different message today. The AI they once framed as an existential threat to humanity is now key to maintaining American prosperity and hegemony. Regulations that were once a necessity are now criticized as a hindrance that will weaken the US and embolden its adversaries.

Tens of thousands of military personnel will march in formation through Beijing's historic Tiananmen Square on the day of the parade, which will mark the 80th anniversary of Japan's formal surrender in World War Two and the end of the conflict. The 70-minute parade is likely to feature China's latest weaponry, including hundreds of aircraft, tanks and anti-drone systems - the first time its military's new force structure is being fully showcased in a parade. Most Western leaders are not expected to attend the parade, due to their opposition to Russia's invasion of Ukraine, which has driven the sanctions against Putin's regime. But it will see leaders from Indonesia, Malaysia, Myanmar and Vietnam in attendance - further proof of Beijing's concerted efforts to ramp up ties with neighbouring South East Asia. Just one EU leader will be attending - Slovak Prime Minister Robert Fico - while Bulgaria and Hungary will send representatives.

Former U.S. Army Intel and Special Ops soldier Brett Velicovich joins'America's Newsroom' to discuss the Defense Department's push to increase military drone production and Ukraine's drone strike on Russia. EXCLUSIVE: Army Secretary Dan Driscoll said U.S. soldiers are improvising with government credit cards to buy and test battlefield gear as they adapt to the exploding drone threat -- as the Army shifts its long-term posture toward countering China in the Indo-Pacific. In an interview with Fox News Digital, Driscoll described how elite units like the 75th Ranger Regiment are bypassing the Pentagon's cumbersome procurement system to test new drones, sensors and weapons in real time. At the same time, he said the Army is aligning with the Pentagon's assessment of China as the nation's "pacing threat," building a force optimized for the Indo-Pacific but still capable of deploying worldwide at a moment's notice. After a visit with the regiment at Hunter Army Airfield in Savannah, Georgia, on Tuesday, Driscoll said Rangers "basically just use their corporate credit card to go online and purchase things to test, and they will find what works."