As leading examples of large language models, ChatGPT and Gemini claim to provide accurate and unbiased information, emphasizing their commitment to political neutrality and avoidance of personal bias. This research investigates the political tendency of large language models and the existence of differentiation according to the query language. For this purpose, ChatGPT and Gemini were subjected to a political axis test using 14 different languages. The findings of the study suggest that these large language models do exhibit political tendencies, with both models demonstrating liberal and leftist biases. A comparative analysis revealed that Gemini exhibited a more pronounced liberal and left-wing tendency compared to ChatGPT. The study also found that these political biases varied depending on the language used for inquiry. The study delves into the factors that constitute political tendencies and linguistic differentiation, exploring differences in the sources and scope of educational data, structural and grammatical features of languages, cultural and political contexts, and the model's response to linguistic features. From this standpoint, and an ethical perspective, it is proposed that artificial intelligence tools should refrain from asserting a lack of political tendencies and neutrality, instead striving for political neutrality and executing user queries by incorporating these tendencies.

Misinformation continues to pose a significant challenge in today's information ecosystem, profoundly shaping public perception and behavior. Among its various manifestations, Out-of-Context (OOC) misinformation is particularly obscure, as it distorts meaning by pairing authentic images with misleading textual narratives. Existing methods for detecting OOC misinformation predominantly rely on coarse-grained similarity metrics between image-text pairs, which often fail to capture subtle inconsistencies or provide meaningful explainability. While multi-modal large language models (MLLMs) demonstrate remarkable capabilities in visual reasoning and explanation generation, they have not yet demonstrated the capacity to address complex, fine-grained, and cross-modal distinctions necessary for robust OOC detection. To overcome these limitations, we introduce EXCLAIM, a retrieval-based framework designed to leverage external knowledge through multi-granularity index of multi-modal events and entities. Our approach integrates multi-granularity contextual analysis with a multi-agent reasoning architecture to systematically evaluate the consistency and integrity of multi-modal news content. Comprehensive experiments validate the effectiveness and resilience of EXCLAIM, demonstrating its ability to detect OOC misinformation with 4.3% higher accuracy compared to state-of-the-art approaches, while offering explainable and actionable insights.

Fleets of robo-taxis offering on-demand transportation services, commonly known as Autonomous Mobility-on-Demand (AMoD) systems, hold significant promise for societal benefits, such as reducing pollution, energy consumption, and urban congestion. However, orchestrating these systems at scale remains a critical challenge, with existing coordination algorithms often failing to exploit the systems' full potential. This work introduces a novel decision-making framework that unites mathematical modeling with data-driven techniques. In particular, we present the AMoD coordination problem through the lens of reinforcement learning and propose a graph network-based framework that exploits the main strengths of graph representation learning, reinforcement learning, and classical operations research tools. Extensive evaluations across diverse simulation fidelities and scenarios demonstrate the flexibility of our approach, achieving superior system performance, computational efficiency, and generalizability compared to prior methods. Finally, motivated by the need to democratize research efforts in this area, we release publicly available benchmarks, datasets, and simulators for network-level coordination alongside an open-source codebase designed to provide accessible simulation platforms and establish a standardized validation process for comparing methodologies. Code available at: https://github.com/StanfordASL/RL4AMOD

Artificial Intelligence (AI) is advancing at an unprecedented pace, with clear potential to enhance decision-making and productivity. Yet, the collaborative decision-making process between humans and AI remains underdeveloped, often falling short of its transformative possibilities. This paper explores the evolution of AI agents from passive tools to active collaborators in human-AI teams, emphasizing their ability to learn, adapt, and operate autonomously in complex environments. This paradigm shifts challenges traditional team dynamics, requiring new interaction protocols, delegation strategies, and responsibility distribution frameworks. Drawing on Team Situation Awareness (SA) theory, we identify two critical gaps in current human-AI teaming research: the difficulty of aligning AI agents with human values and objectives, and the underutilization of AI's capabilities as genuine team members. Addressing these gaps, we propose a structured research outlook centered on four key aspects of human-AI teaming: formulation, coordination, maintenance, and training. Our framework highlights the importance of shared mental models, trust-building, conflict resolution, and skill adaptation for effective teaming. Furthermore, we discuss the unique challenges posed by varying team compositions, goals, and complexities. This paper provides a foundational agenda for future research and practical design of sustainable, high-performing human-AI teams.

Kurt'CyberGuy' Knutsson joins'Fox & Friends' to warn about a disturbing new scam where criminals use AI to clone the voices of loved ones and trick victims into sending money. Charges have been filed against an Oklahoma woman who is being accused of laundering nearly 1.5 million in funds obtained through online romance scams, targeting elderly women. Attorney General Gentner Drummond announced that Christine Joan Echohawk, 53, was arrested Monday, and is accused of laundering money from out-of-state victims between Sept. 30, 2024, and Dec. 26, 2024. Officials said that all the victims were women between the ages of 64 and 79. The victims believed they were sending money to a male subject whom they thought they were in an online relationship with, according to a news release from Drummond's office.

A group of organizations, including nonprofits like LatinoProsperity and labor groups like the California Teamsters, are petitioning California Attorney General Rob Bonta to stop OpenAI from becoming a for-profit entity, The Los Angeles Times reports. OpenAI announced plans to transition to a public-benefit corporation in 2024, and reportedly has two years to pull it off or risk a large portion of the money its raised become debt. The group's primary concerns are that OpenAI "failed to protect its charitable assets" and is actively "subverting its charitable mission to advance safe artificial intelligence." OpenAI started as a nonprofit research organization studying AI, but transitioned to a for-profit company that's overseen and run by a nonprofit in 2019. That structure is legally allowed in the state of California, but the group's petition claims that OpenAI's decision to pursue a new structure is driven by a desire not to further its mission, but to provide "AI's benefits -- the potential for untold profits and control over what may become powerful world-altering technologies -- to a handful of corporate investors and high-level employees."

Finding love in Washington state could come with a price. A bill proposed by two state Democratic lawmakers would impose a tax on dating apps. Under the terms of House Bill 2071, dating app companies would be required to pay 1 per Washington-based user each month, regardless of whether the user pays for the service. The money would be used to fund domestic violence programs. The money would be put into the newly created state Domestic Violence Services Account, which funds intervention programs and support services for victims.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Fox News Digital has learned that the U.S. Office of Personnel Management (OPM) will post an updated Privacy Impact Assessment (PIA) at the close of business Wednesday that paves the way for artificial intelligence to improve government efficiency and enhance the federal record-keeping process. This will be the first time the United States government has applied the use of artificial intelligence for federal employee record-keeping after President Donald Trump issued an executive order in January to "solidify [America's] position as the global leader in AI and secure a brighter future for all Americans." A senior White House official spoke with Fox News Digital, outlining the implementation process, detailing that the Federal Risk and Authorization Management Program (FedRAMP)-approved AI system will be used to drastically speed up the retirement process for the roughly 2.3 million federal employees and improve the accuracy of what is now mostly paper-based record keeping.

In 2034, the first person landed on Mars. While she didn't go there physically, she still experienced the planet intimately. She explored an ancient river delta and built a base. She put up a flag (China's) and conducted a detailed analysis of rock samples. She achieved all this by inhabiting a robot via a sophisticated brain-computer interface.

Dr Mehmet Oz reportedly told federal staffers that artificial intelligence models may be better than frontline human physicians in his first all-staff meeting this week. Oz told staffers that if a patient went to the doctor for a diabetes diagnosis it would cost roughly 100 an hour, compared with 2 an hour for an AI visit, according to unnamed sources who spoke to Wired magazine. He added that patients may prefer an AI avatar. Oz also spent a portion of his first meeting with employees arguing they had a "patriotic duty" to remain healthy, with the goal of decreasing costs to the health insurance system. He made a similar argument at his confirmation hearing.