After ChatGPT burst on the scene last November, some government officials raced to prohibit its use. New York City, Los Angeles Unified, Seattle, and Baltimore School Districts either banned or blocked access to generative AI tools, fearing that ChatGPT, Bard, and other content generation sites could tempt students to cheat on assignments, induce rampant plagiarism, and impede critical thinking. This week, US Congress heard testimony from Sam Altman, CEO of OpenAI, and AI researcher Gary Marcus as it weighed whether and how to regulate the technology. In a rapid about-face, however, a few governments are now embracing a less fearful and more hands-on approach to AI. New York City Schools chancellor David Banks announced yesterday that NYC is reversing its ban because "the knee jerk fear and risk overlooked the potential of generative AI to support students and teachers, as well as the reality that our students are participating in and will work in a world where understanding generative AI is crucial."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Senate Majority Leader Chuck Schumer is attempting to spearhead legislation that will set parameters around the development and use of artificial intelligence. The arch-Democrat is meeting with Sens. Mike Rounds, Martin Heinrich and Todd Young as part of a bipartisan exploratory group, NPR reported Thursday. "Congress must move quickly," Schumer said Thursday from the Senate floor.

Incoming House Speaker Kevin McCarthy of Calif., receives the gavel from House Minority Leader Hakeem Jeffries of N.Y., on the House floor at the U.S. Capitol in Washington, early Saturday, Jan. 7, 2023. END RUN - 210 Democrats endorse plan to sidestep House Republicans in debt limit fight. SHORT LIST - DeSantis reportedly reveals three'credible' 2024 presidential candidates. CHAOTIC CRASH - Multi-vehicle pileup on Oregon interstate results in 7 deaths, police say. 'IT'S ALL GONNA TAKE OVER' - What are you worried about most with AI: Identity theft, job loss, or military takeover?

Artificial general intelligence, the AI with human-like capabilities, could be decades away, said Dr. Michael Capps, CEO of Diveplane Corp. Researchers at a Chinese university last month allegedly handed over control of a satellite to an artificial intelligence (AI) program for 24 hours, showing how far the country will go to find ways to get ahead using AI technology, experts warn. "Many Americans understandably want to hit the pause button on AI development to sort out the risk issues. China, unfortunately, is roaring ahead, as its 24-hour satellite experiment shows," Gordon Chang, a China expert, told Fox News Digital. Researchers at Wuhan University allegedly handed over control of the Qimingxing 1, a small Earth observation satellite, to a ground-based AI program.

MikeRoweWorks Foundation CEO Mike Rowe discusses a surge in white-collar job layoffs and responds to Elon Musk's comments on working from home. Generative artificial intelligence systems are already making it easier for scammers to con elderly Americans out of their money, and several senators are asking the Biden administration to step in and protect people from this quickly emerging threat. Sen. Mike Braun, R-Ind., the top Republican on the Senate Special Committee on Aging, spearheaded a bipartisan letter to the Federal Trade Commission (FTC) on Thursday that asks for an update on what the agency knows about AI-drive scams against the elderly and what it is doing to protect people. The letter, signed by every member of the Senate committee from both parties, asks about AI-powered technology that can be used to replicate people's voices. The letter to FTC Chairwoman Lina Khan warned that voice clones and chatbots are allowing scammers to trick the elderly into making them believe they are talking to a relative or close friend, which leaves them vulnerable to theft.

Apogee Strong co-founder Tim Kennedy joined'Fox & Friends Weekend' to discuss the U.S. response and the importance of fatherhood in America. U.S. military officials are walking back a claim that a senior al Qaeda leader was killed in a recent drone strike in Syria, a senior U.S. defense official confirmed to Fox News. The story was first reported by the Washington Post. The family of 56-year-old Lotfi Hassan Misto identified him as the person killed by the American missile on May 3, according to the Post. U.S. Central Command, or CENTCOM, oversaw the operation and released a statement on the day of the strike saying it conducted a strike "targeting a senior Al Qaeda leader."

Evidence retrieval is a core part of automatic fact-checking. Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: either no access to evidence, access to evidence curated by a human fact-checker, or access to evidence available long after the claim has been made. In this work, we present the first fully automated pipeline to check real-world claims by retrieving raw evidence from the web. We restrict our retriever to only search documents available prior to the claim's making, modeling the realistic scenario where an emerging claim needs to be checked. Our pipeline includes five components: claim decomposition, raw document retrieval, fine-grained evidence retrieval, claim-focused summarization, and veracity judgment. We conduct experiments on complex political claims in the ClaimDecomp dataset and show that the aggregated evidence produced by our pipeline improves veracity judgments. Human evaluation finds the evidence summary produced by our system is reliable (it does not hallucinate information) and relevant to answering key questions about a claim, suggesting that it can assist fact-checkers even when it cannot surface a complete evidence set.

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.

Chemellia is an open-source framework for atomistic machine learning in the Julia programming language. The framework takes advantage of Julia's high speed as well as the ability to share and reuse code and interfaces through the paradigm of multiple dispatch. Chemellia is designed to make use of existing interfaces and avoid ``reinventing the wheel'' wherever possible. A key aspect of the Chemellia ecosystem is the ChemistryFeaturization interface for defining and encoding features -- it is designed to maximize interoperability between featurization schemes and elements thereof, to maintain provenance of encoded features, and to ensure easy decodability and reconfigurability to enable feature engineering experiments. This embodies the overall design principles of the Chemellia ecosystem: separation of concerns, interoperability, and transparency. We illustrate these principles by discussing the implementation of crystal graph convolutional neural networks for material property prediction.

Vehicle teleoperation has numerous potential applications, including serving as a backup solution for autonomous vehicles, facilitating remote delivery services, and enabling hazardous remote operations. However, complex urban scenarios, limited situational awareness, and network delay increase the cognitive workload of human operators and degrade teleoperation performance. To address this, the successive reference pose tracking (SRPT) approach was introduced in earlier work, which transmits successive reference poses to the remote vehicle instead of steering commands. The operator generates reference poses online with the help of a joystick steering and an augmented display, potentially mitigating the detrimental effects of delays. However, it is not clear which minimal set of sensors is essential for the SRPT vehicle teleoperation control loop. This paper tests the robustness of the SRPT approach in the presence of state estimation inaccuracies, environmental disturbances, and measurement noises. The simulation environment, implemented in Simulink, features a 14-dof vehicle model and incorporates difficult maneuvers such as tight corners, double-lane changes, and slalom. Environmental disturbances include low adhesion track regions and strong cross-wind gusts. The results demonstrate that the SRPT approach, using either estimated or actual states, performs similarly under various worst-case scenarios, even without a position sensor requirement. Additionally, the designed state estimator ensures sufficient performance with just an inertial measurement unit, wheel speed encoder, and steer encoder, constituting a minimal set of essential sensors for the SRPT vehicle teleoperation control loop.