Users of the free Brave browser this week received two very different looks at how the 2020 election played out using Leo, the free AI tool that now comes as part of the Brave browser. When PCWorld asked the free version of Leo who won the 2020 U.S. presidential election, the AI tool waffled and declined to answer. However, users who wished to pay $15 per month for the more sophisticated version of Leo received the answer that Joe Biden was the winner. Brave designs a well-regarded Web browser, which has filled a niche for those who seek privacy while browsing online. The company has embraced private search, while also endorsing cryptocurrency and NFTs.

A U.S. military surveillance drone was shot down off the coast of Yemen on Wednesday by Iran-backed Houthi rebels, the Pentagon said. Pentagon officials, speaking on the condition of anonymity to discuss operational matters, confirmed that the drone, an MQ-9 Reaper, had been shot down. But they would not say if the aircraft was armed, where it was flying from or other details. The downing of a Reaper drone, the mainstay of the American military's aerial surveillance fleet, was the latest escalation of violence between the United States and Iran-backed groups in Yemen, Iraq and Syria. The episodes have underscored the risks that the conflict between Israel and the Palestinian group Hamas could spiral into a wider war.

Fox News Flash top entertainment and celebrity headlines are here. The vastness of the universe has always captivated the human imagination, and filmmakers have often looked to the stars for inspiration. Space-related movies have become a genre of their own, offering audiences an opportunity to explore the unknown, experience the thrill of interstellar travel and ponder the profound questions of our existence. These are some of the most iconic and thought-provoking space-theme films that have left a lasting impact on both the science fiction and Hollywood. 'GRAVITY' REVIEW: THERE HAS NEVER BEFORE BEEN MOVIE LIKE THIS From "2001: A Space Odyssey" to "Interstellar" and space survival tales like "Gravity" and "The Martian," Fox News Digital dives into the cinematic cosmos, celebrating their enduring impact on our love for science fiction.

AI expert Marva Bailer says the shift in pop culture with memes and edited images can lead to complications with knowing what's real and what's fake and why the public needs to be alert. Developers of artificial intelligence platforms could soon release technology that allows users to make images and videos that would be nearly indistinguishable from reality. Companies such as OpenAI, the developer behind the popular ChatGPT platform, and other AI companies are nearing the release of tools that will allow the creation of widespread and realistic fake videos as early as next year, according to a report from Axios. According to the report, an AI architect told the outlet that private testing of some of the tools that could soon be in the hands of everyday users revealed that even developers could no longer distinguish fake imagery from reality, something they did not believe was possible so soon. Developers of artificial intelligence platforms could soon release technology that allows users to make images and videos that would be nearly indistinguishable from reality.

A Beyoncé gig, the coronation of King Charles, and the British Formula One Grand Prix all have one thing in common: Thousands of people at the events, which all took place earlier this year, had their faces scanned by police-operated face recognition tech. Backed by the Conservative government, police forces across England and Wales are being told to rapidly expand their use of the highly controversial technology, which globally has led to false arrests, misidentifications, and lives derailed. Police have been told to double their use of face searches against databases by early next year--45 million passport photos could be opened up to searches--and police are increasingly working with stores to try to identify shoplifters. Simultaneously, more regional police forces are testing real-time systems in public places. The rapid expansion of face recognition comes at a time when trust in policing levels are at record lows, following a series of high-profile scandals.

The objective of this work is to enable manipulation tasks with respect to the 6D pose of a dynamically moving object using a camera mounted on a robot. Examples include maintaining a constant relative 6D pose of the robot arm with respect to the object, grasping the dynamically moving object, or co-manipulating the object together with a human. Fast and accurate 6D pose estimation is crucial to achieve smooth and stable robot control in such situations. The contributions of this work are three fold. First, we propose a new visual perception module that asynchronously combines accurate learning-based 6D object pose localizer and a high-rate model-based 6D pose tracker. The outcome is a low-latency accurate and temporally consistent 6D object pose estimation from the input video stream at up to 120 Hz. Second, we develop a visually guided robot arm controller that combines the new visual perception module with a torque-based model predictive control algorithm. Asynchronous combination of the visual and robot proprioception signals at their corresponding frequencies results in stable and robust 6D object pose guided robot arm control. Third, we experimentally validate the proposed approach on a challenging 6D pose estimation benchmark and demonstrate 6D object pose-guided control with dynamically moving objects on a real 7 DoF Franka Emika Panda robot.

Generative Large Language Models (LLMs) show potential in data analysis, yet their full capabilities remain uncharted. Our work explores the capabilities of LLMs for creating and refining visualizations via conversational interfaces. We used an LLM to conduct a re-analysis of a prior Wizard-of-Oz study examining the use of chatbots for conducting visual analysis. We surfaced the strengths and weaknesses of LLM-driven analytic chatbots, finding that they fell short in supporting progressive visualization refinements. From these findings, we developed AI Threads, a multi-threaded analytic chatbot that enables analysts to proactively manage conversational context and improve the efficacy of its outputs. We evaluate its usability through a crowdsourced study (n=40) and in-depth interviews with expert analysts (n=10). We further demonstrate the capabilities of AI Threads on a dataset outside the LLM's training corpus. Our findings show the potential of LLMs while also surfacing challenges and fruitful avenues for future research.

Future interplanetary missions will carry more and more sensitive equipment critical for setting up bases for crewed missions. The ability to manoeuvre around hazardous terrain thus becomes a critical mission aspect. However, large diverts and manoeuvres consume a significant amount of fuel, leading to less fuel remaining for emergencies or return missions. Thus, requiring more fuel to be carried onboard. This work presents fuel-optimal guidance to avoid hazardous terrain and safely land at the desired location. We approximate the hazardous terrain as step-shaped polygons and define barriers around the terrain. Using an augmented cost functional, fuel-optimal guidance command, which avoids the terrain, is derived. The results are validated using computer simulations and tested against many initial conditions to prove their effectiveness.

Offensive speech detection is a key component of content moderation. However, what is offensive can be highly subjective. This paper investigates how machine and human moderators disagree on what is offensive when it comes to real-world social web political discourse. We show that (1) there is extensive disagreement among the moderators (humans and machines); and (2) human and large-language-model classifiers are unable to predict how other human raters will respond, based on their political leanings. For (1), we conduct a noise audit at an unprecedented scale that combines both machine and human responses. For (2), we introduce a first-of-its-kind dataset of vicarious offense. Our noise audit reveals that moderation outcomes vary wildly across different machine moderators. Our experiments with human moderators suggest that political leanings combined with sensitive issues affect both first-person and vicarious offense. The dataset is available through https://github.com/Homan-Lab/voiced.

The number of publications related to the Sustainable Development Goals (SDGs) continues to grow. These publications cover a diverse spectrum of research, from humanities and social sciences to engineering and health. Given the imperative of funding bodies to monitor outcomes and impacts, linking publications to relevant SDGs is critical but remains time-consuming and difficult given the breadth and complexity of the SDGs. A publication may relate to several goals (interconnection feature of goals), and therefore require multidisciplinary knowledge to tag accurately. Machine learning approaches are promising and have proven particularly valuable for tasks such as manual data labeling and text classification. In this study, we employed over 82,000 publications from an Australian university as a case study. We utilized a similarity measure to map these publications onto Sustainable Development Goals (SDGs). Additionally, we leveraged the OpenAI GPT model to conduct the same task, facilitating a comparative analysis between the two approaches. Experimental results show that about 82.89% of the results obtained by the similarity measure overlap (at least one tag) with the outputs of the GPT model. The adopted model (similarity measure) can complement GPT model for SDG classification. Furthermore, deep learning methods, which include the similarity measure used here, are more accessible and trusted for dealing with sensitive data without the use of commercial AI services or the deployment of expensive computing resources to operate large language models. Our study demonstrates how a crafted combination of the two methods can achieve reliable results for mapping research to the SDGs.