Libya's government denied reports it is responsible for drone attacks that hit an airbase in the east used by mercenaries of the Russian paramilitary group Wagner. The origin of the early Friday attack on the Al-Kharruba airbase, 150km (90 miles) southwest of Benghazi, was unclear but it caused no casualties. Army Chief of Staff General Mohamad al-Haddad denied the Tripoli-based authorities had anything to do with the raid. "None of our aircraft targeted any site in the east," al-Haddad said, according to the Libyan news website Addresslibya. "These reports are aimed at stoking a new war between Libyan brothers and involving Libya in a regional conflict."

Speculation is rampant as US lawmakers continue to voice their own opinions about the explosive claims of an illegal, hidden UFO crash retrieval program made public this month by Air Force and intelligence agency veteran David Grusch. Congressman and Marine veteran Mike Gallagher let loose his own theories on the mystery this Tuesday, suggesting that UFOs might be time-traveling craft piloted by humans from the future, as in the 1984 film'The Terminator.' Appearing on ESPN analyst Pat McAfee's sports talk show, the Wisconsin Republican also floated his hypothesis that the unexplained phenomena'could actually be an ancient civilization that's just been hiding here and is suddenly showing itself.' But Rep. Gallagher also brought the conversation back down to Earth, airing his concerns that the airborne mysteries might prove to be breakthrough aerospace technology mastered by a US foreign adversary. 'I'm probably the most interested in is whether it's adversary technology, particularly from China,' said Gallagher, who is also the chair of the House Committee on the Chinese Communist Party. Whether or not we are alone in the universe, the congressman is not alone among his fellow lawmakers in openly airing his UFO concerns.

In the late 1990s, Recife, on Brazil's northeastern coast, was in decline. Its picturesque historic center, made up of 17th century colonial buildings with Dutch, Portuguese, and French influences, had plunged into neglect, reflecting a deep economic crisis worsened by deindustrialization. Many young people were fleeing the city for opportunities in the commercial centers of São Paulo and Rio de Janeiro, or heading overseas. In 2000, a group of businesspeople, government officials, and academics came up with a vision to regenerate Recife's historic center by building a new technology district. With 33 million reais ($6.8 million) raised from the privatization of the local electricity company, they created Porto Digital, a nonprofit organization with the mission of turning Recife into a hub for technology and the creative industries.

Artificial intelligence is either going to save humanity or finish it off, depending on who you speak to. Either way, every week there are new developments and breakthroughs. The US company behind the ChatGPT chatbot, OpenAI, has announced that its first international office will be in London. The move is a boost for the UK prime minister, Rishi Sunak, who has described the AI race as one of the "greatest opportunities" for the country's tech industry. OpenAI said it chose the UK capital because of its "rich culture and exceptional talent pool".

Center for A.I. Safety Director Dan Hendrycks explains concerns about how the rapid growth of artificial intelligence could impact society. House lawmakers are urging federal agencies to quickly and aggressively adopt artificial intelligence technology, at a time when the push from civil rights and industry groups for new AI regulations is still waiting to get off the ground. The House Appropriations Committee, led by Rep. Kay Granger, R-Texas, released several spending bills this week that encourage the government to incorporate AI into everything from national security functions to routine office work to the detection of pests and diseases in crops. Several of those priorities are not just encouraged but would get millions of dollars in new funding under the legislation still being considered by the committee. And while comprehensive AI regulations are likely still months away and are unlikely to be developed this year, lawmakers seem keen on making sure the government is deploying AI where it can. The bills are backed by the GOP majority, and Rep. Don Beyer, D-Va., the vice chair of the Congressional Artificial Intelligence Caucus, said agencies shouldn't have to wait to start using AI.

Climate change is the defining issue of our time, and we are at a defining moment. Various interest groups, social movement organizations, and individuals engage in collective action on this issue on social media. In addition, issue advocacy campaigns on social media often arise in response to ongoing societal concerns, especially those faced by energy industries. Our goal in this paper is to analyze how those industries, their advocacy group, and climate advocacy group use social media to influence the narrative on climate change. In this work, we propose a minimally supervised model soup [57] approach combined with messaging themes to identify the stances of climate ads on Facebook. Finally, we release our stance dataset, model, and set of themes related to climate campaigns for future work on opinion mining and the automatic detection of climate change stances.

Despite its broad practical applications such as in fraud prevention, open-set speaker identification (OSI) has received less attention in the speaker recognition community compared to speaker verification (SV). OSI deals with determining if a test speech sample belongs to a speaker from a set of pre-enrolled individuals (in-set) or if it is from an out-of-set speaker. In addition to the typical challenges associated with speech variability, OSI is prone to the "false-alarm problem"; as the size of the in-set speaker population (a.k.a watchlist) grows, the out-of-set scores become larger, leading to increased false alarm rates. This is in particular challenging for applications in financial institutions and border security where the watchlist size is typically of the order of several thousand speakers. Therefore, it is important to systematically quantify the false-alarm problem, and develop techniques that alleviate the impact of watchlist size on detection performance. Prior studies on this problem are sparse, and lack a common benchmark for systematic evaluations. In this paper, we present the first public benchmark for OSI, developed using the VoxCeleb dataset. We quantify the effect of the watchlist size and speech duration on the watchlist-based speaker detection task using three strong neural network based systems. In contrast to the findings from prior research, we show that the commonly adopted adaptive score normalization is not guaranteed to improve the performance for this task. On the other hand, we show that score calibration and score fusion, two other commonly used techniques in SV, result in significant improvements in OSI performance.

We consider the following well studied problem of metric distortion in social choice. Suppose we have an election with $n$ voters and $m$ candidates who lie in a shared metric space. We would like to design a voting rule that chooses a candidate whose average distance to the voters is small. However, instead of having direct access to the distances in the metric space, each voter gives us a ranked list of the candidates in order of distance. Can we design a rule that regardless of the election instance and underlying metric space, chooses a candidate whose cost differs from the true optimum by only a small factor (known as the distortion)? A long line of work culminated in finding deterministic voting rules with metric distortion $3$, which is the best possible for deterministic rules and many other classes of voting rules. However, without any restrictions, there is still a significant gap in our understanding: Even though the best lower bound is substantially lower at $2.112$, the best upper bound is still $3$, which is attained even by simple rules such as Random Dictatorship. Finding a rule that guarantees distortion $3 - \varepsilon$ for some constant $\varepsilon $ has been a major challenge in computational social choice. In this work, we give a rule that guarantees distortion less than $2.753$. To do so we study a handful of voting rules that are new to the problem. One is Maximal Lotteries, a rule based on the Nash equilibrium of a natural zero-sum game which dates back to the 60's. The others are novel rules that can be thought of as hybrids of Random Dictatorship and the Copeland rule. Though none of these rules can beat distortion $3$ alone, a careful randomization between Maximal Lotteries and any of the novel rules can.

Classifier-Free Guidance (CFG) [37] has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q&A, reasoning, code generation, and machine translation, achieving SOTA on LAMBADA with LLaMA-7B over PaLM-540B; (2) brings improvements equivalent to a model with twice the parameter-count; (3) can stack alongside other inference-time methods like Chain-of-Thought and Self-Consistency, yielding further improvements in difficult tasks; (4) can be used to increase the faithfulness and coherence of assistants in challenging form-driven and content-driven prompts: in a human evaluation we show a 75% preference for GPT4All using CFG over baseline.

This paper introduces the Categorical Graph Model for Conflict Resolution (C-GMCR), a novel framework that integrates category theory into the traditional Graph Model for Conflict Resolution (GMCR). The C-GMCR framework provides a more abstract and general way to model and analyze conflict resolution, enabling researchers to uncover deeper insights and connections. We present the basic concepts, methods, and application of the C-GMCR framework to the well-known Prisoner's Dilemma and other representative cases. The findings suggest that the categorical approach offers new perspectives on stability concepts and can potentially lead to the development of more effective conflict resolution strategies.