Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on "Special Report." FIRST ON FOX: A new Senate Republican-led bill aims to make sure Americans are well aware of what is real online and how to spot content generated by artificial intelligence (AI). Sen. Pete Ricketts, R-Neb., is introducing legislation on Tuesday to direct relevant federal agencies to coordinate on the creation of a watermark for AI-made content, including enforcement rules. That watermark would then be required on any publicly distributed AI images, videos and other materials. "With Americans consuming more media than ever before, the threat of weaponized disinformation confusing and dividing Americans is real," Ricketts told Fox News Digital.

Haywood Talcove, CEO of LexisNexis Risk Solutions' government group, tells Fox News Digital that criminal groups, mostly in other countries, are advertising on social media to market their AI capabilities for fraud and other crimes. A tech company that boasts about its ability to use artificial intelligence to predict crime is in the midst of a privacy lawsuit with Meta, formerly Facebook, that wants it banned from the social media platform. The New York City and Los Angeles police departments, two of the U.S.'s largest police agencies, are among a growing list of law enforcement agencies in the U.S. and around the world to contract with Voyager Labs. In 2018, the New York Police Department agreed to a nearly $9 million deal with Voyager Labs, which claims it can use AI to predict crimes, according to documents obtained by the Surveillance Technology Oversight Project (STOP), The Guardian reported. The company bills itself as a "world leader" in AI-based analytics investigations that can comb through mounds of information from all corners of the internet – including social media and the dark web – to provide insight, uncover potential risks and predict future crimes.

The position paper highlights the range of concerns that are engulfed in the injunction of explainable artificial intelligence in art (XAIxArt). Through a series of quick sub-questions, it points towards the ambiguities concerning 'explanation' and the postpositivist tradition of 'relevant explanation'. Rejecting both 'explanation' and 'relevant explanation', the paper takes a stance that XAIxArt is a symptom of insecurity of the anthropocentric notion of art and a nostalgic desire to return to outmoded notions of authorship and human agency. To justify this stance, the paper makes a distinction between an ornamentation model of explanation to a model of explanation as sense-making.

The year 1998 was a pivotal one in the history of technology: Apple's introduction of the iMac helped set the company back on the path to success after it nearly went bankrupt earlier in the decade; Google was founded by two Stanford students, Larry Page and Sergey Brin; and Microsoft introduced Windows 98, an improved version of its popular computer operating system. That May, Microsoft also became the target of a historic antitrust lawsuit lodged by the Department of Justice and twenty states, accusing it of anticompetitive behavior in two domains: attempting to maintain its monopoly in computer operating systems and trying to monopolize a new market, that of Internet browsers. At the time, residential Wi-Fi connectivity was rapidly expanding across America, and, in the quaintly titled "browser wars," Netscape Navigator, a popular browser released by Mosaic Communications Corporation in 1994, fought Microsoft's Internet Explorer for the growing class of Web-connected consumers. Microsoft, the D.O.J. alleged, had attempted to crush Netscape by making deals with Internet-service providers that prioritized Explorer access at Netscape users' expense. The trial began that fall, and included seventy-six days of testimony that took place over more than eight months, during which a government witness alleged that a Microsoft executive had pledged to "cut off Netscape's air supply" (which a Microsoft attorney denied).

The US Congress is heading back into session, and they're hitting the ground running on AI. We're going to be hearing a lot about various plans and positions on AI regulation in the coming weeks, kicking off with Senate Majority Leader Chuck Schumer's first AI Insight Forum on Wednesday. This and planned future forums will bring together some of the top people in AI to discuss the risks and opportunities it poses and how Congress might write legislation to address them. Although the forums are closed to the public and press, our senior tech policy reporter Tate Ryan-Mosley has chatted with representatives from attendee AI company Hugging Face about what they are expecting, and what exactly these forums are hoping to achieve. Tate's story first appeared in The Technocrat, her weekly newsletter covering policy and Silicon Valley.

This newsletter will break down what exactly these forums are and aren't, and what might come out of them. The forums will be closed to the public and press, so I chatted with people at one company--Hugging Face--that did get the invite about what they are expecting and what their priorities are heading into the discussions. Schumer first announced the forums at the end of June as part of his AI legislation initiative, called SAFE Innovation. In floor remarks on Tuesday, Schumer said he's planning for "an open discussion about how Congress can act on AI: where to start, what questions to ask, and how to build a foundation for SAFE AI innovation." The SAFE framework, as a reminder, is not a legislative proposal but rather a set of priorities that Schumer laid out when it comes to AI regulation.

This paper investigates the strategic decision-making capabilities of three Large Language Models (LLMs): GPT-3.5, GPT-4, and LLaMa-2, within the framework of game theory. Utilizing four canonical two-player games -- Prisoner's Dilemma, Stag Hunt, Snowdrift, and Prisoner's Delight -- we explore how these models navigate social dilemmas, situations where players can either cooperate for a collective benefit or defect for individual gain. Crucially, we extend our analysis to examine the role of contextual framing, such as diplomatic relations or casual friendships, in shaping the models' decisions. Our findings reveal a complex landscape: while GPT-3.5 is highly sensitive to contextual framing, it shows limited ability to engage in abstract strategic reasoning. Both GPT-4 and LLaMa-2 adjust their strategies based on game structure and context, but LLaMa-2 exhibits a more nuanced understanding of the games' underlying mechanics. These results highlight the current limitations and varied proficiencies of LLMs in strategic decision-making, cautioning against their unqualified use in tasks requiring complex strategic reasoning.

The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal textual entailment. We present an analysis of GPT-3.5 (ChatGPT) and GPT-4 performances on COLIEE Task 4 dataset, a prominent benchmark in this domain. The study encompasses data from Heisei 18 (2006) to Reiwa 3 (2021), exploring the models' abilities to discern entailment relationships within Japanese statute law across different periods. Our preliminary experimental results unveil intriguing insights into the models' strengths and weaknesses in handling legal textual entailment tasks, as well as the patterns observed in model performance. In the context of proprietary models with undisclosed architectures and weights, black-box analysis becomes crucial for evaluating their capabilities. We discuss the influence of training data distribution and the implications on the models' generalizability. This analysis serves as a foundation for future research, aiming to optimize GPT-based models and enable their successful adoption in legal information extraction and entailment applications.

In recent years, natural language processing has gained significant popularity in various sectors, including the legal domain. This paper presents NeCo Team's solutions to the Vietnamese text processing tasks provided in the Automated Legal Question Answering Competition 2023 (ALQAC 2023), focusing on legal domain knowledge acquisition for low-resource languages through data enrichment. Our methods for the legal document retrieval task employ a combination of similarity ranking and deep learning models, while for the second task, which requires extracting an answer from a relevant legal article in response to a question, we propose a range of adaptive techniques to handle different question types. Our approaches achieve outstanding results on both tasks of the competition, demonstrating the potential benefits and effectiveness of question answering systems in the legal field, particularly for low-resource languages.

The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over rigidly defined symbolic models, but also serves as a proof-point of the challenges that purely statistics-based approaches have in terms of safety and trustworthiness. As a framework for contextualizing the potential, as well as the limitations of LLMs and other foundation model-based technologies, we propose the concept of a Large Process Model (LPM) that combines the correlation power of LLMs with the analytical precision and reliability of knowledge-based systems and automated reasoning approaches. LPMs are envisioned to directly utilize the wealth of process management experience that experts have accumulated, as well as process performance data of organizations with diverse characteristics, e.g., regarding size, region, or industry. In this vision, the proposed LPM would allow organizations to receive context-specific (tailored) process and other business models, analytical deep-dives, and improvement recommendations. As such, they would allow to substantially decrease the time and effort required for business transformation, while also allowing for deeper, more impactful, and more actionable insights than previously possible. We argue that implementing an LPM is feasible, but also highlight limitations and research challenges that need to be solved to implement particular aspects of the LPM vision.