The bill would need companies to file such documents at least 30 days before publicly debuting their AI tools, or face a financial penalty. Such datasets encompass billions of lines of text and images or millions of hours of music and movies. "AI has the disruptive potential of changing our economy, our political system, and our day-to-day lives. We must balance the immense potential of AI with the crucial need for ethical guidelines and protections," Schiff said in a statement. Schiff's bill, which was first reported by Billboard, has received the support of numerous entertainment industry organizations and unions, including the Recording Industry Association of America, Professional Photographers of America, Directors Guild of America and the Screen Actors Guild-American Federation of Television and Radio Artists.

Generative AI is overblown as an election risk, according to Meta's Nick Clegg, who claims the technology is more useful for defending democracy than attacking it. Speaking at the Meta AI Day event in London on Tuesday, the social network's global affairs chief said that the evidence from major elections that have already been run this year around the world is that technology such as large language models, image and video generators, and speech synthesis tools aren't being used in practice to subvert democracy. "It is right that we should be alert and we should be vigilant," Clegg said. "But of the major elections which have taken place already this year, in Taiwan, Pakistan, Bangladesh and Indonesia, it is striking how little these tools have been used in a systematic basis to really try to subvert and disrupt the elections. "I would urge everyone to think of AI as a sword, not just a shield, when it comes to bad content.

The way China regulates its tech industry can seem highly unpredictable. The government can celebrate the achievements of Chinese tech companies one day and then turn against them the next. But there are patterns in how China approaches regulating tech, argues Angela Huyue Zhang, a law professor at Hong Kong University and author of the new book High Wire: How China Regulates Big Tech and Governs Its Economy. Chinese policies almost always follow a three-phase progression: a lax approach where companies are given relative flexibility to expand and compete, sudden harsh crackdowns that slash profits, and eventually a new loosening of restrictions. Zeyi Yang, our China reporter, recently spoke with Zhang about her new book and how to apply her insights to China's tech industry, including significant new sectors like artificial intelligence.

OpenAI is also at the center of several regulatory investigations, which have forced the company to spend even more on legal support. The Securities and Exchange Commission is looking into whether investors were misled during the chaotic period when Altman briefly left the company. The Federal Trade Commission is probing whether it ran afoul of consumer protection laws in a number of areas, including a data leak and ChatGPT's inaccurate claims. And the commission has had talks with the Justice Department about which agency should probe its multibillion-dollar partnership with Microsoft, amid concerns that such deals are dampening competition in the quickly evolving AI market.

At least three people were killed and eight injured in the southern city of Zaporizhzia after a Russian missile hit several apartment blocks, an industrial building as well as medical and educational facilities. One woman was killed and three others injured after Russia attacked the town of Bilopillia in the northern Sumy region with guided bombs. The attack struck the centre of the town of 15,000 people, damaging shops and a city council building. One person was killed and five injured, including three children, after Russian shelling triggered a fire and the collapse of a building roof, officials said. Officials in Zvyahel in Ukraine's central Zhytomyr region urged people to stay indoors amid fears of "air pollution" after a Russian drone attack hit infrastructure.

Graphs are a natural representation for systems based on relations between connected entities. Combinatorial optimization problems, which arise when considering an objective function related to a process of interest on discrete structures, are often challenging due to the rapid growth of the solution space. The trial-and-error paradigm of Reinforcement Learning has recently emerged as a promising alternative to traditional methods, such as exact algorithms and (meta)heuristics, for discovering better decision-making strategies in a variety of disciplines including chemistry, computer science, and statistics. Despite the fact that they arose in markedly different fields, these techniques share significant commonalities. Therefore, we set out to synthesize this work in a unifying perspective that we term Graph Reinforcement Learning, interpreting it as a constructive decision-making method for graph problems. After covering the relevant technical background, we review works along the dividing line of whether the goal is to optimize graph structure given a process of interest, or to optimize the outcome of the process itself under fixed graph structure. Finally, we discuss the common challenges facing the field and open research questions. In contrast with other surveys, the present work focuses on non-canonical graph problems for which performant algorithms are typically not known and Reinforcement Learning is able to provide efficient and effective solutions.

Text-to-video generative AI models such as Sora OpenAI have the potential to disrupt multiple industries. In this paper, we report a qualitative social media analysis aiming to uncover people's perceived impact of and concerns about Sora's integration. We collected and analyzed comments (N=292) under popular posts about Sora-generated videos, comparison between Sora videos and Midjourney images, and artists' complaints about copyright infringement by Generative AI. We found that people were most concerned about Sora's impact on content creation-related industries. Emerging governance challenges included the for-profit nature of OpenAI, the blurred boundaries between real and fake content, human autonomy, data privacy, copyright issues, and environmental impact. Potential regulatory solutions proposed by people included law-enforced labeling of AI content and AI literacy education for the public. Based on the findings, we discuss the importance of gauging people's tech perceptions early and propose policy recommendations to regulate Sora before its public release.

Large Language Models (LLMs) are increasingly being developed and applied, but their widespread use faces challenges. These include aligning LLMs' responses with human values to prevent harmful outputs, which is addressed through safety training methods. Even so, bad actors and malicious users have succeeded in attempts to manipulate the LLMs to generate misaligned responses for harmful questions such as methods to create a bomb in school labs, recipes for harmful drugs, and ways to evade privacy rights. Another challenge is the multilingual capabilities of LLMs, which enable the model to understand and respond in multiple languages. Consequently, attackers exploit the unbalanced pre-training datasets of LLMs in different languages and the comparatively lower model performance in low-resource languages than high-resource ones. As a result, attackers use a low-resource languages to intentionally manipulate the model to create harmful responses. Many of the similar attack vectors have been patched by model providers, making the LLMs more robust against language-based manipulation. In this paper, we introduce a new black-box attack vector called the Sandwich attack: a multi-language mixture attack, which manipulates state-of-the-art LLMs into generating harmful and misaligned responses. GPT-4, and Claude-3-OPUS, show that this attack vector can be used by adversaries to generate harmful responses and elicit misaligned responses from these models. By detailing both the mechanism and impact of the Sandwich attack, this paper aims to guide future research and development towards more secure and resilient LLMs, ensuring they serve the public good while minimizing potential for misuse. Content Warning: This paper contains examples of harmful language. Ethics and Disclosure This paper introduces a new universal attack method for the SOTA LLMs that could potentially be used to elicit harmful content from publicly available LLMs. The adversarial attack method we used in this paper is easy to design and requires low-cost to implement. Despite the associated risks, we firmly believe that sharing the full details of this research and its methodology will be invaluable to other researchers, scholars, and model creators. It encourages them to delve into the root causes behind these attacks and devise ways to fortify and patch existing models. Additionally, it promotes cooperative initiatives centered around the safety of LLMs in multilingual scenarios.

The accurate evaluation of differential treatment in language models to specific groups is critical to ensuring a positive and safe user experience. An ideal evaluation should have the properties of being robust, extendable to new groups or attributes, and being able to capture biases that appear in typical usage (rather than just extreme, rare cases). Relatedly, bias evaluation should surface not only egregious biases but also ones that are subtle and commonplace, such as a likelihood for talking about appearances with regard to women. We present FairPair, an evaluation framework for assessing differential treatment that occurs during ordinary usage. FairPair operates through counterfactual pairs, but crucially, the paired continuations are grounded in the same demographic group, which ensures equivalent comparison. Additionally, unlike prior work, our method factors in the inherent variability that comes from the generation process itself by measuring the sampling variability. We present an evaluation of several commonly used generative models and a qualitative analysis that indicates a preference for discussing family and hobbies with regard to women.

Substitute relationships are fundamental to people's daily lives across various domains. This study aims to comprehend and predict substitute relationships among products in diverse fields, extensively analyzing the application of machine learning algorithms, natural language processing, and other technologies. By comparing model methodologies across different domains, such as defining substitutes, representing and learning substitute relationships, and substitute reasoning, this study offers a methodological foundation for delving deeper into substitute relationships. Through ongoing research and innovation, we can further refine the personalization and accuracy of substitute recommendation systems, thus advancing the development and application of this field.