Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Gov. Josh Shapiro on Friday remained noncommittal on a strategy to reduce planet-warming greenhouse gases after a task force the Democrat appointed came to an uncertain conclusion over how to make Pennsylvania the first major fossil fuel state to adopt carbon pricing over power plant emissions. The task force sprang from Shapiro questioning his predecessor's use of regulatory authority to join the Regional Greenhouse Gas Initiative, a consortium of 12 eastern states that imposes a price and declining cap on carbon dioxide emissions from power plants. However, the 17-member task force -- comprised of supporters and opponents of former Democratic Gov. Tom Wolf's plan -- could come to no consensus on it. Wolf's regulation allowing Pennsylvania to join the consortium remains hung up in the courts, and Shapiro gave no sign Friday whether he would carry out the consortium's carbon pricing policy should it survive the legal challenge.

California would become the first state to require venture capital firms to disclose the race and gender of the founders of the companies they fund, under a bill currently awaiting governor Gavin Newsom's signature. The business community strongly opposes the legislation, characterizing it as an example of bureaucratic overreach. But civil rights groups and female entrepreneurs say it could go a long way toward equalizing opportunity in Silicon Valley, where startup capital overwhelmingly flows to white men. According to the business data firm PitchBook, companies founded by all-female teams accounted for just 2% of venture capital funding last year. Those led by Black women and Latinas received even less, 0.85%, according to a report from Project Diane, a research effort focused on female founders.

Just when Microsoft's buyout of Activision finally seemed to be near complete -- and we could focus on Google's legal tussles with the Department of Justice -- the Federal Trade Commission said it will revive its attempt to block the $69 billion deal in an adjudicative process. Microsoft received EU approval over the summer when the European Commission endorsed the deal as long as the tech giant could ensure "full compliance with commitments." Normally, the FTC drops its challenges to deals when efforts are lost in federal court. This move will not delay the deal, though in the worst-case scenario, Microsoft might have to sell off parts of the gaming company. Microsoft told Bloomberg it's not concerned about the move preventing its purchase.

The lawyer representing victims of a fatal Tesla crash blamed the company's autopilot driver assistant system, saying that "a car company should never sell consumers experimental vehicles," in the opening statement of a California trial on Thursday. The case stems from a civil lawsuit alleging that the autopilot system caused the owner of a Tesla Model 3 car, Micah Lee, to suddenly veer off a highway east of Los Angeles at 65 mph (105 kph), where his car struck a palm tree and burst into flames. The 2019 crash killed Lee and seriously injured his two passengers, including an eight-year-old boy who was disemboweled, according to court documents. The lawsuit, filed against Tesla by the passengers and Lee's estate, accuses Tesla of knowing that autopilot and other safety systems were defective when it sold the car. Jonathan Michaels, an attorney for the plaintiffs, in his opening statement at the trial in Riverside, California, said that when the 37-year-old Lee bought Tesla's "full self-driving capability package" for $6,000 for his Model 3 in 2019, the system was in "beta", meaning it was not yet ready for release.

Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model sharing options, are explored. The paper concludes with recommendations for developers, standard-setting bodies, and governments for establishing safe and responsible model sharing practices and preserving open-source benefits where safe.

We propose an approach for the structure controllable summarization of long legal opinions that considers the argument structure of the document. Our approach involves using predicted argument role information to guide the model in generating coherent summaries that follow a provided structure pattern. We demonstrate the effectiveness of our approach on a dataset of legal opinions and show that it outperforms several strong baselines with respect to ROUGE, BERTScore, and structure similarity.

As artificial intelligence (AI) technologies spread worldwide, international discussions have increasingly focused on their consequences for democracy, human rights, fundamental freedoms, security, and economic and social development. In this context, UNESCO's Recommendation on the Ethics of Artificial Intelligence, adopted in November 2021, has emerged as the first global normative framework for AI development and deployment. The intense negotiations of every detail of the document brought forth numerous controversies among UNESCO member states. Drawing on a unique set of primary sources, including written positions and recorded deliberations, this paper explains the achievement of global compromise on AI regulation despite the multiplicity of UNESCO member-state positions representing a variety of liberal and sovereignist preferences. Building upon Boltanski's pragmatic sociology, it conceptualises the practice of multilateral negotiations and attributes the multilateral compromise to two embedded therein mechanisms: Structural normative hybridity and situated normative ambiguity allowed to accomplish a compromise by linking macro-normative structures with situated debates of multilateral negotiations.

Many individuals are likely to face a legal dispute at some point in their lives, but their lack of understanding of how to navigate these complex issues often renders them vulnerable. The advancement of natural language processing opens new avenues for bridging this legal literacy gap through the development of automated legal aid systems. However, existing legal question answering (LQA) approaches often suffer from a narrow scope, being either confined to specific legal domains or limited to brief, uninformative responses. In this work, we propose an end-to-end methodology designed to generate long-form answers to any statutory law questions, utilizing a "retrieve-then-read" pipeline. To support this approach, we introduce and release the Long-form Legal Question Answering (LLeQA) dataset, comprising 1,868 expert-annotated legal questions in the French language, complete with detailed answers rooted in pertinent legal provisions. Our experimental results demonstrate promising performance on automatic evaluation metrics, but a qualitative analysis uncovers areas for refinement. As one of the only comprehensive, expert-annotated long-form LQA dataset, LLeQA has the potential to not only accelerate research towards resolving a significant real-world issue, but also act as a rigorous benchmark for evaluating NLP models in specialized domains. We publicly release our code, data, and models.

Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications. In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs. This dataset is collected from 210K unique IP addresses in the wild on our Vicuna demo and Chatbot Arena website. We offer an overview of the dataset's content, including its curation process, basic statistics, and topic distribution, highlighting its diversity, originality, and scale. We demonstrate its versatility through four use cases: developing content moderation models that perform similarly to GPT-4, building a safety benchmark, training instruction-following models that perform similarly to Vicuna, and creating challenging benchmark questions. We believe that this dataset will serve as a valuable resource for understanding and advancing LLM capabilities. The dataset is publicly available at https://huggingface.co/datasets/lmsys/lmsys-chat-1m.

We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-3 decoder-only architecture and are pretrained on a mixture of Arabic and English texts, including source code in various programming languages. With 13 billion parameters, they demonstrate better knowledge and reasoning capabilities in Arabic than any existing open Arabic and multilingual models by a sizable margin, based on extensive evaluation. Moreover, the models are competitive in English compared to English-centric open models of similar size, despite being trained on much less English data. We provide a detailed description of the training, the tuning, the safety alignment, and the evaluation of the models. We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs. Available at https://huggingface.co/inception-mbzuai/jais-13b-chat