Hugging Face has found that data sets have better documentation when they are open, consistently used, and shared, said Yacine Jernite, leader of its machine learning and society team. The open source company has prioritized efforts, like automatically suggesting meta data, to improve documentation. Even with imperfect annotation, openly accessible data sets are the first meaningful step toward more transparency in the field, he said.

Senator Charles Schumer (D-NY) once again played host to Silicon Valley's AI leaders on Tuesday as the US Senate reconvened its AI Insights Forum for a second time. On the guest list this go around: manifesto enthusiast Marc Andreessen and venture capitalist John Doerr, as well as Max Tegmark of the Future of Life Institute and NAACP CEO Derrick Johnson. On the agenda: "the transformational innovation that pushes the boundaries of medicine, energy, and science, and the sustainable innovation necessary to drive advancements in security, accountability, and transparency in AI," according to a release from Sen. Schumer's office. Upon exiting the meeting Tuesday, Schumer told the assembled press, "it is clear that American leadership on AI can't be done on the cheap. Almost all of the experts in today's Forum called for robust, sustained federal investment in private and public sectors to achieve our goals of American-led transformative and sustainable innovation in AI. Per National Security AI Commission estimates, paying for that could cost around $32 billion a year. However, Schumer believes that those funding challenges can be addressed by "leveraging the private sector by employing new and innovative funding mechanisms – like the Grand Challenges prize idea." "We must prioritize transformational innovation, to help create new vistas, unlock new cures, improve education, reinforce national security, protect the global food supply, and more," Schumer remarked. But in doing so, we must act sustainably in order to minimize harms to workers, civil society and the environment. "We need to strike a balance between transformational and sustainable innovation," Schumer said. "Finding this balance will be key to our success." Senators Brian Schatz (D-HI) and John Kennedy (R-LA) also got in on the proposed regulatory action Tuesday, introducing legislation that would provide more transparency on AI-generated content by requiring clear labeling and disclosures. Such technology could resemble the Content Credentials tag that the C2PA and CAI industry advocacy groups are developing. "Our bill is simple," Senator Schatz said in a press statement. "If any content is made by artificial intelligence, it should be labeled so that people are aware and aren't fooled or scammed." The Schatz-Kennedy AI Labeling Act, as they're calling it, would require generative AI system developers to clearly and conspicuously disclose AI-generated content to users. Those developers, and their licensees, would also have to take "reasonable steps" to prevent "systematic publication of content without disclosures." The bill would also establish a working group to create non-binding technical standards to help social media platforms automatically identify such content as well. "It puts the onus where it belongs: on the companies and not the consumers," Schatz said on the Senate floor Tuesday. "Labels will help people to be informed.

"Getting access to many of our public libraries is so difficult, and after a point people will give up asking for access. That's the case in many of our public-funded educational institutes too," says Arul George Scaria, an associate professor at the National Law School of India University Bengaluru, who studies intellectual-property law. One of the best ways to liberate access to these libraries, he says, is through digitization. Technologist Omshivaprakash H L felt the acute lack of such resources when he needed references for writing Wikipedia articles in Kannada, a southwestern Indian language. Around 2019, he heard that Carl Malamud, who runs Public Resource, a registered US charity, was already archiving books like Gandhi's Hind Swaraj collection on Indian self-rule and works of the Indian government in the public domain.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on "America's Newsroom." A study found that artificial intelligence chatbots such as the popular ChatGPT return common debunked medical stereotypes about Black people. Researchers at Stanford University ran nine medical questions through AI chatbots and found that they returned responses that contained debunked medical claims about Black people, including incorrect responses about kidney function and lung capacity, as well as the notion that Black people have different muscle mass than White people, according to a report from Axios. The team of researchers ran the nine questions through four chatbots, including OpenAI's ChatGPT and Google's Bard, that are trained to scour large amounts of internet text, the report noted, but the responses raised concerns about the growing use of AI in the medical field. A study found that artificial intelligence chatbots such as the popular ChatGPT return common debunked medical stereotypes about Black people.

The "worst nightmares" about artificial intelligence-generated child sexual abuse images are coming true and threaten to overwhelm the internet, a safety watchdog has warned. The Internet Watch Foundation (IWF) said it had found nearly 3,000 AI-made abuse images that broke UK law. The UK-based organisation said existing images of real-life abuse victims were being built into AI models, which then produce new depictions of them. It added that the technology was also being used to create images of celebrities who have been "de-aged" and then depicted as children in sexual abuse scenarios. Other examples of child sexual abuse material (CSAM) included using AI tools to "nudify" pictures of clothed children found online.

We propose a human-centred explanation method for rule-based automated decision-making systems in the legal domain. Firstly, we establish a conceptual framework for developing explanation methods, representing its key internal components (content, communication and adaptation) and external dependencies (decision-making system, human recipient and domain). Secondly, we propose an explanation method that uses a graph database to enable question-driven explanations and multimedia display. This way, we can tailor the explanation to the user. Finally, we show how our conceptual framework is applicable to a real-world scenario at the Dutch Tax and Customs Administration and implement our explanation method for this scenario.

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain.

Despite their groundbreaking performance for many generative modeling tasks, diffusion models have fallen short on discrete data domains such as natural language. Crucially, standard diffusion models rely on the well-established theory of score matching, but efforts to generalize this to discrete structures have not yielded the same empirical gains. In this work, we bridge this gap by proposing score entropy, a novel discrete score matching loss that is more stable than existing methods, forms an ELBO for maximum likelihood training, and can be efficiently optimized with a denoising variant. We scale our Score Entropy Discrete Diffusion models (SEDD) to the experimental setting of GPT-2, achieving highly competitive likelihoods while also introducing distinct algorithmic advantages. In particular, when comparing similarly sized SEDD and GPT-2 models, SEDD attains comparable perplexities (normally within $+10\%$ of and sometimes outperforming the baseline). Furthermore, SEDD models learn a more faithful sequence distribution (around $4\times$ better compared to GPT-2 models with ancestral sampling as measured by large models), can trade off compute for generation quality (needing only $16\times$ fewer network evaluations to match GPT-2), and enables arbitrary infilling beyond the standard left to right prompting.

A horrific new era of ultrarealistic, AI-generated, child sexual abuse images is now underway, experts warn. Offenders are using downloadable open source generative AI models, which can produce images, to devastating effects. The technology is being used to create hundreds of new images of children who have previously been abused. Offenders are sharing datasets of abuse images that can be used to customize AI models, and they're starting to sell monthly subscriptions to AI-generated child sexual abuse material (CSAM). The details of how the technology is being abused are included in a new, wide-ranging report released by the Internet Watch Foundation (IWF), a nonprofit based in the UK that scours and removes abuse content from the web.

On Tuesday, 24 AI experts, including Turing Award winners Geoffrey Hinton and Yoshua Bengio, released a paper calling on governments to take action to manage risks from AI. The policy document had a particular focus on extreme risks posed by the most advanced systems, such as enabling large-scale criminal or terrorist activities. The paper makes a number of concrete policy recommendations, such as ensuring that major tech companies and public funders devote at least one-third of their AI R&D budget to projects that promote safe and ethical use of AI. The authors also call for the creation of national and international standards. Bengio, scientific director at the Montreal Institute for Learning Algorithms, says that the paper aims to help policymakers, the media, and the general public "understand the risks, and some of the things we have to do to make [AI] systems do what we want."