An important milestone in the process of EU AI legislation was taken on 14 June when the European parliament voted in favour of adopting the proposed AI act (with 499 votes in favour, 28 against and 93 abstentions). The next step will involve talks with EU member states on the final form of the law. The aim is to reach an agreement by the end of this year. At the core of the proposed act is a risk-based approach, which establishes obligations for providers and those deploying AI systems depending on the level of risk posed. AI systems deemed to present an "unacceptable risk" would be completely prohibited.

A researcher was granted access earlier this year by Facebook's parent company, Meta, to incredibly potent artificial intelligence software – and leaked it to the world. As a former researcher on Meta's civic integrity and responsible AI teams, I am terrified by what could happen next. Though Meta was violated by the leak, it came out as the winner: researchers and independent coders are now racing to improve on or build on the back of LLaMA (Large Language Model Meta AI – Meta's branded version of a large language model or LLM, the type of software underlying ChatGPT), with many sharing their work openly with the world. This could position Meta as owner of the centrepiece of the dominant AI platform, much in the same way that Google controls the open-source Android operating system that is built on and adapted by device manufacturers globally. If Meta were to secure this central position in the AI ecosystem, it would have leverage to shape the direction of AI at a fundamental level, controlling both the experiences of individual users and setting limits on what other companies could and couldn't do.

Retired Brigadier General Robert Spalding joined'Fox & Friends Weekend' to discuss the significance of the report and broader concerns surrounding Chinese espionage targeting the U.S. Circa 1996, U.S. lawmakers wanted to make sure scrappy startups, like AOL and Amazon, had a fighting chance against incumbents. Our government had a straightforward approach: rubberstamp mergers and free tech from any regulatory oversight. These policy approaches were intended to level the playing field for the nascent tech industry and export our values abroad. Our tech policies of yore have turned those startups into the world's first set of trillion-dollar companies. But Big Tech failed to export our values -- and has even been counterproductive on that end.

Mike Gallagher and Raja Krishnamoorthi explain the threat from China amid growing concerns about TikTok and the country's relationship with Russia. Tech firm Ultra has developed an artificial intelligence-powered tool it believes has helped analysts identify products coming from China through the platform Temu that were created using forced labor, possibly from the Uyghur population. "We're looking at Temu from the perspective of the Forced Labor Prevention Act," Ultra founder and CEO Ram Ben Tzion told Fox News Digital. "How many things that we don't want are coming into the country using this method, right? The good cases are counterfeit. The worst cases are poor quality. "I'm quite confident that illicit elements can find themselves going through this platform into the market, so it's time to demand accountability," he added. Ben Tzion's company created the program Publican, which pulls in huge amounts of shipping data to analyze and look for patterns and red flags for any products ...

The robustness of modern machine learning (ML) models has become an increasing concern within the community. The ability to subvert a model into making errant predictions using seemingly inconsequential changes to input is startling, as is our lack of success in building models robust to this concern. Existing research shows progress, but current mitigations come with a high cost and simultaneously reduce the model's accuracy. However, such trade-offs may not be necessary when other design choices could subvert the risk. In this survey we review the current literature on attacks and their real-world occurrences, or limited evidence thereof, to critically evaluate the real-world risks of adversarial machine learning (AML) for the average entity. This is done with an eye toward how one would then mitigate these attacks in practice, the risks for production deployment, and how those risks could be managed. In doing so we elucidate that many AML threats do not warrant the cost and trade-offs of robustness due to a low likelihood of attack or availability of superior non-ML mitigations. Our analysis also recommends cases where an actor should be concerned about AML to the degree where robust ML models are necessary for a complete deployment.

The advent of ChatGPT by OpenAI has prompted extensive discourse on its potential implications for science and higher education. While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice. To investigate this further, we conducted a Delphi study involving 72 experts specialising in research and AI. The study focused on applications and limitations of LLMs, their effects on the science system, ethical and legal considerations, and the required competencies for their effective use. Our findings highlight the transformative potential of LLMs in science, particularly in administrative, creative, and analytical tasks. However, risks related to bias, misinformation, and quality assurance need to be addressed through proactive regulation and science education. This research contributes to informed discussions on the impact of generative AI in science and helps identify areas for future action.

In recent years, discussions of responsible AI practices have seen growing support for "participatory AI" approaches, intended to involve members of the public in the design and development of AI systems. Prior research has identified a lack of standardised methods or approaches for how to use participatory approaches in the AI development process. At present, there is a dearth of evidence on attitudes to and approaches for participation in the sites driving major AI developments: commercial AI labs. Through 12 semi-structured interviews with industry practitioners and subject-matter experts, this paper explores how commercial AI labs understand participatory AI approaches and the obstacles they have faced implementing these practices in the development of AI systems and research. We find that while interviewees view participation as a normative project that helps achieve "societally beneficial" AI systems, practitioners face numerous barriers to embedding participatory approaches in their companies: participation is expensive and resource intensive, it is "atomised" within companies, there is concern about exploitation, there is no incentive to be transparent about its adoption, and it is complicated by a lack of clear context. These barriers result in a piecemeal approach to participation that confers no decision-making power to participants and has little ongoing impact for AI labs. This papers contribution is to provide novel empirical research on the implementation of public participation in commercial AI labs, and shed light on the current challenges of using participatory approaches in this context.

This research delves into the intersection of illustration art and artificial intelligence (AI), focusing on how illustrators engage with AI agents that embody their original characters (OCs). We introduce 'ORIBA', a customizable AI chatbot that enables illustrators to converse with their OCs. This approach allows artists to not only receive responses from their OCs but also to observe their inner monologues and behavior. Despite the existing tension between artists and AI, our study explores innovative collaboration methods that are inspiring to illustrators. By examining the impact of AI on the creative process and the boundaries of authorship, we aim to enhance human-AI interactions in creative fields, with potential applications extending beyond illustration to interactive storytelling and more.

Deciding on an appropriate intervention requires a causal model of a treatment, the outcome, and potential mediators. Causal mediation analysis lets us distinguish between direct and indirect effects of the intervention, but has mostly been studied in a static setting. In healthcare, data come in the form of complex, irregularly sampled time-series, with dynamic interdependencies between a treatment, outcomes, and mediators across time. Existing approaches to dynamic causal mediation analysis are limited to regular measurement intervals, simple parametric models, and disregard long-range mediator--outcome interactions. To address these limitations, we propose a non-parametric mediator--outcome model where the mediator is assumed to be a temporal point process that interacts with the outcome process. With this model, we estimate the direct and indirect effects of an external intervention on the outcome, showing how each of these affects the whole future trajectory. We demonstrate on semi-synthetic data that our method can accurately estimate direct and indirect effects. On real-world healthcare data, our model infers clinically meaningful direct and indirect effect trajectories for blood glucose after a surgery.

Former Google CEO Eric Schmidt has developed a vast network of strategic investments and political relationships that's allowed the tech billionaire to wield significant influence over artificial intelligence and public policy in Washington, D.C., according to an explosive new report. The Bull Moose Project, a nonprofit advocacy group committed to developing "the next generation of America First leaders and policies," has spent months investigating Schmidt's financial disclosures, tax records, business documents and other publicly available information. On Thursday, the group released a report outlining its findings, first obtained by Fox News Digital. "Americans don't want to believe that they live under'the rule of the few,' rather than a democracy's'rule of the many' – but this sobering report is a wake-up call that our elected representatives can't ignore," said Aiden Buzzetti, president of the Bull Moose Project. "What we've put together reinforces the puppet-master role that big tech's leaders play in the public's lives. All items in this database and report are backed by reputable, verifiable sources, and we plan to update this it regularly so that the public has access to Schmidt's dealings, even if government refuses to disclose them. Get ready for your mind to be blown."