The estate of comedian George Carlin settled a lawsuit on Tuesday against the owners of a comedy podcast who claimed they used artificial intelligence to mimic the deceased stand-up's voice. The lawsuit was one of the first in the US to focus on the legality of deepfakes imitating a celebrity's likeness. The Dudesy podcast and its creators – the former Mad TV comedian Will Sasso and the writer Chad Kultgen – agreed to remove all versions of the podcast from the internet and permanently refrain from using Carlin's voice, likeness or image in any content. Danielle Del, a spokesperson for Sasso, declined to comment. Carlin's family and an attorney for his estate both praised the settlement. Neither side disclosed terms of the deal.

Amazon's Just Walk Out technology is touted as an AI-powered checkout system at its Fresh grocery stores, but new reports have claimed it used 1,000 people in India to monitor buyers. The company is now walking out on its own the technology that promised an innovative alternative to cashiers by using cameras and sensors to scan each item and is switching to a self-checkout shopping cart called Dash Cart. An Amazon spokesperson said they do have people watching cameras at Just Walk Out locations to annotate video images, but claimed the associates aren't monitoring customers. The Information first reported that Amazon's artificial intelligence technology just meant outsourcing hundreds of jobs overseas to workers who can watch you shop in real time. Amazon has referred to Just Walk Out as'a combination of sophisticated tools and technologies that added items to the shopper's'virtual cart' when they take an item off a shelf, and remove it when they put it back.

Now in its 16th season, RuPaul's Drag Race has birthed more than a few iconic lip-sync battles, but precious few have featured Muppets. AI Drag Race changed that. In the Instagram account's recent season finale, Miss Piggy, wearing an AI-generated drag look, faced off against lover-turned-rival Kermisha Ihman, who had a thick, 40-inch-long ponytail atop her green felt head. Tackling Lady Gaga's "Telephone," the two whirled and jumped, kicking and bucking in front of head judge Betty Boop. Kermisha worked her faux-nailed webbed feet, sickening in her bejeweled purple corset, but ultimately she fell to Piggy, whose fringe flew as she went for a well-timed jump split at the song's climax.

Machine learning (ML) applications and the organizations that develop them should be accountable. Proposed regulations require impact assessment and there are calls to strengthen enforcement of regulations for ethical business practice regulations.a Responsible organizations should implement a "test-driven ethics" development approach rooted in pragmatist discourse ethics and lessons from test-driven development. This approach extends the popular "principles" approach to ethics seen in industry, government, and the academy.2 Adopting ethical principles will not guarantee ethical actions or outcomes.

The safety features on some of the most powerful AI tools that stop them being used for cybercrime or terrorism can be bypassed simply by flooding them with examples of wrongdoing, research has shown. In a paper from the AI lab Anthropic, which produces the large language model (LLM) behind the ChatGPT rival Claude, researchers described an attack they called "many-shot jailbreaking". The attack was as simple as it was effective. Claude, like most large commercial AI systems, contains safety features designed to encourage it to refuse certain requests, such as to generate violent or hateful speech, produce instructions for illegal activities, deceive or discriminate. A user who asks the system for instructions to build a bomb, for example, will receive a polite refusal to engage.

Fox News Flash top entertainment and celebrity headlines are here. George Carlin's estate has agreed to a settlement with the media company it sued earlier this year over the use of artificial intelligence. In January, Carlin's estate sued the podcast company, Dudesy, for recreating Carlin's iconic comedic style in an hour-long special titled "George Carlin: I'm Glad I'm Dead." The settlement indicates that Dudesy is required to permanently remove the special and cannot use Carlin's image voice or likeness in the future without written consent from the estate. According to The Associated Press, the settlement agreement was approved by both sides and awaits a judge's approval.

There will be no follow-up to that AI-generated George Carlin comedy special released by the podcast Dudesy. Now, the two sides have reached a settlement agreement, which includes the permanent removal of the comedy special from Dudesy's archive. Sasso and Kultgen have also agreed never to repost it on any platform and never to use Carlin's image, voice or likeness without approval from the estate again, according to The New York Times. The AI algorithm that Dudesy used for the special was trained on thousands of hours of Carlin's routines that spanned decades of his career. It generated enough material for an hour-long special, but it did a pretty poor impression of the late comedian with basic punchlines and very little of what characterized Carlin's humor.

For almost a decade now, scholarship in and beyond the ACM FAccT community has been focusing on novel and innovative ways and methodologies to audit the functioning of algorithmic systems. Over the years, this research idea and technical project has matured enough to become a regulatory mandate. Today, the Digital Services Act (DSA) and the Online Safety Act (OSA) have established the framework within which technology corporations and (traditional) auditors will develop the `practice' of algorithmic auditing thereby presaging how this `ecosystem' will develop. In this paper, we systematically review the auditing provisions in the DSA and the OSA in light of observations from the emerging industry of algorithmic auditing. Who is likely to occupy this space? What are some political and ethical tensions that are likely to arise? How are the mandates of `independent auditing' or `the evaluation of the societal context of an algorithmic function' likely to play out in practice? By shaping the picture of the emerging political economy of algorithmic auditing, we draw attention to strategies and cultures of traditional auditors that risk eroding important regulatory pillars of the DSA and the OSA. Importantly, we warn that ambitious research ideas and technical projects of/for algorithmic auditing may end up crashed by the standardising grip of traditional auditors and/or diluted within a complex web of (sub-)contractual arrangements, diverse portfolios, and tight timelines.

Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen and colleagues' (2000) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to their source. Remarkably, they rated the AI's moral reasoning as superior in quality to humans' along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans' raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality.

Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems. Organizations that develop and deploy frontier systems have significant access to such information. By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new and emerging risks posed by frontier systems. Equipped with this information, developers could make better informed decisions on risk management, while policymakers could design more targeted and robust regulatory infrastructure. We outline the key features of responsible reporting and propose mechanisms for implementing them in practice. Evaluate current models for novel risks (including risks discovered by other organizations) Update model safeguards and risk mitigations Developer Other developers (e.g., revise scaling policy, security practices) Documents and Evaluates information Consult with domain experts in government reports safety and decides on (e.g., experts in national security, public health) information response plan Solicit additional information from developer Government actor (e.g., design decisions, organizational processes) Request or conduct further safety evaluations (incl. in collaboration with independent auditors) Domain experts in