Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Had you scanned any of the latest headlines around the TAKE IT DOWN Act, legislation that President Donald Trump signed into law Monday, you would have come away with a deeply mistaken impression of the bill and its true purpose. The surface-level pitch is that this is a necessary law for addressing nonconsensual intimate images--known more widely as revenge porn. Obfuscating its intent with a classic congressional acronym (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks), the TAKE IT DOWN Act purports to help scrub the internet of exploitative, nonconsensual sexual media, whether real or digitally mocked up, at a time when artificial intelligence tools and automated image generators have supercharged its spread. Enforcement is delegated to the Federal Trade Commission, which will give online communities that specialize primarily in user-generated content (e.g., social media, message boards) a heads-up and a 48-hour takedown deadline whenever an appropriate example is reported.

A United Arab Emirates (UAE) academic lab today launched an artificial intelligence world model and agent, two large language models (LLMs) and a new research center in Silicon Valley as it ramps up its investment in the cutting-edge field. The UAE's Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) revealed an AI world model called PAN, which can be used to build physically realistic simulations for testing and honing the performance of AI agents. Eric Xing, President and Professor of MBZUAI and a leading AI researcher, revealed the models and lab at the Computer History Museum in Mountain View, California today. The UAE has made big investments in AI in recent years under the guidance of Sheikh Tahnoun bin Zayed al Nahyan, the nation's tech-savvy national security advisor and younger brother of president Mohamed bin Zayed Al Nahyan. Xing says the UAE's new center in Sunnyvale, California, will help the nation tap into the world's most concentrated source of AI knowledge and talent.

Elon Musk's so-called Department of Government Efficiency (DOGE) used artificial intelligence from Meta's Llama model to comb through and analyze emails from federal workers. Materials viewed by WIRED show that DOGE affiliates within the Office of Personnel Management (OPM) tested and used Meta's Llama 2 model to review and classify responses from federal workers to the infamous "Fork in the Road" email that was sent across the government in late January. The email offered deferred resignation to anyone opposed to changes the Trump administration was making to its federal workforce, including an enforced return to office policy, downsizing, and a requirement to be "loyal." To leave their position, recipients merely needed to reply with the word "resign." This email closely mirrored one that Musk sent to Twitter employees shortly after he took over the company in 2022.

Politico became one of the first newsrooms last year to win a union contract that included rules on how the media outlet can deploy artificial intelligence. The PEN Guild, which represents Politico and its sister publication, environment and energy site E&E News, is now gearing up for another first. The union's members allege that the AI provisions in their contract have been violated, and they're preparing for a groundbreaking legal dispute with management. The outcome could set a precedent for how much input journalists ultimately have over how AI is used in their newsrooms. Last year, Politico began publishing AI-generated live news summaries during big political events like the Democratic National Convention and the US vice presidential debates.

EXCLUSIVE: First lady Melania Trump is launching an audiobook of her memoir using artificial intelligence (AI) audio technology in multiple languages, Fox News Digital has learned. The first lady released her first memoir, "Melania," last year. This week, she is breaking new ground by releasing "Melania, the Audiobook," which has been "created entirely" with AI. "I am proud to be at the forefront of publishing's new frontier – the intersection of artificial intelligence technology and audio," Trump told Fox News Digital. The first lady said ElevenLabs AI developed "an AI-generated replica of my voice under strict supervision, which will establish an unforgettable connection with my personal story, in multiple languages for listeners worldwide." ElevenLabs AI CEO Mati Staniszewski told Fox News Digital that they are "excited that Melania Trump trusted our technology to power this first-of-its-kind audiobook project."

Good Job is Slate's advice column on work. Have a workplace problem big or small? One of my dearest friends was recently squeezed into an unwanted early retirement by DOGE. The work she was doing at the government agency where she's spent most of her career is on the verge of being eliminated or slashed into oblivion, and it kills me to know that her life's work is about to be reversed. I want to support her through this.

Russia's Defence Ministry said air defences shot down 105 Ukrainian drones over Russian regions, including 35 over the Moscow region, after the ministry said a day earlier that it had downed more than 300 Ukrainian drones. Kherson Governor Oleksandr Prokudin said one person was killed in a Russian artillery attack on the region. H said over the past day, 35 areas in Kherson, including Kherson city, came under artillery shelling and air attacks, wounding 11 people. Ukrainian President Zelenskyy said the "most intense situation" is in the Donetsk region, and the army is continuing "active operations in the Kursk and Belgorod regions". Russia's Defence Ministry said air defences shot down 105 Ukrainian drones over Russian regions, including 35 over the Moscow region, after the ministry said a day earlier that it had downed more than 300 Ukrainian drones.

Bayesian optimization (BO) is a popular method for efficiently inferring optima of an expensive black-box function via a sequence of queries. Existing informationtheoretic BO procedures aim to make queries that most reduce the uncertainty about optima, where the uncertainty is captured by Shannon entropy. However, an optimal measure of uncertainty would, ideally, factor in how we intend to use the inferred quantity in some downstream procedure. In this paper, we instead consider a generalization of Shannon entropy from work in statistical decision theory [13, 39], which contains a broad class of uncertainty measures parameterized by a problem-specific loss function corresponding to a downstream task. We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures such as knowledge gradient, expected improvement, and entropy search. We then show how alternative choices for the loss yield a flexible family of acquisition functions that can be customized for use in novel optimization settings.

In this paper, we propose to use the concept of local fairness for auditing and ranking redistricting plans. Given a redistricting plan, a deviating group is a population-balanced contiguous region in which a majority of individuals are of the same interest and in the minority of their respective districts; such a set of individuals have a justified complaint with how the redistricting plan was drawn. A redistricting plan with no deviating groups is called locally fair. We show that the problem of auditing a given plan for local fairness is NP-complete. We present an MCMC approach for auditing as well as ranking redistricting plans. We also present a dynamic programming based algorithm for the auditing problem that we use to demonstrate the efficacy of our MCMC approach. Using these tools, we test local fairness on real-world election data, showing that it is indeed possible to find plans that are almost or exactly locally fair. Further, we show that such plans can be generated while sacrificing very little in terms of compactness and existing fairness measures such as competitiveness of the districts or seat shares of the plans.

A routine training flight over Arizona in January 2023 took an unusual turn when a U.S. Air Force F-16D was struck by what was initially reported as an unidentified object, but now U.S. defense officials say was a small drone. Fox News confirmed that the incident, which occurred near Gila Bend, Arizona, on Jan. 19, 2023, was a routine training mission and was witnessed by the instructor pilot seated in the rear of the two-seat aircraft. According to a U.S. defense official, the pilot observed a "mostly white and orange object" collide with the left side of the aircraft canopy, the transparent covering over the cockpit. Initially, the object was thought to be a bird, a common hazard for aircraft. But after conducting checks during the flight and a detailed inspection upon landing at Tucson International Airport, the crew found "zero evidence" of a bird strike.