Google has been back in federal court to fend off the United States Department of Justice's attempt to topple its internet empire at the same time it is navigating a pivotal shift to artificial intelligence (AI) that could undercut its power. On Friday, the legal and technological threats facing Google were among the key issues being dissected during the closing arguments of a legal proceeding that will determine the changes imposed upon the company in the wake of its dominant search engine being declared an illegal monopoly by US District Judge Amit Mehta last year. Brandishing evidence presented during a recent three-week stretch of hearings, Justice Department lawyers are attempting to persuade Mehta to order a radical shake-up that includes a ban on Google paying to lock its search engine in as the default on smart devices and an order requiring the company to sell its Chrome browser. Google lawyers say only minor concessions are needed, especially as the upheaval triggered by advances in artificial intelligence already are reshaping the search landscape, as alternative, conversational search options are rolling out from AI startups that are hoping to use the Department of Justice's four-and-half-year-old case to gain the upper hand in the next technological frontier. Mehta used Friday's hearing to ask probing and pointed questions to lawyers for both sides while hinting that he was seeking a middle ground between the two camps' proposed remedies.

Breakthroughs, discoveries, and DIY tips sent every weekday. In 1917, two US submarines collided off the coast of San Diego and submarine USS F-1 sank to the bottom of the Pacific Ocean, along with 19 crew members aboard. The horrible accident, whose wreckage was discovered in 1975, represents the US Naval Submarine Force's first wartime submarine loss. Now, researchers from Woods Hole Oceanographic Institution have captured new footage of the 1,300 feet-deep underwater archaeological site. "They were technical dives requiring specialized expertise and equipment," Anna Michel, a co-lead of the expedition and chief scientist at the National Deep Submergence Facility, said in a statement. "We were careful and methodical in surveying these historical sites so that we could share these stunning images, while also maintaining the reverence these sites deserve."

On May 16, 2023, Sam Altman appeared before a subcommittee of the Senate Judiciary. The title of the hearing was "Oversight of AI." The session was a lovefest, with both Altman and the senators celebrating what Altman called AI's "printing press moment"--and acknowledging that the US needed strong laws to avoid its pitfalls. "We think that regulatory intervention by governments will be critical to mitigate the risks of increasingly powerful models," he said. The legislators hung on Altman's every word as he gushed about how smart laws could allow AI to flourish--but only within firm guidelines that both lawmakers and AI builders deemed vital at that moment.

US Secretary of State Marco Rubio's salvo against Chinese students, promising to "aggressively revoke" their visas, is the latest move in heightening tensions between the world's two largest economies. Despite a temporary tariff truce reached between them earlier this month, divisions between Washington and Beijing remain wide, with recent ruptures over higher education, artificial intelligence (AI) chips and rare earth minerals. Here's all we know about how relations between China and the United States are worsening despite diplomatic efforts. A US-China trade spat escalated after Trump's administration raised tariffs on Chinese goods to 145 percent earlier this year, with cumulative US duties on some Chinese goods reaching a staggering 245 percent. Under an agreement reached on May 12 following two days of trade talks in Geneva, tariffs on both sides were dropped by 115 percentage points for 90 days, during which time negotiators hope to secure a longer-term agreement.

It's late morning on a Monday in March and I am, for reasons I will explain momentarily, in a private bowling alley deep in the bowels of a 65 million mansion in Utah. Jesse Armstrong, the showrunner of HBO's hit series Succession, approaches me, monitor headphones around his neck and a wide grin on his face. "I take it you've seen the news," he says, flashing his phone and what appears to be his X feed in my direction. Everyone had: An hour earlier, my boss Jeffrey Goldberg had published a story revealing that U.S. national-security leaders had accidentally added him to a Signal group chat where they discussed their plans to conduct then-upcoming military strikes in Yemen. "Incredibly fucking depressing," Armstrong said.

Back in April, OpenAI announced it was rolling back an update to its GPT-4o model that made ChatGPT's responses to user queries too sycophantic. An AI model that acts in an overly agreeable and flattering way is more than just annoying. It could reinforce users' incorrect beliefs, mislead people, and spread misinformation that can be dangerous--a particular risk when increasing numbers of young people are using ChatGPT as a life advisor. And because sycophancy is difficult to detect, it can go unnoticed until a model or update has already been deployed. A new benchmark called Elephant that measures the sycophantic tendencies of major AI models could help companies avoid these issues in the future.

The war between Ukraine and Russia is being fought increasingly via drone --and NATO and US military leadership is training troops for future conflicts that will pit man against machine. Subscribe to Slate Plus to access ad-free listening to the whole What Next family and all your favorite Slate podcasts. Subscribe today on Apple Podcasts by clicking "Try Free" at the top of our show page. Sign up now at slate.com/whatnextplus to get access wherever you listen.

Goodhart's law is a famous adage in policy-making that states that ``When a measure becomes a target, it ceases to be a good measure''. As machine learning models and the optimisation capacity to train them grow, growing empirical evidence reinforced the belief in the validity of this law without however being formalised. Recently, a few attempts were made to formalise Goodhart's law, either by categorising variants of it, or by looking at how optimising a proxy metric affects the optimisation of an intended goal. In this work, we alleviate the simplifying independence assumption, made in previous works, and the assumption on the learning paradigm made in most of them, to study the effect of the coupling between the proxy metric and the intended goal on Goodhart's law. Our results show that in the case of light tailed goal and light tailed discrepancy, dependence does not change the nature of Goodhart's effect. However, in the light tailed goal and heavy tailed discrepancy case, we exhibit an example where over-optimisation occurs at a rate inversely proportional to the heavy tailedness of the discrepancy between the goal and the metric. %

Robust routing under uncertainty is central to real-world logistics, yet most benchmarks assume static, idealized settings. We present SVRPBench, the first open benchmark to capture high-fidelity stochastic dynamics in vehicle routing at urban scale. Spanning more than 500 instances with up to 1000 customers, it simulates realistic delivery conditions: time-dependent congestion, log-normal delays, probabilistic accidents, and empirically grounded time windows for residential and commercial clients. Our pipeline generates diverse, constraint-rich scenarios, including multi-depot and multi-vehicle setups. Benchmarking reveals that state-of-the-art RL solvers like POMO and AM degrade by over 20% under distributional shift, while classical and metaheuristic methods remain robust. To enable reproducible research, we release the dataset and evaluation suite. SVRPBench challenges the community to design solvers that generalize beyond synthetic assumptions and adapt to real-world uncertainty.

Though neural networks trained on large datasets have been successfully used to describe and predict many physical phenomena, there is a sense among scientists that, unlike traditional scientific models comprising simple mathematical expressions, their findings cannot be integrated into the body of scientific knowledge. Critics of machine learning's inability to produce human-understandable relationships have converged on the concept of "interpretability" as its point of departure from more traditional forms of science. As the growing interest in interpretability has shown, researchers in the physical sciences seek not just predictive models, but also to uncover the fundamental principles that govern a system of interest. However, clarity around a definition of interpretability and the precise role that it plays in science is lacking in the literature. In this work, we argue that researchers in equation discovery and symbolic regression tend to conflate the concept of sparsity with interpretability. We review key papers on interpretable machine learning from outside the scientific community and argue that, though the definitions and methods they propose can inform questions of interpretability for scientific machine learning (SciML), they are inadequate for this new purpose. Noting these deficiencies, we propose an operational definition of interpretability for the physical sciences. Our notion of interpretability emphasizes understanding of the mechanism over mathematical sparsity. Innocuous though it may seem, this emphasis on mechanism shows that sparsity is often unnecessary. It also questions the possibility of interpretable scientific discovery when prior knowledge is lacking. We believe a precise and philosophically informed definition of interpretability in SciML will help focus research efforts toward the most significant obstacles to realizing a data-driven scientific future.