A single panel of a comic book can say a lot: it can depict not only where the characters currently are, but also their motions, their motivations, their emotions, and what they might do next. More generally, humans routinely infer complex sequences of past and future events from a of a, even in situations they have never seen before.In this paper, we model how humans make such rapid and flexible inferences. Building on a long line of work in cognitive science, we offer a Monte Carlo algorithm whose inferences correlate well with human intuitions in a wide variety of domains, while only using a small, cognitively-plausible number of samples. Our key technical insight is a surprising connection between our inference problem and Monte Carlo path tracing, which allows us to apply decades of ideas from the computer graphics community to this seemingly-unrelated theory of mind task.

In several real world applications, machine learning models are deployed to make predictions on data whose distribution changes gradually along time, leading to a drift between the train and test distributions. Such models are often re-trained on new data periodically, and they hence need to generalize to data not too far into the future. In this context, there is much prior work on enhancing temporal generalization, e.g.

Safe reinforcement learning is a promising path toward applying reinforcement learning algorithms to real-world problems, where suboptimal behaviors may lead to actual negative consequences. In this work, we focus on the setting where unsafe states can be avoided by planning ahead a short time into the future. In this setting, a model-based agent with a sufficiently accurate model can avoid unsafe states.We devise a model-based algorithm that heavily penalizes unsafe trajectories, and derive guarantees that our algorithm can avoid unsafe states under certain assumptions. Experiments demonstrate that our algorithm can achieve competitive rewards with fewer safety violations in several continuous control tasks.

Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and in turn teach itself. To obtain valid supervision, active attempts typically employ a momentum teacher for pseudo-label prediction yet observe the confirmation bias issue, where the incorrect predictions may provide wrong supervision signals and get accumulated in the training process. The primary cause of such a drawback is that the prevailing self-training framework acts as guiding the current state with previous knowledge because the teacher is updated with the past student only. To alleviate this problem, we propose a novel self-training strategy, which allows the model to learn from the future.

Hamstrung by lawsuits, the state can't officially keep its goal to ban new gas-powered car sales by 2035. But it's going to keep trying. It's been a weird and confusing few weeks for the auto industry--especially for those who hoped to see more batteries on the road in the coming decade. Just this month: Ford announced a retrenchment in its EV business, canceling some battery-powered vehicle plans and delaying others; the European Commission proposed to backtrack its goal to transition fully to zero-emission cars by 2035; the US government said it would loosen rules that would have required automakers to ratchet up the fuel economy of their fleets. BloombergNEF projects 14 million fewer EVs will be sold in the US by 2030 than it did last year--a 20 percent drop.

Pillay is an editorial fellow at TIME. Albania's new AI-generated minister Diella speaks during the parliamentary session for the voting of the new government of Albania, in Tirana, on September 18, 2025. Albania's new AI-generated minister Diella speaks during the parliamentary session for the voting of the new government of Albania, in Tirana, on September 18, 2025. Pillay is an editorial fellow at TIME. In September, Albania appointed an AI system to a cabinet-level position--a world-first. Called Diella (Albanian for "sun"), the system was declared "Minister of State for Artificial Intelligence," and tasked by Albania's Prime Minister with addressing corruption in government contracting.

The latest film in the franchise seems to have not learned any lessons from sci-fi movies past--or from current reality. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Ares, named after the Greek god of war, was built to be an AI super-soldier. Then he found out about, started listening to Depeche Mode, and realized the tech bro who made him might be a hack.

Thinking about science and technology in terms of return on investment misses the point. Here's what kids really need to know. That's what led me to become a professor. As a high school student, one of my major life goals was to figure out how to build an actual light sword. Doing so is all but impossible, so it didn't really matter if I went into engineering or science, but I pursued STEM just the same.

Conversations around birth control usually focus on women, but Kevin Eisenfrats, one of the MIT Technology Review 2025 Innovators Under 35, is working to change that. His company, Contraline, is working toward testing new birth control options for men . Exclusive: A record-breaking baby has been born from an embryo that's over 30 years old Jessica Hamzelou Therapists are secretly using ChatGPT. Exclusive: A record-breaking baby has been born from an embryo that's over 30 years old The embryos were created in 1994, while the expectant father was still a toddler, and donated via a Christian "embryo adoption" agency. Therapists are secretly using ChatGPT. Some therapists are using AI during therapy sessions.

Sign up for our daily newsletter to get the best of The New Yorker in your inbox. The microchip maker Nvidia is a Silicon Valley colossus. After years as a runner-up to Intel and Qualcomm, Nvidia has all but cornered the market on the parallel processors essential for artificial-intelligence programs like ChatGPT. "Nvidia was there at the beginning of A.I.," the tech journalist Stephen Witt tells David Remnick. "They really kind of made these systems work for the first time. We think of A.I. as a software revolution, something called neural nets, but A.I. is also a hardware revolution."