The government has launched a series of free AI training courses designed to help people learn how to use the technology at work. The online lessons give advice on things such as how to prompt chatbots or use them to assist with admin tasks. Many of the courses are free, with others subsidised, and the government aims to reach 10 million workers by 2030 - calling it the most ambitious training scheme since the launch of the Open University in 1971. But the Institute for Public Policy Research (IPPR) has warned workers will need to know more than just how to prompt a chatbot as the workforce adapts to the growth of AI. Skills for the age of AI can't be reduced to short technical courses alone, said Roa Powell, senior research fellow at the IPPR.

This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirical modeling, and data-intensive infrastructures. The town hall was structured around open panel discussion and extensive audience Q&A, with the aim of eliciting candid, experience-driven perspectives rather than formal presentations or prepared statements. This document preserves the extended exchanges among panelists and audience members, with minimal editorial intervention, and organizes the conversation around five recurring questions concerning disciplinary culture and practices, data curation and "data work," engagement with modern empirical modeling, training for large-scale AI applications, and partnerships with key AI stakeholders. By providing an archival record of this discussion, the preprint aims to support transparency, community reflection, and ongoing dialogue about the evolving role of statistics in the data- and AI-centric future.

Confirming which users are kids is politically fraught and a technical nightmare. Here's what moves from OpenAI and the FTC tell us. How do tech companies check if their users are kids? This question has taken on new urgency recently thanks to growing concern about the dangers that can arise when children talk to AI chatbots. For years Big Tech asked for birthdays (that one could make up) to avoid violating child privacy laws, but they weren't required to moderate content accordingly. Two developments over the last week show how quickly things are changing in the US and how this issue is becoming a new battleground, even among parents and child-safety advocates.

On this episode of, our hosts unpack the news from Davos, where Trump and major AI companies shared the stage at the World Economic Forum. This week, WIRED's Brian Barrett and Leah Feiger are joining the show as the new cohosts, alongside Zoë Schiffer. And our attention has been drawn to the drama going down in the quaint little town of Davos. Zoë tells us how at the World Economic Forum's event, major AI players like Anthropic have been the protagonists--sharing the spotlight with President Donald Trump, who insists on invading Greenland. Brian has been looking at how ICE activity is developing, and Leah is forcing us to think about this year's midterms because tech giants are already pouring millions into it. Plus, we dive into why OpenAI's decision to roll out ads in ChatGPT was a long time coming. Ads Are Coming to ChatGPT. Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . Today, we're starting a bit of a new chapter here on the show, and I want to introduce you to my brand new cohost, Brian Barrett, our executive editor here at WIRED, and Leah Feiger, our senior politics editor. So thrilled to be here. So longtime listeners know the show has taken on a bunch of different formats since it launched. We had the Gadget Lab days, the roundtable, news episodes. We really created this podcast because we want to bring you the best stories and the best takes about what's happening in tech and politics. That's all going to stay the same, but this time we're going to go even deeper. What trends you should be watching for, the news that's already happened or about to break, and how we are thinking about all of it.

Reinforcement learning (RL) has achieved remarkable success in real-world decision-making across diverse domains, including gaming, robotics, online advertising, public health, and natural language processing. Despite these advances, a substantial gap remains between RL research and its deployment in many practical settings. Two recurring challenges often underlie this gap. First, many settings offer limited opportunity for the agent to interact extensively with the target environment due to practical constraints. Second, many target environments often undergo substantial changes, requiring redesign and redeployment of RL systems (e.g., advancements in science and technology that change the landscape of healthcare delivery). Addressing these challenges and bridging the gap between basic research and application requires theory and methodology that directly inform the design, implementation, and continual improvement of RL systems in real-world settings. In this paper, we frame the application of RL in practice as a three-component process: (i) online learning and optimization during deployment, (ii) post- or between-deployment offline analyses, and (iii) repeated cycles of deployment and redeployment to continually improve the RL system. We provide a narrative review of recent advances in statistical RL that address these components, including methods for maximizing data utility for between-deployment inference, enhancing sample efficiency for online learning within-deployment, and designing sequences of deployments for continual improvement. We also outline future research directions in statistical RL that are use-inspired -- aiming for impactful application of RL in practice.

Lego also recognized that it had to build a course that'll work regardless of a teacher's fluency in such subjects. So a big part of developing the course was making sure that teachers had the tools they needed to be on top of whatever lessons they're working on. "When we design and we test the products, we're not the ones testing in the classroom," Silwinski said. "We give it to a teacher and we provide all of the lesson materials, all of the training, all of the notes, all the presentation materials, everything that they need to be able to teach the lesson." Lego also took into account the fact that some schools might introduce its students to these things starting in Kindergarten, whereas others might skip to the grade 3-5 or 6-8 sets.

We (AIhub) will be running a short course on science communication on Wednesday 21 January, from 13:00 - 14:30. In this brief tutorial, science communication experts will teach you how to clearly and concisely explain your research to non-specialists.

A contract justification published in a federal register on Tuesday says that 31 ICE vehicles operating in the Twin Cities area "lack the necessary emergency lights and sirens" to be "compliant." Paul area "currently lack the necessary emergency lights and sirens" required to be "compliant with law enforcement requirements," according to a contract justification published in a federal register on Tuesday. The document justifies ICE paying Whelen Engineering Company, a Connecticut-based firm specializing in "emergency warning and lighting technology," $47,330.49 The document explains that the ATLAS kits would "allow vehicles to be immediately operational and compliant with law enforcement requirements to support the current surge operation" out of Homeland Security Investigations (HSI)'s St. Paul office, which conducts operations in Minnesota, North Dakota, and South Dakota. "These vehicles were deployed prior to being permanently retrofitted and currently lack the necessary emergency lights and sirens required for operational use," the document says.

Grab a lifetime membership and learn up to 25 languages instead of scrolling your life away in 2026. We may earn revenue from the products available on this page and participate in affiliate programs. Learning a new language is a very common resolution. It's useful, stimulating, and can even be fun if you choose the right method. Right now, Rosetta Stone has discounted its lifetime memberships, which means you can pay once and learn forever.

Over the course of the year, we've had the pleasure of working with many talented researchers from across the globe. As 2025 draws to a close, we take a look back at some of the excellent blog posts, interviews and podcasts from our contributors. Jiahui Zhang and Jesse Zhang to tell us about their framework for learning robot manipulation tasks solely from language instructions without per-task demonstrations. Hui Zhang writes about work presented at CoRL2025 on RobustDexGrasp, a novel framework that tackles different grasping challenges with targeted solutions. In this podcast from AAAI, host Ella Lan asked Professor Marynel Vázquez about what inspired her research direction, how her perspective on human-robot interactions has changed over time, robots navigating the social world, and more.