We asked teens what schools should teach them about work. More than four in 10 UK teenagers want more work experience opportunities at school, with a similar proportion calling for more guidance on how to apply for jobs, a survey for the BBC suggests. The online survey, by Survation,, external asked 2,000 young people aged 13 to 18 about the world of work and the help they need to secure employment once they leave full-time education. Some called for more support with their communications skills and raised concerns about stress management and personal finances. Skills Minister Jacqui Smith said it was the government's mission to ensure teenagers leave school confident about their next step and equipped with the skills and experience they need to succeed.

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The actor-director discussed his least favorite currency and read a series of mean tweets--about us!--at our inaugural WIRED@Night event. Ben McKenzie had a question: "When did WIRED die?" Specifically, the actor-director wanted to know when did WIRED "'DIE,' all caps." McKenzie wasn't asking for himself; he was engaging in the time-honored celebrity tradition of reading mean tweets . McKenzie, who famously played Ryan on before becoming a leading voice of crypto skepticism, was sharing the stage with WIRED senior correspondent Andy Greenberg for the first of what will hopefully be a series of smaller events that we are calling WIRED@Night. On April 16, about 100 people gathered at event partner Ace Hotel Brooklyn to sip drinks from Aplos, Faccia Brutto, The Sorting Table, and Manojo and ponder the future of cryptocurrency.

In some ways, Gigi is like any other young social media influencer. With perfect hair and makeup, she logs on and talks to her fans. She shares clips: eating, doing skin care, putting on lipstick. She even has a cute baby who appears in some videos. But after a few seconds, something may seem a little off.

The Consumer Electronics Show (CES), which starts today in Las Vegas, is an odd beast. It is the biggest technology event of the year, a sprawling conference that spills over multiple casinos and convention centres to dominate a city that is hard to overshadow. But for the better part of a decade it has been an afterthought for some of the world's biggest businesses, led by Apple realising that if you can get the press to come to you, you don't need to risk burying your product launches under hundreds of competing newslines. The result is that CES is no longer where you see the future, but where you learn how that future will get copied into a thousand cheap plastic knockoffs. There are, of course, exceptions.

Systems of competing agents can often be modeled as games. Assuming rationality, the most likely outcomes are given by an equilibrium (e.g. a Nash equilibrium). In many practical settings, games are influenced by context, i.e. additional data beyond the control of any agent (e.g. weather for traffic and fiscal policy for market economies). Often the exact game mechanics are unknown, yet vast amounts of historical data consisting of (context, equilibrium) pairs are available, raising the possibility of learning a solver which predicts the equilibria given only the context. We introduce Nash Fixed Point Networks (N-FPNs), a class of neural networks that naturally output equilibria. Crucially, N- FPNs employ a constraint decoupling scheme to handle complicated agent action sets while avoiding expensive projections. Empirically, we find N-FPNs are compatible with the recently developed Jacobian-Free Backpropagation technique for training implicit networks, making them significantly faster and easier to train than prior models. Our experiments show N-FPNs are capable of scaling to problems orders of magnitude larger than existing learned game solvers.

Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy. Instead of sending private data to a central server as in traditional approaches, FL decentralizes computations: devices train locally and share updates with a global server. A primary challenge in this setting is achieving fast and accurate model training--vital for recommendation systems where delays can compromise user engagement. This paper introduces FedFNN, an algorithm that accelerates decentralized model training. In FL, only a subset of users are involved in each training epoch. FedFNN employs supervised learning to predict weight updates from unsampled users, using updates from the sampled set. Our evaluations, using real and synthetic data, show: (i) FedFNN achieves training speeds 5x faster than leading methods, maintaining or improving accuracy; (ii) the algorithm's performance is consistent regardless of client cluster variations; (iii) FedFNN outperforms other methods in scenarios with limited client availability, converging more quickly.

This morning, the New York Times' Kevin Roose called what has been a big week for generative AI tools a "coming out" party [subscription required]. He detailed an actual party, on Monday night, which celebrated a massive funding round for Stability AI, the startup behind Stable Diffusion, the uber-popular image-generating algorithm that was only launched publicly two months ago. But this week was chock-full of other significant news around generative AI (which refers to using unsupervised learning algorithms to learn from existing text, audio or images and create new content -- and now includes popular tools including GPT-3, DALL-E 2 and Imagen as well as nascent text-to-video options from OpenAI and Google). There was the news that Microsoft would add DALL-E to its Office suite and to Azure AI, while Adobe was planning to add generative AI tools to Photoshop and also committed to transparency in its use of generative AI. Then, besides Stable Diffusion's news, content generator Jasper also announced a massive funding round of $125 million, solidifying VC interest in the generative AI space.

It's been just a few weeks since OpenAI began allowing customers to commercially use images created by DALL-E 2, its remarkably powerful AI text-to-image system. But in spite of the current technical limitations and lack of volume licensing, not to mention API, some pioneers say they're already testing the system for various business use cases -- awaiting the day when DALL-E 2 becomes stable enough to deploy into production. Stitch Fix, the online service that uses recommendation algorithms to personalize apparel, says it has experimented with DALL-2 to visualize its products based on specific characteristics like color, fabric and style. For example, if a Stitch Fix customer asked for a "high-rise, red, stretchy, skinny jean" during the pilot, DALL-E 2 was tapped to generate images of that item, which a stylist could use to match with a similar product in Stitch Fix's inventory. "DALL-E 2 helps us surface the most informative characteristics of a product in a visual way, ultimately helping stylists find the perfect item that matches what a client has requested in their written feedback," a spokesperson told TechCrunch via email.