"Everyone thinks they're on this big now," Debbie said, refilling her glass. "I've had it with the journey. I've had it with you people." "I don't think I'm on a journey," Burt said. Life's too short to find out who we really are." It was the first time the six of them had got together for dinner in more than a year (since Maria's diagnosis), and after such a long time (and in celebration of Maria's remission) they'd expected to have more interesting things to tell one another, deeper things, but they were entering dessert territory now, a cake was on the table, and only superficial topics had been broached: Ervin's promotion, Jane and Burt's move to the suburbs, Katherine's recent purchase of a metabolism-tracking device--a pen-shaped item and the cause of Debbie's rant. "How much can you know about yourself, exactly?" she said. "The therapy, the vision quests, the birth charts--do we really need the data on metabolic flexibility, too?" Jane, in Katherine's defense, said that, the more you knew about yourself, the more useful you could be to society. Knowing whether Kat is in fat-or carb-burning mode doesn't help anyone." As a result of Katherine declining cake five minutes earlier, no one had touched it. No one, Debbie included, really wanted to. They'd all overeaten already, drunk too much, made private plans to atone for it the next day. The cake presented a challenge, it sat there taunting them, and Debbie knew this, that you couldn't serve cake to a group of fortysomethings without causing ripples, but what else could she have done? She got it, no one wanted to put on weight, but this was a gorgeous princess cake, just gorgeous, she'd had to drive all the way to Andersonville to get it from that Swedish bakery everyone talked about. Staring at it now, though, she wondered if the cake didn't look a little bit like a tit, the smooth half sphere, the small pink marzipan flower nippling the top of it--and, oh, God, did think it looked like a tit?

The Nobel-winning author, whose newest book is out this week, discusses work by a few of her favorite writers. The Nobel Prize winner Olga Tokarczuk's fiction is known for its interest in the porosity of boundaries--between nations, between ethnicities, between fiction and reality, consciousness and dreams. As her novels and stories stage the constant flux of national borders, particularly in Eastern Europe (Tokarczuk is Polish), they also delight in supernatural and science-fictional elements. In " House of Day, House of Night," out from Riverhead this week, she writes, "All over the world, wherever people are sleeping, small, jumbled worlds are flaring up in their heads, growing over reality like scar tissue." Not long ago, Tokarczuk sent us some remarks about a few of her favorite sci-fi and speculative-fiction writers, whose books mix the fantastical and the prosaic masterfully.

'U.S. sanctions equate us with drug traffickers,' ICC deputy prosecutor says The Hague - The deputy prosecutor of the International Criminal Court on Friday lashed out at U.S. sanctions, arguing they effectively put top court officials on a par with terrorists and drug traffickers. In a wide-ranging interview, Mame Mandiaye Niang also said it would be conceivable to hold an in-absentia hearing against high-level ICC targets such as Israeli Prime Minister Benjamin Netanyahu. Sixty-five-year-old Niang, along with top ICC judges, is subject to sanctions from the administration of U.S. President Donald Trump, in retaliation at the court's arrest warrants for Netanyahu over Israel's campaign in Gaza. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

WIRED Roundup: DOGE Isn't Dead, Facebook Dating Is Real, and Amazon's AI Ambitions In this episode of, we bring you the news of the week, then dive into how some DOGE operatives are still at work in the federal government--despite reports claiming otherwise. Uncanny Valley host Zoë Schiffer is joined by senior editor Leah Feiger to discuss five stories you need to know about this week, from how Amazon is trying to catch up in the AI race to why Facebook Dating is more popular than ever. Then, they dive into how--despite recent reports claiming that it's over--DOGE operatives are still very much working across federal agencies. Who the Hell Is Actually Using Facebook Dating? Sex Workers Built an'Anti-OnlyFans' to Take Control of Their Profits Here's What Its Operatives Are Doing Now 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 on the show, we're bringing you five stories that you need to know about this week, including how despite some reports claiming that the so-called Department of Government Efficiency is pretty much over, DOGE people are actually still at work across federal agencies. I'm joined today by our senior politics editor, Leah Feiger. How are you doing today? I am great because I've spent the day with you, but our gentle listeners don't know that. So the first story this week is one that I saw and I thought, you know what? Leah's going to want to talk about Amazon's artificial intelligence prowess.

In 2023, Noam Chomsky, considered the founder of modern linguistics, wrote that LLMs "learn humanly possible and humanly impossible languages with equal facility." However, in the Mission: Impossible Language Models paper that received a Best Paper award at the 2024 Association of Computational Linguistics (ACL) conference, researchers shared the results of their testing of Chomsky's theory, having discovered that language models actually struggle with learning languages with non-standard characters. Rogers Jeffrey Leo John, CTO of DataChat Inc., a company that he cofounded while working at the University of Wisconsin as a data science researcher, said the Mission: Impossible paper challenged the idea that LLMs can learn impossible languages as effectively as natural ones. "The models [studied for the paper] exhibited clear difficulties in acquiring and processing languages that deviate significantly from natural linguistic structures," said John. "Further, the researchers' findings support the idea that certain linguistic structures are universally preferred or more learnable both by humans and machines, highlighting the importance of natural language patterns in model training. This finding could also explain why LLMs, and even humans, can grasp certain languages easily and not others."

Data-driven methods have become paramount in modern systems and control problems characterized by growing levels of complexity . In safety-critical environments, deploying these methods requires rigorous guarantees, a need that has motivated much recent work at the interface of statistical learning and control. However, many existing approaches achieve this goal at the cost of sacrificing valuable data for testing and calibration, or by constraining the choice of learning algorithm, thus leading to suboptimal performances. In this paper, we describe Pick-to-Learn (P2L) for Systems and Control, a framework that allows any data-driven control method to be equipped with state-of-the-art safety and performance guarantees. P2L enables the use of all available data to jointly synthesize and certify the design, eliminating the need to set aside data for calibration or validation purposes. In presenting a comprehensive version of P2L for systems and control, this paper demonstrates its effectiveness across a range of core problems, including optimal control, reachability analysis, safe synthesis, and robust control. In many of these applications, P2L delivers designs and certificates that outperform commonly employed methods, and shows strong potential for broad applicability in diverse practical settings.

Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts impede appropriate clinical care. We introduce and evaluate SmartAlert, a machine learning (ML)-driven clinical decision support (CDS) system integrated into the electronic health record that predicts stable laboratory results to reduce unnecessary repeat testing. This case study describes the implementation process, challenges, and lessons learned from deploying SmartAlert targeting complete blood count (CBC) utilization in a randomized controlled pilot across 9270 admissions in eight acute care units across two hospitals between August 15, 2024, and March 15, 2025. Results show significant decrease in number of CBC results within 52 hours of SmartAlert display (1.54 vs 1.82, p <0.01) without adverse effect on secondary safety outcomes, representing a 15% relative reduction in repetitive testing. Implementation lessons learned include interpretation of probabilistic model predictions in clinical contexts, stakeholder engagement to define acceptable model behavior, governance processes for deploying a complex model in a clinical environment, user interface design considerations, alignment with clinical operational priorities, and the value of qualitative feedback from end users. In conclusion, a machine learning-driven CDS system backed by a deliberate implementation and governance process can provide precision guidance on inpatient laboratory testing to safely reduce unnecessary repetitive testing.

In this episode of, we talk to writer Evan Ratliff about how he created a small startup made entirely of AI employees--and what his findings reveal about the reality of an agentic future. This year, AI agents have been at the forefront of tech companies' ambitions. OpenAI's Sam Altman has often talked about a possible billion-dollar company being spun up with just one human and an army of AI agents. And so last summer, journalist Evan Ratliff decided to try to become that unicorn himself--by creating HarumoAI, a small startup that's made up of AI employees and executives. Hosts Michael Calore and Lauren Goode sit down with Evan to discuss how it's going, and the current promises and realities of AI agents. 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 . Hey, Lauren, how are you doing? It was so fantastic that I had a hard time coming back, honestly. And I saw a lot of really beautiful art. Not a bad place to go for vacation, I have to say. I've heard this before, I confirmed it. And after seeing so much incredible art and just people doing stuff with their hands and tangible goods, I was like, I don't want to go back to the world of AI. I didn't want to go back to sitting in a coffee shop and hearing everyone pitching their AI startups and driving on the 101 and seeing the billboards. I was just like, What? No, keep me in the land of Burrata and Caravaggio. Well, Lauren, I'm sorry to tell you that you came back on the show just in time to talk about AI agents. It's something that we've talked about a lot this year and our listeners have heard about it a lot, and we're not sick of talking about it.

Migrating a set of software variants into a software product line (SPL) is an expensive and potentially challenging endeavor. Indeed, SPL engineering can significantly impact a company's development process and often requires changes to established developer practices. The work presented in this paper stems from a collaboration with a Small and Medium-sized Enterprise (SME) that decided to migrate its existing code base into an SPL. In this study, we conducted an in-depth evaluation of the company's current development processes and practices, as well as the anticipated benefits and risks associated with the migration. Key stakeholders involved in software development participated in this evaluation to provide insight into their perceptions of the migration and their potential resistance to change. This paper describes the design of the interviews conducted with these stakeholders and presents an analysis of the results. Among the qualitative findings, we observed that all participants, regardless of their role in the development process, identified benefits of the migration relevant to their own activities. Furthermore, our results suggest that an effective risk mitigation strategy involves keeping stakeholders informed and engaged throughout the process, preserving as many good practices as possible, and actively involving them in the migration to ensure a smooth transition and minimize potential challenges.

We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingual representation. Unlike many prior models that release weights without reproducible data pipelines or regard for content-owner rights, Apertus models are pretrained exclusively on openly available data, retroactively respecting `robots.txt` exclusions and filtering for non-permissive, toxic, and personally identifiable content. To mitigate risks of memorization, we adopt the Goldfish objective during pretraining, strongly suppressing verbatim recall of data while retaining downstream task performance. The Apertus models also expand multilingual coverage, training on 15T tokens from over 1800 languages, with ~40% of pretraining data allocated to non-English content. Released at 8B and 70B scales, Apertus approaches state-of-the-art results among fully open models on multilingual benchmarks, rivalling or surpassing open-weight counterparts. Beyond model weights, we release all scientific artifacts from our development cycle with a permissive license, including data preparation scripts, checkpoints, evaluation suites, and training code, enabling transparent audit and extension.