Proton's Andy Yen Says It Will Have To Proton's CEO is a champion of encryption for everyone. So why is he going all in on un-encryptable AI? In the eyes of many privacy advocates, AI is just another surveillance technology . It supercharges online tracking and real-world spying tools, sifts through vast troves of everyone's data to spit out its results, and then encourages users to share their deepest secrets and desires with a cloud-hosted large-language model. Surveillance capitalism made AI possible, and AI returns the favor by making surveillance more powerful and the businesses that run on it more data-rich and profitable than ever. Andy Yen is among the most influential voices in the tech industry who equates AI with privacy invasion. He says the push to integrate AI into every consumer tech product--whether people want it or not--is driving new users in droves to Proton's products, which are, put simply, the end-to-end encrypted versions of every Google service from Gmail to Google Docs, Sheets, Calendar, and Meet. Yet Yen is anti-AI, he says, any more than he is anti-internet. In late June, Proton released the latest version of its very own AI chatbot, Lumo--just one way that Proton is integrating AI ever more deeply into its suite of tools, but seeking to do so in a way that's more voluntary and preserves users' privacy far more than the typical tech giant. That willingness to embrace the apparent contradiction between AI and privacy is just one way in which Yen's views don't cut cleanly across party lines--from current US politics to the lessons of Edward Snowden's leaks to the question of the best corporate structure for a tech company that's not solely designed to maximize profit. You can read our conversation about all of it here or listen wherever you get your podcasts. ANDY GREENBERG: I want to start by asking about your background and the origin story of Proton. You're a trained particle physicist. You worked at CERN, the nuclear research facility, but you were inspired by the leaks of Edward Snowden back in 2013 to create Proton, right? I'm not actually a tech person or even an entrepreneur by training, so I really happened into this field a little bit by accident. I used to work at the Large Hadron Collider, which is a particle accelerator.

Robotics company Foundation says its humanoid robots could conduct autonomous surveillance and reconnaissance along the border, though DHS says no formal contract is in place.

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Akari describes her work on augmented language models, where language models are trained to use other models and tools. This has already resulted in OpenScholar, an open-source model which helps scientists to synthesize vast amounts of scientific literature. You were awarded the 2025 AAAI Doctoral Dissertation Award. What was the topic of your dissertation research, and why was this an interesting area of study to you? My PhD dissertation was on Retrieval-Augmented Language Models.

The CEO of Foundation Future Industries, which counts the president's son as its chief strategy adviser, tells WIRED it's exploring some "kinetic things." Some companies want their humanoid robots to fold your clothes. Others want them in the workplace . Sankaet Pathak and his startup Foundation Future Industries have a slightly different goal: produce an all-American robot supersoldier. Pathak, Foundation's CEO, says his company plans to start giving its humanoids lethal capabilities soon, although he declined to share specifics.

Mathematicians put AI to work on Fermat's last theorem At an event in London, mathematicians have made unexpectedly fast progress on formalising Fermat's last theorem using AI In the lobby of a central London hotel, tourists are bracing themselves for a day of sightseeing in a heatwave. Meanwhile, staff are resetting the dining room after breakfast. And in a windowless meeting room, assembled academics are contemplating whether humans have a role to play in the future of mathematics, now that AI can prove theorems by itself. The general mood in the room is one of bewilderment at the recent jump in computer intelligence and excitement about the potential it unlocks - and perhaps a slight unease about what the future holds for them personally. Twenty-five researchers from diverse fields and countries are here to spend a week working on formalising Fermat's last theorem with cutting-edge AI models.

As AI reshapes how work gets done, organizations with strong process frameworks are best positioned to lead and maintain operational rigor at scale. Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos--a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to embed habits of measurement, analysis, and accountability into day-to-day company culture. But today, those time-tested playbooks are evolving as companies seek to embed AI into established process excellence methodologies. By some estimates, the market for AI-powered process optimization is projected to exceed $113 billion within the next decade.

The increasing societal integration of Large Language Models (LLMs) and agentbased AI demands a new civic competency: adversarial reasoning. This position paper argues that K-12 AI education must move beyond passive literacy to actively equip students with skills in responsible adversarial prompting and ethical system "hacking." Such capabilities are essential for citizens to critically probe AI systems, understand their inherent limitations, identify manipulative patterns, and hold them accountable. We posit that cultivating a generation skilled in "red-teaming" AI is vital for maintaining transparency, preventing undue influence, and fostering a democratic engagement with these transformative technologies.

Kolmogorov-Arnold Networks (KANs) have demonstrated remarkable expressive capacity and predictive power in symbolic learning. However, existing generalization errors of KANs primarily focus on approximation errors while neglecting estimation errors, leading to a suboptimal bias-variance trade-off and poor generalization performance. Meanwhile, the unclear generalization mechanism hinders the design of more effective KANs variants. As the authors of KANs highlighted, they ``would like to explore ways to restrict KANs' hypothesis space so that they can achieve good performance''. To address these challenges, we explore the generalization mechanism of KANs and design more effective KANs with lower model complexity and better generalization. We define \textit{Lipschitz complexity} as the first structural measure for deep functions represented by KANs and derive novel generalization bounds based on \textit{Lipschitz complexity}, establishing a theoretical foundation for understanding their generalization behavior. To reduce \textit{Lipschitz complexity} and boost the generalization mechanism of KANs, we propose Lipschitz-Enhanced KANs ($\textbf{LipKANs}$) by integrating the Lip layer and pioneering the $L_{1.5}$-regularized

They define and propose it as sufficient for protection.