Apple could unveil Gemini-powered Siri in Feb. Prism can edit and format LaTeX. OpenAI is releasing a new app called Prism today, and it hopes it does for science what coding agents like Claude Code and its own Codex platform have done for programming. Prism builds on Crixet, a cloud-based LaTeX platform the company is announcing it acquired today. For the uninitiated, LaTeX is a typesetting system for formatting scientific documents and journals. Nearly the entire scientific community relies on LaTeX, but it can make some tasks, such as drawing diagrams through TikZ commands, time-consuming to do.

PCWorld reports that OpenAI is testing an updated Temporary Chat feature for ChatGPT that retains personal customizations without saving conversations. This hidden setting functions like Incognito Mode, allowing private chats while maintaining user personalization preferences across sessions. AI engineer Tibor Blaho discovered the optional feature, though OpenAI may still store chat copies for up to 30 days for security purposes. OpenAI is testing an update to ChatGPT's "Temporary Chat" feature . Temporary Chat allows you to "have a conversation with a blank slate" where "ChatGPT won't be aware of previous conversations or access memories." It's a lot like Incognito Mode in Chrome, which you can use to "privately" browse the web--except here, it's chatting. The update to Temporary Chat will sort of tweak this, allowing ChatGPT to retain your personal customizations without the conversation itself being saved or affecting your overall account. Temporary chat in ChatGPT has a new, currently hidden option that lets you still use personalization (memory, chat history, style and tone preferences) even though the chat is only temporary ChatGPT web app now also mentions new ChatGPT FinServ plans (Enterprise-like), shopping pic.twitter.com/CaMaeYlCmP

Apple could unveil Gemini-powered Siri in Feb. Gemini 3 is now Google's default model for AI Overviews Plus, you can start an AI Mode conversation directly from a summary. The Google logo and lettering can be seen on the façade of the company's Munich headquarters building in Munich (Bavaria). Google has begun rolling out two upgrades for Search. Starting today, Gemini 3 is the default model powering AI Overviews. When the company debuted its new family of AI systems last November, it first deployed Gemini 3 in AI Overviews through a router that was programmed to direct the most difficult questions to the new system.

PCWorld reports that DuckDuckGo's recent poll revealed 90% of respondents prefer searching without AI integration, highlighting growing user resistance to AI-powered search features.

He Xiaopeng launches Xpeng's next-gen Iron humanoid robot during a press conference at the company's headquarters in Guangzhou on November 5, 2025. He Xiaopeng launches Xpeng's next-gen Iron humanoid robot during a press conference at the company's headquarters in Guangzhou on November 5, 2025. It was a controversy laced with pride for He Xiaopeng. In November, He, the founder and CEO of Chinese physical AI firm XPeng, had just debuted his new humanoid robot, IRON, whose balance, posture shifts, and coquettish swagger mirrored human motion with such eerie precision that a slew of netizens accused him of faking the demonstration by putting a human in a bodysuit. To silence the naysayers, He boldly cut open the robot's leg live on stage to reveal the intricate mechanical systems that allow it to adapt to uneven surfaces and maintain stability just like the human body. "At first, it made me sad," He tells TIME in his Guangzhou headquarters.

In the three years since ChatGPT's explosive debut, OpenAI's technology has upended a remarkable range of everyday activities at home, at work, and in schools. Now OpenAI is making an explicit play for scientists. In October, the firm announced that it had launched a whole new team, called OpenAI for Science, dedicated to exploring how its large language models could help scientists and tweaking its tools to support them. How does a push into science fit with OpenAI's wider mission? And what exactly is the firm hoping to achieve? I put these questions to Kevin Weil, a vice president at OpenAI who leads the new OpenAI for Science team, in an exclusive interview.

We asked tech CEOs, journalists, entertainers, students, and more about the promise and peril of artificial intelligence. The future never feels fully certain. But in this time of rapid, intense transformation--political, technological, cultural, scientific--it's as difficult as it ever has been to get a sense of what's around the next corner. Here at WIRED, we're obsessed with what comes next. Our pursuit of the future most often takes the form of vigorously reported stories, in-depth videos, and interviews with the people helping define it.

When you purchase through links in our articles, we may earn a small commission. Get a 1min.AI Advanced Business Plan lifetime subscription on sale for $74.97 and never switch tools or worry about fees again (MSRP $540). Monthly AI subscriptions add up fast--and once you've enlisted ChatGPT for writing, Gemini for research, and another tool for images or audio, you might already feel like you're in too deep. But 1min.AI is combining them all into a single, lifetime AI platform without subscription fees. So, you need to generate articles for work?

Unsupervised text segmentation is crucial because boundary labels are expensive, subjective, and often fail to transfer across domains and granularity choices. We propose Embed-KCPD, a training-free method that represents sentences as embedding vectors and estimates boundaries by minimizing a penalized KCPD objective. Beyond the algorithmic instantiation, we develop, to our knowledge, the first dependence-aware theory for KCPD under $m$-dependent sequences, a finite-memory abstraction of short-range dependence common in language. We prove an oracle inequality for the population penalized risk and a localization guarantee showing that each true change point is recovered within a window that is small relative to segment length. To connect theory to practice, we introduce an LLM-based simulation framework that generates synthetic documents with controlled finite-memory dependence and known boundaries, validating the predicted scaling behavior. Across standard segmentation benchmarks, Embed-KCPD often outperforms strong unsupervised baselines. A case study on Taylor Swift's tweets illustrates that Embed-KCPD combines strong theoretical guarantees, simulated reliability, and practical effectiveness for text segmentation.

Load balancing-the allocation of work across parallel resources to reduce delay, energy and cost-is a pervasive challenge in science and engineering, from large-scale simulation and data processing to cloud and manufacturing operations. Motivated by the emerging bottleneck in large language model (LLM) serving, we study a particularly stringent regime of load balancing that arises in barrier-synchronized, stateful systems: work cannot be freely migrated and progress is gated by the slowest participant at each step, so heterogeneity and temporal drift in workloads create persistent stragglers and substantial idle time. LLM serving under data-parallel decoding provides a prominent modern instance: in production traces, barrier-induced idle can exceed 40% of compute time per decode step. Here we develop a universal load-balancing principle, which admits a step-wise finite-horizon integer-optimization formulation and yields worst-case guarantees: across LLM decode models and a broader class of non-decreasing workload drift processes, it reduces long-run imbalance by a factor that grows with batch size and system scale. Extensive experiments corroborate the theory, showing substantial improvements in throughput and latency together with reductions in energy consumption. These results provide a general, theoretically grounded framework for load balancing, with immediate implications for sustainable LLM serving and broad relevance to other synchronization-gated resource-allocation problems.