PCWorld reports that Nvidia's rumored N1X chip could revolutionize Windows laptops with a 20-core CPU, Blackwell GPU, and impressive AI performance potentially rivaling Qualcomm's Snapdragon X2 Elite. The N1X represents Nvidia's entry into laptop processors, promising better battery life and AI capabilities as laptop costs soar and consumers seek affordable alternatives. However, gaming performance may suffer due to x86 emulation challenges that plague all Arm-based processors, limiting the chip's appeal for gamers. Nvidia is evidently not content to be the world's most valuable company, as the AI and GPU giant now appears primed to dive headfirst into the choppy waters of the laptop processor market. Whether that will help or hurt its fortunes remains to be seen, as the Internet has been aflame this month with rumors that Nvidia will unveil a new "N1X" chip this week at Computex alongside a weaker N1 chip - and the word is both will be SoC (system-on-chip) silicon aimed at Windows laptops. That could be a big deal for anyone who wants to buy a laptop in the next few years, because everything I've heard about the N1X suggests it's optimized for AI performance, battery life, and perhaps even gaming. If Nvidia's efforts to partner with companies like MediaTek and Intel has produced a capable CPU married to a svelte Nvidia GPU on a single chip, utilizing Nvidia's expertise in building high-performance systems for AI and enterprise use, that's potentially a game-changer for the laptop market - and a big challenge to AMD, Apple, and Qualcomm's flagship laptop chips.

The CEO of Google DeepMind tells WIRED that companies should use the productivity gains of AI to do more, not lay people off. Demis Hassabis, the CEO of Google DeepMind, is keen to talk about the coding skills of his company's newest model, Gemini 3.5 Flash. The model has been trained to perform complex agentic coding tasks: translate large code bases from one language to another; find and fix bugs lurking deep in knotty code; and even write entire operating systems from scratch. Hassabis does not, however, think this spells doom for software developers. "I have no idea why people are going around talking with certainty about that," Hassabis tells WIRED ahead of the new model reveal at today's Google's I/O event .

Why does Amazon have no Western rivals? Vitamins, repair tape and a jar of mango chutney - just some of what my household bought last month via Amazon's sprawling online shopping platform. We also shopped at the company's supermarket chain Whole Foods, streamed its TV shows, read books on Kindle e-readers, and browsed countless websites no doubt powered by Amazon Web Services (AWS), its highly profitable cloud-computing business. And that isn't half of the interconnected products and services offered by the global behemoth, which earlier this year overtook US superstore giant Walmart to become the world's largest company by annual sales. But why does Amazon, launched by Jeff Bezos in 1995 as an online bookstore out of a rented garage, have so few serious rivals in the West when it comes to e-commerce?

We propose PathBoost, a gradient tree boosting method for graph-level classification and regression that learns discriminative path-based features directly from the input graph structure. Building on a previous work, which was tailored to a specific chemistry application, PathBoost introduces three key extensions: (i) adaptation to binary classification through gradient boosting with a logistic loss, (ii) incorporation of multiple node and edge attributes into the path feature space via a prefix-based decomposition, and (iii) automatic anchor node selection based on categorical attribute diversity, eliminating the need for the user to specify the starting point of the considered path features. We compared PathBoost to graph neural networks and graph kernel approaches on several benchmark datasets, obtaining better results in half of them, and comparable results in the rest. PathBoost shows better performances on graphs with larger average node counts. Overall, the results demonstrate that path-based boosting methods can be competitive with more complex black-box approaches.

Large language models often improve reasoning by sampling multiple outputs and aggregating their final answers, but precise and efficient control of error levels remains a challenging task. In particular, deciding when to stop sampling remains difficult when the stopping rule is data-dependent and the set of possible response labels is not known in advance. We study anytime-valid certification of a prespecified target answer as the unique mode of the model's response distribution, a guarantee distinct from answer correctness. We propose the Certification by Intersection-union Testing with Eprocesses (CITE) algorithm, which provably controls false certification at any prescribed level under arbitrary data-driven stopping, without requiring prior knowledge of the answer category set. We also prove a category-set-size-free stopping-time rate, establish matching minimax lower bounds up to constants in the main regime, and extend the construction to confidence-weighted voting. Simulations and LLM self-consistency experiments show empirical error control and improved certification in diffuse-tail settings.

Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI's models Musk kept his cool, and OpenAI's lawyer bulldozed him with piercing questions about his motivations for suing the company. In the first week of the landmark trial between Elon Musk and OpenAI, Musk took the stand in a crisp black suit and tie and argued that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into bankrolling the company. Along the way, he warned that AI could destroy us all and sat through revelations that he had poached OpenAI employees for his own companies. He even confessed, to some audible gasps in the courtroom, that his own AI company, xAI, which makes the chatbot Grok, uses OpenAI's models to train its own. The federal courthouse in Oakland, California, was packed with armies of lawyers carrying boxes of exhibits, journalists typing away at their laptops, and a handful of concerned OpenAI employees. Outside, protesters lined the streets, carrying signs urging people to quit ChatGPT, boycott Tesla, or both.

Musk accuses OpenAI lawyer of trying to'trick' him in combative testimony In his second day on the stand, Elon Musk was at times combative under questioning by OpenAI's lawyer, whom he accused of asking overly complicated questions. Your questions are not simple, he told lawyer William Savitt at one point. They're designed to trick me essentially, Musk is suing fellow OpenAI co-founder Altman and the AI firm, alleging they misled him by shifting the organisation away from its non-profit roots toward a for-profit model. OpenAI says Musk is motivated by jealousy and regret for walking away from the company in 2018. It has also accused Musk, head of xAI, of trying to derail one of his key rivals.

A trial pitting two founders of OpenAI - Sam Altman and Elon Musk - against each other has opened in California, with the sides presenting duelling narratives about the company's history and obligations to consumers. Musk, wearing a dark suit and tie, was asked by one of his lawyers what the lawsuit was about when he took the stand. It's actually very simple, he said. It's not okay to steal a charity... If it's okay to loot a charity, the entire foundation of charitable giving will be destroyed.

The bitter feud between Elon Musk and OpenAI boss Sam Altman has raged for years, but has mostly played out online in the form of accusations, counter-accusations and jibes. But starting on Tuesday, the beef between the two tech billionaires will shift to a much higher-profile forum: a federal courtroom in California, where their row will be the focus of a month-long trial. Being considered is Musk's claim that Altman - with whom he founded OpenAI - has swindled him out of millions of dollars and reneged on the ChatGPT-maker's original non-profit mission. Musk and Altman themselves will be among those to testify in a case in which the future of AI could be at stake. And while one will presumably emerge the winner, it's plausible that neither will emerge from the saga unscathed.