Elon Musk, who co-founded OpenAI, is suing the ChatGPT developer and its CEO, Sam Altman, left, over claims its leaders violated founding nonprofit mission. Elon Musk, who co-founded OpenAI, is suing the ChatGPT developer and its CEO, Sam Altman, left, over claims its leaders violated founding nonprofit mission. Judge says there is plenty of evidence to suggest OpenAI's leaders made assurances nonprofit structure would be kept Elon Musk's lawsuit against OpenAI is to go to trial after a US judge said there is plenty of evidence to support the billionaire's case. The world's richest man, who co-founded OpenAI, is suing the ChatGPT developer and its chief executive, Sam Altman, over claims its leaders violated the organisation's founding mission by shifting to a for-profit model. The US district judge Yvonne Gonzalez Rogers in Oakland, California, told a hearing there was plenty of evidence that suggested OpenAI's leaders made assurances that its original nonprofit structure was going to be maintained.

The supply shortage of the RAM needed to build phones and PCs isn't going away. But a few companies have a plan to solve it. A supply shortage is the last thing tech companies want to talk about at CES . The annual trade show is their chance to promote new products and drum up excitement for what's coming, not discuss the one thing that could make selling new products in 2026 an uphill battle. But I've read the reports.

Natural languages exhibit striking regularities in their statistical structure, including notably the emergence of Zipf's and Heaps' laws. Despite this, it remains broadly unclear how these properties relate to the modern tokenisation schemes used in contemporary transformer models. In this note, we analyse the information content (as measured by the Shannon entropy) of various corpora under the assumption of a Zipfian frequency distribution, and derive a closed-form expression for the slot entropy expectation value. We then empirically investigate how byte--pair encoding (BPE) transforms corpus statistics, showing that recursive applications of BPE drive token frequencies toward a Zipfian power law while inducing a characteristic growth pattern in empirical entropy. Utilizing the ability of transformers to learn context dependent token probability distributions, we train language models on corpora tokenised at varying BPE depths, revealing that the model predictive entropies increasingly agree with Zipf-derived predictions as the BPE depth increases. Attention-based diagnostics further indicate that deeper tokenisation reduces local token dependencies, bringing the empirical distribution closer to the weakly dependent (near IID) regime. Together, these results clarify how BPE acts not only as a compression mechanism but also as a statistical transform that reconstructs key informational properties of natural language.

We introduce Neural Optimal Design of Experiments, a learning-based framework for optimal experimental design in inverse problems that avoids classical bilevel optimization and indirect sparsity regularization. NODE jointly trains a neural reconstruction model and a fixed-budget set of continuous design variables representing sensor locations, sampling times, or measurement angles, within a single optimization loop. By optimizing measurement locations directly rather than weighting a dense grid of candidates, the proposed approach enforces sparsity by design, eliminates the need for l1 tuning, and substantially reduces computational complexity. We validate NODE on an analytically tractable exponential growth benchmark, on MNIST image sampling, and illustrate its effectiveness on a real world sparse view X ray CT example. In all cases, NODE outperforms baseline approaches, demonstrating improved reconstruction accuracy and task-specific performance.

Be cautious if you opt to use it. OpenAI is launching a new facet for its AI chatbot called ChatGPT Health . This new feature will allow users to connect medical records and wellness apps to ChatGPT in order to get more tailored responses to queries about their health. The company noted that there will be additional privacy safeguards for this separate space within ChatGPT, and said that it will not use conversations held in Health for training foundational models. ChatGPT Health is currently in a testing stage, and there are some regional restrictions on which health apps can be connected to the AI company's platform.

An AI model that learns without human input--by posing interesting queries for itself--might point the way to superintelligence. Even the smartest artificial intelligence models are essentially copycats. They learn either by consuming examples of human work or by trying to solve problems that have been set for them by human instructors. But perhaps AI can, in fact, learn in a more human way--by figuring out interesting questions to ask itself and attempting to find the right answer. A project from Tsinghua University, the Beijing Institute for General Artificial Intelligence (BIGAI), and Pennsylvania State University shows that AI can learn to reason in this way by playing with computer code.

The new Atlas will be deployed at Hyundai and Google DeepMind first. Boston Dynamics' Atlas is finally entering production. After years of testing this humanoid robot (and forcing it to dance), the robotics company announced at CES 2026 that the final version of the machine is being built now. The first companies to receive deployments will be Hyundai, Boston Dynamics' majority shareholder, and Google DeepMind, the firm's new AI partner . This final enterprise version of Atlas can perform a wide array of industrial tasks, according to Boston Dynamics, and is specifically designed with consistency and reliability in mind.

LLMs contain a LOT of parameters. They're the mysterious numbers that make your favorite AI models tick. What are they and what do they do? I am writing this because one of my editors woke up in the middle of the night and scribbled on a bedside notepad: "What is a parameter?" Unlike a lot of thoughts that hit at 4 a.m., it's a really good question--one that goes right to the heart of how large language models work. A large language model's parameters are often said to be the dials and levers that control how it behaves.

AI chatbots miss urgent issues in queries about women's health AI models such as ChatGPT and Gemini fail to give adequate advice for 60 per cent of queries relating to women's health in a test created by medical professionals Many women are using AI for health information, but the answers aren't always up to scratch Commonly used AI models fail to accurately diagnose or offer advice for many queries relating to women's health that require urgent attention. Thirteen large language models, produced by the likes of OpenAI, Google, Anthropic, Mistral AI and xAI, were given 345 medical queries across five specialities, including emergency medicine, gynaecology and neurology. The queries were written by 17 women's health researchers, pharmacists and clinicians from the US and Europe. The answers were reviewed by the same experts. Any questions that the models failed at were collated into a benchmarking test of AI models' medical expertise that included 96 queries.

When you purchase through links in our articles, we may earn a small commission. Consolidate your AI apps into one platform built for creators and businesses -- now a flat $59.99 Get three years of access to 1min.AI's Advanced Business Plan for $59.99 (MSRP $299) and unlock a massive suite of AI tools powered by leading models like GPT-4o, Claude, Gemini, and more. Wrangling a different AI tool for every task gets messy fast -- one for writing, one for images, one for video, another for audio, and suddenly you have more subscriptions than actual productivity. That's where this $59.99 1min.AI deal steps in, giving you one platform that can do just about everything in your workflow. From content writing to SEO research to image editing to video generation, it's all baked in, powered by top models from OpenAI, Anthropic, Google, Meta, Mistral, and Cohere.