OpenAI has signed deals with fintech startups, tech giants and even Disney, but it's breaking new ground by announcing a world's first partnership with the country of Malta. In a post on its website, OpenAI said that it would provide ChatGPT Plus for one year to every Maltese resident or citizen. Malta is the first country to launch a partnership of this scale because we refuse to let our citizens stay behind in the digital age, Silvio Schembri, Malta's minister for Economy, Enterprise and Strategic Projects, said in a statement. We are putting our people at the very forefront of global change. For the approximately 574,250 residents living in Malta, they'll have to complete a course developed by the University of Malta before launching the ChatGPT Plus subscription, which costs $20 a month in the US.

Both Musk and Altman took the stand for hours, facing combative cross-examinations that painted them each as untrustworthy. Both Musk and Altman took the stand for hours, facing combative cross-examinations that painted them each as untrustworthy. Two of the world's richest people faced an airing of their dirty laundry amid their messy, bitter feud over OpenAI A nine-person jury is set to decide whether Elon Musk's allegations of "stealing a charity" against Sam Altman and OpenAI are legitimate, with deliberations to begin in earnest on Monday. Whatever its outcome, the case has been an illuminating, at times exhausting, look behind the scenes at the history of OpenAI and how some of the most powerful figures in the tech industry operate. Attorneys for both sides have introduced reams of private text messages, emails and even diary entries to support their arguments.

Musk v. Altman week 3: Elon Musk and Sam Altman traded blows over each other's credibility. Now the jury will pick a side. The trial spilled plenty of dirt--and raised more questions than answers about how the AI giant should be governed. In the final week of the trial, lawyers traded blows over Elon Musk's and OpenAI CEO Sam Altman's credibility. Altman was grilled on his alleged history of lying and self-dealing involving companies that do business with OpenAI. But he fired back, painting Musk as a power-seeker who wanted to control the development of artificial general intelligence (AGI)--powerful AI that can compete with humans on most cognitive tasks.

OpenAI is rolling out a preview of a new personal finance feature inside of ChatGPT. Starting today, Pro users in the US can connect their financial accounts to ChatGPT in order to get more personalized advice from the chatbot. To hear OpenAI tell it, every month more than 200 million users already turn to ChatGPT for guidance on managing their money. By building a framework that allows those people to connect their accounts to its servers, ChatGPT can go from offering generic advice to helping those same users take actions that more directly improve their lives. The integration is made possible through a partnership OpenAI has signed with Plaid, which offers connections to more than 12,000 financial institutions, including banks like Citi and Chase, in addition to services like Affirm and Robinhood.

Plus: as their trial goes to the jury, Musk and Altman face lying accusations. China's short drama industry is fueled by bite-sized, melodramatic, and smutty shows built for smartphone scrolling. Now, many are being made entirely with AI: no actors, camera operators, cinematographers, or CGI specialists required. An average of 470 AI-generated short dramas were released every day in January. Production timelines have shrunk from months to weeks, while costs have dropped by up to 90%. Storytelling is also increasingly driven by performance data.

Mira Murati Wants Her AI to'Keep Humans in the Loop' The Thinking Machines Lab founder and former CTO of OpenAI tells WIRED she isn't interested in automating people out of jobs. Instead, she's building AI that can collaborate. Mira Murati still wants to build AI superintelligence. But the ex-CTO of OpenAI sees human intelligence as a critical part of the equation. At a time of rising worry over AI eliminating jobs and increasing the power of few big companies, Murati's startup, Thinking Machines Lab, offers a radically different vision of the technology.

Claim, counter-claim and tech's seedy side exposed: Five things we learned in the Musk-Altman trial It is the legal showdown that has pitted two of the biggest names in tech, Elon Musk and Sam Altman, against each other. At stake is the future of one of the world's most valuable start-ups, ChatGPT-maker OpenAI, along with the reputations of Altman - the company's boss - and Musk, the man he founded it with. The central claim the jury has now retired to consider is Musk's argument his former friend stole a charity, cheating him out of a fortune (albeit a tiny one, by Musk's standards) along the way - something Altman strongly rejects. But there's been much more to the trial than that. Over the past three weeks, myself and other reporters have been glued to our seats at the federal court in California as the evidence ranged from explosive text messages to revelations of free Teslas allegedly offered in exchange for power.

A federal jury is now deciding whether Elon Musk will win his lawsuit against OpenAI and Sam Altman--but the trial has made everyone look bad. Attorneys delivered closing arguments in the trial on Thursday in a final attempt to convince a judge and jury that their respective clients, Elon Musk and Sam Altman, are the most well-intentioned, truth-telling stewards of OpenAI's founding nonprofit mission. A judgement could be delivered as soon as next week, ending a decade-long battle between two of the technology industry's most influential entrepreneurs. But regardless of the outcome, there is a wide set of losers in this case. Based on ample amounts of evidence, it appears that the people worst off are the employees, policymakers, and members of the public who believed in the mission of a nonprofit research lab--and supported OpenAI because of it.

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving PDE-constrained inverse problems, but their extension to Bayesian inversion still faces a fundamental difficulty: prior distributions are typically defined in the weight space of neural networks, whereas physically meaningful prior assumptions are more naturally expressed in function space. In this study, we introduce a unified framework, termed functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks (fpBPINN), to incorporate functional priors into Bayesian PINN-based inversion. We consider two complementary approaches. The first is a functional-prior-informed Bayesian PINN (FPI-BPINN), in which a neural network weight prior is learned to be consistent with a prescribed functional prior, and Bayesian inference is subsequently performed in weight space. The second is function-space particle-based variational inference for PINNs (fParVI-PINN), which performs Bayesian estimation using ParVI directly in function space. We also show that random Fourier features (RFF) play an important role in representing Gaussian functional priors with neural networks and in improving posterior approximation. We applied the proposed approaches to one-dimensional seismic traveltime tomography and two-dimensional Darcy-flow permeability inversion. These numerical experiments showed that both approaches accurately estimated posterior distributions, highlighting the significance of introducing physically interpretable functional priors into Bayesian PINN-based inverse problems. We also identified the contrasting advantages of FPI-BPINN and fParVI-PINN, namely flexibility and accuracy, respectively.

Reinforcement learning (RL) for large language models (LLMs) is dominated by the cost of rollout generation, which has motivated the use of low-precision rollouts (e.g., FP8) paired with a BF16 trainer to improve throughput and reduce memory pressure. This introduces a rollout-training mismatch that biases the policy gradient and can cause training to collapse outright on reasoning benchmarks. We show that the mismatch is non-stationary and acts as a double-edged sword: early in training it provides a stochastic exploration bonus, exposing the gradient to trajectories the trainer would otherwise under-sample, but the same perturbation transitions into a destabilizing source of bias as the policy concentrates. To solve this, we propose Adaptive Importance Sampling (AIS), a correction framework that adjusts the strength of its intervention on a per-batch basis. AIS combines three real-time diagnostics, namely weight reliability, divergence severity, and variance amplification, into a single mixing coefficient that interpolates between the uncorrected and fully importance-weighted gradients, suppressing the destabilizing component of the mismatch while preserving its exploratory benefit. We integrate AIS into GRPO and evaluate it on the diffusion-based LLaDA-8B-Instruct and the autoregressive Qwen3-8B and Qwen3.5-9B across mathematical reasoning and planning benchmarks. AIS matches the BF16 baseline on most tasks while retaining the 1.5 to 2.76x rollout speedup of FP8.