Luke Littler, the youngest darts world champion in history, has applied to the Intellectual Property Office to trademark his face. The move is intended to prevent his face being reproduced, including by generative AI, without permission. Littler has won two World Championship titles in a row and has had his image used legally on darts merchandise, as well as by multiple brands such as KP Nuts. The 19-year-old joins celebrities such as actor Matthew McConaughey who have filed to protect their likeness from AI misuse in recent months. Littler has already trademarked his nickname the Nuke in the United States.

Peter Vandermeersch admitted using AI to'wrongly put words into people's mouths'. Peter Vandermeersch admitted using AI to'wrongly put words into people's mouths'. Mediahuis suspends Peter Vandermeersch, who says he'fell into trap of hallucinations', after investigation by newspaper where he was once editor-in-chief The publisher of the Dutch newspaper De Telegraaf and the Irish Independent has suspended one of its senior journalists after he admitted using AI to "wrongly put words into people's mouths". Peter Vandermeersch, the former head of the Irish operations at Mediahuis, said he "fell into the trap of hallucinations" - the term for AI-generated errors - when using the technology . Vandermeersch, a fellow of "journalism and society" at the European publishing group, has been suspended from his role.

OpenAI is developing a unified desktop superapp that will integrate ChatGPT, Codex, and Atlas into a single application, according to PCWorld's coverage of The Wall Street Journal report. This consolidation aims to reduce service fragmentation and improve overall quality for users accessing OpenAI's various AI tools. The superapp represents a significant shift toward streamlined AI services, potentially making OpenAI's offerings more accessible and efficient for desktop users. It seems you'll soon be able to access most of OpenAI's services in one place on your computer.

Tech companies believe in intellectual property, but not yours. In April 2024, Eric Schmidt, the former Google CEO and a current AI evangelist, gave a closed-door lecture to a group of Stanford students. If these young people hoped to be Silicon Valley entrepreneurs, Schmidt explained, then they should be prepared to breach some ethical boundaries. Yet Schmidt told the students to go ahead and download whatever they need to build an accurate "test" version of their AI product. If the product takes off, "then you hire a whole bunch of lawyers to go clean the mess up," he said.

At Palantir's Developer Conference, AI Is Built to Win Wars As business soars, Palantir is doubling down on a vision of AI built for battlefield advantage--and attracting customers who agree. The defense contractors, military officers, and corporate executives in attendance are unprepared for the weather; they'd assumed the previous day's mid-70s temperatures would hold. A cold rain turns to steady snowfall, and Palantir passes out heavy blankets. As people move between open-air pavilions, it looks like they were pulled from shipwrecks. To this self-selecting crowd, Palantir is delivering on its promises.

PCWorld reports that Windows 11's March update KB5079473 is causing login failures across Microsoft apps including Teams, OneDrive, Xbox app, and Microsoft Store. Users encounter "You'll need the Internet for this" errors or code 0x800704cf despite having active internet connections after the problematic update. Microsoft recommends restarting your PC while connected to the internet as a temporary workaround, with an official patch expected soon. Ever since Windows 11's big March update, users have reported login issues with certain apps. At the very least, apps that require a Microsoft account are affected, including Teams, OneDrive, Microsoft 365 Copilot, the Xbox app, and the Microsoft Store.

A startup is transforming polluted air into apparel. At least 15 major brands, including H&M and Walmart, are testing new technology for carbon neutral clothing. Breakthroughs, discoveries, and DIY tips sent six days a week. It might not seem like it when you nonchalantly click a Buy Now button while online shopping, but that new t-shirt is part of a complex global web of commerce taking a toll on the environment . Consulting giant McKinsey estimates that the fashion industry alone accounts for as much as 4 percent of total global climate emissions.

Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics. However, the immense scale of the chemical space makes it challenging to explore all possible materials experimentally. In this paper, we introduce FlowLLM, a novel generative model that combines large language models (LLMs) and Riemannian flow matching (RFM) to design novel crystalline materials. FlowLLM first fine-tunes an LLM to learn an effective base distribution of meta-stable crystals in a text representation. After converting to a graph representation, the RFM model takes samples from the LLM and iteratively refines the coordinates and lattice parameters. Our approach significantly outperforms state-of-the-art methods, increasing the generation rate of stable materials by over three times and increasing the rate for stable, unique, and novel crystals by $\sim50$% - a huge improvement on a difficult problem. Additionally, the crystals generated by FlowLLM are much closer to their relaxed state when compared with another leading model, significantly reducing post-hoc computational cost.

Existing Goal-Conditioned Reinforcement Learning (GCRL) algorithms are built upon Hindsight Experience Replay (HER), which densifies rewards through hindsight replay and leverages historical goal-achieving information to construct a learning curriculum. However, when the task is characterized by a non-Markovian reward (NMR), whose computation depends on multiple steps of states and actions, HER can no longer densify rewards by treating a single encountered state as the hindsight goal. The lack of informative rewards hinders policy learning, resulting in rolling out failed trajectories. Consequently, the replay buffer is overwhelmed with failed trajectories, impeding the establishment of an applicable curriculum. To circumvent these limitations, we deviate from existing HER-based methods and propose an on-policy GCRL framework, GCPO, which is applicable to both multi-goal Markovian reward (MR) and NMR problems.GCPO consists of (1) Pre-training from Demonstrations, which pre-trains the policy to possess an initial goal-achieving capability, thereby diminishing the difficulty of subsequent online learning.

Surveys have recently gained popularity as a tool to study large language models. By comparing models' survey responses to those of different human reference populations, researchers aim to infer the demographics, political opinions, or values best represented by current language models. In this work, we critically examine language models' survey responses on the basis of the well-established American Community Survey by the U.S. Census Bureau. Evaluating 43 different language models using de-facto standard prompting methodologies, we establish two dominant patterns. First, models' responses are governed by ordering and labeling biases, for example, towards survey responses labeled with the letter "A".