The elite Wall Street law firm Sullivan & Cromwell has told a court that a major filing it made in a high-profile case contained errors resulting from hallucinations generated by artificial intelligence. Andrew Dietderich, the co-head of the firm's global restructuring group, apologised in a letter to the New York federal judge Martin Glenn on Saturday for the string of mistakes, which included inaccurate citations. The errors, uncovered by the law firm Boies Schiller Flexner (BSF), which was also working on the case, included misquoting the US bankruptcy code and citing cases incorrectly in a filing made on 9 April. In multiple instances, S&C, which employs more than 900 lawyers and has one of the top reputations for corporate work in the US, filed inaccurately summarised conclusions made in other cases using AI. "We deeply regret that this has occurred," said Dietderich in the letter.

Terra Drone's Terra A1 interceptor drone has entered active combat use in Ukraine after being deployed to a military unit tasked with countering Russian uncrewed aerial systems. Japanese drone company Terra Drone said its Terra A1 interceptor -- developed with its Ukrainian partner Amazing Drones -- has moved from the lab to the front lines, entering active combat use in Ukraine against Russian-made Shahed drones. "Deployment for defense purposes has already begun with a military unit, and evaluation and feedback collection under actual operating conditions are currently under way," the Tokyo-based firm, which recently made a strategic investment in the Ukrainian startup, said in a recent statement. Terra Drone explained that this initial "real-world operational deployment" -- carried out via its local partner -- will follow a phased rollout, where new equipment is first issued to a single unit and then expanded to further deployments depending on evaluations from the field. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

We study the problem of completing a binary matrix in an online learning setting. On each trial we predict a matrix entry and then receive the true entry. We propose a Matrix Exponentiated Gradient algorithm [1] to solve this problem. We provide a mistake bound for the algorithm, which scales with the margin complexity [2, 3] of the underlying matrix. The bound suggests an interpretation where each row of the matrix is a prediction task over a finite set of objects, the columns. Using this we show that the algorithm makes a number of mistakes which is comparable up to a logarithmic factor to the number of mistakes made by the Kernel Perceptron with an optimal kernel in hindsight. We discuss applications of the algorithm to predicting as well as the best biclustering and to the problem of predicting the labeling of a graph without knowing the graph in advance.

Taiwan President Lai Ching-te has cancelled a presidential trip to the African nation of Eswatini, accusing Beijing of putting pressure on its neighbours to bar his aircraft from flying over their territories. Seychelles, Mauritius and Madagascar revoked Lai's overflight permits after intense pressure and economic coercion from China, said a Taiwan official. China denied coercion, while praising the three African countries saying it had high appreciation for them. This is the first publicly known instance where a Taiwanese leader has had to cancel a foreign trip due to revoked flight permits. Eswatini, formerly known as Swaziland, is Taiwan's only diplomatic ally in Africa.

The U.S. military is launching a new autonomous warfare command under SOUTHCOM to deploy unmanned systems across Latin America, SOUTHCOM Gen. Francis Donovan said.

A soldier carries a drone during a military parade in Washington on June 14, 2025. The Pentagon's largest-ever budget request earmarks $75 billion for drones and technologies to counter them, mainly for a massive increase for a little-known office working with U.S. commandos to test and evaluate various systems, according to defense officials. The drone-funding proposal includes $54.6 billion for the Defense Autonomous Working Group, or DAWG, from just $225.9 million this year. That would appear to be the largest single year-over-year boost of any defense program or office, meaning it's likely to draw particular congressional and public scrutiny in an already eye-catching $1.5 trillion request that's 42% larger than this year's budget. The big boost for the Pentagon's little-known drone unit comes as the U.S. and Israeli war against Iran illustrates how drones can help level the playing field against even the world's most well-funded armed forces.

Things to Do in L.A. Tap to enable a layout that focuses on the article. A drone delivered her lethal dose of fentanyl in a church parking lot. The Drug Enforcement Administration was among agencies involved in the investigation. This is read by an automated voice. Please report any issues or inconsistencies here .

Meta to capture U.S. employee mouse movements and keystrokes to train AI NEW YORK - Meta is installing new tracking software on U.S.-based employees' computers to capture mouse movements, clicks and keystrokes for use in training its artificial intelligence models, part of a broad initiative to build AI agents that can perform work tasks autonomously, the company told staffers in internal memos. The tool, called Model Capability Initiative (MCI), will run on work-related apps and websites and will also take occasional snapshots of the content on employees' screens, according to one of the memos, posted by a staff AI research scientist on Tuesday in a channel for the company's model-building Meta SuperIntelligence Labs team. The purpose, according to the memo, was to improve the company's AI models in areas where they struggle to replicate how humans interact with computers, like choosing from dropdown menus and using keyboard shortcuts. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Michael Dell and Susan Dell became the first donors to give more than $1 billion to the University of Texas at Austin, funding a massive AI-native hospital and research campus.

Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data local and confidential. However, traditional federated models often assume homogeneity in the degradation processes across clients, an assumption that may not hold in many industrial settings. To overcome this, this paper proposes a personalized federated prognostic model designed to accommodate clients with heterogeneous degradation processes, allowing them to build tailored prognostic models. The prognostic model iteratively facilitates the underlying pairwise collaborations between clients with similar degradation patterns, which enhances the performance of personalized federated learning. To estimate parameters jointly using decentralized datasets, we develop a federated parameter estimation algorithm based on proximal gradient descent. The proposed approach addresses the limitations of existing federated prognostic models by simultaneously achieving model personalization, preserving data privacy, and providing comprehensive failure time distributions. The superiority of the proposed model is validated through extensive simulation studies and a case study using the turbofan engine degradation dataset from the NASA repository.