Samsung Electronics is the world's biggest supplier of the memory chips that go into everything from smartphones and electric vehicles to servers at artificial intelligence data centers. Samsung Electronics union members have voted in favor of a compensation deal that will hand chip workers an average bonus of about $340,000, staving off a strike that threatened to disrupt global chip supply. The company's largest union said the deal was signed after about 74% of its members voted in favor of the agreement. Workers accepted a wage proposal that was tentatively agreed by labor leaders last week, just 90 minutes before a planned strike at the world's largest memory chipmaker. Samsung's shares rose as much as 8% in Seoul on Wednesday.

'My job is going': U.K. workers squeezed out by AI In the U.K., the IMF estimated in 2024 that more than two-thirds of British workers perform tasks that AI could potentially carry out. London - When a client asked her a year ago to design a glossary to train an artificial intelligence system, translator Jessica Spengler realized she was going to train her own replacement. "That was the day I really thought ... my job is going," said the 52-year-old, who translates into English for German educational and historical organizations. In the U.K., where services account for around 80% of the economy, AI has become flexible, fast and inexpensive competition for many white-collar workers, with the impacts beginning to emerge. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Meet the UK's very own LeBron James... but not as you know it Look out LeBron James and Michael Jordan, there's a new basketball champ around. But it was made in Lisburn rather than Los Angeles or Chicago. The name 25416 may not appear on many replica vests, but it can shoot hoops like no-one else. And the basketball-playing robot won a school in Lisburn first prize at the UK-wide First Tech Challenge robotics competition. The team of sixth-formers from Friends' School came top of 48 schools from across the UK at the competition held in London's Copper Box Arena. Going down and working on it with my friends is honestly one of the highlights of my last year in school, he said.

Wall Street's AI winner hunt leads to seasoning maker in Japan Ajinomoto, known more as a seasonings and foods maker, holds more than 95% of global market share for insulating materials used in personal computers and data center servers. The beneficiaries of the artificial intelligence buildout are spreading far beyond technology high-flyers. Laura Lau found one in seasoning maker Ajinomoto. The Tokyo-based company is best known for making monosodium glutamate, or MSG, a flavor enhancer used in soups and vegetables. Its lesser-known business, called Build-Up Film, or ABF, makes insulating film used to package high-performance semiconductors.

People visit an Alibaba booth during the World Artificial Intelligence Conference in Shanghai on July 26, 2025. China is restricting overseas travel for top AI professionals in private firms such as Alibaba Group and DeepSeek, suggesting an escalation in measures intended to safeguard its technology and catch up to the U.S. in a pivotal sphere. Government agencies have begun imposing restrictions on individuals involved in advanced AI work and considered strategically important to the country, people familiar with the matter said. That means they need approval from relevant authorities before embarking on overseas travel, the people said, asking for anonymity to discuss a sensitive issue. Beijing has for years imposed travel restrictions on key personnel from prominent college researchers to nuclear scientists and executives at state firms.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

An ethical hacker who just won major prizes at a prestigious international competition says her days of competing could be numbered due to the rise of AI tools like Claude Mythos. Valentina Palmiotti - better known as Chompie - was the most successful individual at the annual Pwn2Own hacking competition in Berlin. She told BBC News that, for now, AI tools were helping her to win bug bounties - money given to hackers who spot vulnerabilities in online systems before they can be exploited by cyber-criminals. But she said systems like Mythos were so powerful that even champion hackers like her would soon struggle to compete with them. AI has shaken the cyber-security world, with concerns focussing on Mythos in particular.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

Lagrangian Relaxation (LR) is a powerful technique for solving large-scale Mixed Integer Linear Programming (MILP), particularly those with decomposable structures, such as vehicle routing or unit commitment problems. By relaxing the coupling constraints, LR enables parallel subproblem solving and often yields tighter dual bounds than standard linear programming relaxations, which is crucial for efficient branch-and-bound pruning. While recent empirical work has shown promising results using machine learning to predict these multipliers, a theoretical understanding of such methods remains an open question. In this work, we bridge this gap by analyzing the problem of learning LR through the lens of Data-driven Algorithm Design, i.e., a statistical learning problem over a distribution of problem instances. Our contributions are as follows: first, we derive a generalization bound of $\mathcal{O}(s^{1.5}/\sqrt{N})$ for the learned multipliers, where $s$ is the number of coupling constraints and $N$ is the sample size. Second, we provide a minimax lower-bound of $Ω(s/\sqrt{N})$, proving that a linear dependency is unavoidable. Third, we constructively close this theoretical gap by proving that Stochastic Gradient Ascent (SGA) with averaging achieves the minimax optimal rate $Θ(s/\sqrt{N})$. Finally, we extend our framework to the learning-to-warm-start setting, proving that it achieves a fast, minimax-optimal rate of $Θ(s/N)$ and establishing a theoretical advantage over direct multiplier prediction.

Selecting a finite dictionary of observables whose span is Koopman-invariant is a central challenge in data-driven Koopman operator approximation. We address this problem by exploiting zero-block structure in Extended Dynamic Mode Decomposition (EDMD) matrices. We show that any sub-dictionary whose span is Koopman-invariant induces an exact zero block in the EDMD matrix, even for finite data. We then show that such blocks can be detected by applying PageRank to a row-normalized EDMD matrix constructed from a large initial dictionary. The theory extends to approximately invariant subspaces and yields stronger guarantees for personalized PageRank (PPR) when the seed observables lie inside the target block and reach all observables in that block. Combining EDMD concentration bounds with PageRank perturbation theory gives end-to-end detection guarantees with $O(1/\sqrt{M})$ finite-sample scaling and explicit constants. More generally, without assuming an invariant subspace exists, high PPR mass on a sub-dictionary controls discounted multi-step leakage from the seed observables. Numerical experiments on the Duffing oscillator, Van der Pol oscillator, Lorenz system, and a three-well Ramachandran potential suggest that the method identifies compact, interpretable dictionaries with accurate predictions.