'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.

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.

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This material may not be published, broadcast, rewritten, or redistributed.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 .

Predictive models trained on observational data often fail to generalise to the distributions they encounter when deployed, especially when the training data is a product of the system being optimised. Recommender systems are a canonical example: they are trained on interaction logs confounded by the deployed policy, past user behaviour, and platform filtering. As a result, the training distribution differs substantially from the candidate distribution scored at serving time, a gap that makes offline metrics unreliable predictors of online performance. We address the distribution shift problem with a method motivated by causal representation learning (CRL). We propose an information-theoretic disentanglement criterion and prove that its optimum depends only on the causal components of the input. We then derive a tractable variational lower bound that makes the criterion optimisable from finite observational data alone. The scope of our method is narrower than that of much of the CRL literature, in that we target better generalisation under distribution shift, not full identification of all latent causal factors. This narrower target is what makes the method practical, requiring only the existing confounded logs, applying to any standard supervised model, and adding no inference-time cost. Our headline evaluation is an A/B test with millions of users on Spotify, applied to a production ranker for personalised playlist generation. A capacity-matched CRL variant performed on par offline but delivered substantial online gains in listener engagement. Complementary evidence on the public KuaiRand recommendation dataset and a synthetic benchmark with known causal structure shows the same pattern: offline parity with baseline, gains under distribution shift. Across all three settings, adding our causal disentanglement objective yields meaningfully better out-of-distribution generalisation.

Sharrell Shaw's request for strangers to visit her husband's Arlington grave on Memorial Day drew over 6 million views and visits from Trump administration officials.

Paul Anka says Toronto's crime rate has spiked amid the arrival over 400,000 new immigrants, telling Bill Maher that Canada was homogenous until recently.

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Cuban Foreign Minister Bruno Rodriguez Parrilla calls State Secretary Marco Rubio a liar, accusing him of deceiving the American people about Cuba being a threat to the United States.