Bang Chun-ja, a 78-year-old South Korean woman living alone, holding Hyodol, an artificial intelligence-powered healthcare doll designed for the elderly, during an interview at her home in Yongin in April. Yongin, South Korea - In her tiny apartment in South Korea where she lives alone, 78-year-old Bang Chun-ja spends her days with a childlike artificial intelligence-powered doll she says she prefers to people. The doll greets Bang when she returns home, sings to her when she feels bored, reminds her not to skip meals or medication -- helping her maintain a routine -- and tells her it loves her. Bang has limited contact with her grown-up daughter, and fell into severe depression after major back surgery, spending hours alone staring at the ceiling in pain. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

China-based actors are likely behind the use of ChatGPT for "covert influence operations" aimed at stoking opposition to data centres in the United States, OpenAI has said. In a research report released on Wednesday, the company behind the world's most popular AI chatbot said it had banned a cluster of accounts likely based in China for attempting to "manipulate a legitimate debate about American AI". Among other content, the accounts generated a comic strip showing a cigar-chomping businessman holding bags marked with dollar signs as a family reacted in shock to their electricity bill, according to the San Francisco-based company. OpenAI said a second cluster of accounts had generated content casting US tariffs as an effort to "dominate technological competition" with China, and specified that the material should not mention Chinese leader Xi Jinping. While the campaign sought to "exploit and amplify existing public concerns" about energy prices, OpenAI found no evidence that it had a "meaningful" influence, the company said.

A group of high school students arrested over allegedly trying to extort money from a boy in western Tokyo may have used ChatGPT to decide how much to demand, police said. A group of high school students in Tokyo arrested over allegedly assaulting a boy and trying to extort money from him may have used ChatGPT to decide how much to demand, media reports have recently revealed. Five teenagers, including a 17-year-old girl and four boys ranging in age from 16 to 17, were arrested in January over the alleged assault and attempted extortion of a 17-year-old high school student in the city of Hachioji in western Tokyo, according to the Metropolitan Police Department. Police said the suspects assaulted the boy in a plaza in Hachioji's Shiroyamate district, breaking his nose and causing other injuries, before allegedly trying to extort ¥150,000 ($935) from him. The girl, who was the victim's ex-girlfriend, allegedly first confronted him, accusing him of touching her younger sister's leg. She then challenged him, saying, "Give me the money or fight me one-on-one," according to reports by Fuji TV.

Dipu Tamang from Nepal is among more than 400,000 international students in Japan. When Dipu Tamang arrived in Japan from Nepal in 2024, he joined a growing stream of young people who see the country less as a traditional study destination and more as a structured route into work and long-term opportunity. The 22-year-old graduated from Shinjuku Heiwa Japanese Language School in March and now studies international business at a vocational college in Tokyo. He juggles part-time work as a convenience store clerk and hotel housekeeper to help cover his living expenses. "At first, I was interested in Japanese pop culture," he said. "Then I wanted to learn the language.

North Korea will'never' get nuclear recognition, EU and South Korea say European Commission President Ursula von der Leyen shakes hands with South Korean President Lee Jae Myung next to European Council President Antonio Costa on the day of an EU-South Korea summit in Brussels on Wednesday. South Korea and the European Union have said that North Korea will "never" be recognized as a nuclear-weapon state, reaffirming their commitment to denuclearization days after China and North Korea pledged closer ties at a summit that made no public mention of the issue. South Korean President Lee Jae Myung held talks with European Commission President Ursula von der Leyen and European Council President Antonio Costa on Wednesday in Brussels, where they agreed to step up defense ties, including efforts to facilitate the exchange of classified information. "The DPRK will never be accepted as a nuclear-weapon state," the EU and South Korea said in a joint statement, referring to North Korea's formal name, the Democratic People's Republic of Korea. 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.

Several countries have been considering tightening rules around AI use as well as social media use for children. OTTAWA - The Canadian government introduced a digital safety bill on Wednesday that would ban social media for children under 16 with exemptions for platforms that meet certain safety standards, months after Australia enacted the world's first social media ban for young people. The bill also aims to make AI chatbots safer by setting up a digital regulator to establish safety standards, a government official said. Companies could face penalties of 3% of global revenue or up to 10 million Canadian dollars ($7.2 million), whichever is more, for failing to comply. "Social media platforms and AI chatbots are designed to capture attention. They do not support healthy childhood development and have become a source of anxiety, isolation, depression and a range of other mental health challenges for many young Canadians," said Marc Miller, minister of Canadian identity and culture.

We study online estimation for high-dimensional generalized linear models with streaming data. First, for the non-distributed setting, we propose a gradient-enhanced surrogate loss that approximates the cumulative loss using only historical summaries, which modifies and improves upon the existing renewable estimation approach for the same model in the high-dimensional setting, and removes the batch-number constraint in previous studies. We then extend the method to distributed streaming data under the master-client architecture, where batches are partitioned across sites and only summaries (gradient vectors) are exchanged. Instead of directing applying the popular method of Jordan et al. (2019) to the surrogate quadratic loss, our adjusted approach does not require the clients to compute the full surrogate loss. We derive non-asymptotic error bounds under the high-dimensional scaling, without the stringent constraint on the number of batches in the previous studies. Simulation results under linear and logistic models, together with a real-data application, show improved accuracy over existing renewable estimators.

Attention is the key mechanism underlying in-context learning in transformers, and attention patterns have been observed empirically to emerge abruptly during training. We present a Bayesian theory of feature learning in attention; we then focus on how the copy subcircuit in the first layer of an induction head is learned by analyzing a single-layer softmax attention network trained on a copy task. We derive a closed-form posterior over the attention matrix and reduce it to a low-dimensional order parameter space. This reduction reveals a phase transition in the amount of training data, which we verify using both Bayesian sampling and standard training with Adam. We contrast our results with linear attention and find that softmax attention exhibits a \emph{first-order phase transition} while in linear attention an initial \emph{second-order phase transition} is followed by a smooth, continuous evolution toward the structured attention pattern (\emph{crossover}). Our work provides a first-principles theoretical account of the abrupt emergence of the copy subcircuit, reminiscent of the one observed in training large language models.

Given this law, the evolution of a continuoustime autonomous1 dynamical system is determined by its Uncertainty has become a central topic in machine learning initial state. Or, stated differently, the evolution is given by (ML), with increasing interest in the distinction between aleatoric and epistemic uncertainty. Aleatoric uncertainty the solution to the initial value problem reflects randomness inherent in a process, whereas epistemicx (t) = f(x(t)), x(0) = x0, (1) uncertainty originates from a lack of knowledge about that process. The former is therefore irreducible, while the latterwhere x(t) X is the state at time t and x0 X the can, in principle, be reduced by gathering more information (Hullermeier and Waegeman, 2021).

We study distributed optimization with stochastic gradients and finite-bit communication modeled by random (unbiased) quantization. We propose q-PDGD, a quantized stochastic primal-dual method, and analyze it under relaxed global geometry. Under restricted secant inequality (RSI), a constant step-size yields linear contraction to an explicit neighborhood determined by gradient noise, quantization distortion, and network connectivity, while a diminishing step-size achieves O(1/k) convergence without shared-minimizer assumptions. Under Polyak-Lojasiewicz (PL) inequality, we obtain linear-to-neighborhood convergence in the same stochastic quantized setting. Our results match the best-known centralized stochastic rates in oracle complexity, and are supported by experiments demonstrating the predicted tradeoffs between quantization level, step-size choice, and graph structure.