A U.S. judge has ordered South Korean game developer Krafton to reinstate the head of one of its video game studios after ruling that he had been improperly removed as part of a takeover plan hatched by ChatGPT. WILMINGTON, DELAWARE - A Delaware judge on Monday ordered that South Korean game developer Krafton reinstate the head of one of its video game studios, ruling he had been improperly removed as part of a takeover plan hatched by ChatGPT. Krafton CEO Changhan Kim had largely followed the advice of artificial intelligence tool ChatGPT during a $250 million dispute with the leaders of the Subnautica game maker Unknown Worlds Entertainment, which Krafton had acquired, according to the ruling by Vice Chancellor Lori Will of the Court of Chancery in Delaware. Businesses and governments are scrambling for new ways to use AI, and the technology has been blamed for mass layoffs, fears of autonomous weapons and concerns about civil rights. Companies caught in takeover-related legal battles often spend millions of dollars on teams of attorneys and advisers from top-flight Wall Street firms. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Deep Learning (DL) have significantly enhanced Network Intrusion Detection Systems (NIDS), improving the effectiveness of cybersecurity operations.

Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs across different languages. In this paper, we show how disparity in the treatment of different languages arises at the tokenization stage, well before a model is even invoked. The same text translated into different languages can have drastically different tok-enization lengths, with differences up to 15 times in some cases. These disparities persist even for tokenizers that are intentionally trained for multilingual support.

Normalization methods play an important role in enhancing the performance of deep learning while their theoretical understandings have been limited.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Students originally from overseas attend entrance exam preparation classes for high school advancement at YSC Global School in the city of Fussa, Tokyo, on Jan. 22. As policies related to foreign nationals are expected to be a major issue in Sunday's Lower House election in Japan, some are calling for improvements to Japanese language education for the children of foreign residents. In 2010, Youth Support Center, a nonprofit organization in the city of Fussa, Tokyo, established YSC Global School to provide Japanese language education and support for high school entry for children and young people with foreign roots, tailored to their proficiency levels. The school offers a total of 14 face-to-face and online courses and annually admits about 250 to 300 children from countries such as China, the Philippines and Nepal. Limited classrooms and instructors, however, hinder its ability to accommodate more students. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Elon Musk said on Monday that SpaceX has acquired his artificial intelligence startup, xAI, in a record-setting deal that unifies the billionaire's AI and space ambitions by combining the rocket-and-satellite company with the maker of the Grok chatbot. The deal, first reported last week, represents one of the most ambitious tie-ups in the technology sector yet, combining a space-and-defense contractor with a fast-growing AI developer whose costs are largely driven by chips, data centers and energy. It could also bolster SpaceX's data-center ambitions as Musk competes with rivals such as Alphabet's Google, Meta, Amazon-backed Anthropic and OpenAI in the AI sector. 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. With your current subscription plan you can comment on stories.

A Mos Food Services employee places an order via a microphone at an artificial intelligence drive-thru facility, which was unveiled to members of the media in Yoshikawa City, Saitama Prefecture, on Wednesday. The Japanese hamburger chain aims to improve store management efficiency by automating part of customer interaction with conversational AI amid a serious labor shortage. The company plans to introduce the new AI system at multiple outlets in fiscal 2026, which begins in April. In a media demonstration held at a store in the city of Yoshikawa, Saitama Prefecture, a Mos Food employee acting as a customer spoke into a microphone to place a drive-thru order. The AI system took the order after making suggestions such as, We recommend a limited-time avocado burger. Once the system is introduced, store employees will prepare food based on customer orders transmitted from the AI system.

Real-world tabular databases routinely combine continuous measurements and categorical records, yet missing entries are pervasive and can distort downstream analysis. We propose Statistical-Neural Interaction (SNI), an interpretable mixed-type imputation framework that couples correlation-derived statistical priors with neural feature attention through a Controllable-Prior Feature Attention (CPFA) module. CPFA learns head-wise prior-strength coefficients $\{λ_h\}$ that softly regularize attention toward the prior while allowing data-driven deviations when nonlinear patterns appear to be present in the data. Beyond imputation, SNI aggregates attention maps into a directed feature-dependency matrix that summarizes which variables the imputer relied on, without requiring post-hoc explainers. We evaluate SNI against six baselines (Mean/Mode, MICE, KNN, MissForest, GAIN, MIWAE) on six datasets spanning ICU monitoring, population surveys, socio-economic statistics, and engineering applications. Under MCAR/strict-MAR at 30\% missingness, SNI is generally competitive on continuous metrics but is often outperformed by accuracy-first baselines (MissForest, MIWAE) on categorical variables; in return, it provides intrinsic dependency diagnostics and explicit statistical-neural trade-off parameters. We additionally report MNAR stress tests (with a mask-aware variant) and discuss computational cost, limitations -- particularly for severely imbalanced categorical targets -- and deployment scenarios where interpretability may justify the trade-off.