Judge puts a one-year limit on Google's contracts for default search placement It comes after the September ruling that Google will not have to sell off Chrome, but must make changes. A federal judge has expanded on the remedies decided for the Department of Justice's antitrust case against Google, ruling in favor of putting a one-year limit on the contracts that make Google's search and AI services the default on devices, reports. Judge Amit Mehta's ruling on Friday means Google will have to renegotiate these contacts every year, which would create a fairer playing field for its competitors. The new details come after Mehta ruled in September that Google would not have to sell off Chrome, as the DOJ proposed at the end of 2024. This all follows the ruling last fall that Google illegally maintained an internet search monopoly through actions including paying companies such as Apple to make its search engine the default on their devices and making exclusive deals around the distribution of services such as Search, Chrome and Gemini.

Senate Republicans are winning the AI regulation moratorium battle as debate continues over federal framework versus states' rights in artificial intelligence policy.

'U.S. sanctions equate us with drug traffickers,' ICC deputy prosecutor says The Hague - The deputy prosecutor of the International Criminal Court on Friday lashed out at U.S. sanctions, arguing they effectively put top court officials on a par with terrorists and drug traffickers. In a wide-ranging interview, Mame Mandiaye Niang also said it would be conceivable to hold an in-absentia hearing against high-level ICC targets such as Israeli Prime Minister Benjamin Netanyahu. Sixty-five-year-old Niang, along with top ICC judges, is subject to sanctions from the administration of U.S. President Donald Trump, in retaliation at the court's arrest warrants for Netanyahu over Israel's campaign in Gaza. 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.

Then its sales blew up. But fails to meet the lofty goals of its own ideas. Shortly before the December 2 release of horror game, developer Santa Ragione shared some news: the game would not be available on Valve's mega platform, Steam . Valve had already banned an early, incomplete version of the game two years ago and offered, according to Santa Ragione, little clarification about why at the time. Then, hours before the game's release, the Epic Games Store banned as well.

New York Times newspaper office building is seen in Manhattan on 26 October 2022. New York Times newspaper office building is seen in Manhattan on 26 October 2022. The New York Times sued an embattled artificial intelligence startup on Friday, accusing the firm of illegally copying millions of articles. The newspaper alleged Perplexity AI had distributed and displayed journalists' work without permission en masse. The Times said that Perplexity AI was also violating its trademarks under the Lanham Act, claiming the startup's generative AI products create fabricated content, or "hallucinations", and falsely attribute them to the newspaper by displaying them alongside its registered trademarks.

Ruby the turtle needs a new greenhouse. Dance companies are stepping up. A performance of Moss Anthology#5 for Buglisi Dance Theatre (left). Ruby, an endangered Burmese roofed turtle who lives at a turtle sanctuary in New Jersey (right). Breakthroughs, discoveries, and DIY tips sent every weekday.

Both publications claim the AI company scraped their works for LLM training and often reproduced their content verbatim. The said it had sent Perplexity several cease-and-desist demands to stop using its content until the two reached an agreement, but the AI company persisted in doing so. First, by scraping its website (including in real time) to train AI models and feed content into the likes of the Claude chatbot and Comet browser . The also says Perplexity damaged its brand by falsely attributing completely fabricated information (aka hallucinations) to the newspaper. The also filed a lawsuit against Perplexity for similar reasons.

An AI image generator startup's database was left accessible to the open internet, revealing more than 1 million images and videos, including photos of real people who had been "nudified." An AI image generator startup left more than 1 million images and videos created with its systems exposed and accessible to anyone online, according to new research reviewed by WIRED. The "overwhelming majority" of the images involved nudity and were "depicted adult content," according to the researcher who uncovered the exposed trove of data, with some appearing to depict children or the faces of children swapped onto the AI-generated bodies of nude adults. Multiple websites--including MagicEdit and DreamPal--all appeared to be using the same unsecured database, says security researcher Jeremiah Fowler, who discovered the security flaw in October. At the time, Fowler says, around 10,000 new images were being added to the database every day.

This thesis develops methods for causal inference and causal representation learning (CRL) in high-dimensional, time-varying data. The first contribution introduces the Causal Dynamic Variational Autoencoder (CDVAE), a model for estimating Individual Treatment Effects (ITEs) by capturing unobserved heterogeneity in treatment response driven by latent risk factors that affect only outcomes. CDVAE comes with theoretical guarantees on valid latent adjustment and generalization bounds for ITE error. Experiments on synthetic and real datasets show that CDVAE outperforms baselines, and that state-of-the-art models greatly improve when augmented with its latent substitutes, approaching oracle performance without access to true adjustment variables. The second contribution proposes an efficient framework for long-term counterfactual regression based on RNNs enhanced with Contrastive Predictive Coding (CPC) and InfoMax. It captures long-range dependencies under time-varying confounding while avoiding the computational cost of transformers, achieving state-of-the-art results and introducing CPC into causal inference. The third contribution advances CRL by addressing how latent causes manifest in observed variables. We introduce a model-agnostic interpretability layer based on the geometry of the decoder Jacobian. A sparse self-expression prior induces modular, possibly overlapping groups of observed features aligned with shared latent influences. We provide recovery guarantees in both disjoint and overlapping settings and show that meaningful latent-to-observed structure can be recovered without anchor features or single-parent assumptions. Scalable Jacobian-based regularization techniques are also developed.

Agentic AI systems, software agents with autonomy, decision-making ability, and adaptability, are increasingly used to execute complex tasks on behalf of organisations. Most such systems rely on Large Language Models (LLMs), whose broad semantic capabilities enable powerful language processing but lack explicit, institution-specific grounding. In enterprises, data rarely comes with an inspectable semantic layer, and constructing one typically requires labour-intensive "data archaeology": cleaning, modelling, and curating knowledge into ontologies, taxonomies, and other formal structures. At the same time, explainability methods such as saliency maps expose an "interpretability gap": they highlight what the model attends to but not why, leaving decision processes opaque. In this preprint, we present a case study, developed by Kaiasm and Avantra AI through their work with The Turing Way Practitioners Hub, a forum developed under the InnovateUK BridgeAI program. This study presents a collaborative human-AI approach to building an inspectable semantic layer for Agentic AI. AI agents first propose candidate knowledge structures from diverse data sources; domain experts then validate, correct, and extend these structures, with their feedback used to improve subsequent models. Authors show how this process captures tacit institutional knowledge, improves response quality and efficiency, and mitigates institutional amnesia. We argue for a shift from post-hoc explanation to justifiable Agentic AI, where decisions are grounded in explicit, inspectable evidence and reasoning accessible to both experts and non-specialists.