Valve's Steam Machine: Everything we know MetaAI is essentially the only AI assistant now available on WhatsApp. The EU could take interim measures against WhatsApp as it investigates AI providers' access to the app. On Monday, the EU's regulatory arm announced its preliminary view that Meta, WhatsApp's parent company, violated antitrust laws by blocking third-party AI assistants from operating on WhatsApp. The European Commission's is concerned that Meta's actions will limit competitors from entering the AI assistant market. We must protect effective competition in this vibrant field, which means we cannot allow dominant tech companies to illegally leverage their dominance to give themselves an unfair advantage, Teresa Ribera, executive vice-president for Clean, Just and Competitive Transition said in a statement. Ribera continued: AI markets are developing at rapid pace, so we also need to be swift in our action.

The integration of knowledge graphs (KGs) with large language models (LLMs) offers significant potential to improve the retrieval phase of retrieval-augmented generation (RAG) systems. In this study, we propose KG-CQR, a novel framework for Contextual Query Retrieval (CQR) that enhances the retrieval phase by enriching the contextual representation of complex input queries using a corpus-centric KG. Unlike existing methods that primarily address corpus-level context loss, KG-CQR focuses on query enrichment through structured relation representations, extracting and completing relevant KG subgraphs to generate semantically rich query contexts. Comprising subgraph extraction, completion, and contextual generation modules, KG-CQR operates as a model-agnostic pipeline, ensuring scalability across LLMs of varying sizes without additional training. Experimental results on RAGBench and MultiHop-RAG datasets demonstrate KG-CQR's superior performance, achieving a 4-6% improvement in mAP and a 2-3% improvement in Recall@25 over strong baseline models. Furthermore, evaluations on challenging RAG tasks such as multi-hop question answering show that, by incorporating KG-CQR, the performance consistently outperforms the existing baseline in terms of retrieval effectiveness

Google will not be forced to sell its Chrome browser, a federal judge ruled on Tuesday in the tech giant's ongoing legal battle over being ruled a monopoly last year. The company will be barred from certain exclusive deals with device makers and must share data from its search engine with competitors, the judge ruled. Judge Amit Mehta's ruling follows months of speculation surrounding what penalties Google would face as a result of his decision last year that the company violated antitrust laws as it built what he called an online search monopoly. The ruling, one of the most significant antitrust cases in decades, resulted in an additional hearing in April to determine what actions the government should take as a remedy. Mehta's decision to allow Google to keep Chrome represents a more lenient outcome for the company than what federal prosecutors requested: force the tech giant sell off its marquee search product and to ban it from entering the browser market for five years.

Elon Musk has threatened legal action against Apple on behalf of his artificial intelligence startup xAI, accusing the iPhone maker of favoring OpenAI and breaching antitrust regulations in managing the rankings in its App Store. The posts elicited snide responses from Sam Altman, the OpenAI CEO, and began a spat between the two former business partners on X. "Apple is behaving in a manner that makes it impossible for any AI company besides OpenAI to reach #1 in the App Store, which is an unequivocal antitrust violation. In a post earlier that day, he wrote: "Hey @Apple App Store, why do you refuse to put either X or Grok in your'Must Have' section when X is the #1 news app in the world and Grok is #5 among all apps? OpenAI's ChatGPT currently holds the top spot in the App Store's "Top Free Apps" section in the US, while xAI's Grok ranks fifth. Apple has a partnership with OpenAI that integrates ChatGPT into iPhones, iPads and Macs.

Google has a monopoly, and that's the official line of the US federal government. In fact, it has two of them, losing two separate antitrust cases that threaten to cripple the tech giant. The Department of Justice has proposed forcing Google to sell or otherwise divest itself of the Chrome browser as its first and preferred remedy. But who would buy it? Unsurprisingly, there are beaucoup business beaus lining up around the block for this browser bachelorette.

Google has been paying Samsung tons of cash every month to pre-install the AI app Gemini on its smartphones, according to a report by Bloomberg . This information comes to us as part of a pre-existing antitrust case against Google. Peter Fitzgerald, Google's VP of platforms and device partnerships, testified in federal court that it began paying Samsung for this service back in January. The pair of companies have a contract that's set to run at least two years. Fitzgerald told Judge Amit Metha, who is overseeing the case, that Google provides Samsung with both fixed monthly payments and a percentage of revenue earned from advertisers within the Gemini app.

Educational tech company Chegg has sued Google in federal court claiming that its "AI Overviews" that appear ahead of search results have hurt its traffic and revenue. In order to be included in Google's search results, Chegg alleges, it must "supply content that Google republishes without permission in AI-generated answers that unfairly compete for the attention of users on the internet in violation of antitrust laws of the United States." However, Chegg is taking another approach, instead accusing Google of abusing its monopoly position to force companies to supply materials for its "AI Overviews" on its search page. Failing to do so, it says, means it could effectively be excluded from Google Search altogether. Chegg included a screenshot of a Google AI Overview that takes details from Chegg's website without attribution, though the page in question appears lower down in the search results.

Group max-min fairness (MMF) is commonly used in fairness-aware recommender systems (RS) as an optimization objective, as it aims to protect marginalized item groups and ensures a fair competition platform. However, our theoretical analysis indicates that integrating MMF constraint violates the assumption of sample independence during optimization, causing the loss function to deviate from linear additivity. Such nonlinearity property introduces the Jensen gap between the model's convergence point and the optimal point if mini-batch sampling is applied. Both theoretical and empirical studies show that as the mini-batch size decreases and the group size increases, the Jensen gap will widen accordingly. Some methods using heuristic re-weighting or debiasing strategies have the potential to bridge the Jensen gap. However, they either lack theoretical guarantees or suffer from heavy computational costs. To overcome these limitations, we first theoretically demonstrate that the MMF-constrained objective can be essentially reformulated as a group-weighted optimization objective. Then we present an efficient and effective algorithm named FairDual, which utilizes a dual optimization technique to minimize the Jensen gap. Our theoretical analysis demonstrates that FairDual can achieve a sub-linear convergence rate to the globally optimal solution and the Jensen gap can be well bounded under a mini-batch sampling strategy with random shuffle. Extensive experiments conducted using six large-scale RS backbone models on three publicly available datasets demonstrate that FairDual outperforms all baselines in terms of both accuracy and fairness. Our data and codes are shared at https://github.com/XuChen0427/FairDual.

End-to-end encryption (E2EE) has become the gold standard for securing communications, bringing strong confidentiality and privacy guarantees to billions of users worldwide. However, the current push towards widespread integration of artificial intelligence (AI) models, including in E2EE systems, raises some serious security concerns. This work performs a critical examination of the (in)compatibility of AI models and E2EE applications. We explore this on two fronts: (1) the integration of AI "assistants" within E2EE applications, and (2) the use of E2EE data for training AI models. We analyze the potential security implications of each, and identify conflicts with the security guarantees of E2EE. Then, we analyze legal implications of integrating AI models in E2EE applications, given how AI integration can undermine the confidentiality that E2EE promises. Finally, we offer a list of detailed recommendations based on our technical and legal analyses, including: technical design choices that must be prioritized to uphold E2EE security; how service providers must accurately represent E2EE security; and best practices for the default behavior of AI features and for requesting user consent. We hope this paper catalyzes an informed conversation on the tensions that arise between the brisk deployment of AI and the security offered by E2EE, and guides the responsible development of new AI features.

Artificial intelligence (AI) model creators commonly attach restrictive terms of use to both their models and their outputs. These terms typically prohibit activities ranging from creating competing AI models to spreading disinformation. Often taken at face value, these terms are positioned by companies as key enforceable tools for preventing misuse, particularly in policy dialogs. But are these terms truly meaningful? There are myriad examples where these broad terms are regularly and repeatedly violated. Yet except for some account suspensions on platforms, no model creator has actually tried to enforce these terms with monetary penalties or injunctive relief. This is likely for good reason: we think that the legal enforceability of these licenses is questionable. This Article systematically assesses of the enforceability of AI model terms of use and offers three contributions. First, we pinpoint a key problem: the artifacts that they protect, namely model weights and model outputs, are largely not copyrightable, making it unclear whether there is even anything to be licensed. Second, we examine the problems this creates for other enforcement. Recent doctrinal trends in copyright preemption may further undermine state-law claims, while other legal frameworks like the DMCA and CFAA offer limited recourse. Anti-competitive provisions likely fare even worse than responsible use provisions. Third, we provide recommendations to policymakers. There are compelling reasons for many provisions to be unenforceable: they chill good faith research, constrain competition, and create quasi-copyright ownership where none should exist. There are, of course, downsides: model creators have fewer tools to prevent harmful misuse. But we think the better approach is for statutory provisions, not private fiat, to distinguish between good and bad uses of AI, restricting the latter.