The European Union has fined Google 890 million euros ($1bn), saying the technology giant broke digital antitrust rules by steering users of Google Play and its search engine towards its own services and apps at the expense of rivals. Thursday's penalty is the latest in Brussels' crackdown on Big Tech, which has seen the bloc lead the world in reining in the largest firms from Silicon Valley to Beijing. The European Commission, the bloc's executive branch, said it was acting in the interest of consumers. "The best products should succeed because they're better, not because they're owned by the company running the search engine. And European consumers have a right to be told by app developers where to sign up to the best offers, even when the app store owner does not get a cut," said Teresa Ribera, the commission's executive vice president for clean, just and competitive transition.

Google Search AI poses'unacceptable risk' to kids, report finds A new report from a California-based nonprofit says the artificial intelligence features built in to Alphabet's Google Search pose an unacceptable risk" to children. The artificial intelligence features built in to Alphabet's Google Search pose an "unacceptable risk" to children, according to a new report from the Youth AI Safety Institute at Common Sense Media, a California-based nonprofit. The study found that on test accounts configured for minors, the ubiquitous search engine's AI features failed to detect suicide risks, said an eating disorder symptom was normal, and provided instructions for creating deepfakes, or sexually explicit fake content. The report added that unlike standalone chatbots, Google's AI tools are built directly into the default search experience on school and personal devices, currently offering administrators or parents no way to disable them. The report specifically evaluated AI Overviews, which generate automated answers above traditional search results, and AI Mode, a conversational feature that lets users chat back and forth with the search engine. Rather than matching searches to a list of external links, the new features use generative AI to answer complex questions directly on the search page -- a format critics say presents the automated summaries as if they are definitive answers.

The hosting platform wants sites to have more control over how AI companies use their content. Cloudflare has announced plans to automatically block mixed-use web crawlers that index websites for search engines and act as AI agents and trainers at the same time. The company previously offered its customers the optional ability to prevent crawlers from scraping their sites for AI chatbots, but now Cloudflare's stance is becoming more defensive by default. Now that the majority of traffic on the Internet is non-human, we must go further and act faster so that a sustainable ecosystem can emerge, Matthew Prince, Cloudflare's CEO and co-founder shared in a statement. Cloudflare's new tools and partnerships give website owners increased visibility and commercial opportunities and benefit AI companies that have bots with clear and transparent intent.

Russia’s “Project 2026” wasn’t just to spread misinformation on social media — its main goal to create an alternative information ecosystem.

Large Language Models (LLMs) are transforming search engines into Conversational Search Engines (CSE). Consequently, Search Engine Optimization (SEO) is being shifted into Conversational Search Engine Optimization (C-SEO). We are beginning to see dedicated C-SEO methods for modifying web documents to increase their visibility in CSE responses. However, they are often tested only for a limited breadth of application domains; we do not know whether certain C-SEO methods would be effective for a broad range of domains. Moreover, existing evaluations consider only a single-actor scenario where only one web document adopts a C-SEO method; in reality, multiple players are likely to competitively adopt the cutting-edge C-SEO techniques, drawing an analogy from the dynamics we have seen in SEO.

The ruling holds that a company that designs, trains, operates, and manages an AI system must assume legal liability for any damages caused by the responses it generates. A local court in Germany has issued a ruling that could reshape the operation of search engines and artificial-intelligence-based chatbots worldwide. The Munich Regional Court preliminarily ruled that Google is liable for a series of false statements generated by its AI Overviews feature, requiring the company to prevent the dissemination of erroneous or inaccurate claims through its search engine. The ruling stems from a case first reported by the Decoder, in which two publishers discovered that Google's AI-generated summaries linked them, in certain searches, to questionable business practices, scams, and subscription-related frauds, without any basis for doing so. Earlier this year, the affected companies sent the tech giant a cease-and-desist letter, according to the report.

Effectively retrieving, reasoning and understanding visually rich information remains a challenge for traditional Retrieval-Augmented Generation (RAG) methods. On the one hand, traditional text-based methods cannot handle visual-related information. On the other hand, current vision-based RAG approaches are often limited by fixed pipelines and frequently struggle to reason effectively due to the insufficient activation of the fundamental capabilities of models. As reinforcement learning (RL) has been proven to be beneficial for model reasoning, we introduce VRAG-RL, a novel RL framework tailored for complex reasoning across visually rich information. With this framework, VLMs interact with search engines, autonomously sampling single-turn or multi-turn reasoning trajectories with the help of visual perception tokens and undergoing continual optimization based on these samples.

Instead of clicking on a bunch of random website links, I was reading an AI summary positioned at the top of my search results and sometimes clicking through to double-check the accuracy of the output. The next evolution of Search that Google is building asks for even less active participation from users. You're really the most involved at the start of the journey, and that's it. You tell the agents what you want to know, and they do the clicking and even calling on your behalf. Rather than you going off on some online adventure, it's the agent that's hoovering up anything it can find and bouncing between different sites.

Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller specialized models excel. In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q&A system, a search engine, a translation system, and a calendar. Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.

The unbiased learning to rank (ULTR) problem has been greatly advanced by recent deep learning techniques and well-designed debias algorithms. However, promising results on the existing benchmark datasets may not be extended to the practical scenario due to some limitations of existing datasets. First, their semantic feature extractions are outdated while state-of-the-art large-scale pre-trained language models like BERT cannot be utilized due to the lack of original text. Second, display features are incomplete; thus in-depth study on ULTR is impossible such as the displayed abstract for analyzing the click necessary bias. Third, synthetic user feedback has been adopted by most existing datasets and real-world user feedback is greatly missing. To overcome these disadvantages, we introduce the Baidu-ULTR dataset. It involves randomly sampled 1.2 billion searching sessions and 7,008 expert annotated queries (397,572 query document pairs).