However, existing music datasets suffer from limited size, accessibility, and lack of audio resources.

Google has enhanced its'Results About You' privacy tool to help users monitor and remove personal information like government ID numbers from search results, according to PCWorld. The update includes a streamlined process for removing explicit images through a three-dot menu option and allows bulk removal requests. These privacy improvements are rolling out in the US first as part of Google's Safer Internet Day initiatives, giving users better control over their digital footprint. In celebration of Safer Internet Day, Google rolled out an update to its "Results About You" feature and launched a new, simpler tool for removing unwanted explicit images from search results. With Results About You, you can tell Google to keep track of search results where your personal information appears, and then ask Google to remove those search results. The feature has previously been able to track search results with your home address, email address, and phone number, but now the search engine will also be able to warn you about search results that include your government documents, such as passports or driver's licenses. The update to Results About You will first be rolled out in the United States over the coming days, but Google is working on adapting the feature for other regions as well.

How the'confident authority' of Google AI Overviews is putting public health at risk Experts say tool can give'completely wrong' medical advice which could put users at risk of serious harm Do I have the flu or Covid? Why do I wake up feeling tired? What is causing the pain in my chest? For more than two decades, typing medical questions into the world's most popular search engine has served up a list of links to websites with the answers. Google those health queries today and the response will likely be written by artificial intelligence.

When you purchase through links in our articles, we may earn a small commission. Here's how to get rid of them Are you tired of AI summaries in Google Search? Want the search results to look like they used to? Here are some tricks you can use. Unless you live under a rock, you've probably seen that Google Search has been showing "AI Overviews" at the top of its search results.

Google has said AI Overviews, which use generative AI to provide snapshots of information on a topic or question, are'helpful and reliable'. Google has said AI Overviews, which use generative AI to provide snapshots of information on a topic or question, are'helpful and reliable'. 'Dangerous and alarming': Google removes some of its AI summaries after users' health put at risk Google has removed some of its artificial intelligence health summaries after a Guardian investigation found people were being put at risk of harm by false and misleading information. The company has said its AI Overviews, which use generative AI to provide snapshots of essential information about a topic or question, are " helpful " and " reliable ". But some of the summaries, which appear at the top of search results, served up inaccurate health information, putting users at risk of harm.

LLM-based Search Engines (LLM-SEs) introduces a new paradigm for information seeking. Unlike Traditional Search Engines (TSEs) (e.g., Google), these systems summarize results, often providing limited citation transparency. The implications of this shift remain largely unexplored, yet raises key questions regarding trust and transparency. In this paper, we present a large-scale empirical study of LLM-SEs, analyzing 55,936 queries and the corresponding search results across six LLM-SEs and two TSEs. We confirm that LLM-SEs cites domain resources with greater diversity than TSEs. Indeed, 37% of domains are unique to LLM-SEs. However, certain risks still persist: LLM-SEs do not outperform TSEs in credibility, political neutrality and safety metrics. Finally, to understand the selection criteria of LLM-SEs, we perform a feature-based analysis to identify key factors influencing source choice. Our findings provide actionable insights for end users, website owners, and developers.

Recent advances in Multimodal Large Language Models (MLLMs) have enabled their use as intelligent agents for smartphone operation. However, existing methods depend on the Android Debug Bridge (ADB) for data transmission and action execution, limiting their applicability to Android devices. In this work, we introduce the novel Embodied Smartphone Operation (ESO) task and present See-Control, a framework that enables smartphone operation via direct physical interaction with a low-DoF robotic arm, offering a platform-agnostic solution. See-Control comprises three key components: (1) an ESO benchmark with 155 tasks and corresponding evaluation metrics; (2) an MLLM-based embodied agent that generates robotic control commands without requiring ADB or system back-end access; and (3) a richly annotated dataset of operation episodes, offering valuable resources for future research. By bridging the gap between digital agents and the physical world, See-Control provides a concrete step toward enabling home robots to perform smartphone-dependent tasks in realistic environments.

The EU has opened an investigation into Google over its artificial intelligence (AI) summaries which appear above search results. The European Commission said it would examine whether the firm used data from websites to provide this service - and if it failed to offer appropriate compensation to publishers. It is also investigating how YouTube videos may have been used to improve its broader AI systems, and whether content creators were able to opt-out. A Google spokesperson said the probe risks stifling innovation in a market that is more competitive than ever. Europeans deserve to benefit from the latest technologies and we will continue to work closely with the news and creative industries as they transition to the AI era, they said.

This study examined how AI-generated summaries, which have become visually prominent in online search results, affect how users think about different issues. In a preregistered randomized controlled experiment, participants (N = 2,004) viewed mock search result pages varying in the presence (vs. absence), placement (top vs. middle), and stance (benefit-framed vs. harm-framed) of AI-generated summaries across four publicly debated topics. Compared to a no-summary control group, participants exposed to AI-generated summaries reported issue attitudes, behavioral intentions, and policy support that aligned more closely with the AI summary stance. The summaries placed at the top of the page produced stronger shifts in users' issue attitudes (but not behavioral intentions or policy support) than those placed at the middle of the page. We also observed moderating effects from issue familiarity and general trust toward AI. In addition, users perceived the AI summaries more useful when it emphasized health harms versus benefits. These findings suggest that AI-generated search summaries can significantly shape public perceptions, raising important implications for the design and regulation of AI-integrated information ecosystems.

The advancement in Large Language Models has driven the creation of complex agentic systems, such as Deep Research Agents (DRAs), to overcome the limitations of static Retrieval Augmented Generation (RAG) pipelines in handling complex, multi-turn research tasks. This paper introduces the Static Deep Research Agent (Static-DRA), a novel solution built upon a configurable and hierarchical Tree-based static workflow. The core contribution is the integration of two user-tunable parameters, Depth and Breadth, which provide granular control over the research intensity. This design allows end-users to consciously balance the desired quality and comprehensiveness of the research report against the associated computational cost of Large Language Model (LLM) interactions. The agent's architecture, comprising Supervisor, Independent, and Worker agents, facilitates effective multi-hop information retrieval and parallel sub-topic investigation. We evaluate the Static-DRA against the established DeepResearch Bench using the RACE (Reference-based Adaptive Criteria-driven Evaluation) framework. Configured with a depth of 2 and a breadth of 5, and powered by the gemini-2.5-pro model, the agent achieved an overall score of 34.72. Our experiments validate that increasing the configured Depth and Breadth parameters results in a more in-depth research process and a correspondingly higher evaluation score. The Static-DRA offers a pragmatic and resource-aware solution, empowering users with transparent control over the deep research process. The entire source code, outputs and benchmark results are open-sourced at https://github.com/SauravP97/Static-Deep-Research/