Is Google's new AI Mode feature theft? The News/Media Alliance, trade association representing news media organizations in the U.S. and Canada, certainly thinks so. At Google's I/O showcase earlier this week, the tech company announced the public release of AI Mode in Google Search. AI Mode expands AI Overviews in search and signifies a pivot away from Google's traditional search. Users will see a tab at the top of their Google Search page that takes them to a chatbot interface much like, say, ChatGPT, instead of your typical Google Search results.

In the high desert east of Reno, Nevada, construction crews are flattening the golden foothills of the Virginia Range, laying the foundations of a data center city. Google, Tract, Switch, EdgeCore, Novva, Vantage, and PowerHouse are all operating, building, or expanding huge facilities nearby. Meanwhile, Microsoft has acquired more than 225 acres of undeveloped property, and Apple is expanding its existing data center just across the Truckee River from the industrial park. The corporate race to amass computing resources to train and run artificial intelligence models and store information in the cloud has sparked a data center boom in the desert--and it's just far enough away from Nevada's communities to elude wide notice and, some fear, adequate scrutiny. This story is part of Power Hungry: AI and our energy future--our new series shining a light on the energy demands and carbon costs of the artificial intelligence revolution.

Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning. Heuristic methods, leveraging a range of different pairwise measures such as common neighbors and shortest paths, often rival the performance of vanilla Graph Neural Networks (GNNs). Therefore, recent advancements in GNNs for link prediction (GNN4LP) have primarily focused on integrating one or a few types of pairwise information. In this work, we reveal that different node pairs within the same dataset necessitate varied pairwise information for accurate prediction and models that only apply the same pairwise information uniformly could achieve suboptimal performance. As a result, we propose a simple mixture of experts model Link-MoE for link prediction. Link-MoE utilizes various GNNs as experts and strategically selects the appropriate expert for each node pair based on various types of pairwise information. Experimental results across diverse real-world datasets demonstrate substantial performance improvement from Link-MoE. Notably, Link-MoE achieves a relative improvement of 18.71% on the MRR metric for the Pubmed dataset and 9.59% on the Hits@100 metric for the ogbl-ppa dataset, compared to the best baselines. The code is available at https://github.com/ml-ml/Link-MoE/.

The past two Google I/O developer conferences have mainly been AI events, and this year is no different. The tech giant used the stage to unveil features across all its most popular products, even bringing AI experiments that were previously announced to fruition. This means that dozens of AI features and tools were unveiled. They're meant to transform how you use Google offerings, including how you shop, video call, sort your inbox, search the web, create images, edit video, code, and more. Since such a firehose of information is packed into a two-hour keynote address, you may be wondering which features are actually worth paying attention to.

Graph diffusion, which iteratively propagates real-valued substances among the graph, is used in numerous graph/network-involved applications. However, releasing diffusion vectors may reveal sensitive linking information in the data such as transaction information in financial network data. Protecting the privacy of graph data is challenging due to its interconnected nature. This work proposes a novel graph diffusion framework with edge-level differential privacy guarantees by using noisy diffusion iterates. The algorithm injects Laplace noise per diffusion iteration and adopts a degree-based thresholding function to mitigate the high sensitivity induced by low-degree nodes. Our privacy loss analysis is based on Privacy Amplification by Iteration (PABI), which to our best knowledge, is the first effort that analyzes PABI with Laplace noise and provides relevant applications. We also introduce a novel -Wasserstein distance tracking method, which tightens the analysis of privacy leakage and makes PABI practically applicable. We evaluate this framework by applying it to Personalized Pagerank computation for ranking tasks. Experiments on real-world network data demonstrate the superiority of our method under stringent privacy conditions.

If the AI lovefest of Google I/O 2025 were a TV show, you might be tempted to call it It's Always Sunny in Mountain View. But here's a better sitcom analogy for the event that added AI Mode to all U.S. search results, whether we want it or not. It's The Good Place, in which our late heroes are repeatedly assured that they've gone to a better world. A place where everything is fine, all is as it seems, and search quality just keeps getting better. Don't worry about ever-present and increasing AI hallucinations here in the Good Place, where the word "hallucination" isn't even used.

From the opening AI-influenced intro video set to "You Get What You Give" by New Radicals to CEO Sundar Pichai's sign-off, Google I/O 2025 was packed with news and updates for the tech giant and its products. And when we say packed, we mean it, as this year's Google I/O clocked in at nearly two hours long. During that time, Google shared some big wins for its AI products, such as Gemini topping various categories on the LMArena leaderboard. Another example that Google seemed really proud of was the fact that Gemini completed Pokémon Blue a few weeks ago. But, we know what you're really here for: Product updates and new product announcements.

Google just cracked open the future of search, and it talks back. During today's Google I/O 2025 keynote event, Google announced that it is now rolling out the AI Mode search tool to everyone in the United States. Powered by Gemini, AI Mode will now include new "Deep Search" features and some agentic capabilities. AI Mode represents the biggest shift in Google Search since its inception. It's no longer just a place to find links.

The past two Google I/O developer conferences have mainly been AI events, and this year is no different. The tech giant used the stage to unveil features across all its most popular products, even bringing AI experiments that were previously announced to fruition. This means that dozens of AI features and tools were unveiled. They're meant to transform how you use Google offerings, including how you shop, video call, sort your inbox, search the web, create images, edit video, code, and more. Since such a firehose of information is packed into a two-hour keynote address, you may be wondering which features are actually worth paying attention to.

Google is introducing a new artificial intelligence (AI) mode that more firmly embeds chatbot capabilities into its search engine, aiming to give users the experience of having a conversation with an expert. The "AI Mode" was made available in the US on Tuesday, appearing as an option in Google's search bar. The change, unveiled at the company's annual developers conference in Mountain View, California, is part of the tech giant's push to remain competitive against ChatGPT and other AI services, which threaten to erode Google's dominance of online search. The company also announced plans for its own augmented reality glasses and said it planned to offer a subscription AI tool.