If you're one of the 8% of Americans who say they're willing to pay for AI, Google has a deal for you -- a 250 per month AI subscription. The company unveiled Google AI Ultra today, a plan with the biggest usage limits for Google's suite of AI tools and access to the highest versions of those tools. Google AI Ultra is intended for filmmakers, developers, and creative professionals and gives users access to tools like Veo, Imagen, Whisk, NotebookLM, and a new tool called Flow. Also: Google's popular AI tool gets its own Android app - how to use NotebookLM on your phone Subscribers also get a massive expansion in storage across Google platforms, plus YouTube Premium ( 13.99 per month on its own). Here's a full breakdown of what the new plan includes: Google said the current AI Premium plan is also getting an upgrade -- to Gemini AP Pro.

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.

Graph Neural Networks (GNNs) often perform better for high-degree nodes than low-degree nodes on node classification tasks. This degree bias can reinforce social marginalization by, e.g., privileging celebrities and other high-degree actors in social networks during social and content recommendation. While researchers have proposed numerous hypotheses for why GNN degree bias occurs, we find via a survey of 38 degree bias papers that these hypotheses are often not rigorously validated, and can even be contradictory. Thus, we provide an analysis of the origins of degree bias in message-passing GNNs with different graph filters. We prove that high-degree test nodes tend to have a lower probability of misclassification regardless of how GNNs are trained. Moreover, we show that degree bias arises from a variety of factors that are associated with a node's degree (e.g., homophily of neighbors, diversity of neighbors). Furthermore, we show that during training, some GNNs may adjust their loss on low-degree nodes more slowly than on high-degree nodes; however, with sufficiently many epochs of training, message-passing GNNs can achieve their maximum possible training accuracy, which is not significantly limited by their expressive power. Throughout our analysis, we connect our findings to previouslyproposed hypotheses for the origins of degree bias, supporting and unifying some while drawing doubt to others. We validate our theoretical findings on 8 common real-world networks, and based on our theoretical and empirical insights, describe a roadmap to alleviate degree bias.

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If you like Google's AI services (and I mean really like them), there's a new subscription for you. At its Google I/O keynote event (and in a company blog post), Google revealed that a new AI subscription plan for professionals is ready to roll out in the United States. The new Google AI Ultra subscription is intended for the hardest of hardcore AI users, and it costs a whopping 250 a month. Yes, you read that right: Two hundred and fifty U.S. dollars per month. While business owners and professionals may be used to paying for Google Workspace access, the average user is probably not accustomed to paying for Google services.

When I was a child, computers were a fixture in my home, from the giant Atari on which I learned my ABCs, to the Commodore Amiga that my dad used for his videography business, to the PC towers that facilitated my first forays onto the internet. But tech was still a niche hobby back then. Even in college in the late 1990s and early 2000s, many of my friends got by just fine without computers. For people in college now--namely, my students--things are decidedly different. Gadgets are everywhere, and are increasingly designed to insert themselves into every aspect of our consciousness, colonizing every spare moment of our time and attention.

Exp. Psychology, Oxford ELSC, HebrewU Department of Computing Brain Mind Institute, EPFL Gatsby Unit, UCL Imperial College London Andrew M. Saxe Christopher Summerfield Ali Hummos

Google I/O 2025 is nearly upon us. The tech giant's annual developers conference begins Tuesday, May 20, in Mountain View, California, and all things AI are on the menu. As in years past, Google I/O is set to open with a company keynote (likely led by CEO Sundar Pichai) followed by a developers keynote. The company is expected to drop its biggest I/O news and announcements during these presentations. If you're not attending I/O in person, here's how you can watch both keynotes live from the comfort of your own couch.

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