From real-time translation in Google Meet to AI-video creation tools, we're recapping all the biggest Google Workspace updates revealed during the Google I/O 2025 keynote address. This year's Google I/O 2025 was packed with big announcements, and the event signalled the start of a new era of AI search. So, it should come as no surprise that the event focused primarily on AI and Google Gemini. Google has already integrated AI into Google Workspace, the subscription-based suite of productivity apps and business tools from the company, which includes popular tools like Gmail, Drive, Sheets, and Meet. But Google is just getting started with AI in Workspace, and today the company announced a ton of new AI-related features coming to the subscription service.

At Google I/O 2025, the company revealed a bunch of new Gemini updates and features that position the app as your AI assistant for practically everything. Today, Google announced a new Sora competitor called Flow, updates to your AI-powered researcher, and free Gemini Live. Free is the operative word here, since many of the other features are bundled into the paid subscription plans -- Google AI Pro (formerly AI Premium) for 20 a month and a new plan called AI Ultra for a whopping 250 a month. The features showcased today demonstrate AI's increasingly agentic capabilities, in other words, AI tools that can perform tasks on your behalf. "This is our ultimate goal for the Gemini app," said Google Labs and Gemini lead Josh Woodward in a pre-event briefing, "an AI that's personal, proactive and powerful."

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.

A.1 Rotations in 2 dimensions In 2-dimensional settings, there exists a single scalar angular position, the yaw angle θ. In order to perform the transformation, we have to express the angular positions in a format suitable for linear transformations; we do so by transforming them to rotation matrices, perform a matrix multiplication, and then transform the angular positions back to angle format. In 2 dimensions, we use eq. After the rotation, we can convert them back to angle format using the 2-argument arc-tangent function: θ = atan2(sin θ, cos θ) (14) Simplified rotations In 2 dimensions, the computations can be simplified since rotations commute. In practice, we use the 2-argument arc-tangent function atan2(y, x) to compute θ.

Modelling interactions is critical in learning complex dynamical systems, namely systems of interacting objects with highly non-linear and time-dependent behaviour. A large class of such systems can be formalized as geometric graphs, i.e., graphs with nodes positioned in the Euclidean space given an arbitrarily chosen global coordinate system, for instance vehicles in a traffic scene. Notwithstanding the arbitrary global coordinate system, the governing dynamics of the respective dynamical systems are invariant to rotations and translations, also known as Galilean invariance. As ignoring these invariances leads to worse generalization, in this work we propose local coordinate frames per node-object to induce roto-translation invariance to the geometric graph of the interacting dynamical system. Further, the local coordinate frames allow for a natural definition of anisotropic filtering in graph neural networks. Experiments in traffic scenes, 3D motion capture, and colliding particles demonstrate that the proposed approach comfortably outperforms the recent state-of-the-art.

Epoch-GD) proposed by [16] was deemed a breakthrough for stochastic strongly convex minimization, which achieves the optimal convergence rate of O(1/T) with T iterative updates for the objective gap. However, its extension to solving stochastic min-max problems with strong convexity and strong concavity still remains open, and it is still unclear whether a fast rate of O(1/T) for the duality gap is achievable for stochastic min-max optimization under strong convexity and strong concavity. Although some recent studies have proposed stochastic algorithms with fast convergence rates for min-max problems, they require additional assumptions about the problem, e.g., smoothness, bi-linear structure, etc. In this paper, we bridge this gap by providing a sharp analysis of epoch-wise stochastic gradient descent ascent method (referred to as Epoch-GDA) for solving strongly convex strongly concave (SCSC) min-max problems, without imposing any additional assumption about smoothness or the function's structure. To the best of our knowledge, our result is the first one that shows Epoch-GDA can achieve the optimal rate of O(1/T) for the duality gap of general SCSC min-max problems. We emphasize that such generalization of Epoch-GD for strongly convex minimization problems to Epoch-GDA for SCSC min-max problems is non-trivial and requires novel technical analysis. Moreover, we notice that the key lemma can also be used for proving the convergence of Epoch-GDA for weakly-convex strongly-concave min-max problems, leading to a nearly optimal complexity without resorting to smoothness or other structural conditions.

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.

CLIP described in 2, we train two more instantiations of it by keeping either of the two consistency regularizers active in the loss objective (Eq. CLIP as only cross-modal consistency regularizer term is added to the loss objective. CLIP on most of the experiments discussed in the main text to understand their zero-shot transfer ability on standard datasets and robustness to natural distribution shifts. A.1 Zero-shot Transfer Table 7 presents our results of the zero-shot transfer experiment described in 3.1. CLIP outperforms its sub-variants and the CLIP model on the ImageNet1K dataset.

At first glance, "Heat Index" appears as inoffensive as newspaper features get. A "summer guide" sprawling across more than 50 pages, the feature, which was syndicated over the past week in both the Chicago Sun-Times and The Philadelphia Inquirer, contains "303 Must-Dos, Must-Tastes, and Must-Tries" for the sweaty months ahead. Readers are advised in one section to "Take a moonlight hike on a well-marked trail" and "Fly a kite on a breezy afternoon." In others, they receive tips about running a lemonade stand and enjoying "unexpected frozen treats." Yet close readers of the guide noticed that something was very off.

Online shopping has never been perfect. If I'm online scouting for a good deal on a pair of shoes, I have to sort through several options, sizes, colors, and price points to find what I want. And if I want to buy the shoes at a discount, I have to wait several weeks or months and come back when the price drops. At Google's annual developer event, Google IO, the tech giant is debuting an AI-powered solution to online shoppers' problems in the form of AI Mode. AI Mode offers online shoppers several options for finding exactly what they want, virtually trying on the clothing before they buy, and tracking prices to buy when a product is at its lowest.