Google's Chrome browser for MacOS and Windows is receiving an infusion of new Gemini-powered capabilities, including an AI browsing assistant contextually sensitized to a user's browsing activities. Google made the announcement this week at Google I/O 2025. Dubbed Gemini-in-Chrome, the feature will be available May 21 to Google AI Pro and Google AI Ultra subscribers in the US as well as Chrome Beta, Dev, and Canary users. The general idea behind Gemini-in-Chrome is to reorganize, aggregate, and then more sensibly redisplay the data found on one or more browser tabs while also embellishing the final output with additional but relevant Gemini-generated information. For example, during a pre-event press briefing attended by ZDNET, Google director of Chrome product management Charmaine D'Silva demonstrated how Gemini-in-Chrome could not only organize a head-to-head feature comparison chart of individual sleeping bags -- to which multiple Chrome tabs (one tab per sleeping bag) were pointing -- but could respond to text prompts about each bag's suitability to the expected temperatures for an upcoming camping trip in Maine.

For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? Graph Convolutional Networks (GCNs) could be crucial tools for a broad range of applications, including social networks, computer vision, natural language processing, traffic prediction, chemistry, protein design, recommendation system and so on [64, 58]. Any of these applications may have a different social effect. The use of GCNs could improve protein design efficiency and lead to the development of new medicines, but it could also result in job losses.

Social media platforms were conceived to act as online'town squares' where people could get together, share information and communicate with each other peacefully. However, harmful content borne out of bad actors are constantly plaguing these platforms slowly converting them into'mosh pits' where the bad actors take the liberty to extensively abuse various marginalised groups. Accurate and timely detection of abusive content on social media platforms is therefore very important for facilitating safe interactions between users. However, due to the small scale and sparse linguistic coverage of Indic abusive speech datasets, development of such algorithms for Indic social media users (one-sixth of global population) is severely impeded.

AI's energy use already represents as much as 20 percent of global data-center power demand, research published Thursday in the journal Joule shows. That demand from AI, the research states, could double by the end of this year, comprising nearly half of all total data-center electricity consumption worldwide, excluding the electricity used for bitcoin mining. The new research is published in a commentary by Alex de Vries-Gao, the founder of Digiconomist, a research company that evaluates the environmental impact of technology. De Vries-Gao started Digiconomist in the late 2010s to explore the impact of bitcoin mining, another extremely energy-intensive activity, would have on the environment. Looking at AI, he says, has grown more urgent over the past few years because of the widespread adoption of ChatGPT and other large language models that use massive amounts of energy. According to his research, worldwide AI energy demand is now set to surpass demand from bitcoin mining by the end of this year.

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.

Artificial intelligence systems could account for nearly half of datacentre power consumption by the end of this year, analysis has revealed. The estimates by Alex de Vries-Gao, the founder of the Digiconomist tech sustainability website, came as the International Energy Agency forecast that AI would require almost as much energy by the end of this decade as Japan uses today. De Vries-Gao's calculations, to be published in the sustainable energy journal Joule, are based on the power consumed by chips made by Nvidia and Advanced Micro Devices that are used to train and operate AI models. The paper also takes into account the energy consumption of chips used by other companies, such as Broadcom. The IEA estimates that all data centres – excluding mining for cryptocurrencies – consumed 415 terawatt hours (TWh) of electricity last year.

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.

The necessity to align two graphs, minimizing a structural distance metric, is prevalent in biology, chemistry, recommender systems, and social network analysis. Due to the problem's NP-hardness, prevailing graph alignment methods follow a modular and mediated approach, solving the problem restricted to the domain of intermediary graph representations or products like embeddings, spectra, and graph signals. Restricting the problem to this intermediate space may distort the original problem and are hence predisposed to miss high-quality solutions.

Despite their remarkable successes, state-of-the-art large language models (LLMs), including vision-and-language models (VLMs) and unimodal language models (ULMs), fail to understand precise semantics. For example, semantically equivalent sentences expressed using different lexical compositions elicit diverging representations. The degree of this divergence and its impact on encoded semantics is not very well understood.

Cutaway view of a fictional space colony concept painted by artist Rick Guidice as part of a NASA art program in the 1970s. This story was originally published by Undark and is reproduced here as part of the Climate Desk collaboration. Elon Musk once joked: "I would like to die on Mars. Musk is, in fact, deadly serious about colonizing the Red Planet. Part of his motivation is the idea of having a "back-up" planet in case some future catastrophe renders the Earth uninhabitable. Musk has suggested that a million people may be calling Mars home by 2050 -- and he's hardly alone in his enthusiasm. Venture capitalist Marc Andreessen believes the world can easily support 50 billion people, and more than that once we settle other planets. And Jeff Bezos has spoken of exploiting the resources of the moon and the asteroids to build giant space stations. "I would love to see a trillion humans living in the solar system," he has said. Not so fast, cautions science journalist Adam Becker.