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.

Google on Tuesday unleashed another wave of artificial intelligence technology to accelerate a year-long makeover of its search engine that is changing the way people get information and curtailing the flow of internet traffic to other websites. The next phase outlined at Google's annual developers conference includes releasing a new "AI mode" option in the United States. The company says the feature will make interacting with its search engine more like having a conversation with an expert capable of answering a wide array of questions. AI mode is being offered to all users in the US just two-and-a-half months after the company began testing with a limited Labs division audience. Google is also feeding its latest AI model, Gemini 2.5, into its search algorithms and will soon begin testing other AI features, such as the ability to automatically buy concert tickets and conduct searches through live video feeds.

Problem with selecting oracles based on initial value. Alternatively, we can switch between the oracles once to get a reward of 3/4, and twice to get the optimal reward of 1. All terminal states not shown give a reward of 0 and intermediate states have no rewards. The optimal terminal state is outlined in bold. Consequently it goes right and eventually obtains a suboptimal reward of 3/4.

Texas high school student Elliston Berry joins'Fox & Friends' to discuss the House's passage of a new bill that criminalizes the sharing of non-consensual intimate images, including content created with artificial intelligence. Parents love capturing their kids' big moments, from first steps to birthday candles. But a new study out of the U.K. shows many of those treasured images may be scanned, analyzed and turned into data by cloud storage services, and nearly half of parents don't even realize it. A survey of 2,019 U.K. parents, conducted by Perspectus Global and commissioned by Swiss privacy tech company Proton, found that 48% of parents were unaware providers like Google Photos, Apple iCloud, Amazon Photos and Dropbox can access and analyze the photos they upload. First lady Melania Trump, joined by President Donald Trump, delivers remarks before President Trump signed the Take it Down Act into law in the Rose Garden of the White House May 19, 2025, in Washington, D.C. (Chip Somodevilla/Getty Images) These companies use artificial intelligence to sort images into albums, recognize faces and locations and suggest memories.

We initiate the work towards a comprehensive picture of the worst average-case satisfaction of voting axioms in semi-random models, to provide a finer and more realistic foundation for comparing voting rules. We adopt the semi-random model and formulation in [54], where an adversary chooses arbitrarily correlated "ground truth" preferences for the agents, on top of which random noises are added. We focus on characterizing the semi-random satisfaction of two well-studied voting axioms: Condorcet criterion and participation.

Image Restoration (IR), a classic low-level vision task, has witnessed significant advancements through deep models that effectively model global information. Notably, the emergence of Vision Transformers (ViTs) has further propelled these advancements. When computing, the self-attention mechanism, a cornerstone of ViTs, tends to encompass all global cues, even those from semantically unrelated objects or regions. This inclusivity introduces computational inefficiencies, particularly noticeable with high input resolution, as it requires processing irrelevant information, thereby impeding efficiency. Additionally, for IR, it is commonly noted that small segments of a degraded image, particularly those closely aligned semantically, provide particularly relevant information to aid in the restoration process, as they contribute essential contextual cues crucial for accurate reconstruction. To address these challenges, we propose boosting IR's performance by sharing the key semantics via Transformer for IR (i.e., SemanIR) in this paper.