Google adds separate work and personal accounts to Chrome on iOS
Chrome now supports easy account switching and data separation on iOS, similar to its approach on Android and Chrome for desktop . The browser keeps data such as tabs, history and passwords confined to the corresponding account, protecting employer information from exposure to personal activities.
When users first sign or select their managed account, they will be taken through an onboarding process that explains the separation between managed accounts and personal, as well as insights into how their company is handling their data. Anytime a user switches to the managed account, they are notified that they are entering a managed experience.
Google has also added safeguards for companies using Chrome Enterprise. IT teams can now apply URL filtering to work accounts in Chrome on iOS, and security teams can stream audit logs from iOS and Android directly into the Admin console or any SIEM, both of which are features already available on desktop.
These features are available for managed devices through Chrome Enterprise today.
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CNET
an hour ago
- CNET
AI Is Taking Over Your Search Engine. Here's a Look Under the Hood
For decades, the way we find information on the internet changed only in small ways. Doing a traditional Google search today doesn't feel all that different from when, in the 1990s, you would Ask Jeeves. Sure, a lot has changed under the hood, the results are likely far more relevant and the interface has some new features, but you're still typing in keywords and getting a list of websites that might hold the answer. That way of searching, it seems, is starting to go the way of AltaVista, may it rest in peace. In May, Google announced the rollout of its new AI Mode for search, which uses a generative AI model (based on the company's Gemini large language model) to give you conversational answers that feel a lot more like having a chat and less like combing through a set of links. Other companies, like Perplexity and OpenAI, have also deployed search tools based on gen AI. These tools, which merge the functionality of a chatbot and a traditional search engine, are quickly gaining steam. You can't even escape AI by doing just a regular Google search: AI Overviews have been popping up atop those results pages since last year, and about one in five searches are now showing this kind of summary, according to a Pew Research Center report. I'm surprised it's not even more than that. These newfangled search tools feel a lot like your typical chatbot, like ChatGPT, but they do things a little differently. Those differences share a lot of DNA with their search engine ancestors. Here's a look under the hood at how these new tools work, and how you can use them effectively. Everything Announced at Google I/O 2025 Everything Announced at Google I/O 2025 Click to unmute Video Player is loading. Play Video Pause Skip Backward Skip Forward Next playlist item Unmute Current Time 0:13 / Duration 15:40 Loaded : 6.33% 00:13 Stream Type LIVE Seek to live, currently behind live LIVE Remaining Time - 15:27 Share Fullscreen This is a modal window. Beginning of dialog window. Escape will cancel and close the window. Text Color White Black Red Green Blue Yellow Magenta Cyan Opacity Opaque Semi-Transparent Text Background Color Black White Red Green Blue Yellow Magenta Cyan Opacity Opaque Semi-Transparent Transparent Caption Area Background Color Black White Red Green Blue Yellow Magenta Cyan Opacity Transparent Semi-Transparent Opaque Font Size 50% 75% 100% 125% 150% 175% 200% 300% 400% Text Edge Style None Raised Depressed Uniform Drop shadow Font Family Proportional Sans-Serif Monospace Sans-Serif Proportional Serif Monospace Serif Casual Script Small Caps Reset Done Close Modal Dialog End of dialog window. Close Modal Dialog This is a modal window. This modal can be closed by pressing the Escape key or activating the close button. Close Modal Dialog This is a modal window. This modal can be closed by pressing the Escape key or activating the close button. Everything Announced at Google I/O 2025 Search engines vs. AI search: What's the difference? The underlying technology of a search engine is kinda like an old library card catalog. The engine uses bots to crawl the vast expanses of the internet to find, analyze and index the endless number of web pages. Then, when you do a search to ask who played Dr. Angela Hicks on ER, because you're trying to remember what else you've seen her in, it will return pages for things like the cast of ER or the biography of the actor, CCH Pounder. From there, you can click through those pages, whether they're on Wikipedia or IMDB or somewhere else, and learn that you know CCH Pounder from her Emmy-winning guest appearance on an episode of The X-Files. "When customers have a certain question, they can type that question into Google and then Google runs their ranking algorithms to find what content is the best for a particular query," Eugene Levin, president of the marketing and SEO tool company Semrush, told me. Generally, with a traditional search, you have to click through to other websites to get the answer you're looking for. When I was trying to figure out where I recognized CCH Pounder from, I clicked on at least half a dozen different sites to track it down. That included using Google's video search -- which combs an index of videos across different hosting platforms -- to find clips of her appearance on The X-Files. Google announced AI Mode at its I/O developer conference in May. Google/Screenshot by Joe Maldonado/CNET These multiple searches don't necessarily have to happen. If I just want to know the cast of ER, I can type in "cast of ER" and click on the Wikipedia page at the top. You'll usually find Wikipedia or another relevant, trustworthy site at or near the top of a search result page. That's because a main way today's search algorithms work is by tracking which sites and pages get most links from elsewhere on the web. That model, which "changed the game for search" when Google launched it in the 1990s, was more reliable than indexing systems that relied on things like just how many times a keyword appeared on a page, said Sauvik Das, associate professor at Carnegie Mellon University's Human-Computer Interaction Institute. "There's lots of cookie recipes on the web, but how do you know which ones to show first?" Das said. "Well, if a bunch of other websites are linking to this website for the keywords of 'cookie recipe,' that's pretty difficult to game." AI-powered search engines work a little differently, but operate on the same basic infrastructure. In my quest to see where I recognized CCH Pounder from, I asked Google's AI Mode, literally, "Where do I recognize the actress who plays Dr. Angie Hicks on ER from?" In a conversation that felt far more like chatting with a bot than doing searches, I narrowed it down. The first result gave me a list of shows and movies I hadn't seen, so I asked for a broader list, which featured her guest appearances on other shows. Then I could ask for more details about her X-Files appearance, and that narrowed it down. While the way I interacted with Google was different, the search mechanisms were basically the same. AI Mode just used its Gemini model to develop and process dozens of different web searches to gather the information needed, Robby Stein, vice president of product for Google Search, told me. "A user could've just queried each of those queries themselves." Basically, AI Mode did the same thing I did, just a lot faster. So many searches, so little time The approach here is called "query fan-out." The AI model takes your request and breaks it down into a series of questions, then conducts searches to answer those components of the request. It then takes the information it gathers from all those searches and websites and puts it together in an answer for you. In a heartbeat. Those searches are using the same index that a traditional search would. "They work on the same foundation," Levin said. "What changes is how they pull information from this foundation." This fan-out process allows the AI search to pull in relevant information from sites that might not have appeared on the first page of traditional search results, or to pull a paragraph of good information from a page that has a lot more irrelevant information. Instead of you going down a rabbit hole to find one tiny piece of the answer you want, the AI goes down a wide range of rabbit holes in a few seconds. "They will anticipate, if you're looking for this, what is the next thing you might be interested in?" Levin said. Read more: AI Essentials: 29 Ways You Can Make Gen AI Work for You, According to Our Experts The number of searches the AI model will do depends on the tool you're using and on how complicated your question is. AI Mode that uses Google's Deep Search will spend more time and conduct more searches, Stein said. "Increasingly, if you ask a really hard question, it will use our most powerful models to reply," Stein said. The large language models that power these search engines also have their existing training data to pull from or use to guide their searches. While a lot of the information is coming from the up-to-date content it finds by searching the web, some may come from that training data, which could include reams of information ranging from websites like this one to whole libraries of books. That training data is so extensive that lawsuits over whether AI companies actually had the right to use that information are quickly multiplying. (Disclosure: Ziff Davis, CNET's parent company, in April filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.) AI search isn't just a chatbot Not relying on training data is one thing that sets an AI-powered search engine apart from a traditional chatbot, even though the underlying language model might be largely the same. While ChatGPT Search will scour the internet for relevant sites and answers, regular ChatGPT might rely on its own training data to answer your question. "The right answer might be in there," Das said. "It might also hallucinate a likely answer that isn't anywhere in the pre-training data." The AI search uses a concept called retrieval-augmented generation to incorporate what it finds on the internet into its answer. It collects information from a source you point it to (in this case, the search engine index) and tells it to look there instead of making something up if it can't find it in its training data. "You're telling the AI the answer is here, I just want you to find where," Das said. "You get the top 10 Google results, and you're telling the AI the answer is probably in here." Perplexity offers AI-powered search through its app and through a newly announced browser. Stefani Reynolds/Bloomberg via Getty Images Can you really trust AI search results? These AI-powered search tools might be more reliable than just using a chatbot itself, because they're pulling from current, relevant information and giving you links, but you still have to think critically about it. Here are some tips from the experts: Bring your human skepticism Consider how bad people are at telling when you're sarcastic on the internet. Then think about how bad a large language model might be at it. That's how Google's AI Overviews came up with the idea to put glue on pizza -- by pulling information from a humorous Reddit post and repeating it as if it were real culinary advice. "The AI doesn't know what is authentic and what is humorous," Das said. "It's going to treat all that information the same." Remember to use your own judgement and look for the sources of the information. They might not be as accurate as the LLM thinks, and you don't want to make important life decisions based on somebody's joke on an internet forum that a robot thought was real. AI can still make stuff up Even though they're supposed to be pulling from search results, these tools can still make things up in the absence of good information. That's how AI Overviews started creating fake definitions for nonsensical sayings. The retrieval-augmented generation might reduce the risk of outright hallucinations but doesn't eliminate it, according to Das. Remember that an LLM doesn't have a sense of what the right answer to a question is. "It's just predicting what is the next English word that would come after this previous stream of other English words or other language words," Das said. "It doesn't really have a concept of truthiness in that sense." Check your sources Traditional search engines are very hands-off. They will give you a list of websites that appear relevant to your search and let you decide whether you want to trust them. Because an AI search is consolidating and rewriting that information itself, it may not be obvious when it's using an untrustworthy source. "Those systems are not going to be entirely error-free, but I think the challenge is that over time you will lose an ability to catch them," Levin said. "They will be very convincing and you will not know how to really go and verify, or you will think you don't need to go and verify." But you can check every source. But that's exactly the kind of work you were probably hoping to avoid using this new system that's designed to save you time and effort. "The problem is if you're going to do this analysis for every query you perform in ChatGPT, what is the purpose of ChatGPT?" Levin said.
Business Insider
2 hours ago
- Business Insider
China Calls for Global AI Rules as U.S. Escalates Tech Fight – What Investors Should Watch
China is proposing to lead the creation of a new international body to shape the future of artificial intelligence. Speaking at the World Artificial Intelligence Conference in Shanghai, Premier Li Qiang called for a World AI Cooperation Organization, aiming to make AI development more inclusive and to prevent it from being dominated by a handful of nations or companies. Elevate Your Investing Strategy: Take advantage of TipRanks Premium at 50% off! Unlock powerful investing tools, advanced data, and expert analyst insights to help you invest with confidence. The proposal comes as the global AI race accelerates. Premier Li cited the need for shared governance to address the risks tied to AI, from job losses to security concerns. Former Google (GOOG) chief executive Eric Schmidt backed the idea of global collaboration, saying the U.S. and China should work together to maintain stability and ensure human control over powerful AI systems. Tensions Rise as China Courts Allies and the U.S. Doubles Down However, turning that vision into a working framework will not be easy, as the U.S. is taking a different path. Just days before the conference, President Donald Trump signed new executive orders to ease regulations and boost energy access for AI infrastructure, including data centers. These moves are designed to strengthen companies like OpenAI and Google while reinforcing America's lead in advanced AI. In the meantime, geopolitical friction remains high. U.S. restrictions on Nvidia Corporation (NVDA) chips continue to limit China's access to high-end semiconductors. Premier Li acknowledged these supply chain issues and reaffirmed China's goal to reduce its reliance on foreign technology. That includes support for homegrown companies like DeepSeek, which has gained attention for scaling up open-sourced models and AI agents. China's strategy also includes outreach to the Global South, including partnerships with Brazil and African nations. However, international trust remains a hurdle. Western companies and governments are hesitant to align with a governance model led by Beijing, especially regarding concerns around data access, intellectual property, and dual-use technologies. Takeaway for Investors For investors, the gap between cooperation and competition is clear. Chinese firms are racing to set their own benchmarks, while U.S. players double down on domestic infrastructure and AI regulation. The idea of a global AI framework may gain traction diplomatically, but market dynamics suggest a more fragmented path forward. Whether this initiative reshapes AI development or becomes another diplomatic flashpoint will depend on how governments and companies balance access, risk, and control in the months ahead. Using TipRanks' Comparison Tool, we've analyzed several leading AI stocks that could be influenced by geopolitical tensions, shifting regulations, and broader market dynamics.
Business Insider
2 hours ago
- Business Insider
The Week That Was, The Week Ahead: Macro & Markets, July 27, 2025
Everything to Know about Macro and Markets The S&P 500 (SPX) touched new intraday highs on Friday, ending the week up 1.5%. Major indices finished the week on a strong note, thanks to solid corporate earnings and favorable trade developments. The Nasdaq-100 (NDX) and the Dow Jones Industrial Average (DJIA) ended the week 0.9% and 1.3% higher, respectively. Elevate Your Investing Strategy: Take advantage of TipRanks Premium at 50% off! Unlock powerful investing tools, advanced data, and expert analyst insights to help you invest with confidence. Weekly Gains on Strong Earnings and Trade Deals The S&P 500 posted its fifth straight day of closing records on Friday. Interestingly, the index ended above the 6,300 level for the first time on Monday. Strong earnings reported by several companies, notably by Google parent Alphabet (GOOGL) and telecom giant Verizon (VZ), fueled last week's rally. According to FactSet, out of the 34% of S&P 500 companies that have reported Q2 results so far, 80% have exceeded earnings expectations, a proportion that is above the 5-year average of 78%. However, the magnitude of earnings surprises is below average levels. Additionally, the trade agreement between the U.S. and Japan eased concerns about the ongoing tariff wars and contributed to last week's rally. U.S. President Donald Trump called it 'perhaps the largest Deal ever made,' while adding that Japan would invest $550 billion in the U.S. The deal lowers the tariffs on auto exports, a major driver of Japan's economy, to 15% from the existing 25% that is imposed across countries. A Big Week Ahead The week ahead is expected to be quite eventful, given that it includes the August tariff deadline, the July Federal Open Market Committee (FOMC) meeting, earnings reports from four of the Magnificent 7 stocks, and some key economic releases. The Federal Reserve's two-day policy meeting will be in focus this week, especially due to President Trump's persistent attacks on the central bank's Chair Jerome Powell. The independence of the Fed is being debated, with reporters expected to question Powell about the same at the post-meeting press conference. Despite growing pressure from Trump to slash interest rates, the Fed is expected to hold the benchmark short-term borrowing rate steady in a target range of 4.25% to 4.50%. Meanwhile, the August 1 deadline to raise tariffs is approaching this week. Trump's erratic trade policy decisions have been making headlines. Following the trade agreement with Japan, all eyes are on the possibility of a deal with the European Union (EU). Aside from Trump's trade stance, earnings from four of the Magnificent 7 stocks – Meta Platforms (META), Microsoft (MSFT), Amazon (AMZN), and Apple (AAPL), are eagerly awaited. Investors will be focusing on the commentary from management of these companies regarding the business backdrop and artificial intelligence (AI) spending. Finally, the Personal Consumption Expenditure (PCE) price index report, scheduled for release on Thursday, and the July nonfarm payrolls report, due on Friday, could impact investor sentiment. Experts expect the jobs report to indicate a deceleration in job additions in July compared to the previous month. Meanwhile, the PCE price index is expected to show a rise in inflation. Stocks That Made the News Alphabet impressed investors with its market-beating second-quarter revenue and earnings. The company raised its capital expenditure guidance for 2025 from $75 billion to $85 billion to support robust demand for its cloud offerings amid the ongoing AI boom. Verizon also delivered upbeat results for the June quarter, thanks to strength in its wireless service business. The company raised the lower end of its full-year earnings guidance to reflect strong demand for its premium plans and the Trump administration's new tax law. IBM (IBM) stock fell even after delivering better-than-expected second-quarter results. The company's software business lagged the Street's revenue expectations. The tech giant also noted that some clients are being cautious in signing new deals due to geopolitical pressures. Tesla (TSLA) reported dismal second-quarter results, with the electric vehicle (EV) maker's CEO Elon Musk cautioning investors about a 'few rough quarters' ahead due to the expiration of the federal tax credits. TSLA stock is down 22% year-to-date, as investors are concerned about weak demand due to macro pressures and rising competition, a lack of innovation, and Musk's political ambitions. Intel (INTC) reported mixed results for the second quarter. The chip giant exceeded the consensus revenue expectations, but the bottom line fell short of estimates. Further, CEO Lip-Bu Tan announced significant cuts in chip factory construction. The company is streamlining its operations and cutting costs in an attempt to revive its business. Kohl's (KSS) made headlines this week as it became the latest stock to join the meme stock frenzy. Retail investors are buying stocks of companies like Kohl's and online residential real estate platform Opendoor Technologies (OPEN), which generally have high short seller interest, to trigger rapid rallies. UnitedHealth (UNH) stock declined after the health insurer disclosed in an SEC filing that it 'proactively' reached out to the U.S. Department of Justice (DOJ) after reports of investigations into certain aspects of the company's participation in the Medicare program surfaced. The company added that it has started complying with formal criminal and civil requests from the DOJ. Data analytics company Palantir Technologies (PLTR) stock continued to rally this week, thanks to AI tailwinds. PLTR stock is now one of the 20 most valuable companies in the U.S. Paramount (PARA) secured the Federal Communications Commission's (FCC) approval for the $8 billion merger with Skydance Media. The Q2 2025 earnings season is in full swing, with many prominent U.S. companies' earnings scheduled for this week. Spotlight will be on the earnings reports of PayPal (PYPL), Visa (V), Boeing (BA), SoFi Technologies (SOFI), UnitedHealth (UNH), Microsoft (MSFT), Meta Platforms (META), Alibaba (BABA), Ford (F), Apple (AAPL), Amazon (AMZN), Exxon Mobil (XOM), and Chevron (CVX).
