If there was any doubt about Google’s commitment to move fast and break things, its new policy position should put that to rest. “For too long, AI policymaking has paid disproportionate attention to the risks,” the document says.
Google urges the US to invest in AI not only with money but with business-friendly legislation. The company joins the growing chorus of AI firms calling for federal legislation that clarifies how they can operate. It points to the difficulty of complying with a “patchwork” of state-level laws that impose restrictions on AI development and use. If you want to know what keeps Google’s policy wonks up at night, look no further than the vetoed SB-1047 bill in California, which would have enforced AI safety measures.
According to Google, a national AI framework that supports innovation is necessary to push the boundaries of what artificial intelligence can do. Taking a page from the gun lobby, Google opposes attempts to hold the creators of AI liable for the way those models are used. Generative AI systems are non-deterministic, making it impossible to fully predict their output. Google wants clearly defined responsibilities for AI developers, deployers, and end users—it would, however, clearly prefer most of those responsibilities fall on others. “In many instances, the original developer of an AI model has little to no visibility or control over how it is being used by a deployer and may not interact with end users,” the company says.
There are efforts underway in some countries that would implement stringent regulations that force companies like Google to make their tools more transparent. For example, the EU’s AI Act would require AI firms to publish an overview of training data and possible risks associated with their products. Google believes this would force the disclosure of trade secrets that would allow foreign adversaries to more easily duplicate its work, mirroring concerns that OpenAI expressed in its policy proposal.
Google wants the government to push back on these efforts at the diplomatic level. The company would like to be able to release AI products around the world, and the best way to ensure it has that option is to promote light-touch regulation that “reflects US values and approaches.” That is, Google’s values and approaches.
Not all devices can simply download an updated app—after almost a decade, Assistant is baked into many Google products. The company says Google-powered cars, watches, headphones, and other devices that use Assistant will receive updates that transition them to Gemini. It’s unclear if all Assistant-powered gadgets will be part of the migration. Most of these devices connect to your phone, so the update should be relatively straightforward, even for accessories that launched early in the Assistant era.
There are also plenty of standalone devices that run Assistant, like TVs and smart speakers. Google says it’s working on updated Gemini experiences for those devices. For example, there’s a Gemini preview program for select Google Nest speakers. It’s unclear if all these devices will get updates. Google says there will be more details on this in the coming months.
Meanwhile, Gemini still has some ground to make up. There are basic features that work fine in Assistant, like setting timers and alarms, that can go sideways with Gemini. On the other hand, Assistant had its fair share of problems and didn’t exactly win a lot of fans. Regardless, this transition could be fraught with danger for Google as it upends how people interact with their devices.
One of the best mostly invisible updates in iOS 18 was Apple’s decision to finally implement the Rich Communications Services (RCS) communication protocol, something that is slowly helping to fix the generally miserable experience of texting non-iPhone users with an iPhone. The initial iOS 18 update brought RCS support to most major carriers in the US, and the upcoming iOS 18.4 update is turning it on for a bunch of smaller prepaid carriers like Google Fi and Mint Mobile.
Now that Apple is on board, iPhones and their users can also benefit from continued improvements to the RCS standard. And one major update was announced today: RCS will now support end-to-end encryption using the Messaging Layer Security (MLS) protocol, a standard finalized by the Internet Engineering Task Force in 2023.
“RCS will be the first large-scale messaging service to support interoperable E2EE between client implementations from different providers,” writes GSMA Technical Director Tom Van Pelt in the post announcing the updates. “Together with other unique security features such as SIM-based authentication, E2EE will provide RCS users with the highest level of privacy and security for stronger protection from scams, fraud and other security and privacy threats. ”
A new study from Columbia Journalism Review’s Tow Center for Digital Journalism finds serious accuracy issues with generative AI models used for news searches. The research tested eight AI-driven search tools equipped with live search functionality and discovered that the AI models incorrectly answered more than 60 percent of queries about news sources.
Researchers Klaudia Jaźwińska and Aisvarya Chandrasekar noted in their report that roughly 1 in 4 Americans now uses AI models as alternatives to traditional search engines. This raises serious concerns about reliability, given the substantial error rate uncovered in the study.
Error rates varied notably among the tested platforms. Perplexity provided incorrect information in 37 percent of the queries tested, whereas ChatGPT Search incorrectly identified 67 percent (134 out of 200) of articles queried. Grok 3 demonstrated the highest error rate, at 94 percent.
A graph from CJR shows “confidently wrong” search results. Credit: CJR
For the tests, researchers fed direct excerpts from actual news articles to the AI models, then asked each model to identify the article’s headline, original publisher, publication date, and URL. They ran 1,600 queries across the eight different generative search tools.
The study highlighted a common trend among these AI models: rather than declining to respond when they lacked reliable information, the models frequently provided confabulations—plausible-sounding incorrect or speculative answers. The researchers emphasized that this behavior was consistent across all tested models, not limited to just one tool.
Surprisingly, premium paid versions of these AI search tools fared even worse in certain respects. Perplexity Pro ($20/month) and Grok 3’s premium service ($40/month) confidently delivered incorrect responses more often than their free counterparts. Though these premium models correctly answered a higher number of prompts, their reluctance to decline uncertain responses drove higher overall error rates.
Issues with citations and publisher control
The CJR researchers also uncovered evidence suggesting some AI tools ignored Robot Exclusion Protocol settings, which publishers use to prevent unauthorized access. For example, Perplexity’s free version correctly identified all 10 excerpts from paywalled National Geographic content, despite National Geographic explicitly disallowing Perplexity’s web crawlers.
Google’s venerable 2015 Chromecast attempted to self-destruct earlier this week, upsetting a huge number of people who were still using the decade-old streaming dongles. Google was seemingly caught off guard by the devices glitching out all at the same time, but it promised to address the problem, and it has. Google says it has a fix ready to roll out, and most affected devices should be right as rain in the coming days.
Google is still not confirming the cause of the Chromecast outage, but it was almost certainly the result of a certificate expiring after 10 years. It would seem there was no one keeping an eye on the Chromecast’s ticking time bomb, which isn’t exactly surprising—Google has moved on from the Chromecast brand, focusing instead on the more capable Google TV streamer. Even if Google is done with the Chromecast, its customers aren’t.
If you left your 2015 Chromecast or Chromecast Audio alone to await a fix, you’re in good shape. The update should be delivered automatically to the device soon. “We’ve started rolling out a fix for the problem with Chromecast (2nd gen) and Chromecast Audio devices, which will be completed over the next few days. Users must ensure their device is connected to WiFi to receive the update,” says Google.
Gemini 2.0 is also coming to Deep Research, Google’s AI tool that creates detailed reports on a topic or question. This tool browses the web on your behalf, taking its time to assemble its responses. The new Gemini 2.0-based version will show more of its work as it gathers data, and Google claims the final product will be of higher quality.
You don’t have to take Google’s word on this—you can try it for yourself, even if you don’t pay for advanced AI features. Google is making Deep Research free, but it’s not unlimited. The company says everyone will be able to try Deep Research “a few times a month” at no cost. That’s all the detail we’re getting, so don’t go crazy with Deep Research right away.
Lastly, Google is also rolling out Gems to free accounts. Gems are like custom chatbots you can set up with a specific task in mind. Google has some defaults like Learning Coach and Brainstormer, but you can get creative and make just about anything (within the limits prescribed by Google LLC and applicable laws).
Some of the newly free features require a lot of inference processing, which is not cheap. Making its most expensive models free, even on a limited basis, will undoubtedly increase Google’s AI losses. No one has figured out how to make money on generative AI yet, but Google seems content spending more money to secure market share.
The annual Game Developers Conference is about to kick off, and even though Stadia is dead and buried, Google has a lot of plans for games. It’s expanding tools that help PC developers bring premium games to Android, and games are heading in the other direction, too. The PC-based Play Games platform is expanding to bring every single Android game to Windows. Google doesn’t have a firm timeline for all these changes, but 2025 will be an interesting year for the company’s gaming efforts.
Google released the first beta of Google Play Games on PC back in 2022, allowing you to play Android games on a PC. It has chugged along quietly ever since, mostly because of the anemic and largely uninteresting game catalog. While there are hundreds of thousands of Android games, only a handful were made available in the PC client. That’s changing in a big way now that Google is bringing over every Android game from Google Play.
Starting today, you’ll see thousands of new games in Google Play Games on PC. Developers actually have to opt out if they don’t want their games available on Windows machines via Google Play Games. Google says this is possible thanks to improved custom controls, making it easy to map keyboard and gamepad controls onto games that were designed for touchscreens (see below). The usability of these mapped controls will probably vary dramatically from game to game.
While almost every Android game will soon be available on Windows, not all will get top billing. Google Play Games on PC has a playability badge, indicating a game has been tested on Windows. Games that have been specifically optimized for PC get a more prominent badge. Games with the “Playable” or “Optimized” distinction will appear throughout the client in lists of suggested titles, but untested games will only appear if you search for them. However, you can install them all just the same, and they’ll work better on AMD-based machines, support for which has been lacking throughout the beta.
Researchers have discovered multiple Android apps, some that were available in Google Play after passing the company’s security vetting, that surreptitiously uploaded sensitive user information to spies working for the North Korean government.
Samples of the malware—named KoSpy by Lookout, the security firm that discovered it—masquerade as utility apps for managing files, app or OS updates, and device security. Behind the interfaces, the apps can collect a variety of information including SMS messages, call logs, location, files, nearby audio, and screenshots and send them to servers controlled by North Korean intelligence personnel. The apps target English language and Korean language speakers and have been available in at least two Android app marketplaces, including Google Play.
Think twice before installing
The surveillanceware masquerades as the following five different apps:
휴대폰 관리자 (Phone Manager)
File Manager
스마트 관리자 (Smart Manager)
카카오 보안 (Kakao Security) and
Software Update Utility
Besides Play, the apps have also been available in the third-party Apkpure market. The following image shows how one such app appeared in Play.
Credit: Lookout
The image shows that the developer email address was mlyqwl@gmail[.]com and the privacy policy page for the app was located at https://goldensnakeblog.blogspot[.]com/2023/02/privacy-policy.html.
“I value your trust in providing us your Personal Information, thus we are striving to use commercially acceptable means of protecting it,” the page states. “But remember that no method of transmission over the internet, or method of electronic storage is 100% secure and reliable, and I cannot guarantee its absolute security.”
The page, which remained available at the time this post went live on Ars, has no reports of malice on Virus Total. By contrast, IP addresses hosting the command-and-control servers have previously hosted at least three domains that have been known since at least 2019 to host infrastructure used in North Korean spy operations.
On Wednesday, Google DeepMind announced two new AI models designed to control robots: Gemini Robotics and Gemini Robotics-ER. The company claims these models will help robots of many shapes and sizes understand and interact with the physical world more effectively and delicately than previous systems, paving the way for applications such as humanoid robot assistants.
It’s worth noting that even though hardware for robot platforms appears to be advancing at a steady pace (well, maybe not always), creating a capable AI model that can pilot these robots autonomously through novel scenarios with safety and precision has proven elusive. What the industry calls “embodied AI” is a moonshot goal of Nvidia, for example, and it remains a holy grail that could potentially turn robotics into general-use laborers in the physical world.
Along those lines, Google’s new models build upon its Gemini 2.0 large language model foundation, adding capabilities specifically for robotic applications. Gemini Robotics includes what Google calls “vision-language-action” (VLA) abilities, allowing it to process visual information, understand language commands, and generate physical movements. By contrast, Gemini Robotics-ER focuses on “embodied reasoning” with enhanced spatial understanding, letting roboticists connect it to their existing robot control systems.
For example, with Gemini Robotics, you can ask a robot to “pick up the banana and put it in the basket,” and it will use a camera view of the scene to recognize the banana, guiding a robotic arm to perform the action successfully. Or you might say, “fold an origami fox,” and it will use its knowledge of origami and how to fold paper carefully to perform the task.
Gemini Robotics: Bringing AI to the physical world.
In 2023, we covered Google’s RT-2, which represented a notable step toward more generalized robotic capabilities by using Internet data to help robots understand language commands and adapt to new scenarios, then doubling performance on unseen tasks compared to its predecessor. Two years later, Gemini Robotics appears to have made another substantial leap forward, not just in understanding what to do but in executing complex physical manipulations that RT-2 explicitly couldn’t handle.
While RT-2 was limited to repurposing physical movements it had already practiced, Gemini Robotics reportedly demonstrates significantly enhanced dexterity that enables previously impossible tasks like origami folding and packing snacks into Zip-loc bags. This shift from robots that just understand commands to robots that can perform delicate physical tasks suggests DeepMind may have started solving one of robotics’ biggest challenges: getting robots to turn their “knowledge” into careful, precise movements in the real world.
Better generalized results
According to DeepMind, the new Gemini Robotics system demonstrates much stronger generalization, or the ability to perform novel tasks that it was not specifically trained to do, compared to its previous AI models. In its announcement, the company claims Gemini Robotics “more than doubles performance on a comprehensive generalization benchmark compared to other state-of-the-art vision-language-action models.” Generalization matters because robots that can adapt to new scenarios without specific training for each situation could one day work in unpredictable real-world environments.
That’s important because skepticism remains regarding how useful humanoid robots currently may be or how capable they really are. Tesla unveiled its Optimus Gen 3 robot last October, claiming the ability to complete many physical tasks, yet concerns persist over the authenticity of its autonomous AI capabilities after the company admitted that several robots in its splashy demo were controlled remotely by humans.
Here, Google is attempting to make the real thing: a generalist robot brain. With that goal in mind, the company announced a partnership with Austin, Texas-based Apptronik to”build the next generation of humanoid robots with Gemini 2.0.” While trained primarily on a bimanual robot platform called ALOHA 2, Google states that Gemini Robotics can control different robot types, from research-oriented Franka robotic arms to more complex humanoid systems like Apptronik’s Apollo robot.
Gemini Robotics: Dexterous skills.
While the humanoid robot approach is a relatively new application for Google’s generative AI models (from this cycle of technology based on LLMs), it’s worth noting that Google had previously acquired several robotics companies around 2013–2014 (including Boston Dynamics, which makes humanoid robots), but later sold them off. The new partnership with Apptronik appears to be a fresh approach to humanoid robotics rather than a direct continuation of those earlier efforts.
Other companies have been hard at work on humanoid robotics hardware, such as Figure AI (which secured significant funding for its humanoid robots in March 2024) and the aforementioned former Alphabet subsidiary Boston Dynamics (which introduced a flexible new Atlas robot last April), but a useful AI “driver” to make the robots truly useful has not yet emerged. On that front, Google has also granted limited access to the Gemini Robotics-ER through a “trusted tester” program to companies like Boston Dynamics, Agility Robotics, and Enchanted Tools.
Safety and limitations
For safety considerations, Google mentions a “layered, holistic approach” that maintains traditional robot safety measures like collision avoidance and force limitations. The company describes developing a “Robot Constitution” framework inspired by Isaac Asimov’s Three Laws of Robotics and releasing a dataset unsurprisingly called “ASIMOV” to help researchers evaluate safety implications of robotic actions.
This new ASIMOV dataset represents Google’s attempt to create standardized ways to assess robot safety beyond physical harm prevention. The dataset appears designed to help researchers test how well AI models understand the potential consequences of actions a robot might take in various scenarios. According to Google’s announcement, the dataset will “help researchers to rigorously measure the safety implications of robotic actions in real-world scenarios.”
The company did not announce availability timelines or specific commercial applications for the new AI models, which remain in a research phase. While the demo videos Google shared depict advancements in AI-driven capabilities, the controlled research environments still leave open questions about how these systems would actually perform in unpredictable real-world settings.
Google has a new mission in the AI era: to add Gemini to as many of the company’s products as possible. We’ve already seen Gemini appear in search results, text messages, and more. In Google’s latest update to Workspace, Gemini will be able to add calendar appointments from Gmail with a single click. Well, assuming Gemini gets it right the first time, which is far from certain.
The new calendar button will appear at the top of emails, right next to the summarize button that arrived last year. The calendar option will show up in Gmail threads with actionable meeting chit-chat, allowing you to mash that button to create an appointment in one step. The Gemini sidebar will open to confirm the appointment was made, which is a good opportunity to double-check the robot. There will be a handy edit button in the Gemini window in the event it makes a mistake. However, the robot can’t invite people to these events yet.
The effect of using the button is the same as opening the Gemini panel and asking it to create an appointment. The new functionality is simply detecting events and offering the button as a shortcut of sorts. You should not expect to see this button appear on messages that already have calendar integration, like dining reservations and flights. Those already pop up in Google Calendar without AI.
On the Frontier Math benchmark by EpochAI, o3 solved 25.2 percent of problems, while no other model has exceeded 2 percent—suggesting a leap in mathematical reasoning capabilities over the previous model.
Benchmarks vs. real-world value
Ideally, potential applications for a true PhD-level AI model would include analyzing medical research data, supporting climate modeling, and handling routine aspects of research work.
The high price points reported by The Information, if accurate, suggest that OpenAI believes these systems could provide substantial value to businesses. The publication notes that SoftBank, an OpenAI investor, has committed to spending $3 billion on OpenAI’s agent products this year alone—indicating significant business interest despite the costs.
Meanwhile, OpenAI faces financial pressures that may influence its premium pricing strategy. The company reportedly lost approximately $5 billion last year covering operational costs and other expenses related to running its services.
News of OpenAI’s stratospheric pricing plans come after years of relatively affordable AI services that have conditioned users to expect powerful capabilities at relatively low costs. ChatGPT Plus remains $20 per month and Claude Pro costs $30 monthly—both tiny fractions of these proposed enterprise tiers. Even ChatGPT Pro’s $200/month subscription is relatively small compared to the new proposed fees. Whether the performance difference between these tiers will match their thousandfold price difference is an open question.
Despite their benchmark performances, these simulated reasoning models still struggle with confabulations—instances where they generate plausible-sounding but factually incorrect information. This remains a critical concern for research applications where accuracy and reliability are paramount. A $20,000 monthly investment raises questions about whether organizations can trust these systems not to introduce subtle errors into high-stakes research.
In response to the news, several people quipped on social media that companies could hire an actual PhD student for much cheaper. “In case you have forgotten,” wrote xAI developer Hieu Pham in a viral tweet, “most PhD students, including the brightest stars who can do way better work than any current LLMs—are not paid $20K / month.”
While these systems show strong capabilities on specific benchmarks, the “PhD-level” label remains largely a marketing term. These models can process and synthesize information at impressive speeds, but questions remain about how effectively they can handle the creative thinking, intellectual skepticism, and original research that define actual doctoral-level work. On the other hand, they will never get tired or need health insurance, and they will likely continue to improve in capability and drop in cost over time.
The unexpected appearance of notification cooldown, along with smaller changes to haptics globally, could be responsible for the complaints. Maybe this is working as intended and Pixel owners are just caught off guard; or maybe Google broke something. It wouldn’t be the first time.
The unexpected appearance of Notification Cooldown in the update might have something to do with the reports—it’s on by default.
Credit: Ryan Whitwam
The unexpected appearance of Notification Cooldown in the update might have something to do with the reports—it’s on by default. Credit: Ryan Whitwam
In 2022, Google released an update that weakened haptic feedback on the Pixel 6, making it so soft that people were missing calls. Google released a fix for the problem a few weeks later. If there’s something wrong with the new Pixel Drop, it’s a more subtle problem. People can’t even necessarily explain how it’s different, but most seem to agree that it is.
After testing several Pixel phones both before and after the update, there may be some truth to the complaints. The length and intensity of haptic notification feedback feel different on a Pixel 9 Pro XL post-update, but our Pixel 9 Pro feels the same after installing the Pixel Drop. The different models may simply have been tuned differently in the update, or there could be a bug involved. We’ve reached out to Google to ask about this possible issue and have been told the Pixel team is actively investigating the reports.
