On the heels of its release of new Gemini models last week, Google has announced a pair of new features for its flagship AI product. Starting today, Gemini has a new Canvas feature that lets you draft, edit, and refine documents or code. Gemini is also getting Audio Overviews, a neat capability that first appeared in the company’s NotebookLM product, but it’s getting even more useful as part of Gemini.
Canvas is similar (confusingly) to the OpenAI product of the same name. Canvas is available in the Gemini prompt bar on the web and mobile app. Simply upload a document and tell Gemini what you need to do with it. In Google’s example, the user asks for a speech based on a PDF containing class notes. And just like that, Gemini spits out a document.
Canvas lets you refine the AI-generated documents right inside Gemini. The writing tools available across the Google ecosystem, with options like suggested edits and different tones, are available inside the Gemini-based editor. If you want to do more edits or collaborate with others, you can export the document to Google Docs with a single click.
Credit: Google
Canvas is also adept at coding. Just ask, and Canvas can generate prototype web apps, Python scripts, HTML, and more. You can ask Gemini about the code, make alterations, and even preview your results in real time inside Gemini as you (or the AI) make changes.
Google Assistant is not long for this world. Google confirmed what many suspected last week, that it will transition everyone to Gemini in 2025. Assistant holdouts may find it hard to stay on Google’s old system until the end, though. Google has confirmed some popular Assistant features are being removed in the coming weeks. You may not miss all of them, but others could force a change to your daily routine.
As Google has increasingly become totally consumed by Gemini, it was a foregone conclusion that Assistant would get the ax eventually. In 2024, Google removed features like media alarms and voice messages, but that was just the start. The full list of removals is still available on its support page (spotted by 9to5Google), but there’s now a new batch of features at the top. Here’s a rundown of what’s on the chopping block.
Favorite, share, and ask where and when your photos were taken with your voice
Change photo frame settings or ambient screen settings with your voice
Translate your live conversation with someone who doesn’t speak your language with interpreter mode
Get birthday reminder notifications as part of Routines
Ask to schedule or hear previously scheduled Family Bell announcements
Get daily updates from your Assistant, like “send me the weather everyday”
Use Google Assistant on car accessories that have a Bluetooth connection or AUX plug
Some of these are no great loss—you’ll probably live without the ability to get automatic birthday reminders or change smart display screensavers by voice. However, others are popular features that Google has promoted aggressively. For example, interpreter mode made a splash in 2019 and has been offering real-time translations ever since; Assistant can only translate a single phrase now. Many folks also use the scheduled updates in Assistant as part of their morning routine. Family Bell is much beloved, too, allowing Assistant to make custom announcements and interactive checklists, which can be handy for getting kids going in the morning. Attempting to trigger some of these features will offer a warning that they will go away soon.
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.
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.
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.
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.
Google’s AI ambitions know no bounds. A new report claims Google’s next phones will herald the arrival of a feature called Pixel Sense that will ingest data from virtually every Google app on your phone, fueling a new personalized experience. This app could be the premiere feature of the Pixel 10 series expected out late this year.
According to a report from Android Authority, Pixel Sense is the new name for Pixie, an AI that was supposed to integrate with Google Assistant before Gemini became the center of Google’s universe. In late 2023, it looked as though Pixie would be launched on the Pixel 9 series, but that never happened. Now, it’s reportedly coming back as Pixel Sense, and we have more details on how it might work.
Pixel Sense will apparently be able to leverage data you create in apps like Calendar, Gmail, Docs, Maps, Keep Notes, Recorder, Wallet, and almost every other Google app. It can also process media files like screenshots in the same way the Pixel Screenshots app currently does. The goal of collecting all this data is to help you complete tasks faster by suggesting content, products, and names by understanding the context of how you use the phone. Pixel Sense will essentially try to predict what you need without being prompted.
Samsung is pursuing a goal that is ostensibly similar to Now Brief, a new AI feature available on the Galaxy S25 series. Now Brief collects data from a handful of apps like Samsung Health, Samsung Calendar, and YouTube to distill your important data with AI. However, it rarely offers anything of use with its morning, noon, and night “Now Bar” updates.
Pixel Sense sounds like a more expansive version of this same approach to processing user data—and perhaps the fulfillment of Google Now’s decade-old promise. The supposed list of supported apps is much larger, and they’re apps people actually use. If pouring more and more data into a large language model leads to better insights into your activities, Pixel Sense should be better at guessing what you’ll need. Admittedly, that’s a big “if.”
At Mobile World Congress, Google confirmed that a long-awaited Gemini AI feature it first teased nearly a year ago is ready for launch. The company’s conversational Gemini Live will soon be able to view live video and screen sharing, a feature Google previously demoed as Project Astra. When Gemini’s video capabilities arrive, you’ll be able to simply show the robot something instead of telling it.
Right now, Google’s multimodal AI can process text, images, and various kinds of documents. However, its ability to accept video as an input is spotty at best—sometimes it can summarize a YouTube video, and sometimes it can’t, for unknown reasons. Later in March, the Gemini app on Android will get a major update to its video functionality. You’ll be able to open your camera to provide Gemini Live a video stream or share your screen as a live video, thus allowing you to pepper Gemini with questions about what it sees.
Gemini Live with video.
It can be hard to keep track of which Google AI project is which—the 2024 Google I/O was largely a celebration of all things Gemini AI. The Astra demo made waves as it demonstrated a more natural way to interact with the AI. In the original video, which you can see below, Google showed how Gemini Live could answer questions in real time as the user swept a phone around a room. It had things to say about code on a computer screen, how speakers work, and a network diagram on a whiteboard. It even remembered where the user left their glasses from an earlier part of the video.
The researchers’ explanations about how “Set-of-Mark” and “Trace-of-Mark” work. Credit: Microsoft Research
The Magma model introduces two technical components: Set-of-Mark, which identifies objects that can be manipulated in an environment by assigning numeric labels to interactive elements, such as clickable buttons in a UI or graspable objects in a robotic workspace, and Trace-of-Mark, which learns movement patterns from video data. Microsoft says those features allow the model to complete tasks like navigating user interfaces or directing robotic arms to grasp objects.
Microsoft Magma researcher Jianwei Yang wrote in a Hacker News comment that the name “Magma” stands for “M(ultimodal) Ag(entic) M(odel) at Microsoft (Rese)A(rch),” after some people noted that “Magma” already belongs to an existing matrix algebra library, which could create some confusion in technical discussions.
Reported improvements over previous models
In its Magma write-up, Microsoft claims Magma-8B performs competitively across benchmarks, showing strong results in UI navigation and robot manipulation tasks.
For example, it scored 80.0 on the VQAv2 visual question-answering benchmark—higher than GPT-4V’s 77.2 but lower than LLaVA-Next’s 81.8. Its POPE score of 87.4 leads all models in the comparison. In robot manipulation, Magma reportedly outperforms OpenVLA, an open source vision-language-action model, in multiple robot manipulation tasks.
Magma’s agentic benchmarks, as reported by the researchers. Credit: Microsoft Research
As always, we take AI benchmarks with a grain of salt since many have not been scientifically validated as being able to measure useful properties of AI models. External verification of Microsoft’s benchmark results will become possible once other researchers can access the public code release.
Like all AI models, Magma is not perfect. It still faces technical limitations in complex step-by-step decision-making that requires multiple steps over time, according to Microsoft’s documentation. The company says it continues to work on improving these capabilities through ongoing research.
Yang says Microsoft will release Magma’s training and inference code on GitHub next week, allowing external researchers to build on the work. If Magma delivers on its promise, it could push Microsoft’s AI assistants beyond limited text interactions, enabling them to operate software autonomously and execute real-world tasks through robotics.
Magma is also a sign of how quickly the culture around AI can change. Just a few years ago, this kind of agentic talk scared many people who feared it might lead to AI taking over the world. While some people still fear that outcome, in 2025, AI agents are a common topic of mainstream AI research that regularly takes place without triggering calls to pause all of AI development.
On Tuesday, Hugging Face researchers released an open source AI research agent called “Open Deep Research,” created by an in-house team as a challenge 24 hours after the launch of OpenAI’s Deep Research feature, which can autonomously browse the web and create research reports. The project seeks to match Deep Research’s performance while making the technology freely available to developers.
“While powerful LLMs are now freely available in open-source, OpenAI didn’t disclose much about the agentic framework underlying Deep Research,” writes Hugging Face on its announcement page. “So we decided to embark on a 24-hour mission to reproduce their results and open-source the needed framework along the way!”
Similar to both OpenAI’s Deep Research and Google’s implementation of its own “Deep Research” using Gemini (first introduced in December—before OpenAI), Hugging Face’s solution adds an “agent” framework to an existing AI model to allow it to perform multi-step tasks, such as collecting information and building the report as it goes along that it presents to the user at the end.
The open source clone is already racking up comparable benchmark results. After only a day’s work, Hugging Face’s Open Deep Research has reached 55.15 percent accuracy on the General AI Assistants (GAIA) benchmark, which tests an AI model’s ability to gather and synthesize information from multiple sources. OpenAI’s Deep Research scored 67.36 percent accuracy on the same benchmark.
As Hugging Face points out in its post, GAIA includes complex multi-step questions such as this one:
Which of the fruits shown in the 2008 painting “Embroidery from Uzbekistan” were served as part of the October 1949 breakfast menu for the ocean liner that was later used as a floating prop for the film “The Last Voyage”? Give the items as a comma-separated list, ordering them in clockwise order based on their arrangement in the painting starting from the 12 o’clock position. Use the plural form of each fruit.
To correctly answer that type of question, the AI agent must seek out multiple disparate sources and assemble them into a coherent answer. Many of the questions in GAIA represent no easy task, even for a human, so they test agentic AI’s mettle quite well.
Samsung announced the Galaxy S25, S25+, and S25 Ultra at its Unpacked event today. What is different from last year’s models? With the phones themselves, not much, other than a new chipset and a wide camera. But pure AI optimism? Samsung managed to pack a whole lot more of that into its launch event and promotional materials.
The corners on the S25 Ultra are a bit more rounded, the edges are flatter, and the bezels seem to be slightly thinner. The S25 and S25+ models have the same screen size as the S24 models, at 6.2 and 6.7 inches, respectively, while the Ultra notches up slightly from 6.8 to 6.9 inches.
Samsung’s S25 Ultra, in titanium builds colored silver blue, black, gray, and white silver.
Credit: Samsung
Samsung’s S25 Ultra, in titanium builds colored silver blue, black, gray, and white silver. Credit: Samsung
The S25 Ultra, starting at $1,300, touts a Snapdragon 8 Elite processor, a new 50-megapixel ultra-wide lens, and what Samsung claims is improved detail in software-derived zoom images. It comes with the S Pen, a vestige of the departed Note line, but as The Verge notes, there is no Bluetooth included, so you can’t pull off hand gestures with the pen off the screen or use it as a quirky remote camera trigger.
Samsung’s S25 Plus phones, in silver blue, navy, and icy blue.
Credit: Samsung
Samsung’s S25 Plus phones, in silver blue, navy, and icy blue. Credit: Samsung
It’s much the same with the S25 and S25 Plus, starting at $800. The base models got an upgrade to a default of 12GB of RAM. The displays, cameras, and general shape and build are the same. All the Galaxy devices released in 2025 have Qi2 wireless charging support—but not by default. You’ll need a “Qi2 Ready” magnetic case to get a sturdy attachment and the 15 W top charging speed.
One thing that hasn’t changed, for the better, is Samsung’s recent bump up in longevity. Each Galaxy S25 model gets seven years of security updates and seven of OS upgrades, which matches Google’s Pixel line in number of years.
Side view of the Galaxy S25 Edge, which is looking rather thin. Samsung
At the very end of Samsung’s event, for less than 30 seconds, a “Galaxy S25 Edge” was teased. In a mostly black field with some shiny metal components, Samsung seemed to be teasing the notably slimmer variant of the S25 that had been rumored. The same kinds of leaks about an “iPhone Air” have been circulating. No details were provided beyond its name, and a brief video suggesting its svelte nature.
