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Latest AI News
Indian Clinicians Trust AI More Than Global Peers, But Adoption Still Lags, Reveals Report
While only 42% of Indian clinicians currently use AI at work, the Elsevier report reveals higher trust, better AI governance, and stronger digital readiness than global averages.
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Samsung Sets Up Robotics Division to Drive Humanoid Push, Says Report
This new division will report to the CEO and lead Samsung’s global robotics strategy and commercialisation efforts.
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From CCTVs to Body Scanners, Indian AI Now Powers Real-Time Threat Detection
Indian firms are now deploying screening systems, crowd-monitoring platforms, and traffic sensors at airports, metro networks, etc to make surveillance intelligent.
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TCS Acquires Land in Vizag, Pune for OpenAI Data Centres
The two sites are expected to support OpenAI’s infrastructure expansion in India as the company scales its computing capacity to serve growing demand for AI services.
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Google to Develop Frozen v2 Chip to Improve Gemini Efficiency
Google has expanded its custom silicon efforts over the past decade to reduce reliance on third-party AI hardware.
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How 104 Seconds at Sriharikota Changed the Course of Indian Space
Skyroot Aerospace’s Vikram-1 launch shifts India’s private space sector from promise to proof.
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Accenture Names Ex-McKinsey Partner Pradeep Prabhala to Lead India Business
Prabhala succeeds as lead of Accenture’s India Market Unit at a time when the consulting and IT services major is sharpening its focus on AI-led enterprise transformation.
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The Simple Habits in Chess That Separate Winners From Everyone Else
Winning rapid chess isn't about deep calculation, but avoiding blunders and following fundamentals.
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Z.ai Builds 1 GW AI Data Centre Built on Chinese Chips
Z.ai’s facility will train the company’s GLM models without NVIDIA hardware as Beijing accelerates investment in domestic AI infrastructure.
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Anthropic’s landmark $1.5B copyright settlement is approved
Anthropic can finally start cutting checks to a group of authors and book publishers that sued the AI lab over copyright infringement. A federal judge gave final approval Monday of Anthropic’s landmark $1.5 billion settlement of a class action copyright lawsuit,Reuters reported. Judge William Alsup of the U.S. District Court for the Northern District of California issued a preliminary approval of the settlement last year, after ruling that Anthropic had illegally downloaded and stored millions of copyrighted books. Alsup has since retired and Judge Araceli Martinez-Olguin signed off on the settlement on Monday. The payout will deliver $3,000 per work across an estimated 500,000 works, shared among the authors and publishers who hold rights to them. While the settlement is believed to be thelargest in the history of U.S. copyright law,many authors and creators still don’t view it as a win. That’s because of how the legal question was resolved. Alsup sided with Anthropic on the core issue. He ruled that training an AI model on copyrighted text counts as fair use — a decision widely seen as a turning point for the AI industry. But the ruling didn’t excuse how Anthropic obtained the books in the first place. Anthropic had built its training library from two sources: books it purchased and scanned (fine), and books it downloaded from pirate sites like Library Genesis and Pirate Library Mirror. Alsup found the second method illegal on its own terms and said that piracy question could go to trial; Anthropic agreed to a settlement soon after to avoid a trial and whatever damages a jury might have awarded. While the final approval closes out this case, it doesn’t settle the legal question industry-wide because Alsup’s ruling was a single district court decision, and Anthropic’s decision to settle means the case will never reach an appeals court to become binding precedent. Other judges are still free to reach their own conclusions on their own facts, which is exactly what’s playing out elsewhere. There is still a string of copyright lawsuits against companies such as Google, Meta, Midjourney, and OpenAI over whether it’s legal to train AI models on copyrighted works. Just last week, a group of publishers and authors, including Hachette, Cengage, Elsevier, author Scott Turow, and S.C.R.I.B.E. filed aclass action lawsuitagainst Google over accusations that the company used their copyrighted works to train its AI platform, Gemini.
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Adobe camera app’s new feature will critique your photos using AI
Adobe is adding new AI-powered features to its experimental iOS camera app calledProject Indigo, launched last year. The app previously offered pro controls, multi-frame super-resolution, and different capture modes, and is now adding features that will use LLMs (large language models) to critique photos and provide editing suggestions. It’s also adding other AI features, like advanced object removal, depth of field generation, and the ability to add different styles to photos. Marc Levoy, the person heading Adobe’s project, had previously developed Pixel’s camera chops. He said that most generative AI tools provide prompt-based editing and, often, finding the perfect prompt to tweak a photo or get the right result can be a tricky endeavor. That’s why most of the new experimental AI features are buttons that can generate more deterministic outputs. For instance, Google’scamera coach feature for Pixel phones, launched last year, offered more generic framing suggestions. Project Indigo’s features, by comparison, are fairly descriptive and could help you learn some photography tricks, even if you don’t agree with the AI assessment. There are two features in this category. First is the photo critique, which includes a “professional” opinion about framing, lighting, colors, and emotional impact. The second is capture and edit suggestions, which give you tips about reshooting the photo on how you can change framing, exposure, and objects in the viewfinder. For instance, the app told me to remove the hexagonal white object in the frame from a photo I took. A second section tells you how you can use an existing photo to make it better using Adobe Lightroom controls. Photography apps, including Apple Photos, Google Photos, and Adobe Photoshop, have offered object removal for years. But the feature often depends on the user circling or selecting an object by drawing on the screen, which is not perfect every time. Project Indigo’s new feature gives you toggles for things to remove from an image, like people in the background, trash and trash cans, wires and poles, fences, vehicles, and other clutter. You can also describe a custom object to remove. The results of this feature are pretty impressive. The app removed my friend and the object he was holding from the background without creating any strange artifacts. The app also allows you to use AI to create depth of field for a photo to simulate a blurred background. With this new update, Adobe is also experimenting with the style transfer feature, which lets you turn your picture into tones like watercolor, pen and ink, ink line with color wash, monochromatic, and backlit subject. Some styles remind me ofthe early days of the Prisma app. With advanced models, the outputs look refined, but style transfer is not a new or useful feature. Despite Levoy’s criticism of the prompt-based implementation, Project Indigo has its own feature that lets you describe your edit. Users can use it to perform edit flows that are not available in preset tools. But this is also a potential road to slopland. The company is using Google’s Gemini-based Nano Banana for these features, but it’s open to swapping in other models, including its own Adobe Firefly model. These features, bundled under the AI playground tab, are still in a testing phase, and only select users will get access to them. These features might not ever make it to a wider audience, but it is good to see some features that can make people aware of nuances in photography, rather than just creating more slop.
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OpenAI is scared of open-weight models. Should the US be?
The impressive capabilities of Chinese lab Moonshot’s Kimi K3, the biggest open-weight large language model, has kicked off a debate that conflates two things: the economic possibilities of American AI giants and the future of LLMs as a technology. OpenAI’s head of strategic futures, Dean W. Ball, went so far as toarguethat the US government should find a pretext to create regulatory fear, uncertainty, and distrust around the new models, since open-weight models must necessarily deter capital spending by the frontier labs. Peoplefreaked out, with tech luminaries likeYann LeCunandMartin Casadoarguing that open software can accelerate innovation and coexist with proprietary projects. Ball soonretractedhis claims that a regulatory crackdown was the White House’s “best strategy” and that open-weight models necessarily slow down advances in the technology. However, Axiosreportsthat the Trump administration is considering banning K3 and other advanced Chinese models at the behest of American frontier labs. Anotherreportfrom Politico said that the Department of Commerce would not take that step anytime soon. The benefit for major AI companies is clear: Open-weight models, running on independent infrastructure or inside major enterprises, offers cheaper intelligence than Anthropic or OpenAI’s class-leading models. If users increasingly spend more outside the closed labs, that means smaller return on their massive investments in model training. That view extends far beyond OpenAI. “Strong, frontier-caliber open source models will place a squeeze on the margins and will bring down the prices of the frontier companies,” Braden Hancock, the co-founder of Snorkel AI and a research partner at the Laude Institute, told TechCrunch. “It will not necessarily mean that the amount of AI usage goes down a little bit. You know, obviously, quite the opposite.” That’s not a problem for people without shares in Anthropic and OpenAI. AI will still proliferate. So what’s the justification for the government to block Americans from purchasing something in our ostensibly free markets? Concerns over Chinese models come in several flavors. One is protecting US data from the Chinese government; the US banned the import of modern Chinese EVs over concerns about their data gathering. But experts tend to think that open-weight models run on US servers are unlikely to leak data back to China, although it’s not impossible that such a thing could be done. Another is that the models may have implicit bias toward the PRC — but it’s not clear what that might mean for, say, coding tasks. A third common worry is that Chinese models lack the guardrails that the US government has mandated (through an opaque process), which aim to prevent leading US LLMs from being used to exploit closed computer systems or create weapons. However, those same guardrails may make US companies more vulnerable: David Sacks, the venture capitalist and Trump adviser, has beensharing casesof US companies turning to Chinese LLMs to close security gaps when US frontier models refuse to do the tasks. But the most significant motivation for restricting the models is that fear that China will be able to outpace the US if the frontier labs slow down. Sam Bresnick, a China-focused research fellow at Georgetown’s Center for Security and Emerging Technology, says the growing importance of AI to the US military operations gives the US a reason to support continued investment in AI at the frontier labs. But the whole question, he says, is fraught. “Why should the weight of the U.S. government be aimed at protecting these these companies from competitors that are being locked out from the U.S. market based on their origins?” Bresnick asks. Advocates for open AI say that the frontier companies are creating a false binary between innovation and closed models. “The bigger impact of having these open source models come from China is less that they’re sneaking in back doors, and more that they are owning the innovation,” Hancock told TechCrunch. “You end up with, effectively, an expanded workforce on your model. PyTorch became the industry standard because it was open source, and so the whole community could contribute to it rather than just one company, and it grew and grew, and all the rest of the deep learning libraries kind of died in comparison.” Hancock and other advocates fear that Chinese LLMs will become the locus of international research. Already, US graduate programs mainly build on open-weight Chinese models, and Hancock says that half of the papers students study are coming from Chinese institutions, with American frontier labs increasingly reticent about sharing their work widely. “Restricting open models wouldn’t make AI safer,” said Clem Delangue, the CEO of Hugging Face, a platform for open AI collaboration. “It would simply hide the risks, concentrate power in the hands of a few and make it harder for the next generation of builders, researchers, academia, non-profits, governments to participate in making AI safer and more beneficial for all.” Bresnick says that the real way to slow China would be to focus more on chip export controls. A better way to preserve US AI leadership would be to stop selling Nvidia H200 processors to China. “That,” he says, “could potentially keep us out of this thorny debate about banning open source technologies that huge numbers of US companies want to use.” Part of the problem is that uncertainty around AI economics. “The open business model, the proprietary business model — neither one is figured out. AI companies are struggling to figure out how to make money on their tools, especially as training costs need to go up and up,” Bresnick points out. The same challenges that play out in the US are also playing out in China, where AI companies are also struggling to generate revenue and access compute power, and the government is seen as encouraging open releases for policy reasons despite the challenge in capitalizing on them. Some US companies, including Thinking Machines Lab and Nvidia, are trying to make a business around releasing open models. Hancock points out that Nvidia would do better “if there are dozens or hundreds of companies building AI than rather than two or three that are well capitalized enough to make their own chips,” which is one reason behind its investment inNemotron, a collection of open models. “The main point is the U.S. would be very well served to have its own very capable, much less expensive open models,” Bresnick said. “It just clashes with the approach the frontier labs have taken.” With additional reporting from Rebecca Bellan.
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