AI NewsIndia Can Build Indigenous GPU by 2029, Says C-DAC Bengaluru Chief

India Can Build Indigenous GPU by 2029, Says C-DAC Bengaluru Chief

5:30 PM IST · August 19, 2026

C-DAC Bengaluru is developing a broader homegrown computing stack spanning CPUs, AI accelerators, RISC-V servers, and chiplets, with a fully indigenous HPC system targeted for 2030.

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US government sides with OpenAI on issue of training LLMs on copyrighted material

In a lawsuit that The New York Times filed against OpenAI, the Trump administration has contributeda 20-page briefin defense of the ChatGPT maker’s unlicensed use of copyrighted material to train its LLMs. “The United States has a strong interest in continuing to develop a robust and competitive artificial intelligence industry that sets the standard for the practice and procedure of AI use globally… As such, it is critical for the United States to ‘retain global leadership in artificial intelligence,’” the brief reads, referencingan executive orderthat President Donald Trump signed last year. The LLMs powering chatbots like ChatGPT, Claude, and Gemini are trained on incomprehensibly massive databases of published works, including copyrighted books, articles, and other media that AI companies feed into these databases without permission. Many publishers,including The New York Timesin this case, have sought to argue that it is illegal for AI companies like OpenAI to train AI models on their copyrighted material. This question —can you use copyrighted material to train an AI?— isn’t black and white, hence the extensive legal debate around the subject. These conversations often center on fair use, a carve out of copyright law that makes exceptions for certain scenarios when it can be ruled legal to use someone else’s copyrighted work without permission. In this case, the fair use debate addresses whether AI companies’ use of copyrighted work is “transformative” enough for a judge to rule it legal.“Constraining LLM development under a misunderstanding of fair use doctrine would thwart such creative and scientific progress while hindering American prosperity and economic mobility,” the brief says. So far, cases about AI training and copyright infringement have largely been favorable to AI companies. Last year, Judge William Alsup ordered Anthropic to pay a$1.5 billion copyright settlementto a group of writers whose works were used to train the company’s AI models; but Anthropic wasn’t dinged for its AI training. Rather, the company was fined for using illegal shadow libraries to pirate the books it used for training. “Like any reader aspiring to be a writer, Anthropic’s LLMs trained upon works not to race ahead and replicate or supplant them — but to turn a hard corner and create something different,” Judge Alsup wrote, comparing the LLM’s training to a human reading a book. This new Trump administration brief is not a ruling, as the case is being tried in the U.S. District Court for the Southern District of New York, and the authors of the brief do not have jurisdiction. However, this intervention by the Trump administration could still carry weight.

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We’re ‘dangerously close’ to dead internet theory, says Pangram’s CEO

The internet has a trust problem, and it’s not just because social media feeds are filling up with AI slop. AI-generated text and images are now making their way into job applications, product reviews, and even insurance claims, leaving platforms and users alike scrambling to figure out what’s real. A handful of startups have cropped up in the past couple of years to become the “trust layer” the internet needs — including Pangram. The startuprecently snapped up $9 millionfor its AI detection system and landed apartnership with Substack, which is now using Pangram’s tech to show readers which of their favorite authors use AI to write their newsletters. Pangram also recently dropped a new AI image detection tool. On this episode of TechCrunch’sEquitypodcast, Pangram co-founder and CEO Max Spero joins Rebecca Bellan to dig into the promise of AI detection tools and where to draw the line between AI assisted and AI generated. Listen to the full episode to hear more about: Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotifyand all the casts. You also can follow Equity onXandThreads, at @EquityPod.

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Pangram’s Max Spero on why AI detection is harder than ‘Real or Fake’

Loading the player… The internet has a trust problem, and it’s not just because social media feeds are filling up with AI slop. AI-generated text and images are now making their way into job applications, product reviews, and even insurance claims, leaving platforms and users alike scrambling to figure out what’s real. A handful of startups have cropped up in the past couple of years to become the “trust layer” the internet needs — including Pangram. The startuprecently snapped up $9 millionfor its AI detection system and landed apartnership with Substack, which is now using Pangram’s tech to show readers which of their favorite authors use AI to write their newsletters. Pangram also recently dropped a new AI image detection tool. Watch as Pangram co-founder and CEO Max Spero joins TechCrunch’sEquitypodcast to dig into the promise of AI detection tools and where to draw the line between AI assisted and AI generated. Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotifyand all the casts. You also can follow Equity onXandThreads, at @EquityPod.

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OpenAI’s new reasoning technique alarms AI safety experts

OpenAI’s new Astra model will use a reasoning technique called “recurrent depth” that allows it to operate outside of the sequential thinking that characterizes most reasoning models, theThe Information reportedon Tuesday. This technique, also called “opaque recurrence,” will likely make the model’s chain of thought more difficult to monitor — and that has AI safety experts rattled. While Astra’s use of the technique is reportedly limited, its emergence has still raised significant concerns among AI safety experts. “I am extremely concerned by the reporting that Astra uses opaque recurrence,” wrote Redwood CEO Buck Shlegerisin a postafter the news broke. “I don’t know whether Astra is much less CoT monitorable than previous models. But if OpenAI pushes this technique further, they’ll have the option to massively increase the recurrence and totally destroys CoT monitorability.” Longtime AI safety advocate Zvi Mowshowitz also weighed inand wrotethat laws might be necessary to prevent a “race to the bottom” among AI labs. “The technique is playing with fire, risking a taboo that OpenAI and Anthropic have fought to establish that we work hard to maintain Chain of Thought faithfulness and monitorability for as long as we can,” Mowshowitz wrote. “More intensive use of such techniques would probably damage monitorability.” Under normal circumstances, a reasoning model’s chain of thought provides the sequential steps taken by the model as it attempts to solve a problem. While the representation is imperfect, it still serves as a valuable tool for monitoring misbehavior or misalignment. In the case of OpenAI’s recent rogue agent activity, chain-of-thought records were an important tool in teasing out why agents behaved the way they did. In opaque recurrence, the model takes a less linear approach, processing the same query several times in a loop. The result leaves fewer legible traces, effectively side-stepping a conventional chain-of-thought record. Crucially, Astra’s use of the technique appears to be limited. The model’s chain of thought is still expected to be legible, and the company pushed back against any suggestion that it would shift to “neuralese.” OpenAI has already announced plans for extensive chain-of-thought monitoring systems as part of its forward-looking safety plans. In a post on X, OpenAI chief scientist Jakub Pachocki emphasized the lab’s commitment to legible chains of thought. “OpenAI has worked to preserve and utilize chain-of-thought monitoring since our very first reasoning models,” Pachocki wrote. “It’s a core goal of our current research program. All AI models do some quantity of opaque reasoning, and few researchers take chain-of-thought logs as a direct representation of a model’s reasoning. Still, those caveats don’t dispel the concern that opaque recurrence may make AI reasoning harder to monitor, particularly as it grows in use across different models. In a follow-up report Wednesday morning, The Information reported that both Anthropic and Google DeepMind were already discussing the technique. In a post responding to the news,Redwood Research chief scientist Ryan Greenblatt said opaque reasoning could easily scale faster than conventional chain-of-thought reasoning, effectively removing all reasoning from visible channels. “My biggest concern is that a natural progression from here would involve scaling up the opaque reasoning to the point where the model reasons entirely or almost entirely in latent space,” Greenblatt wrote. “I hope it isn’t too late to avoid the most concerning architectures and that OpenAI will stop here.”

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India Can Build Indigenous GPU by 2029, Says C-DAC Bengaluru Chief

最新 AI 资讯

US government sides with OpenAI on issue of training LLMs on copyrighted material

We’re ‘dangerously close’ to dead internet theory, says Pangram’s CEO

Pangram’s Max Spero on why AI detection is harder than ‘Real or Fake’

OpenAI’s new reasoning technique alarms AI safety experts

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