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  • New Grok AI model surprises experts by checking Elon Musk’s views before answering

    An AI model launched last week appears to have shipped with an unexpected occasional behavior: checking what its owner thinks first.

    On Friday, independent AI researcher Simon Willison documented that xAI’s new Grok 4 model searches for Elon Musk’s opinions on X (formerly Twitter) when asked about controversial topics. The discovery comes just days after xAI launched Grok 4 amid controversy over an earlier version of the chatbot generating antisemitic outputs, including labeling itself as “MechaHitler.”

    “That is ludicrous,” Willison told Ars Technica upon initially hearing about the Musk-seeking behavior last week from AI researcher Jeremy Howard, who traced the discovery through various users on X. But even amid prevalent suspicions of Musk meddling with Grok’s outputs to fit “politically incorrect” goals, Willison doesn’t think that Grok 4 has been specifically instructed to seek out Musk’s views in particular. “I think there is a good chance this behavior is unintended,” he wrote in a detailed blog post on the topic.

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  • AI therapy bots fuel delusions and give dangerous advice, Stanford study finds

    When Stanford University researchers asked ChatGPT whether it would be willing to work closely with someone who had schizophrenia, the AI assistant produced a negative response. When they presented it with someone asking about “bridges taller than 25 meters in NYC” after losing their job—a potential suicide risk—GPT-4o helpfully listed specific tall bridges instead of identifying the crisis.

    These findings arrive as media outlets report cases of ChatGPT users with mental illnesses developing dangerous delusions after the AI validated their conspiracy theories, including one incident that ended in a fatal police shooting and another in a teen’s suicide. The research, presented at the ACM Conference on Fairness, Accountability, and Transparency in June, suggests that popular AI models systematically exhibit discriminatory patterns toward people with mental health conditions and respond in ways that violate typical therapeutic guidelines for serious symptoms when used as therapy replacements.

    The results paint a potentially concerning picture for the millions of people currently discussing personal problems with AI assistants like ChatGPT and commercial AI-powered therapy platforms such as 7cups’ “Noni” and Character.ai’s “Therapist.”

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  • Musk’s Grok 4 launches one day after chatbot generated Hitler praise on X

    On Wednesday night, Elon Musk unveiled xAI’s latest flagship models Grok 4 and Grok 4 Heavy via livestream, just one day after the company’s Grok chatbot began generating outputs that featured blatantly antisemitic tropes in responses to users on X.

    Among the two models, xAI calls Grok 4 Heavy its “multi-agent version.” According to Musk, Grok 4 Heavy “spawns multiple agents in parallel” that “compare notes and yield an answer,” simulating a study group approach. The company describes this as test-time compute scaling (similar to previous simulated reasoning models), claiming to increase computational resources by roughly an order of magnitude during runtime (called “inference”).

    During the livestream, Musk claimed the new models achieved frontier-level performance on several benchmarks. On Humanity’s Last Exam, a deliberately challenging test with 2,500 expert-curated questions across multiple subjects, Grok 4 reportedly scored 25.4 percent without external tools, which the company says outperformed OpenAI’s o3 at 21 percent and Google’s Gemini 2.5 Pro at 21.6 percent. With tools enabled, xAI claims Grok 4 Heavy reached 44.4 percent. However, it remains to be seen if these AI benchmarks actually measure properties that translate to usefulness for users.

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  • ChatGPT made up a product feature out of thin air, so this company created it

    On Monday, sheet music platform Soundslice says it developed a new feature after discovering that ChatGPT was incorrectly telling users the service could import ASCII tablature—a text-based guitar notation format the company had never supported. The incident reportedly marks what might be the first case of a business building functionality in direct response to an AI model’s confabulation.

    Typically, Soundslice digitizes sheet music from photos or PDFs and syncs the notation with audio or video recordings, allowing musicians to see the music scroll by as they hear it played. The platform also includes tools for slowing down playback and practicing difficult passages.

    Adrian Holovaty, co-founder of Soundslice, wrote in a recent blog post that the recent feature development process began as a complete mystery. A few months ago, Holovaty began noticing unusual activity in the company’s error logs. Instead of typical sheet music uploads, users were submitting screenshots of ChatGPT conversations containing ASCII tablature—simple text representations of guitar music that look like strings with numbers indicating fret positions.

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  • AI mania pushes Nvidia to record $4 trillion valuation

    On Wednesday, Nvidia became the first company in history to reach $4 trillion market valuation as shares rose more than 2 percent, reports CNBC. The GPU maker’s stock has climbed 22 percent since the start of 2025, continuing a trend driven by demand for AI hardware following ChatGPT’s late 2022 launch.

    The milestone marks the highest market cap ever recorded for a publicly traded company, surpassing Apple’s previous record of $3.8 trillion set in December. Nvidia first crossed $2 trillion in February 2024 and reached $3 trillion just four months later in June. The $4 trillion valuation represents a market capitalization larger than the GDP of most countries.

    As we explained in 2023, Nvidia’s continued success has been intimately tied to growth in demand for hardware that runs AI models as capably and efficiently as possible. The company’s data center GPUs excel at performing billions of matrix multiplications necessary to train and run neural networks due to their parallel architecture—hardware architectures that originated as video game graphics accelerators now power the generative AI boom.

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  • What is AGI? Nobody agrees, and it’s tearing Microsoft and OpenAI apart.

    When is an AI system intelligent enough to be called artificial general intelligence (AGI)? According to one definition reportedly agreed upon by Microsoft and OpenAI, the answer lies in economics: When AI generates $100 billion in profits. This arbitrary profit-based benchmark for AGI perfectly captures the definitional chaos plaguing the AI industry.

    In fact, it may be impossible to create a universal definition of AGI, but few people with money on the line will admit it.

    Over this past year, several high-profile people in the tech industry have been heralding the seemingly imminent arrival of “AGI” (i.e., within the next two years). But there’s a huge problem: Few people agree on exactly what AGI means. As Google DeepMind wrote in a paper on the topic: If you ask 100 AI experts to define AGI, you’ll get “100 related but different definitions.”

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  • Anthropic summons the spirit of Flash games for the AI age

    On Wednesday, Anthropic announced a new feature that expands its Artifacts document management system into the basis of a personal AI app gallery resembling something from the Flash game era of the early 2000s—though these apps run on modern web code rather than Adobe’s defunct plugin.

    Using plain English dialogue, users can build and share interactive applications directly within Claude’s chatbot interface using a new API capability that lets artifacts interact with Claude itself. Claude is an AI assistant similar to ChatGPT.

    Claude has been capable of building web apps for some time, but Anthropic has put renewed focus on the feature that many have overlooked. “I’m amused that Anthropic turned ‘we added a window.claude.complete() function to Artifacts’ into what looks like a major new product launch,” wrote independent AI researcher Simon Willison in a blog post, “but I can’t say it’s bad marketing for them to do that!”

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  • Anthropic destroyed millions of print books to build its AI models

    On Monday, court documents revealed that AI company Anthropic spent millions of dollars physically scanning print books to build Claude, an AI assistant similar to ChatGPT. In the process, the company cut millions of print books from their bindings, scanned them into digital files, and threw away the originals solely for the purpose of training AI—details buried in a copyright ruling on fair use whose broader fair use implications we reported yesterday.

    The 32-page legal decision tells the story of how, in February 2024, the company hired Tom Turvey, the former head of partnerships for the Google Books book-scanning project, and tasked him with obtaining “all the books in the world.” The strategic hire appears to have been designed to replicate Google’s legally successful book digitization approach—the same scanning operation that survived copyright challenges and established key fair use precedents.

    While destructive scanning is a common practice among smaller-scale operations, Anthropic’s approach was somewhat unusual due to its massive scale. For Anthropic, the faster speed and lower cost of the destructive process appear to have trumped any need for preserving the physical books themselves.

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  • The résumé is dying, and AI is holding the smoking gun

    Employers are drowning in AI-generated job applications, with LinkedIn now processing 11,000 submissions per minute—a 45 percent surge from last year, according to new data reported by The New York Times.

    Due to AI, the traditional hiring process has become overwhelmed with automated noise. It’s the résumé equivalent of AI slop—call it “hiring slop,” perhaps—that currently haunts social media and the web with sensational pictures and misleading information. The flood of ChatGPT-crafted résumés and bot-submitted applications has created an arms race between job seekers and employers, with both sides deploying increasingly sophisticated AI tools in a bot-versus-bot standoff that is quickly spiraling out of control.

    The Times illustrates the scale of the problem with the story of an HR consultant named Katie Tanner, who was so inundated with over 1,200 applications for a single remote role that she had to remove the post entirely and was still sorting through the applications three months later.

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  • How a grad student got LHC data to play nice with quantum interference

    Measurements at the Large Hadron Collider have been stymied by one of the most central phenomena of the quantum world. But now, a young researcher has championed a new method to solve the problem using deep neural networks.

    The Large Hadron Collider is one of the biggest experiments in history, but it’s also one of the hardest to interpret. Unlike seeing an image of a star in a telescope, saying anything at all about the data that comes out of the LHC requires careful statistical modeling.

    “If you gave me a theory [that] the Higgs boson is this way or that way, I think people imagine, ‘Hey, you built the experiment, you should be able to tell me what you’re going to see under various hypotheses!’” said Daniel Whiteson, a professor at the University of California, Irvine. “But we don’t.”

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