Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Mashable Selects Mashable Voices Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Switch Off Trending Now In My Bag All Series Deliberately destroying rare books might be the final straw in the eyes of the Federal Trade Commission. Earlier this month, we reported on the disturbing trend of AI companies using physical copies of books to train their agentic AI, in a practice so eerily reminiscent of book burning that it's spooking even devotees of artificial intelligence . Well, according to Axios, the FTC is now being urged to investigate the practice, not, as we might like, for crimes against humanity, but for violations of antitrust law, since every book destroyed is one less book available for competing AI agents to use. SEE ALSO: AI companies keep destroying old books. Axios reports that [m]ore than a dozen civil society groups, including Demand Progress Education Fund, the Consumer Federation of America, and the Institute for Local Self-Reliance, are urging the FTC to investigate these practices, particularly those involving rare books.

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China's new moon mission could unlock secret of lunar ice: Why that matters China is set to launch its Chang'e-7 unmanned, robotic space mission, possibly as early as Monday morning, to look for ice water in the permanently shadowed craters of the moon's south pole. This marks China's seventh and most ambitious moon mission so far. Here is what we know about it. What do we know about Chang'e-7? The Chang'e 7 launch window runs from Monday, August 24 to Monday, August 31, according to launch observers.

A women uses the new IBM 650 Magnetic Drum Data Processing Machine in a New York office in 1954. A women uses the new IBM 650 Magnetic Drum Data Processing Machine in a New York office in 1954. Many hoped that the march of technology would usher in an egalitarian utopia - but some foresaw the threat it would pose to liberal society. One of the stranger things about this dizzying, headlong moment in time is that it doesn't have much of a past. Everything is about the future of this, the future of that: the future of work, the future of humanity, the future of the planet. It's as if everyone is screaming (some ecstatically, most terror-stricken): robots are taking over the world! Meanwhile, you can't put down your phone, unplug, delete your AI apps, tell Zoom to piss off; it feels as if you are racing toward something, and can't stop, or look back, or think straight. But of course this weird moment in history does have a past. Things could have turned out differently.

Akari describes her work on augmented language models, where language models are trained to use other models and tools. This has already resulted in OpenScholar, an open-source model which helps scientists to synthesize vast amounts of scientific literature. You were awarded the 2025 AAAI Doctoral Dissertation Award. What was the topic of your dissertation research, and why was this an interesting area of study to you? My PhD dissertation was on Retrieval-Augmented Language Models.

Are AI companies to blame? Last week, the internet raged as social-media posts and news articles accused AI companies of "destroying the world's books" --including "millions of rare" ones--by chopping off their spines to scan them more easily, and discarding them afterward. One article said the news was "sparking concerns that the last remaining copies of out-of-print texts are being destroyed." The investor and AI skeptic Michael Burry called the practice of sacrificing rare books "evil incarnate." Even Elon Musk weighed in, posting that he had asked the engineers training xAI's models to "preserve any rare books in a library and scan them the hard way."

Japan's AI gamble: Can technology offset the cost of an ageing society? Beneath the business towers of Tokyo's Otemachi district, a test is taking place to see whether artificial intelligence can help solve one of Japan's biggest economic challenges: a shrinking workforce. Deep underground, Marunouchi Heat Supply Company operates a 30km (18.6-mile) network of heating and cooling pipelines serving offices, commercial buildings and transport facilities in one of Japan's most important business areas. The company is now using AI to manage this complex infrastructure with the goal of moving towards more automated operations by 2027. Developed with the Tokyo-based AI company Preferred Networks, the project uses PlantPilot, an AI system trained on historical operating data and the expertise of experienced engineers.

Apple has sued OpenAI and two former employees, alleging misappropriation of its trade secrets as the artificial intelligence company seeks to build its own hardware for ChatGPT, a major rupture in a partnership between the iPhone maker and the AI giant. The complaint, filed in a California federal court on Friday, alleges a coordinated effort to steal Apple's confidential information, including product designs, manufacturing processes and supply chain strategies. The lawsuit names Chang Liu, a former senior system electrical engineer, and Tang Yew Tan, a former vice president of product design for the iPhone and Apple Watch, as defendants, along with the OpenAI Foundation, OpenAI Group PBC and io Products. Neither defendant immediately responded to a request for comment. Apple alleged that Liu failed to return a company-issued work laptop and later used an authentication bug to access Apple's internal network, downloading "dozens of Apple's confidential hardware-related files".

From AI with Hannah Fry to David Attenborough's early days, these are the five must-watch science documentaries of the year to date, says Bethan Ackerley In 2015, an amateur trophy hunter from the US shot and killed the largest lion in Africa. The vitriol unleashed after Cecil's death isn't surprising (or entirely unwarranted), but what is remarkable is how this delicately-crafted film uses the case as a locus for all sorts of arguments about conservation. A symbol in life and in death, Cecil and other large, charismatic animals exist in a complex balance with humans who, one way or another, invariably stake a claim on them. Almost everyone in the world now needs to have some knowledge of how AI technologies work, from all the chatbots they encounter to driverless cars and more. Mathematician Hannah Fry is an excellent person to impart such knowledge: across three episodes, she guides us through recent cases where AI has become entangled with very human problems.

Transfer learning leverages knowledge from related source domains to improve learning in a target domain. Recent theoretical advances cover a broad range of regression settings within (generalized) linear models. Despite their diversity, these methods share two common constraints: they assume a known link function or linear structure and require direct access to raw source data. To move beyond these constraints, we propose a source-data-free transfer learning framework based on the single-index model (SIM). Instead of requiring raw source data, our method transfers only summary statistics derived from a generalized Stein's lemma in a one-time communication. This design preserves privacy and avoids side effects caused by dissimilarities of unknown nonlinear link functions across domains. To capture flexible, unknown nonlinearity, we employ a multilayer perceptron guided by the pre-estimated index from the transferred statistics, which significantly mitigates overfitting. Extensive experiments on synthetic data and a real-world application demonstrate consistent improvements over existing (generalized) linear model-based approaches. The proposed framework thus offers a practical, privacy-preserving, and nonlinear-adaptive solution for transfer learning.