AI at School Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Look Up Say More Safety Net Versus Creator Playbook In My Bag Trending Now Back to School Good Connection: Uplifting stories for a digital age All Series Bluetti's Elite 300 portable power station gets steep discount on Amazon -- save $400 Basically a portable power grid for your home or RV, now with $400 shaved off the top. Soumya is a deals writer who covers consumer tech, shopping deals, and the products people use every day. With experience writing about everything from AI tools and software to smartphones and home gadgets, she enjoys breaking down product research into clear, useful recommendations. When she's not tracking deals, she's usually comparing products, digging through reviews, and figuring out what actually makes a purchase worth it. All products featured here are independently selected by our editors and writers.

When AI art has no author: Study finds generated images often can't be traced to training data When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Policymakers want a way to assign responsibility. New work from a team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, the question may often have no answer. It's not that the tools for finding it are inadequate.

The largest planned AI data center in New Jersey has been accused of violating federal law after an investigation found the site running dozens of gas-powered generators without a permit, according to Floodlight and The Guardian. Thermal drone footage showed the partly-finished DataOne facility was operating at least 45 of the site's 62 generators simultaneously. The facility allegedly doesn't have permits for any of them. We flew a thermal drone over one of the largest data centers planned on the East Coast. What we found: 60 unpermitted generators, tractor-trailer-sized, burning a mile from two schools.https://t.co/iuGazzdnSM

One of the largest datacenters on the east coast, DataOne is also the first in New Jersey seeking to build its own power plant. One of the largest datacenters on the east coast, DataOne is also the first in New Jersey seeking to build its own power plant. One of east coast's largest datacenters accused of'violating federal law' Thu 27 Aug 2026 11.00 EDTLast modified on Thu 27 Aug 2026 12.25 EDT The largest planned AI datacenter in New Jersey is being accused of violating federal law after a visual investigation found the site running dozens of unpermitted gas-powered generators. Thermal drone footage captured by the non-profit newsroom Floodlight last week showed the partly completed DataOne facility in Vineland was operating at least 45 of the site's 62 generators at the time. The datacenter has stirred controversy since breaking ground in 2025 due to persistent noise complaints, unpermitted construction and a contentious zoning process.

In the spring of 2022, Google began reaching out to a series of writers, including award-winning science-fiction authors and New York Times bestsellers, for feedback on an experimental writing tool. Four years later, their participation has corners of the literary world up in arms. Google researchers knew at the time that they were approaching a breakthrough point with large language models (LLMs). DeepMind, the company's AI division, was internally testing a natural language generation model called LaMDA which had demonstrated emergent writing capabilities. That model would eventually form the basis for Google Gemini.

By 2050, scientists estimate that antibiotic-resistant infections will be associated with more than eight million deaths around the world every year. These are bacterial infections that resist traditional antibiotics like penicillin. They can develop when you eat contaminated food, have an open wound or undergo surgery. E. coli is a good example, as several strains have become highly resistant to conventional antibiotics . They can also arrive as secondary infections, like pneumonia after a virus .

Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series Save on laptops, generators, and more. Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School. You can find more of her work in various online publications, including,, and .

OpenAI has partnered with datacentre operator NextDC to build a multibillion dollar computing cluster in Sydney. The NSW environment minister, Penny Sharpe, says the city is'a highly desirable location'. OpenAI has partnered with datacentre operator NextDC to build a multibillion dollar computing cluster in Sydney. The NSW environment minister, Penny Sharpe, says the city is'a highly desirable location'. NSW government'absolutely thrilled' to welcome OpenAI ... until someone mentioned the Terminator films Emails sent between MP Anoulak Chanthivong's staff take cautious approach to AI giant arriving in Sydney - despite the government's encouragement The NSW technology minister's office removed a reference to being "absolutely thrilled" about OpenAI opening a Sydney office after staffers joked a dystopian Skynet could be headed for the city within five years.

Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher-generated training data, or, when the generator runs unsupervised, reward it by a difficulty heuristic that need not improve the solver. We introduce INFUSER, an iterative co-training framework with two co-evolving roles: a Generator that drafts questions and reference golden answers from a pool of unstructured, automatically collected documents, and a Solver that improves by training on them. The solver is trained with standard correctness rewards against the generator-provided answers, while the generator is rewarded by an optimizer-aware influence score that measures whether each proposed question would actually improve the solver on the target distribution. Because this continuous, noisy influence score is poorly served by standard GRPO, we propose DuGRPO, a dual-normalized variant of GRPO, for generator training. Together, these turn the document pool into an adaptive curriculum that favors questions useful to the current solver, not just hard ones. On Qwen3-8B-Base, INFUSER outperforms strong self-evolution baselines with over 20% relative improvement on Olympiad and SuperGPQA benchmarks, and an 8B INFUSER co-evolving generator outperforms a frozen 32B thinking generator on math and coding. Ablations confirm each design choice is necessary, and two extensions, applying INFUSER to an instruction-finetuned anchor and augmenting it with rule-verifiable RLVR data, further demonstrate the flexibility and generalizability of the framework. Code is available at https://github.com/FFishy-git/INFUSER.

Unsupervised Environment Design (UED) seeks to automatically generate training curricula for reinforcement learning (RL) agents, with the goal of improving generalisation and zero-shot performance. However, designing effective curricula remains a difficult problem, particularly in settings where small subsets of environment parameterisations result in significant increases in the complexity of the required policy. Current methods struggle with a difficult credit assignment problem and rely on regret approximations that fail to identify challenging levels, both of which are compounded as the size of the environment grows. We propose Dynamic Environment Generation for UED (DEGen) to enable a denser level generator reward signal, reducing the difficulty of credit assignment and allowing for UED to scale to larger environment sizes. We also introduce a new regret approximation, Maximised Negative Advantage (MNA), as a significantly improved metric to optimise for, that better identifies more challenging levels. We show empirically that MNA outperforms current regret approximations and when combined with DEGen, consistently outperforms existing methods, especially as the size of the environment grows. We have made all our code available here: https://github.