This year, RoboCup took place in Incheon, South Korea, from 2-6 July. The event saw teams take part in competitions, training sessions, and a symposium. Take a look at what the participants got up to in our round up from social media. RoboCup 2026 officially begins today! A post shared by RoboCup Federation (@robocup.official)

My dog ate my homework isn't an excuse you can use anymore. Chegg has adapted with the times, incorporating homework help and tutoring with technology to bring students the assistance they need to ace tests and essays. From its beginnings as a textbook rental service, Chegg is now primarily known for its subscription-based homework help, 24/7 question portal, and AI-assisted learning. Chegg is designed to help you find the answers your child or student is looking for, with study tools like learning and math assistants, flashcards for study help, and writing tools like plagiarism and grammar checkers. Plus, keeping in line with their first mission, Chegg still has a large inventory of physical and digital textbooks available for students to rent or purchase, helping to significantly reduce rising academic costs and lower barriers.

When you purchase through links in our articles, we may earn a small commission. From phones and laptops to cameras and game consoles, iFixit offers thousands of repair guides that can save you time and money. We've all been in a spot where we wish we had the technical know-how to fix a cracked phone screen, deal with a laptop that won't charge, or bring an old game console back to life. I've been in these exact situations and instead of needlessly tossing my electronics and buying new ones, I first turn to iFixit . It's the best place on the web to learn how to repair your own electronics.

Amid live coding sessions and Silicon Valley optimism, the UN's AI for Good summit wrestled with an increasingly urgent question: Can global governance catch up before the technology races beyond its control? Dodge past the live onstage coding sessions, AI refresher courses, an obstacle course of gizmos, round people walking round with glowing green silent-disco-style headphones blaring UN panel discussions into your ears, and you can take a pause for breath. But you might find yourself in the Networking Zone, on a rotating seating contraption called UFOTECH that looks more like the kind of lazy Susan you'd encounter at a Chinese restaurant than the networking bench it is designed to function as. This is the AI for Good summit, organized by the United Nations' International Telecommunication Union (ITU), where representatives from the private and public sectors try to discuss how to harness the technology for the benefit, rather than the detriment, of humanity. While Silicon Valley execs and AI lab leaders are testifying to lawmakers in Washington about the risks of superintelligence, and the White House slaps export controls on chips, the UN AI for Good Summit--now in its 10th year--is focused on much more idealistic goals.

In a closed-door simulation, insurers played out their response to a mass disruption by China's Volt Typhoon hackers--and found a nightmare scenario. It's around an hour and 10 minutes into the role-playing game I've been invited to observe, a simulated catastrophic cyberattack on US water utilities, when the whole thing begins to feel less like a fun afternoon playing Dungeons & Dragons and more like a plausible threat to civilization. A full 24 hours of in-game time have passed since hackers disrupted 5,000 water utilities across the United States in this imagined scenario. Joshua Corman, the former Cybersecurity and Infrastructure Security Agency strategist serving as our dungeon master, stands at the front of a conference space in an office tower high above Times Square, narrating the latest updates to the game's participants, a few dozen insurance executives set up in six teams. All of them have gone disturbingly silent. It's about to get harder," Corman says. "I'm going to share a few things, and it's going to hurt." It is, of course, still the same April afternoon as when we started--but in game time, the second-order effects of widespread water outages have started to become clear. Food refrigeration systems are failing at cold storage warehouses. Water-dependent drug and chemical manufacturing has been bottlenecked, leading to insulin shortages. Data centers' cooling systems are failing, causing outages of cloud services. Most critically, 2,000 hospitals are without water, hampering patient care and in some cases leading to evacuations as HVAC systems shut down and the July heat--the game takes place just before Independence Day in 2027--bakes facilities. Worse yet, Corman is playing a looping video onscreen, at the front of the room, showing a burst water main: The hackers have managed to trigger not just IT disruption but also, in at least some cases, real physical destruction that will take far longer to fix. "Everyone downstream is without water pressure," Corman says. "There are no breaks in real incident response," Corman explains just before the giant water pipe starts gushing onscreen. "If you have to go to the bathroom, go to the bathroom.

Follow the day's latest updates Court approves $23.5m fine and costs order against ASX Shadow arts minister says AI companies need to do what everyone else does: 'ask permission and pay for it' Albanese defends gambling reforms, says he's'not against someone having a punt' Pocock says it's'tragic' gambling reforms don't go nearly far enough Shadow arts minister says AI companies need to do what everyone else does: 'ask permission and pay for it' If AI companies want to use Australian creative work, they should do what everyone else does: ask permission and pay for it. Australian creativity is one of our greatest national assets - not a free resource for multinational tech companies. The Coalition will always back the right of artists to control their work and be fairly compensated when others profit from it. This is about consent, fairness and respect for Australian creativity. Court approves $23.5m fine and costs order against ASX Shadow arts minister says AI companies need to do what everyone else does: 'ask permission and pay for it' Albanese defends gambling reforms, says he's'not against someone having a punt' Pocock says it's'tragic' gambling reforms don't go nearly far enough Court approves $23.5m fine and costs order against ASX A federal court judge has ordered the ASX operator to pay $23.5m in penalties and costs after the company admitted to making a misleading statement about a troubled upgrade for technology required to run the stock exchange.

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.

We generalize the universal approximation theorem for functional input neural networks (FNN) to differentiable maps by including the approximation of the derivatives. A FNN maps the input from a possibly infinite-dimensional weighted manifold to the real-valued hidden layer, on which a non-linear scalar activation function is applied, and then returns the output into a Banach space via some linear readouts. By proving a weighted Nachbin theorem, we establish a universal approximation theorem for differentiable maps, which goes beyond the usual formulation on compact sets and also includes the approximation of the derivatives. This leads us to approximation results for non-anticipative functionals including the horizontal and vertical derivatives. As a further application, we show that linear functions of the signature are able to approximate path space functionals including their directional derivatives.

Flexion Robotics, a startup founded by ex-Nvidia engineers, has a clever way of training robots to do useful work. Humanoid robots might be able to run, dance, and occasionally kick people, but to become human, they're going to need to learn how to do all sorts of menial chores at work. Flexion Robotics, a Swiss startup founded by ex-Nvidia robotics researchers, thinks it has the solution. The company has developed a way to train robots to perform complex tasks that involve simple skills like opening doors, climbing stairs, and carrying boxes. The key is to teach the robots individual skills in simulation, then have a master AI algorithm determine how to use them.

Binary classification from positive-only samples is a variant of PAC learning in which the learner receives i.i.d. samples from the positive region of an unknown target concept, but is evaluated under the original distribution (which places mass on both positive and negative regions). This model dates back to Natarajan [1987, STOC], and the characterization of improper learning is well-known -- it even appears in textbooks. The characterization of proper positive-only learning, however, has long remained open. In this work, we revisit and settle this question: a concept class is properly learnable from positive-only samples if and only if it has finite VC dimension and satisfies a new combinatorial condition, which we call uniform exterior separability. Together with several separation results, this characterization reveals a surprisingly rich landscape that differs sharply from standard PAC learning: proper and improper learning are separated, randomized and deterministic proper learning are separated, there are classes for which no ERM is a learner, and finite VC dimension does not suffice even for non-uniform learning. Along the way, we introduce new combinatorial dimensions that we believe can be of broader interest in learning theory.