YouTube should be included in the ban on under-16s accessing social media, the nation's online safety chief has said as she urges the Albanese government to rethink its decision to carve out the video sharing platform from new rules which apply to apps such as TikTok, Snapchat and Instagram. The eSafety commissioner, Julie Inman Grant, also recommended the government update its under-16s social media ban to specifically address features such as stories, streaks and AI chatbots which can disproportionately pose risk to young people. The under-16s ban will come into effect in December 2025, despite questions over how designated online platforms would verify users' ages, and the government's own age assurance trial reporting last week that current technology is not "guaranteed to be effective" and face-scanning tools have given incorrect results. Although then communications minister Michelle Rowland initially indicated YouTube would be part of the ban legislated in December 2024, the regulations specifically exempted the Google-owned video site. Guardian Australia revealed YouTube's global chief executive personally lobbied Rowland for an exemption shortly before she announced the carve out.

We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standard reinforcement learning as a special case. Previous distributional DP methods could optimize the same class of expected utilities as classic DP. To go beyond expected utilities, we combine distributional DP with stock augmentation, a technique previously introduced for classic DP in the context of risk-sensitive RL, where the MDP state is augmented with a statistic of the rewards obtained so far (since the first time step). We find that a number of recently studied problems can be formulated as stock-augmented return distribution optimization, and we show that we can use distributional DP to solve them. We analyze distributional value and policy iteration, with bounds and a study of what objectives these distributional DP methods can or cannot optimize. We describe a number of applications outlining how to use distributional DP to solve different stock-augmented return distribution optimization problems, for example maximizing conditional value-at-risk, and homeostatic regulation. To highlight the practical potential of stock-augmented return distribution optimization and distributional DP, we combine the core ideas of distributional value iteration with the deep RL agent DQN, and empirically evaluate it for solving instances of the applications discussed.

X-Ray specs and invisibility cloaks are the stuff of sci-fi and fantasy, but sometimes science is just stranger than fiction. A food dye that helps give certain sodas and snacks their hallmark orange hue renders mouse skin almost completely see-through in a reversible, potentially non-toxic research method that could transform medical and scientific imaging. Because of a counterintuitive fundamental physics principle, Tartrazine, also known as Yellow 5, can temporarily turn biological tissue transparent to the naked eye, as described in a study published September 5 in the journal Science. So far, the scientists behind the new discovery have used the method to see the organs in a mouse's intact abdomen, glimpse the pulsing vessels surrounding a rodent skull, and to get an exceptionally clear view of muscle tissue through a microscope. With further safety and efficacy research, the method may spur new scientific findings, boost microscopy advances, and improve medical diagnostic strategies and treatments.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Australia's government said Monday the online dating industry must improve safety standards or be forced to make changes through legislation, responding to research that says three-in-four Australian users suffer some form of sexual violence through the platforms. Communications Minister Michelle Rowland said popular dating companies such as Tinder, Bumble and Hinge have until June 30 to develop a voluntary code of conduct that addresses user safety concerns. The code could include improving engagement with law enforcement, supporting at-risk users, improving safety policies and practices, and providing greater transparency about harms, she said.

Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage self-supervised learning techniques on the UK-Biobank activity tracker dataset--the largest of its kind to date--containing more than 700,000 person-days of unlabelled wearable sensor data. Our resulting activity recognition model consistently outperformed strong baselines across seven benchmark datasets, with an F1 relative improvement of 2.5%-100% (median 18.4%), the largest improvements occurring in the smaller datasets. In contrast to previous studies, our results generalise across external datasets, devices, and environments. Our open-source model will help researchers and developers to build customisable and generalisable activity classifiers with high performance.

Background checks and ID verification systems in dating apps are among the measures being considered as governments around the country grapple with how to keep people safe while they are looking for love online. The strategies were discussed by ministers, victim-survivors, authorities and technology companies as part of national dating app roundtable talks in Sydney on Wednesday. The federal communications minister, Michelle Rowland, said it was an "important first step", flagging discussion of possible longer-term changes like background checks for dating app users. "None of us underestimate the complex issues around privacy, user safety, data collection and management that are involved," she said. "There's no one law that is going to fix this issue."

When six-foot-four inch Marty first rolled into Stop & Shop, the robot walked into history. Social robot experts say it is among the first instance of a robot deployed in a customer environment, namely supermarkets in the Northeast. Marty rolls around the store looking for spills with its three cameras. It does take the place of the human worker, called an associate, that did the same thing, but it means the associate can do something else. Doing the walk-around of the store is seen as a mundane task.

As night falls in London, Georgina Rowlands and Anna Hart start applying makeup. Rowlands has long narrow blue triangles and thin white rectangles criss-crossing her face. Hart has a collection of red, orange and white angular shapes on hers. They're two of the four founders of the Dazzle Club, a group of artists set up last year to provoke discussion about the growing using of facial recognition technology. The group holds monthly silent walks through different parts of London to raise awareness about the technology, which they say is being used for "rampant surveillance."

A wheeled robot named Marty is rolling into nearly 500 grocery stores to alert employees if it encounters spilled granola, squashed tomatoes or a broken jar of mayonnaise. But there could be a human watching from behind its cartoonish googly eyes. Badger Technologies CEO Tim Rowland says its camera-equipped robots stop after detecting a potential spill. But to make sure, humans working in a control center in the Philippines review the imagery before triggering a cleanup message over the loudspeaker. Rowland says 25 of the robots are now operating at certain Giant, Martin's and Stop & Shop stores, with 30 more arriving each week. Carlisle, Pennsylvania-based Giant says it has two robots now working at stores in the state, and plans to expand to all 172 Giant stores by the middle of this year.

A wheeled robot named Marty is rolling into nearly 500 grocery stores to alert employees if it encounters spilled granola, squashed tomatoes or a broken jar of mayonnaise. But there could be a human watching from behind its cartoonish googly eyes. Badger Technologies CEO Tim Rowland says its camera-equipped robots stop after detecting a potential spill. The'Marty' robots will roam grocery store aisles looking for spills and hazards. When it spots an accident, it will alert staff to come and clean up spills.