Pigs could be issued with biometric passports based on facial recognition technology, giving farmers a more practical and welfare-friendly way of identifying individuals than ear notches or tags, the current industry standards. Identifying pigs based on their unique facial features could enable them to receive individualised food and veterinary care, and be traced as they go through meat processing.

Where writing is concerned, the best of today's AIs can be very, very good. A few years ago, a text generator called GPT-2 analyzed a sample of writing by Harvard psychologist Steven Pinker, then produced an imitation that hardly anyone could distinguish from the real thing. A more recent AI called Copilot, which has been customized for programming uses, is speeding up the work of practiced coders–it sometimes knows more than they do. A sample from a writing assistant called Jasper (formerly known as Jarvis) struck an editor as better than the work of some professional writers. The machines seem to have a particular knack for conversations. This may not be writing per se, but it's a language challenge that leaves some humans floundering.

Harvard Medical School scientists and colleagues at Stanford University have developed an artificial intelligence diagnostic tool that can detect diseases on chest X-rays directly from natural-language descriptions contained in accompanying clinical reports. The step is deemed a major advance in clinical AI design because most current AI models require laborious human annotation of vast reams of data before the labeled data are fed into the model to train it. A report on the work, published Sept. 15 in Nature Biomedical Engineering, shows that the model, called CheXzero, performed on par with human radiologists in its ability to detect pathologies on chest X-rays. The team has made the code for the model publicly available for other researchers. Most AI models require labeled datasets during their "training" so they can learn to correctly identify pathologies. This process is especially burdensome for medical image-interpretation tasks since it involves large-scale annotation by human clinicians, which is often expensive and time-consuming.

A soft robot made from droplets of a magnetic fluid can break itself up and reconstitute itself later when it encounters obstacles or narrow passages. Researchers say it could be used for targeted drug delivery in the future. Xinjian Fan at Soochow University in Taiwan and his colleagues used droplets of a ferrofluid, in this case magnetic iron oxide nanoparticles suspended in oil, to make a soft robot about a centimetre in size. A set of controllable magnets can direct the robot to move or change shape, as needed, by acting on the nanoparticles. To make it move through a narrow channel, the researchers used their magnets to squeeze the robot into a thin, elongated shape.

Example image of the 3D printer nozzle used by the machine learning algorithm to detect and correct errors in real time. Engineers from the University of Cambridge have developed a machine learning algorithm that can detect and correct a wide variety of different errors in real time, and can be easily added to new or existing machines to enhance their capabilities. Details of their low-cost approach are reported in the journal Nature Communications. However, it is also vulnerable to production errors, from small-scale inaccuracies and mechanical weaknesses through to total build failures. Currently, the way to prevent or correct these errors is for a skilled worker to observe the process.

As Tesla faces a federal investigation and lawsuits over fatal accidents involving its Autopilot system, shaking public confidence in robotic cars, could a pared-down approach like the one described -- variously called "partial autonomy" or "driver assistance" systems -- be the more realistic future of hands-free driving? This type of system, more like a no-nonsense chaperone than one you would find in a fully robotic car, is a necessary component for top scores from the Insurance Institute for Highway Safety's forthcoming ratings of partial-autonomous tech; high ratings from the independent nonprofit are prized. And though General Motors is taking the lead with their Super Cruise system, they not alone; Ford, BMW and Mercedes-Benz are making similar attempts. Super Cruise combines minutely detailed, 3-D laser-scanned roadway maps with cameras, radar and onboard GPS. By the end of this year, the company intends to expand the system's network to two-way highways for the first time and double its total operational domain to 400,000 miles.

The more data you can feed a machine learning algorithm, the better it can spot patterns, make decisions, predict behaviours, personalise content, diagnose medical conditions, power smart everything, detect cyber threats and fraud; indeed, AI and data make for a happy partnership: "The algorithm without data is blind. Data without algorithms is dumb." Not everyone wants to share, at least, not under the current rules of digital engagement. Some individuals disengage entirely, becoming digital hermits. Others proceed with caution, using privacy-enhancing technologies (PETs) to plug the digital leak: a kind karate chop, digital self defense -- they don't trust website privacy notices, they verify them with tools like DuckDuckGo's Privacy Grade extension and soon, machine-readable privacy notices.

New research published in AI Magazine explores how advanced AI could hack reward systems to dangerous effect. Researchers at the University of Oxford and Australian National University analyzed the behavior of future advanced reinforcement learning (RL) agents, which take actions, observe rewards, learn how their rewards depend on their actions, and pick actions to maximize expected future rewards. As RL agents get more advanced, they are better able to recognize and execute action plans that cause more expected reward, even in contexts where reward is only received after impressive feats. Lead author Michael K. Cohen says, "Our key insight was that advanced RL agents will have to question how their rewards depend on their actions." Answers to that question are called world-models.

When a demo of the software was released in late August, users quickly found that certain words--both explicit mentions of political leaders' names and words that are potentially controversial only in political contexts--were labeled as "sensitive" and blocked from generating any result. China's sophisticated system of online censorship, it seems, has extended to the latest trend in AI. It's not rare for similar AIs to limit users from generating certain types of content. DALL-E 2 prohibits sexual content, faces of public figures, or medical treatment images. The ERNIE-ViLG model is part of Wenxin, a large-scale project in natural-language processing from China's leading AI company, Baidu.

The second edition of the Global AI Summit kicked off Tuesday morning in the Saudi capital Riyadh, bringing together various stakeholders and academics to discuss the future of artificial intelligence and the Kingdom's contribution to this field. Over 200 speakers representing 90 countries have come together for the global summit that will run until September 15 at the King Abdul Aziz International Conference Center and under the patronage of Saudi Crown Prince Mohammed bin Salman. The Global AI Summit, organized by Saudi Data & AI Authority (SDAIA), will touch on topics such as the impact of AI on the public and private sectors, healthcare, environment, transportation, smart cities and culture among other matters. For all the latest headlines follow our Google News channel online or via the app. SDAIA said on its website that "tech companies, startups, investors, and entrepreneurs [will] meet at the Global AI Summit to shape the future of AI." Speaking at the opening ceremony of the summit, Saudi Minister of Communications & Information Technology Abdullah Alswaha said that Saudi Arabia "has become the largest tech force of coders and data scientists," adding that there are currently more than 70,000 trainees in this field.