The ongoing revolution in artificial intelligence (AI)--in image recognition, natural language processing and translation, and much more--has been driven by neural networks, specifically many-layer versions known as deep learning. These systems have well-known weaknesses, but their capability continues to grow, even as they demand ever more data and energy. At the same time, other critical applications need much more than just powerful pattern recognition, and deep learning does not provide the sorts of performance guarantees that are customary in computer science. To address these issues, some researchers favor combining neural networks with older tools for artificial intelligence. In particular, neurosymbolic AI incorporates the long-studied symbolic representation of objects and their relationships.

An operator controls a Fetch driverless car from the office of Imperium Drive, during driverless car trials, in Milton Keynes, Britain, June 8, 2022. MILTON KEYNES, England (Reuters) -Autonomous vehicle (AV) startups have raised tens of billions of dollars based on promises to develop truly self-driving cars, but industry executives and experts say remote human supervisors may be needed permanently to help robot drivers in trouble. The central premise of autonomous vehicles – that computers and artificial intelligence will dramatically reduce accidents caused by human error – has driven much of the research and investment. But there is a catch: Making robot cars that can drive more safely than people is immensely tough because self-driving software systems simply lack humans' ability to predict and assess risk quickly, especially when encountering unexpected incidents or "edge cases." "Well, my question would be, 'Why?'" said Kyle Vogt, CEO of Cruise, a unit of General Motors (NYSE:GM), when asked if he could see a point where remote human overseers should be removed from operations.

You could see it coming. Flippy started acting weird, jerking and hitching. The worker on the fry station had witnessed this behavior before. Even Joe Garcia, the Miso Robotics "robot support specialist" assigned to troubleshoot at Jack in the Box, had seen it. Garcia, a mechanical engineering graduate from Loyola Marymount University who one day wants to work for NASA, is spending his days swooping in when Flippy occasionally loses his mind as he encounters tacos.

More than one-third of artificial intelligence researchers around the world agree that AI decisions could cause a catastrophe as bad as all-out nuclear war in this century. The findings come from a survey covering the opinions of 327 researchers who had recently co-authored papers on AI research in natural language processing.

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.