Yejin Choi, a University of Washington computer science professor and senior research manager at Seattle's Allen Institute for Artificial Intelligence (AI2), won a $800,000 "genius grant" given annually by the John D. and Catherine T. MacArthur Foundation. Choi, one of 25 MacArthur Fellows for 2022 revealed Wednesday, is an expert in natural language processing. Her work aims to improve the ability of computers and artificial intelligence systems to perform commonsense reasoning and understand implied meaning in human language. "This is such a great honor because there have been only two other researchers in the natural language processing field who have received this award," Choi told UW News. Choi spoke to GeekWire earlier this year about the debate over a robot's ability to have human-like feelings.

An exoskeleton boot that lets you walk faster while using less energy could help older people or those with disabilities move around.Existing exoskeletons have failed to make the step into the real world because they need to be fine-tuned to a person's gait over long periods. Without such personalisation, the hardware may provide only a minimal boost or even make walking harder. "Despite all the things you see in the comic books and superhero movies, exoskeletons are really, really tricky," says Steve Collins at Stanford University in California. Collins and his colleagues have previously found tailoring an exoskeleton to an individual to be a lengthy task. The wearer had to visit the lab for five consecutive days and walk on a treadmill for 2 hours each day while wearing an uncomfortable respirator and sensors so that the content of the air they breathed in and out, and therefore their metabolic effort, could be measured. Now, the researchers have come up with a computer model that absorbs the data from 3600 of their previous laboratory tests to learn how to approximate the metabolic effort based on physical data from the exoskeleton's sensors alone.

A humanoid answered pre-submitted questions in the House of Lords about technology and art in London.

AlphaTensor was designed to perform matrix multiplications, but the same approach could be used to tackle other mathematical challenges.Credit: DeepMind An artificial intelligence (AI) developed by machine-learning company DeepMind in London has tackled a type of calculation called matrix multiplication. The system -- called AlphaTensor -- leverages the skills that DeepMind's game-playing AIs use to beat human players at games such as Go and chess. Matrix multiplication is a widely used mathematical technique that involves multiplying numbers arranged in grids, or matrices, that might represent sets of pixels in images, air conditions in a weather model or the internal workings of an artificial neural network. AlphaTensor broke ground by finding shortcuts to solve these problems with fewer steps (A. The same general approach could have applications in other kinds of mathematical operation, its developers say, such as decomposing complex waves or other mathematical objects into simpler ones.

A group of robotics companies including Boston Dynamics has pledged not to add weapons to their devices and to push back against attempts by other people to do so. But how big an effect will it have if other firms will be developing robots with military applications? An open letter signed by Agility Robotics, ANYbotics, Boston Dynamics, Clearpath Robotics, Open Robotics and Unitree says that "untrustworthy people" could use the companies' devices to "invade civil rights or to threaten, harm, or …

Schmickl, who now leads the Artificial Life Lab at the University of Graz in Austria, wasn't wrong. Studies in various parts of the world have since found that insect populations are declining or changing. After working in the field of swarm robotics for several years--using nature to inspire robots--Schmickl decided to flip his work around and design robots to help nature, a concept he calls ecosystem hacking. Honeybees and other pollinators face habitat loss, pesticide exposure, and other challenges, and Schmickl believes that coming to their aid could help strengthen entire ecosystems. Already, some companies offer augmented beehives that monitor conditions inside, or even robotically tend the bees.

In 2017, spurred by advocacy from civil society groups, the New York City Council created a task force to address the city's growing use of artificial intelligence. But the task force quickly ran aground attempting to come to a consensus on the scope of "automated decision systems." In one hearing, a city agency argued that the task force's definition was so expansive that it might include simple calculations such as formulas in spreadsheets. By the end of its eighteen-month term, the task force's ambitions had narrowed from addressing how the city uses automated decision systems to simply defining the types of systems that should be subject to oversight. As policymakers around the world have attempted to create guidance and regulation for AI's use in settings ranging from school admissions and home loan approvals to military weapon targeting systems, they all face the same problem: AI is really challenging to define.

Machine learning-based computational models have been successfully applied to a broad range of complex information processing tasks, including those that involve retrieving specific data items from large archives. Researchers at the Sony Computer Science Laboratories (CSL) in France have been trying to develop machine learning techniques that could help music producers to easily identify and retrieve specific audio samples from a database. To this end, Stefan Lattner, a researcher at Sony CSL, recently introduced SampleMatch, a machine learning-based model that can automatically retrieve drum samples that match a specific music track from large archives. His model is set to be presented in December at the ISMIR 2022 conference, a leading event that focuses on music information retrieval. "Our music team at Sony CSL is working on AI that could make the life of music producers easier," Stefan Lattner, one of the researchers who carried out the study, told TechXplore.

Fuller's team is working to construct a robotic fly. Similar to the cyborg cockroaches, the flies could be used in search-and-rescue missions. They could also be unleashed to fly around and look for chemical leaks in the air or cracks in piping infrastructure. "You open a suitcase and these little robotic flies fly around," he said. "Then, once you know where the leak is, you can patch it."

In early 2021, Bollinger's winemakers were able to get their first taste of La Grande Année 2014, a prestige fizz that had been aging in the champagne house's cellars since it was blended. La Grande Année, Bollinger's flagship vintage champagne, is produced only in years when the broad quality is deemed sufficiently high, and enjoys seven years of aging under cork before it's launched. Ahead of opening up the 2014 vintage, questions lingered over just how strong a year it really was, given a roller-coaster growing season that saw record-breaking heat in June followed by a cold, wet summer that slowed grape maturation. Moreover, for a champagne house known for its forthright pinot noir character, it was a vintage that distinctly favored chardonnay. But for Denis Bunner, Bollinger's deputy head winemaker (or chef de cave), the answer was clear-cut even before the bottles were opened.