A French Paralympian has been able to walk again – all thanks to a technology. Tennis player Kevin Piette, 36, who lost the use of his legs in an accident aged 11, has made history by carrying the Olympic torch wearing a robotic exoskeleton. Heartwarming footage shows him smiling as he passes waving crowds in Poissy, northwest Paris, as the traditional torch relay nears the end of its route. X user @Brink_Thinker posted the clip, which has been described as'inspirational' by fellow social media users. Replying to the post, someone said: 'I have never seen a happier face!!!' Kevin Piette, paraplegic since an accident, made history today by carrying the Olympic flame with his exoskeleton! 'Exoskeleton' is used to describe a mechanical shell that covers the user and them provides robotic support.

A total of 13 teams from around the state came to the Washington County Fairgrounds last weekend for a robotics competition, formally called a "First Tech Challenge robotics meet." Such competitions change objectives every year. At this tournament, the games involved strategically stacking cones onto markers around an arena, a task each robot was specifically designed to do. Jim Pitcher, Coach Mentor of Washington 4-H's team, called Eaglebots, said hosting the event was its own team-building exercise. "It's for the love of the sport," he said.

You are free to share this article under the Attribution 4.0 International license. Cognitive behavioral therapy for chronic pain supported by artificial intelligence can yield the same results as programs delivered by therapists, a new study shows. Cognitive behavioral therapy (CBT) is an effective alternative to opioid painkillers for managing chronic pain. But getting patients to complete those programs is challenging, especially because psychotherapy often requires multiple sessions and mental health specialists are scarce. AI-supported therapy requires substantially less clinician time, making it more accessible to patients, the researchers report.

Many games often share common ideas or aspects between them, such as their rules, controls, or playing area. However, in the context of General Game Playing (GGP) for board games, this area remains under-explored. We propose to formalise the notion of "game concept", inspired by terms generally used by game players and designers. Through the Ludii General Game System, we describe concepts for several levels of abstraction, such as the game itself, the moves played, or the states reached. This new GGP feature associated with the ludeme representation of games opens many new lines of research. The creation of a hyper-agent selector, the transfer of AI learning between games, or explaining AI techniques using game terms, can all be facilitated by the use of game concepts. Other applications which can benefit from game concepts are also discussed, such as the generation of plausible reconstructed rules for incomplete ancient games, or the implementation of a board game recommender system.

Adam Shelly (left), an Xwing software engineer, stands with CEO and founder Marc Piette (center) and ... [ ] CTO Maxime Gariel in front of a Cessna 208B Grand Caravan the startup has retrofitted to fly autonomously. Scores of companies are working to develop pilotless robot aircraft that are electrically powered and takeoff and land vertically. That's an awful lot of change to pull off at once. Xwing is among a handful of aviation startups that are aiming to get to market sooner by taking on just one piece of that puzzle, in its case, making aircraft fly autonomously. The San Francisco-based company claims to have pulled off the first fully autonomous flight of a Cessna 208B Grand Caravan, a small workhorse cargo plane, and it's hoping to win approval from the Federal Aviation Administration to launch commercial cargo deliveries with unmanned Grand Caravans over unpopulated areas in 2022.

There's no doubt that AI has usurped big data as the enterprise technology industry's favorite new buzzword. While progress was slow during the first few decades, AI advancement has rapidly accelerated during the last decade. Some people say AI will augment humans and maybe even make us immortal; other pessimistic individuals say AI will lead to conflict and may even automate our society out of jobs. Despite the differences in opinion, the fact is, only a few people can identify what AI really is. Today, we are surrounded by minute forms of AI, like the voice assistants that we all hold in our smart phones, without us knowing or perceiving the efficiency of the service.

Growing demand is straining our existing electricity generation facilities and requires active participation of the utility and the consumers to achieve energy sustainability. One of the most effective and widely used ways to achieve this goal in the smart grid is demand response (DR), whereby consumers reduce their electricity consumption in response to a request sent from the utility whenever it anticipates a peak in demand. To successfully plan and implement demand response, the utility requires reliable estimate of reduced consumption during DR. This also helps in optimal selection of consumers and curtailment strategies during DR. While much work has been done on predicting normal consumption, reduced consumption prediction is an open problem that is under-studied. In this paper, we introduce and formalize the problem of reduced consumption prediction, and discuss the challenges associated with it. We also describe computational methods that use historical DR data as well as pre-DR conditions to make such predictions. Our experiments are conducted in the real-world setting of a university campus microgrid, and our preliminary results set the foundation for more detailed modeling.