Machine Learning (ML) is about statistical patterns in the artificial data sets, while artificial intelligence (AI) is about causal patterns in the real world data sets. The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage. Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Artificial intelligence is important because it automates repetitive learning and discovery through data. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks.
A Field Support Robot was used to retrieve rugby balls on day three of the Tokyo 2020 Olympic Games at Tokyo Stadium. Over the weekend, the FSR will help during track and field events. A Field Support Robot was used to retrieve rugby balls on day three of the Tokyo 2020 Olympic Games at Tokyo Stadium. Over the weekend, the FSR will help during track and field events. The Field Support Robot is a good boy!
You may not be noticing it much on your television, smartphone or tablet screens, but the TV coverage of the Tokyo 2020 Olympics (which of course are being held in 2021 due to the COVID-19 pandemic) are infused with big data and AI to an extent never before experienced in the history of the Olympic games. It's been 53 years since the Olympics officially adopted electronic time-keeping equipment to track racers in Olympic events. Omega's Magic Eye camera, which debuted in 1948, gave us the first of many "photo finishes" for track events, and was soon adopted in other events as well. Now the technology is cranking up a notch in the Tokyo 2020 Olympics (which perhaps should have been called the 2021 games), and Omega is again behind much of it. For example, Omega, which is the official timekeeper for 35 Olympic sports, is using cameras equipped with computer vision capabilities to track the movement of beach volleyball players, as well as the ball in play.
People are increasingly getting onto those banned no-fly types of lists, which could happen with ... [ ] self-driving cars too. People keep getting banned for doing the darndest and seemingly dumbest of acts. Oftentimes getting banned for the rest of their entire life. You might have heard or seen the recent brouhaha in major league baseball when a spectator in Yankee Stadium seated above leftfield opted to throw a baseball down onto the field that then struck the Boston Red Sox player Alex Verdugo in the back. He was not hurt, but you can imagine the personal dismay and shock at suddenly and unexpectedly having a projectile strike him from behind, seemingly out of nowhere. Turns out that Alex had earlier tossed the same baseball up into the stands as a memento for a young Red Sox cheering attendee. By some boorish grabbing, it had ended up in the hands of a New York Yankees fan. Next, after some hysterical urging by other frenetic Yankees to toss it back, the young man did so. Whether this act of defiance was intentionally devised to smack the left-fielder is still unclear and it could have been a happenstance rather than a purposeful aim.
It might not be obvious from the TV coverage, but the Tokyo 2020 Olympics (which of course are being held in 2021) are infused with big data and AI to an extent never before experienced in an Olympic games. It's been 53 years since the Olympics officially adopted electronic time-keeping equipment to track racers in Olympic events. Omega's Magic Eye camera, which debuted in 1948, gave us the first of many "photo-finish" for track events, and was soon adopted in other events too. Now the technology is going up a notch in the Tokyo 2020 Olympics (which perhaps should have been called the 2021 games), and Omega is behind much of it. For example, Omega, which is the official timekeeper for 35 Olympic sports, is using cameras equipped with computer vision capabilities to track the movement of beach volleyball players, as well as the ball.
Cassie has made history as the first bipedal robot to complete a five-kilometer (5K) run, having done so in just over 53 minutes. Developed by Oregon State University, the two-legged machine with knees that bend like those of an ostrich, taught itself how to run through a deep reinforcement learning algorithm. Yesh Godse, an undergraduate in the lab, said in a statement: 'Deep reinforcement learning is a powerful method in AI that opens up skills like running, skipping and walking up and down stairs.' Cassie's total time of 53 minutes, three seconds, included about six and a half minutes of resets following two falls. Cassie first stumbled when its computer overheated and the other came after it took a turn at too high of a speed. The robot's makers foresee it eventually delivering packages, managing warehouse tasks and helping people in their homes.
This year's Olympic Games may be closed to most spectators because of COVID-19, but the eyes of the world are still on the athletes thanks to dozens of cameras recording every leap, dive and flip. Among all that broadcasting equipment, track-and-field competitors might notice five extra cameras--the first step in a detailed 3-D tracking system that supplies spectators with near-instantaneous insights into each step of a race or handoff of a baton. And tracking is just the beginning. The technology on display in Tokyo suggests that the future of elite athletic training lies not merely in gathering data about the human body, but in using that data to create digital replicas of it. These avatars could one day run through hypothetical scenarios to help athletes decide which choices will produce the best outcomes.
Cassie, a bipedal robot that's all legs, has successfully ran five kilometers without having a tether and on a single charge. The machine serves as the basis for Agility Robotics' delivery robot Digit, as TechCrunch notes, though you may also remember it for "blindly" navigating a set of stairs. Oregon State University engineers were able to train Cassie in a simulator to give it the capability to go up and down a flight of stairs without the use of cameras or LIDAR. Now, engineers from the same team were able to train Cassie to run using a deep reinforcement learning algorithm. According to the team, Cassie teaching itself using the technique gave it the capability to stay upright without a tether by shifting its balance while running.
When the Indy Autonomous Challenge takes off later this year, all the race cars will look the same -- and no one will be behind the wheel. The IAC is a university-led self-driving car race taking place Oct. 23 at the Indianapolis Motor Speedway with $1.3 million in prizes on offer. The first three teams to cross the finish line in 25 minutes or less after 20 laps (that's about 50 miles) will win what's believed to be the first head-to-head autonomous race. To make sure the race is about building out the software for autonomous driving at high speeds (the average speed will be about 120mph), each team has the exact same modified Dallara AV-21, a typical race car usually with a human driver. Clemson University students helped develop the base car for the race.