Sports


This is one way Toyota plans to shuttle people around during the 2020 Olympics – TechCrunch

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When thousands of people converge in Tokyo for the 2020 Olympic and Paralympic Games, the city's infrastructure will be tested. Toyota is getting into the mix to handle some of the ways people will get around the city and the Olympics venue. Toyota unveiled Thursday a new product called APM or Accessible People Mover that is designed for the Olympics and Paralympic Games. The aim, according to Toyota, is for this vehicle to provide "mobility for all" and to solve the so-called "last mile" problem. In Toyota's view, that means a vehicle that can transport as many people as possible, including elderly, pregnant women, families with young children and people with disabilities.


How IBM tweaked its Wimbledon highlight-picking AI to remove bias

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IBM has been tweaking the AI-powered highlight picking algorithm it deploys during the Wimbledon tennis championships this year to take into account a wider array of factors to better find and personalise the best points to share with fans around the world. Big Blue is celebrating a 30-year technology partnership with the famous grass court tennis tournament, and in 2017 it unveiled an AI-powered system for picking the best points to insert into a highlights package, with the aim of delivering highlights "better than an international media organisation" as Sam Sneddon, IBM sports and entertainment lead, told Computerworld UK during a tour of its technology bunker on-site at the Championships this year. Whether it was Novak Djokovic and Roger Federer's five-hour epic mens' final, or Simona Halep's swift dismantling of Serena Williams in the ladies' final, IBM was working in the background to map and collect every second of footage before feeding it through a set of machine learning and deep learning algorithms which decide the points that would make for the best 5-10 minute highlight package. The Watson system analyses 39 factors, like player gestures and crowd reactions, from live footage and assigns an'excitement score'. For an idea of scale, IBM collects 4.5 million tennis data points per tournament.


How AI picks the most exciting moments at Wimbledon without bias

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Note: This blog post was authored by Aaron Baughman with Stephen Hammer, Eythan Holladay, Eduardo Morales and Gary Reiss. Wimbledon is one of the most prestigious major events in the world. With over 675 matches played and over 147,000 tennis points played, its size and scale are substantial. In fact, even if fans diligently watch their favorite players, they will miss a high proportion of the played points. Wimbledon uses IBM digital and AI capabilities to provide rapid access to match highlights to serve up the best content to fans.


The NBA just invested in this hot AI startup to train and find its next crop of global superstars

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Today, the AI-powered basketball training app HomeCourt is being drafted by the NBA to help it find and develop the next Williamson. The NBA has announced a new partnership with HomeCourt that uses the app's technology to develop and train players at all skill levels, making it an integral part of the league's youth basketball development initiatives around the world. In addition, the league is making a strategic investment in Nex Team, the San Jose-based startup behind HomeCourt as part of its $8.5 millon series A funding round. Other investors include Will Smith's Dreamers Fund, the Alibaba Entrepreneurship Fund, and a laundry list of pro ballers, including Al Horford, Sue Bird, Bradley Beal, and the Plumlee brothers (Mason and Miles), all of whom join Dallas Mavericks owner Mark Cuban and Brooklyn Nets co-owner (and Alibaba executive vice chairman) Joe Tsai, both of whom also invested in Nex Team's seed round last summer. In the year since its launched, HomeCourt has logged more than 25 million shots, 20 million dribbles, 3.5 million minutes, with users across 170 countries.


An Artificial Intelligence Program Just Beat 5 Poker Professionals In a Texas Hold'em Tournament

TIME - Tech

A new artificial intelligence program the company built with Carnegie Mellon University called Pluribus recently beat five poker professionals in a six-player Texas Hold'em tournament. After 10,000 hands, the system averaged profits of about $1,000 per hour using $1 chips, a "decisive margin of victory," according to a Facebook blog post. AI has been besting humans at poker for a couple of years, but previous programs could compete with just a single player at a time. Given the complexities that come with poker, including techniques like bluffing, beating five humans in a single game is a significant milestone, Facebook said. "No other game embodies the challenge of hidden information quite like poker, where each player has information (his or her cards) that the others lack," Facebook wrote in a blog post.


Predicting NBA Rookie Stats with Machine Learning

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Every year, millions of basketball fans from around the world tune in to the NBA Draft with the hope that their favorite team strikes gold and discovers the next big NBA star. The people in the front offices of these NBA teams spend thousands of hours scouting and evaluating college and international talent trying to find players that can succeed at the pro level and contribute to the team. Following the growth of the field of data science, it makes sense to try and evaluate talent beyond traditional methods. This article documents a project that attempted to do just that by predicting the stat-lines for the newest batch of NBA rookies. The overall objective of this project was to predict how certain players would do in their first year in the NBA in terms of points, assists, rebounds, steals, and blocks, and the first step to achieving that was to create the right dataset.


Scaling Innovation: Whiteboards versus Maps

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I love watching the NBA's Golden State Warriors play basketball. Their offensive "improvisation" is a thing of beauty in their constant ball movement in order to find the "best" shot. The coordinated decision-making is truly a thing of beauty, but here's the challenge: how would you "scale" the Warriors? You can't just add another player to the mix – even a perennial all-star like Boogie Cousins – and have the same level of success. One of the biggest challenges in this age of Digital Transformation is how are organizations going to exploit new technologies such as IoT and AI to "scale innovation?"


Atlantic League to debut robot umpires and allow players to steal first base

USATODAY - Tech Top Stories

The Atlantic League, an independent baseball league, is rolling out a new revolutionary rule and will debut robot umpires to start the second half of the season. On Wednesday, during the league's All-Star Game in York, Pennsylvania, robots will call balls and strikes for the first time in a professional game, according to The Washington Post. The league will also allow batters to steal first base -- yes, steal first -- on a pitch not caught cleanly, similar to a dropped third strike. Except, the runner can attempt to reach first during any count. Using robots will still include a human element, however.


Wimbledon, Steeped In Tradition, Embraces Artificial Intelligence

NPR Technology

Match highlights at Wimbledon are selected and assembled by robots. Artificial intelligence is used to pick the most dramatic moments, making those judgments by crowd noise and facial expressions.


The strawberry-picking robots

Daily Mail - Science & tech

Strawberry-picking robots could collect enough fruit to supply tennis-lovers at Wimbledon for one week. The'Rubion' is programmed to only pick'perfect' strawberries, with just 14 machines being able to gather enough to keep the tournament topped up for seven days. Its clasping mechanism, which leaves the fruit bruise-free, picks and packages a strawberry every five seconds, resulting in up to 360kg of the berry a day. Rubion even has a built in'camera' uses sensors to ensure only the ripest fruit makes it through. Strawberry-picking robots could collect enough fruit to supply tennis-lovers at Wimbledon for one week.