Along with just about every other major sporting event, the Wimbledon grand slam tennis tournament was canceled this year due to the COVID-19 crisis. However, organizers at the All England Lawn Tennis & Croquet Club (AELTC) have tapped long-time partner IBM to create a virtual two-week event consisting of classic matches dating back more than 40 years. To go beyond just bringing old matches to modern digital platforms, IBM and AELTC have leveraged AI techniques to revitalize old footage and appeal to modern viewers. With matches stretching as far back as 1977, a wide range of video quality is to be expected, and older recordings tend to have an issue with visual "noise." "To the naked eye, this noise is like static in the image, with color pixels dancing," IBM executive Sam Seddon told VentureBeat.
BELGRADE, Serbia (AP) -- Novak Djokovic's charity tennis exhibition series, combined with an overall softening of coronavirus restrictions in Serbia and Croatia, has been followed by an increase in the number of positive cases among professional athletes. Two tennis players ranked among the top 40 in the world and five players at Serbia's biggest soccer club have tested positive for the virus after being involved in sporting events where fans packed into the stands and social distancing was not enforced. Djokovic, the top-ranked player in the world who previously said he was against taking a vaccine for the virus even if it became mandatory to travel, will now be tested as well, his media team said Monday. "He is fine, he has no symptoms but nonetheless, he needs to do the test and then we will see what's going on," Djokovic's media team said in a statement. Djokovic was the face behind the Adria Tour, a series of exhibition events that started in Belgrade and moved to Zadar, Croatia, this weekend. Grigor Dimitrov, a three-time Grand Slam semifinalist from Bulgaria, said Sunday he tested positive for the virus.
Welcome to Recharge, a weekly newsletter full of stories that will energize your inner hellraiser. See more editions and sign up here. Like everyone else in the world, Venus and Serena Williams can't play tennis right now to thousands of stadium fans. But they can play Tennis to millions of them. The superstar sisters lit up this weekend's Mario Tennis tournament for coronavirus relief in doubles showdowns with fellow pros Maria Sharapova, Naomi Osaka, and Kei Nishikori, along with Seal, Steve Aoki, and other entertainers and fashion figures.
We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolutionary search (ES) methods acting on CNN-based policy architectures for non-visual inputs and convolving across time learn compact controllers leading to smooth motions. Furthermore, we show that with appropriately tuned curriculum learning on the task and rewards, policies are capable of developing multi-modal styles, specifically forehand and backhand stroke, whilst achieving 80\% return rate on a wide range of ball throws. We observe that multi-modality does not require any architectural priors, such as multi-head architectures or hierarchical policies.
Gaby Ecanow loves listening to music, but never considered writing her own until taking 6.S191 (Introduction to Deep Learning). By her second class, the second-year MIT student had composed an original Irish folk song with the help of a recurrent neural network, and was considering how to adapt the model to create her own Louis the Child-inspired dance beats. "It was cool," she says. "It didn't sound at all like a machine had made it." This year, 6.S191 kicked off as usual, with students spilling into the aisles of Stata Center's Kirsch Auditorium during Independent Activities Period (IAP).
Never before have consumers had such an outstanding lineup of content offerings from which to choose – from award-winning original programming on OTT services like Netflix, to a galaxy of live-streamed and live-broadcast entertainment and sports events. First the good news: "I want my MTV." Some of us are old enough to remember that bygone slogan that expressed viewers' craving for music videos (back when MTV was all about music videos!) and well before the multiplatform internet age. That craving for all types of content has grown exponentially in the ensuing years, creating a potential gold mine of new opportunities for savvy media companies to develop lucrative new revenue streams. As media companies look for ways to realize that new revenue potential, they're running up against some real institutional barriers when they try to get their hands on the valuable content assets they need.
Google has released a neural-network-powered chatbot called Meena that it claims is better than any other chatbot out there. Data slurp: Meena was trained on a whopping 341 gigabytes of public social-media chatter--8.5 times as much data as OpenAI's GPT-2. Google says Meena can talk about pretty much anything, and can even make up (bad) jokes. Why it matters: Open-ended conversation that covers a wide range of topics is hard, and most chatbots can't keep up. At some point most say things that make no sense or reveal a lack of basic knowledge about the world.
The purpose of this course is to teach about how to use Python and machine learning in order to predict sports outcomes. It takes you through through all the steps, from collecting data using a web crawler to making profitable bets based on your predicted results. The course is built around predicting tennis games, but the things taught can be extended to any sport, including team sports. The course includes: 1) Intro to Python and Pandas. This course is geared towards people that have some interest in data science and some experience in Python.
Sign in to report inappropriate content. Instructor: Patrick Winston This lecture begins with a high-level view of learning, then covers nearest neighbors using several graphical examples. We then discuss how to learn motor skills such as bouncing a tennis ball, and consider the effects of sleep deprivation.
Japanese robotics company Omron and its table-tennis-playing bot are back at CES to serve up loads of fresh new tech. This year, though Omron may have reincarnated its crowd-pleasing table tennis bot, called Forpheus, the company managed to up the ante with a new emotional recognition system that gauges players' frustration level and their skill. In addition to being fun, Omron wants Forpheus to showcase its work in AI, computer vision and robotics. Its system, which watches players closely as they battle the bot in ping pong, has the capability of reading a players' face and even their heart rate and then interpreting that information to make inferences on skill and state-of-mind. Forpheus (pictured above) can reach to a volley using computer vision.