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.
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.
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?"
Dallas Mavericks owner Mark Cuban contends artificial intelligence, or AI, will transform our way of life. In a recent interview with Yahoo Finance Editor-in-Chief Andy Serwer, Cuban said the impact of AI across different industries and cultures will surpass the wide-ranging effects of some previous technologies, including personal computers, mobile, even the internet. "There's nothing that AI won't impact," Cuban explained. "So having been around awhile, I saw the impact of PCs. Then I saw the impact [of] the local area networks. Then I saw the impact of wide area networks. Then I saw the impact of the internet. Then I saw the impact of mobile. Then I saw the impact of wireless. And it dwarfs any of those things."
Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system for multi-hop RC that decomposes a compositional question into simpler sub-questions that can be answered by off-the-shelf single-hop RC models. Since annotations for such decomposition are expensive, we recast sub-question generation as a span prediction problem and show that our method, trained using only 400 labeled examples, generates sub-questions that are as effective as human-authored sub-questions. We also introduce a new global rescoring approach that considers each decomposition (i.e. the sub-questions and their answers) to select the best final answer, greatly improving overall performance. Our experiments on HotpotQA show that this approach achieves the state-of-the-art results, while providing explainable evidence for its decision making in the form of sub-questions.
This episode is from a jam-packed day on May 14th. Gary starts the day holding some internal meetings in LA, then flies to Vegas for a keynote where talk about, among other things, his POV on AI and how it can improve our inefficiencies. You'll also see his reaction to the NBA Draft Lottery, as well as an Instagram clip that you might recognize from last week;) Enjoy! -- If you haven't joined my #FirstInLine community, you need to jump on it ASAP! By joining #FirstInLine, my messaging program, you get details on exclusive giveaways that I'm doing, updates regarding my keynotes/conferences, and more;) You can join here: https://garyvee.com/JoinFIL Thank you for watching this video.
The ability of reasoning beyond data fitting is substantial to deep learning systems in order to make a leap forward towards artificial general intelligence. A lot of efforts have been made to model neural-based reasoning as an iterative decision-making process based on recurrent networks and reinforcement learning. Instead, inspired by the consciousness prior proposed by Yoshua Bengio, we explore reasoning with the notion of attentive awareness from a cognitive perspective, and formulate it in the form of attentive message passing on graphs, called neural consciousness flow (NeuCFlow). Aiming to bridge the gap between deep learning systems and reasoning, we propose an attentive computation framework with a three-layer architecture, which consists of an unconsciousness flow layer, a consciousness flow layer, and an attention flow layer. We implement the NeuCFlow model with graph neural networks (GNNs) and conditional transition matrices. Our attentive computation greatly reduces the complexity of vanilla GNN-based methods, capable of running on large-scale graphs. We validate our model for knowledge graph reasoning by solving a series of knowledge base completion (KBC) tasks. The experimental results show NeuCFlow significantly outperforms previous state-of-the-art KBC methods, including the embedding-based and the path-based. The reproducible code can be found by the link below.
The nature of basketball is such that its most cathartic moment--when the ball goes decisively and irretrievably through the hoop--is the same every time. The ball piercing the basket is both a discrete event and a continuous waterfall of motion that, for active players, is constant throughout their careers. They shoot in practice, they shoot in the game, they shoot and shoot and shoot. The motion becomes so ingrained in their muscle memory that the gesture requires only its activation; everything else--the elevation, the aiming at the basket, the cocking of the elbow and the follow-through of the hand--is programmed. I found myself thinking about the waterfall of shots in the wake of one of the more dramatic ones in recent N.B.A. history: Damian Lillard, of the Portland Trail Blazers, hitting the game-winning, series-ending shot against the Oklahoma City Thunder in Game Five.
Utah Jazz head coach Quin Snyder had heavy praise for reigning MVP James Harden ahead of Game 2. Snyder had compared Harden to artificial intelligence on Tuesday and was asked to expand on that before Wednesday night's game. "The way he plays, there's an artistic nature to it… Obviously he's skilled, but I think the way he processes the game… He literally sees the whole court." I think the way he plays, there's an artistic nature to it. The feel that he has for different things on the court. He's able to put the ball on different locations that he wants, to manipulate spacing.
Toyota's basketball robot is getting pretty good. The robot, Cue, was the subject of a demonstration during a Japanese basketball game on Thursday, where it attempted to land shots from outside the three-point line. From near the halfway line, Cue makes pretty light work of what is quite a difficult shot. As per the Associated Press, to make the shot, Cue "computes as a three-dimensional image where the basket is, using sensors on its torso, and adjusts motors inside its arm and knees to give the shot the right angle and propulsion for a swish." While the robot has been touted as having perfect accuracy from free throws, longer distances can be a bit hit and miss, as you'll see in the full video of the demonstration.