If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The movie Moneyball, among many things, can be considered as the prime example of data-driven performance optimization in sports. For those who haven't watched the movie or read the book it is based on, it depicts the story of how the Oakland Athletics' general manager, Billy Beane, used statistical data and analytics to build a competitive team despite the team's small budget. His team, which was assembled by analyzing individual statistics of players, data mainly acquired free, went on to have an unexpectedly prolific season and reached unprecedented heights. During the historic season of 2002, the Oakland Athletics competed with and held their own against the best teams in Major League Baseball (MLB), whose budgets far outweighed their own. The team's achievements -- the most remarkable being their famous 20-game winning streak -- showed how a data-driven approach can, to a great extent, compensate for a lack of resources and enhance performance by enabling effective decision-making.
Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However, to the best of our knowledge, no previous work has ever succeeded in applying DRL to multi-agent problems with discrete-continuous hybrid (or parameterized) action spaces which is very common in practice. Our work fills this gap by proposing two novel algorithms: Deep Multi-Agent Parameterized Q-Networks (Deep MAPQN) and Deep Multi-Agent Hierarchical Hybrid Q-Networks (Deep MAHHQN). We follow the centralized training but decentralized execution paradigm: different levels of communication between different agents are used to facilitate the training process, while each agent executes its policy independently based on local observations during execution. Our empirical results on several challenging tasks (simulated RoboCup Soccer and game Ghost Story) show that both Deep MAPQN and Deep MAHHQN are effective and significantly outperform existing independent deep parameterized Q-learning method.
Zone7 announced last month it had raised $2.5 million in seed funding from a Resolute Ventures-led group, which also included Amicus Capital Partners, PLG Ventures, UpWest Labs, Dave Pell, PLG Ventures and UpWest, as well as former and current athletes, including the National Basketball Association's Kristaps Porzingis. Zone7, a 10-person startup, said Major League Baseball, professional soccer teams and several college programs are using its system, although the company declined to name the teams. However, the Maccabi Tel Aviv Football Club, an Israeli pro team, has tested the technology, according to Jordi Cruyff, who was the team's sports director from 2012 to 2017. Mr. Cruyff is also a former player and one-time member of some of the biggest teams in the sport, including FC Barcelona and Manchester United Football Club. With Maccabi, Mr. Cruyff said he would receive daily alerts from Zone7 that certain players were at high risk.
With the help of assistive technology, a man from Wolverhampton who lost his voice as a child has been able to speak again in his Black Country accent. Jack Smith, 22, had felt that the'posh and boring' voice that he had been previously given did not represent his roots. The new voice was created by inputting hundreds of recordings from someone with an accent similar to Mr Smith's into a communication aid software. As a Wolverhampton Wanderers (Wolves) Football Club fan, Jack's accent has been showcased to his football team when he read out the team names at a Premier League match win. With the help of assistive technology, a man from Wolverhampton who lost his voice as a child has been able to speak in his Black Country accent.
Having notched impressive victories over human professionals in Go, Atari Games, and most recently StarCraft 2 -- Google's DeepMind team has now turned its formidable research efforts to soccer. In a paper released last week, the UK AI company demonstrates a novel machine learning method that trains a team of AI agents to play a simulated version of "the beautiful game." Gaming, AI and soccer fans hailed DeepMind's latest innovation on social media, with comments like "You should partner with EA Sports for a FIFA environment!" Machine learning, and particularly deep reinforcement learning, has in recent years achieved remarkable success across a wide range of competitive games. Collaborative-multi-agent games however remained a relatively difficult research domain.
As the staff at Big D Soccer prepares to release our personal predictions for the coming season, I thought it would be an interesting experiment to release a machine learning series. Today will be the first of (hopefully) many articles on machine learning and soccer throughout the season, and we are starting with season-long predictions. First I will break down the very basics of machine learning, then we will tackle what the predictions are, what might change, and why. Well here is a great starting point. Machine learning refers to the algorithms and methods that build models to predict and reflect data.
In that time the national team has won the world cup (2010), and the winner of the UEFA Champions League has originated from the Iberian nation. Spanish teams have also won the last five champions league finals Barcelona in 2015 and Real Madrid in 2014 and 2016 - 2018. The quality of the players, coaches and the global appeal of the league has now been combined with a variety of technology that use artificial intelligence to keep Spanish teams ahead of the competition. The sustained success has inspired other leagues - including the English Premier League - to follow suit. La Liga gave MailOnline a look behind the scenes at Espanyol's RCDE stadium as part of Mobile World Congress in Barcelona to commemorate the league's 90th anniversary.
The MLS app has been developed in-house because the league fashioned a devoted engineering and improvement crew final yr, and senior vp of media Chris Schlosser mentioned one of many advantages of maintaining the method in-house is the flexibility to push month-to-month releases all year long to make sure that the app is repeatedly up to date. The app was constructed utilizing React Native expertise, which enabled MLS' crew to construct one app throughout iOS, Android and the net. Schlosser mentioned MLS makes use of information from the again finish of the app, in addition to buyer analysis and fan panels, when crafting updates. The Stay Your Colours spot was produced in English and Spanish, and it'll run on MLS associate networks together with ESPN, Fox Sports activities, Univision, TSN and TVA Sports activities, in addition to throughout worldwide broadcast associate platforms and on the league's personal digital channels. Stay Your Colours relies on one of many first issues followers see upon downloading the MLS app: the flexibility to decide on their favourite membership, after which the complete app expertise is personalised based mostly on that membership's colours.
It is not uncommon nowadays for teams at every professional level to have an analytics department, and to have coaching staff that rely on analytics to make informed decisions. Here on we touched on the rise of sports analytics in a previous post, where we defined it as the use of data to build predictive models for informed decision making. Now, imagine sports analytics today. Then throw in artificial intelligence (AI), into the mix. The results are a system in which massive data sets can be analysed quickly and accurately.
We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents' behaviors: from random, to simple ball chasing, and finally showing evidence of cooperation. Our study highlights several of the challenges encountered in large scale multi-agent training in continuous control. In particular, we demonstrate that the automatic optimization of simple shaping rewards, not themselves conducive to co-operative behavior, can lead to long-horizon team behavior. We further apply an evaluation scheme, grounded by game theoretic principals, that can assess agent performance in the absence of pre-defined evaluation tasks or human baselines.