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### Robot umpires? Let's leave baseball to real, live human beings.

I have nothing against progress. Some of my best friends are traveling shoe salesmen, and I can't tell you how many times my stone hand ax has come in handy around the cave. But I can't shake the feeling we've gone a tad too far with technology. The latest assault on our humanity came Thursday, when news broke that Major League Baseball would use an automated strike zone at Triple-A this season. It means robot umpires will be one heartbeat from the big leagues -- a ''heartbeat'' being that thing once used to deduce whether a ''person'' was alive.

### Computing an Optimal Pitching Strategy in a Baseball At-Bat

The field of quantitative analytics has transformed the world of sports over the last decade. To date, these analytic approaches are statistical at their core, characterizing what is and what was, while using this information to drive decisions about what to do in the future. However, as we often view team sports, such as soccer, hockey, and baseball, as pairwise win-lose encounters, it seems natural to model these as zero-sum games. We propose such a model for one important class of sports encounters: a baseball at-bat, which is a matchup between a pitcher and a batter. Specifically, we propose a novel model of this encounter as a zero-sum stochastic game, in which the goal of the batter is to get on base, an outcome the pitcher aims to prevent. The value of this game is the on-base percentage (i.e., the probability that the batter gets on base). In principle, this stochastic game can be solved using classical approaches. The main technical challenges lie in predicting the distribution of pitch locations as a function of pitcher intention, predicting the distribution of outcomes if the batter decides to swing at a pitch, and characterizing the level of patience of a particular batter. We address these challenges by proposing novel pitcher and batter representations as well as a novel deep neural network architecture for outcome prediction. Our experiments using Kaggle data from the 2015 to 2018 Major League Baseball seasons demonstrate the efficacy of the proposed approach.

### Invasion of the Robot Umpires

Grown men wearing tights like to yell terrible things at Fred DeJesus. DeJesus is an umpire in the outer constellations of professional baseball, where he's been spat on and, once, challenged to a postgame fight in a parking lot. He was born in Bushwick, Brooklyn, to Puerto Rican parents, stands five feet three, and is shaped, in his chest protector, like a fire hydrant; he once ejected a player for saying that he suffered from "little-man syndrome." Two years ago, DeJesus became the first umpire in a regular-season game anywhere to use something called the Automated Ball-Strike System. Most players refer to it as the "robo-umpire."

### Robot umps and dogs, minor league ball back after lost year

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. It took just four batters at George Steinbrenner Field before a fan yelled "C'mon, blue!" toward home plate umpire Kaleb Devier after two consecutive close pitches were called balls. Never mind that a computer was making the calls. Didn't matter on Tuesday night as the Tampa Tarpons took on the Dunedin Blue Jays.

### AI (Artificial Intelligence) Lessons From The World Series

Snell, second from left, comes out of the game against the Dodgers in the 6th inning in Game 6 of the World Series at Globe Life Field on October 27, 2020 in Arlington, Texas. I'm a lifelong Dodgers fan and I waited for 32 years for the team to win another World Series. But during this period of time, the sport has certainly seen much change. With the availability of huge amounts of data, sophisticated computers and advanced analytics, the strategies have become increasingly based on the numbers. It seems that AI (Artificial Intelligence) has dominated the decision making process.

### AI (Artificial Intelligence) Lessons From The World Series

ARLINGTON, TEXAS - OCTOBER 27:Rays pitcher Blake Snell, second from left, comes out of the game ... [ ] against the Dodgers in the 6th inning in Game 6 of the World Series at Globe Life Field on October 27, 2020 in Arlington, Texas. I'm a lifelong Dodgers fan and I waited for 32 years for the team to win another World Series. But during this period of time, the sport has certainly seen much change. With the availability of huge amounts of data, sophisticated computers and advanced analytics, the strategies have become increasingly based on the numbers. It seems that AI (Artificial Intelligence) has dominated the decision making process.

### The AI Lords Of Sports: How The SportsTech Is Changing Business World

It is the time of the fall classic, Major League Baseball's World Series. As the two best teams vie for the championship this year, there are some actors in the game beyond the players, coaches, umpires (or referees), and fans… namely big data, analytics, and artificial intelligence. These new actors are also highly prevalent in football, basketball, and hockey, and they are changing these games forever. Sports foray into technology and data really got its start in 2002 with the Oakland Athletics. General Manager Billy Beane and Assistant GM Paul DePodesta would pioneer sabermetrics, which is a new perspective on baseball analytics.

### How Will Artificial Intelligence Change the World of Sports? - ReadWrite

Today, the technological landscape is expanding by all leaps and bounds, and Artificial Intelligence (AI) remains in the thick of it. A technology that is one for the present and future, AI is playing a massive role in shaping businesses to the core. From healthcare and entertainment to commerce and sports, Artificial Intelligence is transforming every industrial vertical for good. Here is how artificial intelligence will change the world of sports. Speaking of the sports industry itself, the presence of AI today is to be seen in just about every major league around the world.

### AI bot predicts World Series winners

America has been glued to their TV screens since the MLB playoffs began on October 1. As the field has whittled down to just four teams, odds makers are eager to figure out which team has the edge. Researchers at DataRobot thought it would be a fun exercise to pull all of the MLB data from the last few decades and have their AI figure out who will win the 2019 World Series. SEE: Artificial intelligence: A business leader's guide (free PDF) (TechRepublic Premium) At the start of the playoffs, the AI predicted the Los Angeles Dodgers were most likely to win the pennant, followed closely by the Houston Astros. In the American League, DataRobot's AI said the Houston Astros had a 40% probability of winning the American League, followed by the New York Yankees at 25% and Minnesota Twins at 18%.

### An Interview with Sophia the Robot

The opening keynote for DevLearn 2019 Conference & Expo was beautiful, poised, and … a robot. Sophia was created by Dr. David Hanson of Hong Kong-based Hanson Robotics. In a conversation with The eLearning Guild's executive director and executive vice president David Kelly, Sophia spoke about artificial intelligence and its impact on work and society. "What really excites me is the opportunity to dispel some common misconceptions humans have about artificial intelligence," said Sophia, who was draped in a black garment and spoke in an eerily polite, feminine voice. "The first is the assumption the AI conversation is about robots. Artificial intelligence is affecting many different aspects of life. Most of us are interacting with AI every day without even realizing it."