As the viewing habits of sports fans continue to shift, more and more sports leagues have attempted to give consumers new and innovative ways to engage with players and games. A number of NBA fans use the league's mobile app to watch their favorite team. Given the growing popularity of its digital platforms, the NBA is tapping into artificial intelligence to enhance fans' viewing experience. During February's Tech Summit, NBA Commissioner Adam Silver demonstrated the AI technology that will likely alter the way fans interact with games whenever they opt to experience the league with the app. "There is a need to see the NBA making strides to use AI," Alex Beckman told Fox News Digital.

Sports analytics is one of the biggest sectors booming in the sports world. Though sports have captured the attention of the public and investors alike, analytics is a behind-the-scenes industry that combines the latest in machine-learning algorithms and data crunching. Some programs, like those that rely on AI, are designed to make predictions by studying huge amounts of historical data. Others, such as analytics software, are designed to make immediate conclusions from live data points. Teams rely on sports analytics to make leaner decisions related to recruitment, training regimens, and more.

"Fallout 76" was a buggy, listless wasteland and became an industry laughingstock. "Anthem" barely got off the ground. Recent updates to "Red Dead Online" are causing players to lose faith as they're nickel-and-dimed for dirty clothes and dances. It's a dark coincidence that a video game about abandoned and lost communities could be host to millions of adrift gamers, outraged and disappointed by the recent dud parade of massive, open-world online shooters. In Washington, where the game is set, Ubisoft held a preview event for "The Division 2," the sequel to the company's most successfully launched game ever.

Teaching robots about objects in their environment requires a multimodal correlation of images and linguistic descriptions to build complete feature and object models.  These models can be created manually by collecting images and related keywords and presenting the pairings to robots, but doing so is tedious and unnatural.  This work abstracts the problem of training robots to learn about the world around them by introducing I Spy , an interactive dialogue- and vision-based game in which players place objects in front of a humanoid robot and challenge it to guess which object they have in mind.  The robot gradually learns about the objects and the features which describe them through repeated games, by updating its knowledge with newly captured training images.  This paper details I Spy's learning and gaming processes, describes the approaches taken to extract information from multiple modalities both before and during gameplay, and finally discusses the results of a study designed to evaluate the game's model accuracy over time, its overall performance, and its appeal to human players.