Through advanced computer vision and machine learning capabilities, … Second Spectrum is the world leader in building machines that understand …

Fast and accurate, that is what most sports are about. Although when computer vision has been around for many years, not many people in sports seem to be aware of its values, feasibility and applications to the real stadium and the world. Computer Vision (CV) is a subfield of artificial intelligence and machine learning that develops techniques to train computers to interpret and understand the contents inside images. Computer Vision aims to replicate parts of the complexities in the human visual system and visual perception by applying deep learning models to accurately detect and classify objects from the dynamic and varying physical world. Many types of sports are often multidimensional systems that incorporate a plethora of data points that make one team or athlete better than the other.

Has it changed sports already? On this episode of StarTalk Sports Edition, Neil deGrasse Tyson and co-hosts Gary O'Reilly and Chuck Nice investigate the rise of AI in sports. To kick things off, Noah Syken, Vice President of Sports and Entertainment Partnerships at IBM, joins the show to take us inside the lab at IBM. You'll learn about IBM's long history in sports. We discuss their current projects partnering with Wimbledon and the US Open. You'll hear how IBM is taking classic tennis matches and updating them to current broadcast standards using artificial intelligence.

The Clippers and Second Spectrum will use AWS machine learning and data analytics services to advance game analyses and drive new experiences for Clippers CourtVision, which launched to great acclaim at the start of the 2018-19 basketball season and has been billed by experts as the future of sports viewing. In addition, Clippers CourtVision will test Amazon SageMaker to build, train, and deploy machine learning-driven stats which will appear on live broadcasts and on-demand NBA game videos. Second Spectrum uses cameras in all 29 NBA arenas to collect 3D spatial data including ball and player locations and movements, which is stored and analyzed on AWS in real time. With help from AWS's broad range of services, Second Spectrum uses that data to generate augmented graphical overlays on Clippers broadcasts in real time, offering users an array of content options and Clippers CourtVision Modes with features ranging from live layouts of basketball plays, to the frame-by-frame probability of a shot going in, to a suite of graphics that animate based on conditions both simple and complex, giving fans a deeper understanding of and interaction with the game as the action unfurls on the court. Clippers CourtVision uses AWS Elemental Media Services to deliver the live game-watching experience.

The Los Angeles Clippers and Second Spectrum have partnered to bring a fan engagement tool that helps those watching at home to have the option to see the game like never before. During live action, stats and fantasy points appear above each player as the basket is scored off an alley-oop. There are animation options that enable users to make a custom highlight of Blake Griffin dunking a cloud as lightning strikes. A screen can be automatically identified. Recommended and trending highlights are available on-demand.

Last week the Data Science community had a major event – Strata Hadoop World 2017 at San Jose. The over-arching theme across all talks and the expo was that the focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand. Even as the limelight shifts from Big Data to Machine Learning (and more advanced aspects like Deep Learning), from an implementation perspective, distributed and scalable processing stays dominant as most of the popular ML models have an insatiable appetite for input data and compute power. Mike Olson, CSO and Chairman, Cloudera talked about "The machine-learning renaissance". Most of the fundamental techniques for ML and AI were invented in 1960s and 1970s.