OCTOBER 2018CIOAPPLICATIONSEUROPE.COM9for better graphics at higher resolutions and higher frame rates. The parallel compute capability of these cards aligned well with the needs of neural network models. A modern GPU (available for less than $700) may have more than 3,500 cores, contrasted with the four that are present in a typical laptop CPU.In 2012, a team led by Geoff Hinton used a deep convolutional neural network running on a pair of NVIDIA GPUs to win the ILSVRC competition, classifying100k images from ImageNet into 1,000 categories after training on 1.2m category labeled images. (The term "deep" indicates that the network has several layers, eight in this case, and "convolutional" that the network has a specific topology well suited to images and other continuous data like text, audio and video). This combination of algorithmic advances, a very large dataset and the use of GPUs finally brought together the ingredients required to trigger the current "AI Spring".The Eternal AI SpringThe broad applicability of machine learning approaches has led to its current domination of the field of artificial intelligence (AI), a field of study that has had a number of booms and busts since its inception in 1956. We refer to the busts as "AI Winters", where funding for academic research dries up after bold claims fail to materialise. The current "AI Spring" is reflected in the appearance of both "machine learning" and "deep learning" at the very peak of the "Peak of Inflated Expectations" in Gartner's 2017 Emerging Technologies Hype Cycle, just waiting to crash into their "Trough of Disillusionment".However, Andrew Ng (adjunct professor at Stanford University, founder of Google's first AI group, Google Brain, and former lead of Baidu's AI group) believes that we've now entered an "eternal AI Spring". This is based on both the impact machine learning is currently having outside of academia across a range of industries, and the clear roadmap for changing almost all other industries, just with current technology.Genius SportsGenius Sports have established a horisontal Machine Learning function, offering services to all Group business units. This team is currently working on a number of projects using Amazon Web Services cloud infrastructure, which includes GPU support (Microsoft and Google also have strong cloud offerings). Cloud computing is well suited to machine learning work, as the compute requirement during training can be significant, often requiring hours of compute time. Conversely, a trained model can be executed in milliseconds.The appetite from machine learning teams for GPU hardware, combined with that from the original video game and the newer virtual/augmented reality markets, and GPUs applicability to blockchain "mining", bode well for further performance and capacity gains in this area.The Genius Sports team are also benefiting from three forms of open source technology:1. Programming languages, specifically Python and R (and their comprehensive, high-performance numerical libraries)2. High-quality, interoperable frameworks from major vendors (e.g. Google's TensorFlow and Facebook's support for PyTorch)3. Large datasets and trained modelsThis mass democratisation of machine learning technology, and the valuable results that can be achieved by skilled practitioners, makes this an area that is well worth investing in for businesses with, or with the capability to acquire, the necessary data assets.
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