Summary: Data scientists in both industry and academia have been using GPUs for AI and machine learning to make groundbreaking improvements across a variety of applications including image classification, video analytics, speech recognition and natural language processing. In particular, Deep Learning – the use of sophisticated, multi-level "deep" neural networks to create systems that can perform feature detection from massive amounts of unlabeled training data – is an area that has been seeing significant investment and research. Although AI has been around for decades, two relatively recent trends have sparked widespread use of Deep Learning within AI: the availability of massive amounts of training data, and powerful and efficient parallel computing provided by GPU computing. Early adopters of GPU accelerators for machine learning include many of the largest web and social media companies, along with top tier research institutions in data science and machine learning. With thousands of computational cores and 10-100x application throughput compared to CPUs alone, GPUs have become the processor of choice for processing big data for data scientists.
Bio: David Williams is a Solutions Architect for NVIDIA, working with Enterprise and Startup companies over the Southeastern section of the United States. Born and raised in Houston, Texas, David left the South for the freezing shores of Lake Michigan to attend Northwestern University for his Bachelor's and Master's degrees in Computer Engineering. Making a much needed return to warmer climates, David moved to Chapel Hill after graduation and joined NVIDIA. Solution Architects serve as customer engineering resources, investigating questions and evaluating proof of concepts for companies interested in NVIDIA technology. As NVIDIA has become the leading artificial intelligence company, the technical challenges faced in this new market cover topics of GPU hardware, system software, data engineering, datacenter architecture, deep learning frameworks, and neural network data science. David is excited to discuss the key drivers and introductory concepts of the world of artificial intelligence and deep learning.