ACM Fellow Simon Fraser University, Canada, ACM KDD Chair, ACM Fellow
加拿大大数据科学研究中心主席，Simon Fraser大学计算机科学学院教授，数据科学、大数据、数据挖掘和数据库系统等领域的著名研究者，专长是为数据密集型应用程序，提供高效的数据分析技术。 他作为ACM Fellow和IEEE Fellow，为“数据挖掘的基础，方法和应用”做出了巨大贡献。
Technical and Business Challenges
The bad news: Big data is not as sexy as it was 3 years ago. All in AI now! More often than not, to questions like how my business can effectively adopt the disruptive AI technologies, a simple answer is to collect and clean a lot of data. To make things even more frustrating, no one knows exactly how much data is needed. This talk will discuss a few important technical and business challenges in collecting and preparing data to enable AI applications, such as salability (though not in a traditional computer science sense!), unbiasedness, and elasticity. The good news: Big Data is the propellant for the AI rocket and high performance distributed computing is the combustion chamber. Most importantly, the big challenge for the near future: how to make AI including deep learning green.