Abstract: 

Over the past decade, we have seen an almost unbelievable rise in the power and influence of data analysis and the broader data community, touching an incredibly broad swath of business and society. We have seen data-driven decision making has gone from fringe to mainstream to the default, and companies at the forefront of data science, like Google and Facebook, have moved towards the top of the S&P 500. Recently, we have had to confront the negative consequences that a myopic focus on metrics and models have had on society, and a spirit of optimism and potential around machine learning and artificial intelligence has given way towards a sense of foreboding and even fear at the consequences of what we do next. I would like to talk about how we go forward from here with a sense of humility about the limits of our ability to understand both the inner workings of our deep learning models and the complexity of the world at large, informed by the perspectives of Berkshire Hathaway, Michel de Montaigne, and the Houston Astros.

Bio: 

Josh Wills is an engineer on Slack's Search, Learning, and Intelligence Team helping to build the company's production search and machine learning infrastructure. He's a recovering manager, having most recently built and led Slack's data engineering team and before that the data science and engineering teams at Cloudera and Google. Josh is a member of the Apache Software Foundation, the founder of the Apache Crunch project, and a co-author of O'Reilly's Advanced Analytics with Spark. In May of 2012, he tweeted a pithy definition of a data scientist as someone who is better at statistics than any software engineer and better at software engineering than any statistician, and his Twitter mentions have never been the same.