AI in Industry - Lessons from 50+ Companies and Example Projects

Recent progress in deep learning has created a compelling opportunity for scientists and engineers with machine learning skills who are looking to join data teams at fast growing tech companies. These roles, focusing on advanced algorithms and leveraging new research results for sophisticated products, are a core component in many organizations, from large corporations to early-stage startups. Unfortunately, leading applied AI teams often seek practical evidence that goes beyond the machine learning and computer science fundamentals that many academic researchers and engineers gain in their current roles.

This talk will focus on strategies that engineers and researchers, who are not currently working in the field of artificial intelligence, can use to break into this cutting-edge industry. These lessons come from speaking with over 50 top Applied AI teams all over the Bay Area and New York, who come to Insight to find Applied AI practitioners. We'll discuss the best practices of several top data teams, as well as the methods Insight Fellows use to rapidly build impressive deep learning modules and products in the span of four weeks, highlighting specific approaches you can take to leverage your experience in machine learning and engineering to become a successful member of one of these teams.

Speakers:

Since 2012, Insight (http://insightdatascience.com/) has helped more than 1000 Insight Fellows join data teams at companies including Facebook, Tesla, Uber, Airbnb, LinkedIn, Netflix, and top startups. A year ago, Insight launched the Insight Artificial Intelligence (http://insightdata.ai/) Fellows program, tailored specifically to scientists and professional engineers (no PhD required) who want to join top companies working on deep learning and artificial intelligence.

Jeremy Karnowski

Jeremy (  / jeremykarnowski  ) has a background in applying machine learning and deep learning to audio and video data to understand social behavior. His Ph.D. work in the Department of Cognitive Science at UC San Diego also included modeling human decision making with Bayesian probability theory and control theory. He has guided the full development lifecycle of 100+ data products, many using advanced ML and deep learning techniques, providing technical feedback and guidance on algorithmic design. He is currently Insight’s AI Lead in Silicon Valley.

Emmanuel Ameisen

Emmanuel (  / ameisen  ) has a profile at the intersection of Artificial Intelligence and business, having earned an Msc. in Artificial Intelligence, an Msc. in Computer Engineering, and an Msc. in Management from three of France’s top schools. Recently, Emmanuel has worked implementing and scaling out predictive analytics and machine learning solutions for Local Motion and Zipcar. He has years of experience going from product ideation to effective implementations. He is currently an AI Program Director and Machine Learning Engineer at Insight.