In this podcast Mike Tamir (@MikeTamir, Head of #DataScience) talked about building a data science AI team. He shared his AI project (FakerFact.org). He shared the lifecycle of an AI project and some things that leaders could keep in mind to help create a successful data science AI team. This podcast is great for leaders learning to build a strong AI workforce.

TIMELINE: 0:28 Micheal's journey. 2:36 Micheal's current role. 3:18 AI and businesses. 5:28 Parameters to consider for AI adoption. 9:30 When do businesses invest in ML resources. 13:20 Tips for candidates in vetting data companies. 16:05 What's the faker fact? 20:45 Getting started on an AI product design. 24:58 Achieving accuracy in data. 27:40 AI the newsmaker and AI the fact-checker. 33:56 Tips for hiring the right data leader for a business. 35:32 Creating a great data science team. 37:19 Challenges in forming a data science team. 39:00 In job training to achieve technological competence. 44:00 Ingredients of a good hire. 47:35 Micheal's secret to success. 50:55 Micheal's favorite reads. 54:20 Key takeaways.

Mike's Recommended Read: What Technology Wants by Kevin Kelly https://amzn.to/2MaNiuN Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville http://www.deeplearningbook.org/

Podcast Link: https://futureofdata.org/building-data-science-ai-teams-by-miketamir-uberatg-futureofdata-podcast/

Mike's BIO: Mike serves as Head of Data Science at Uber ATG, UC Berkeley Data Science faculty, and head of Phronesis ML Labs. He has led teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale/Sears, and CSO for Galvanize, where he founded the galvanizeU-UNH accredited Masters of Science in Data Science degree and oversaw the company's transformation from co-working space to Data Science organization. Mike's most recent passion in research has involved applying Machine Learning techniques to help combat fake news through the FakerFact.org project

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The scale and frequency with which information can be distributed on social media makes the problem of fake news a rapidly metastasizing issue. To do any content filtering or labeling demands an algorithmic solution. In today's episode, Kyle interviews Kai Shu and Mike Tamir about their independent work exploring the use of machine learning to detect fake news. Kai Shu and his co-authors published Fake News Detection on Social Media: A Data Mining Perspective, a research paper which both surveys the existing literature and organizes the structure of the problem in a robust way. Mike Tamir led the development of fakerfact.org, a website and Chrome/Firefox plugin which leverages machine learning to try and predict the category of a previously unseen web page, with categories like opinion, wiki, and fake news.