AI in Action E10: Mike Tamir, Head of Data Science at Uber ATG


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Welcome to episode ten of AI in Action, the podcast that breaks down the hype and explores the impact that Data Science, Machine Learning and Artificial Intelligence are making on our everyday lives.

Powered by Alldus International, our goal is to share with you the insights of technologists and data science enthusiasts to showcase the excellent work that is being done within AI in the United States.

Today’s guest on the show is Mike Tamir, Head of Data Science at Uber ATG in San Francisco. Uber are working to bring the future closer with self-driving technology and urban air transport. They also help people order food quickly and affordably, remove barriers to healthcare, create new freight-booking solutions and help companies provide a seamless employee travel experience.

Mike is a leader in Data science, specializing in deep learning and distributed scalable machine learning. He has a wealth of experience delivering data products for use cases including text comprehension, image recognition, recommender systems, targeted advertising and customer analytics. He is a pioneer in developing machine learning and data science training programs in the industry.

In the show today, Mike will tell you about:

  • How he became interested in Data Science
  • His role at Uber ATG
  • Applying Deep Learning to evaluate article embedding in fake news evaluation
  • Learning and sharing interesting content on social media
  • Advice he would share to Data Scientists moving from academia to industry
  • Emerging trends in AI and Data Science that excite him for the future

If you would like to read more about Mike and his work at Uber, you can head over to or connect with him on Linkedin and Twitter @MikeTamir.

What did you make of Mike’ podcast? Where do you see the future of Data Science and Deep Learning heading in the next few years? We would love to hear your thoughts so please leave a comment below.

If you would like to hear more from AI in Action then please subscribe and don’t forget to like and share with your friends on social media.

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30 episodes available. A new episode about every 6 days averaging 19 mins duration .