29 min listen
Google AI with Jeff Dean
ratings:
Length:
44 minutes
Released:
Sep 12, 2018
Format:
Podcast episode
Description
Jeff Dean, the lead of Google AI, is on the podcast this week to talk with Melanie and Mark about AI and machine learning research, his upcoming talk at Deep Learning Indaba and his educational pursuit of parallel processing and computer systems was how his career path got him into AI. We covered topics from his team’s work with TPUs and TensorFlow, the impact computer vision and speech recognition is having on AI advancements and how simulations are being used to help advance science in areas like quantum chemistry. We also discussed his passion for the development of AI talent in the content of Africa and the opening of Google AI Ghana. It’s a full episode where we cover a lot of ground. One piece of advice he left us with, “the way to do interesting things is to partner with people who know things you don’t.”
Listen for the end of the podcast where our colleague, Gabe Weiss, helps us answer the question of the week about how to get data from IoT core to display in real time on a web front end.
Jeff Dean
Jeff Dean joined Google in 1999 and is currently a Google Senior Fellow, leading Google AI and related research efforts. His teams are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google’s crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google’s initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google’s distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools.
Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing.
Cool things of the week
Google Dataset Search is in beta site
Expanding our Public Datasets for geospatial and ML-based analytics blog
Zip Code Tabulation Area (ZCTA) site
Google AI and Kaggle Inclusive Images Challenge site
We are rated in the top 100 technology podcasts on iTunes site
What makes TPUs fine-tuned for deep learning? blog
Interview
Jeff Dean on Google AI profile
Deep Learning Indaba site
Google AI site
Google AI in Ghana blog
Google Brain site
Google Cloud site
DeepMind site
Cloud TPU site
Google I/O Effective ML with Cloud TPUs video
Liquid cooling system article
DAWNBench Results site
Waymo (Alphabet’s Autonomous Car) site
DeepMind AlphaGo site
Open AI Dota 2 blog
Moustapha Cisse profile
Sanjay Ghemawat profile
Neural Information Processing Systems Conference site
Previous Podcasts
GCP Podcast Episode 117: Cloud AI with Dr. Fei-Fei Li podcast
GCP Podcast Episode 136: Robotics, Navigation, and Reinforcement Learning with Raia Hadsell podcast
TWiML & AI Systems and Software for ML at Scale with Jeff Dean podcast
Additional Resources
arXiv.org site
Chris Olah blog
Distill Journal site
Google’s Machine Learning Crash Course site
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville book and site
NAE Grand Challenges for Engineering site
Senior Thesis Parallel Implementations of Neural Network Training: Two Back-Propagation Approaches by Jeff Dean paper and tweet
Machine Learning for Systems and Systems for Machine Learning slides
Question of the week
How do I get data from IoT core to display in real time on a web front end?
Building IoT Applications on Google Cloud
Listen for the end of the podcast where our colleague, Gabe Weiss, helps us answer the question of the week about how to get data from IoT core to display in real time on a web front end.
Jeff Dean
Jeff Dean joined Google in 1999 and is currently a Google Senior Fellow, leading Google AI and related research efforts. His teams are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google’s crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google’s initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google’s distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools.
Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing.
Cool things of the week
Google Dataset Search is in beta site
Expanding our Public Datasets for geospatial and ML-based analytics blog
Zip Code Tabulation Area (ZCTA) site
Google AI and Kaggle Inclusive Images Challenge site
We are rated in the top 100 technology podcasts on iTunes site
What makes TPUs fine-tuned for deep learning? blog
Interview
Jeff Dean on Google AI profile
Deep Learning Indaba site
Google AI site
Google AI in Ghana blog
Google Brain site
Google Cloud site
DeepMind site
Cloud TPU site
Google I/O Effective ML with Cloud TPUs video
Liquid cooling system article
DAWNBench Results site
Waymo (Alphabet’s Autonomous Car) site
DeepMind AlphaGo site
Open AI Dota 2 blog
Moustapha Cisse profile
Sanjay Ghemawat profile
Neural Information Processing Systems Conference site
Previous Podcasts
GCP Podcast Episode 117: Cloud AI with Dr. Fei-Fei Li podcast
GCP Podcast Episode 136: Robotics, Navigation, and Reinforcement Learning with Raia Hadsell podcast
TWiML & AI Systems and Software for ML at Scale with Jeff Dean podcast
Additional Resources
arXiv.org site
Chris Olah blog
Distill Journal site
Google’s Machine Learning Crash Course site
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville book and site
NAE Grand Challenges for Engineering site
Senior Thesis Parallel Implementations of Neural Network Training: Two Back-Propagation Approaches by Jeff Dean paper and tweet
Machine Learning for Systems and Systems for Machine Learning slides
Question of the week
How do I get data from IoT core to display in real time on a web front end?
Building IoT Applications on Google Cloud
Released:
Sep 12, 2018
Format:
Podcast episode
Titles in the series (100)
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