Date: Wed, 12 Sep 2018 00:00:00 +0000
<p><a href="https://twitter.com/JeffDean">Jeff Dean</a>, the lead of Google AI, is on the podcast this week to talk with <a href="https://twitter.com/nyghtowl">Melanie</a> and <a href="https://twitter.com/Neurotic">Mark</a> 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.”</p> <p>Listen for the end of the podcast where our colleague, <a href="https://twitter.com/gabeweiss_">Gabe Weiss</a>, 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.</p> <h5 id="jeff-dean">Jeff Dean</h5> <p><a href="https://twitter.com/JeffDean">Jeff Dean</a> 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.</p> <p>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.</p> <h5 id="cool-things-of-the-week">Cool things of the week</h5> <ul> <li>Google Dataset Search is in beta <a href="https://toolbox.google.com/datasetsearch">site</a></li> <li>Expanding our Public Datasets for geospatial and ML-based analytics <a href="https://cloud.google.com/blog/products/data-analytics/expanding-our-public-datasets-geospatial-and-ml-based-analytics"> blog</a> <ul> <li>Zip Code Tabulation Area (ZCTA) <a href="https://console.cloud.google.com/marketplace/details/bigquery-public-data/zipcode_area?pli=1"> site</a></li> </ul> </li> <li>Google AI and Kaggle Inclusive Images Challenge <a href="https://www.kaggle.com/c/inclusive-images-challenge">site</a></li> <li>We are rated in the top 100 technology podcasts on iTunes <a href="http://toppodcast.com/top-podcasts/?search_string=&search_cat=23&search_submit=Submit"> site</a></li> <li>What makes TPUs fine-tuned for deep learning? <a href="https://cloud.google.com/blog/products/ai-machine-learning/what-makes-tpus-fine-tuned-for-deep-learning"> blog</a></li> </ul> <h5 id="interview">Interview</h5> <ul> <li>Jeff Dean on Google AI <a href="http://ai.google/research/people/jeff">profile</a></li> <li>Deep Learning Indaba <a href="http://www.deeplearningindaba.com">site</a></li> <li>Google AI <a href="https://ai.google/">site</a></li> <li>Google AI in Ghana <a href="https://www.blog.google/around-the-globe/google-africa/google-ai-ghana/"> blog</a></li> <li>Google Brain <a href="https://ai.google/research/teams/brain">site</a></li> <li>Google Cloud <a href="https://cloud.google.com/">site</a></li> <li>DeepMind <a href="https://deepmind.com/">site</a></li> <li>Cloud TPU <a href="https://cloud.google.com/tpu/">site</a></li> <li>Google I/O Effective ML with Cloud TPUs <a href="https://www.youtube.com/watch?v=zEOtG-ChmZE">video</a></li> <li>Liquid cooling system <a href="https://datacenterfrontier.com/google-shifts-to-liquid-cooling-for-ai-data-crunching/"> article</a></li> <li>DAWNBench Results <a href="https://dawn.cs.stanford.edu/benchmark/">site</a></li> <li>Waymo (Alphabet’s Autonomous Car) <a href="https://waymo.com">site</a></li> <li>DeepMind AlphaGo <a href="https://deepmind.com/research/alphago/">site</a></li> <li>Open AI Dota 2 <a href="https://blog.openai.com/dota-2/">blog</a></li> <li>Moustapha Cisse <a href="http://moustaphacisse.com/">profile</a></li> <li>Sanjay Ghemawat <a href="https://ai.google/research/people/SanjayGhemawat">profile</a></li> <li>Neural Information Processing Systems Conference <a href="https://nips.cc">site</a></li> <li>Previous Podcasts <ul> <li>GCP Podcast Episode 117: Cloud AI with Dr. Fei-Fei Li <a href="https://www.gcppodcast.com/post/episode-117-cloud-ai-with-fei-fei-li/"> podcast</a></li> <li>GCP Podcast Episode 136: Robotics, Navigation, and Reinforcement Learning with Raia Hadsell <a href="https://www.gcppodcast.com/post/episode-136-robotics-navigation-and-reinforcement-learning-with-raia-hadsell/"> podcast</a></li> <li>TWiML & AI Systems and Software for ML at Scale with Jeff Dean <a href="https://twimlai.com/twiml-talk-124-systems-software-machine-learning-scale-jeff-dean/"> podcast</a></li> </ul> </li> <li>Additional Resources <ul> <li>arXiv.org <a href="https://arxiv.org/">site</a></li> <li>Chris Olah <a href="http://colah.github.io">blog</a></li> <li>Distill Journal <a href="https://distill.pub">site</a></li> <li>Google’s Machine Learning Crash Course <a href="https://developers.google.com/machine-learning/crash-course/">site</a></li> <li>Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville <a href="https://mitpress.mit.edu/books/deep-learning">book</a> and <a href="https://www.deeplearningbook.org/">site</a></li> <li>NAE Grand Challenges for Engineering <a href="http://www.engineeringchallenges.org/challenges.aspx">site</a></li> <li>Senior Thesis Parallel Implementations of Neural Network Training: Two Back-Propagation Approaches by Jeff Dean <a href="https://drive.google.com/file/d/1I1fs4sczbCaACzA9XwxR3DiuXVtqmejL/view"> paper</a> and <a href="https://twitter.com/JeffDean/status/1033054187742015489">tweet</a></li> <li>Machine Learning for Systems and Systems for Machine Learning <a href="http://learningsys.org/nips17/assets/slides/dean-nips17.pdf">slides</a></li> </ul> </li> </ul> <h5 id="question-of-the-week">Question of the week</h5> <p>How do I get data from IoT core to display in real time on a web front end?</p> <ul> <li>Building IoT Applications on Google Cloud <a href="https://www.youtube.com/watch?v=RYaprBSDy8A">video</a></li> <li>MQTT <a href="http://mqtt.org">site</a></li> <li>Cloud Pub/Sub <a href="https://cloud.google.com/pubsub/">site</a></li> <li>Cloud Functions <a href="https://cloud.google.com/functions/">site</a></li> <li>Cloud Firestore <a href="https://cloud.google.com/firestore/">site</a></li> </ul> <h5 id="where-can-you-find-us-next">Where can you find us next?</h5> <p>Melanie is at <a href="http://www.deeplearningindaba.com">Deep Learning Indaba</a> and Mark is at <a href="https://cloud.withgoogle.com/next18/tokyo">Tokyo NEXT</a>. We’ll both be at <a href="https://www.thestrangeloop.com">Strangeloop</a> end of the month.</p> <p>Gabe will be at <a href="https://cloud.withgoogle.com/next18/london">Cloud Next London</a> and the <a href="https://www.iotsworldcongress.com/the-event/visit-passes/">IoT World Congress</a>.</p>
