Phil Brown — How IPUs are Advancing Machine Intelligence
Phil shares some of the approaches, like sparsity and low precision, behind the breakthrough performance of Graphcore's Intelligence Processing Units (IPUs). --- Phil Brown leads the Applications team at Graphcore, where they're building high-performance machine learning applications for their Intelligence Processing Units (IPUs), new processors specifically designed for AI compute. Connect with Phil: LinkedIn: https://www.linkedin.com/in/philipsbrown/ Twitter: https://twitter.com/phil_s_brown --- 0:00 Sneak peek, intro 1:44 From computational chemistry to Graphcore 5:16 The simulations behind weather prediction 10:54 Measuring improvement in weather prediction systems 15:35 How high performance computing and ML have different needs 19:00 The potential of sparse training 31:08 IPUs and computer architecture for machine learning 39:10 On performance improvements 44:43 The impacts of increasing computing capability 50:24 The ML chicken and egg problem 52:00 The challenges of converging at scale and bringing hardware to market Links Discussed: Rigging the Lottery: Making All Tickets Winners (Evci et al., 2019): https://arxiv.org/abs/1911.11134 Graphcore MK2 Benchmarks: https://www.graphcore.ai/mk2-benchmarks Check out the transcription and discover more awesome ML projects: http://wandb.me/gd-phil-brown --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google Podcasts: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Check out our Gallery, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/gallery
27 Touko 202157min
Alyssa Simpson Rochwerger — Responsible ML in the Real World
From working on COVID-19 vaccine rollout to writing a book on responsible ML, Alyssa shares her thoughts on meaningful projects and the importance of teamwork. --- Alyssa Simpson Rochwerger is as a Director of Product at Blue Shield of California, pursuing her dream of using technology to improve healthcare. She has over a decade of experience in building technical data-driven products and has held numerous leadership roles for machine learning organizations, including VP of AI and Data at Appen and Director of Product at IBM Watson. Connect with Sean: Personal website: https://seanjtaylor.com/ Twitter: https://twitter.com/seanjtaylor LinkedIn: https://www.linkedin.com/in/seanjtaylor/ --- Topics Discussed: 0:00 Sneak peak, intro 1:17 Working on COVID-19 vaccine rollout in California 6:50 Real World AI 12:26 Diagnosing bias in models 17:43 Common challenges in ML 21:56 Finding meaningful projects 24:28 ML applications in health insurance 31:21 Longitudinal health records and data cleaning 38:24 Following your interests 40:21 Why teamwork is crucial Transcript: http://wandb.me/gd-alyssa-s-rochwerger Links Discussed: My Turn: https://myturn.ca.gov/ "Turn the Ship Around!": https://www.penguinrandomhouse.com/books/314163/turn-the-ship-around-by-l-david-marquet/ --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google Podcasts: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected
20 Touko 202145min
Sean Taylor — Business Decision Problems
Sean joins us to chat about ML models and tools at Lyft Rideshare Labs, Python vs R, time series forecasting with Prophet, and election forecasting. --- Sean Taylor is a Data Scientist at (and former Head of) Lyft Rideshare Labs, and specializes in methods for solving causal inference and business decision problems. Previously, he was a Research Scientist on Facebook's Core Data Science team. His interests include experiments, causal inference, statistics, machine learning, and economics. Connect with Sean: Personal website: https://seanjtaylor.com/ Twitter: https://twitter.com/seanjtaylor LinkedIn: https://www.linkedin.com/in/seanjtaylor/ --- Topics Discussed: 0:00 Sneak peek, intro 0:50 Pricing algorithms at Lyft 07:46 Loss functions and ETAs at Lyft 12:59 Models and tools at Lyft 20:46 Python vs R 25:30 Forecasting time series data with Prophet 33:06 Election forecasting and prediction markets 40:55 Comparing and evaluating models 43:22 Bottlenecks in going from research to production Transcript: http://wandb.me/gd-sean-taylor Links Discussed: "How Lyft predicts a rider’s destination for better in-app experience"": https://eng.lyft.com/how-lyft-predicts-your-destination-with-attention-791146b0a439 Prophet: https://facebook.github.io/prophet/ Andrew Gelman's blog post "Facebook's Prophet uses Stan": https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-prophet-uses-stan/ Twitter thread "Election forecasting using prediction markets": https://twitter.com/seanjtaylor/status/1270899371706466304 "An Updated Dynamic Bayesian Forecasting Model for the 2020 Election": https://hdsr.mitpress.mit.edu/pub/nw1dzd02/release/1 --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google Podcasts: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected
13 Touko 202145min
Polly Fordyce — Microfluidic Platforms and Machine Learning
Polly explains how microfluidics allow bioengineering researchers to create high throughput data, and shares her experiences with biology and machine learning. --- Polly Fordyce is an Assistant Professor of Genetics and Bioengineering and fellow of the ChEM-H Institute at Stanford. She is the Principal Investigator of The Fordyce Lab, which focuses on developing and applying new microfluidic platforms for quantitative, high-throughput biophysics and biochemistry. Twitter: https://twitter.com/fordycelab Website: http://www.fordycelab.com/ --- Topics Discussed: 0:00 Sneak peek, intro 2:11 Background on protein sequencing 7:38 How changes to a protein's sequence alters its structure and function 11:07 Microfluidics and machine learning 19:25 Why protein folding is important 25:17 Collaborating with ML practitioners 31:46 Transfer learning and big data sets in biology 38:42 Where Polly hopes bioengineering research will go 42:43 Advice for students Transcript: http://wandb.me/gd-polly-fordyce Links Discussed: "The Weather Makers": https://en.wikipedia.org/wiki/The_Wea... --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google Podcasts: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected
29 Huhti 202145min
Adrien Gaidon — Advancing ML Research in Autonomous Vehicles
Adrien Gaidon shares his approach to building teams and taking state-of-the-art research from conception to production at Toyota Research Institute. --- Adrien Gaidon is the Head of Machine Learning Research at the Toyota Research Institute (TRI). His research focuses on scaling up ML for robot autonomy, spanning Scene and Behavior Understanding, Simulation for Deep Learning, 3D Computer Vision, and Self-Supervised Learning. Connect with Adrien: Twitter: https://twitter.com/adnothing LinkedIn: https://www.linkedin.com/in/adrien-gaidon-63ab2358/ Personal website: https://adriengaidon.com/ --- Topics Discussed: 0:00 Sneak peek, intro 0:48 Guitars and other favorite tools 3:55 Why is PyTorch so popular? 11:40 Autonomous vehicle research in the long term 15:10 Game-changing academic advances 20:53 The challenges of bringing autonomous vehicles to market 26:05 Perception and prediction 35:01 Fleet learning and meta learning 41:20 The human aspects of machine learning 44:25 The scalability bottleneck Transcript: http://wandb.me/gd-adrien-gaidon Links Discussed: TRI Global Research: https://www.tri.global/research/ todoist: https://todoist.com/ Contrastive Learning of Structured World Models: https://arxiv.org/abs/2002.05709 SimCLR: https://arxiv.org/abs/2002.05709 --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google Podcasts: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected
22 Huhti 202148min
Nimrod Shabtay — Deployment and Monitoring at Nanit
A look at how Nimrod and the team at Nanit are building smart baby monitor systems, from data collection to model deployment and production monitoring. --- Nimrod Shabtay is a Senior Computer Vision Algorithm Developer at Nanit, a New York-based company that's developing better baby monitoring devices. Connect with Nimrod: LinkedIn: https://www.linkedin.com/in/nimrod-shabtay-76072840/ --- Links Discussed: Guidelines for building an accurate and robust ML/DL model in production: https://engineering.nanit.com/guideli... Careers at Nanit: https://www.nanit.com/jobs --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud --- Join our community of ML practitioners where we host AMAs, share interesting projects, and more: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery
15 Huhti 202133min
Chris Mattmann — ML Applications on Earth, Mars, and Beyond
Chris shares some of the incredible work and innovations behind deep space exploration at NASA JPL and reflects on the past, present, and future of machine learning. --- Chris Mattmann is the Chief Technology and Innovation Officer at NASA Jet Propulsion Laboratory, where he focuses on organizational innovation through technology. He's worked on space missions such as the Orbiting Carbon Observatory 2 and Soil Moisture Active Passive satellites. Chris is also a co-creator of Apache Tika, a content detection and analysis framework that was one of the key technologies used to uncover the Panama Papers, and is the author of "Machine Learning with TensorFlow, Second Edition" and "Tika in Action". Connect with Chris: Personal website: https://www.mattmann.ai/ Twitter: https://twitter.com/chrismattmann --- Topics Discussed: 0:00 Sneak peek, intro 0:52 On Perseverance and Ingenuity 8:40 Machine learning applications at NASA JPL 11:51 Innovation in scientific instruments and data formats 18:26 Data processing levels: Level 1 vs Level 2 vs Level 3 22:20 Competitive data processing 27:38 Kerbal Space Program 30:19 The ideas behind "Machine Learning with Tensorflow, Second Edition" 35:37 The future of MLOps and AutoML 38:51 Machine learning at the edge Transcript: http://wandb.me/gd-chris-mattmann Links Discussed: Perseverance and Ingenuity: https://mars.nasa.gov/mars2020/ Data processing levels at NASA: https://earthdata.nasa.gov/collaborate/open-data-services-and-software/data-information-policy/data-levels OCO-2: https://www.jpl.nasa.gov/missions/orbiting-carbon-observatory-2-oco-2 "Machine Learning with TensorFlow, Second Edition" (2020): https://www.manning.com/books/machine-learning-with-tensorflow-second-edition "Tika in Action" (2011): https://www.manning.com/books/tika-in-action Transcript: http://wandb.me/gd-chris-mattmann --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google Podcasts: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected
8 Huhti 202142min
Vladlen Koltun — The Power of Simulation and Abstraction
From legged locomotion to autonomous driving, Vladlen explains how simulation and abstraction help us understand embodied intelligence. --- Vladlen Koltun is the Chief Scientist for Intelligent Systems at Intel, where he leads an international lab of researchers working in machine learning, robotics, computer vision, computational science, and related areas. Connect with Vladlen: Personal website: http://vladlen.info/ LinkedIn: https://www.linkedin.com/in/vladlenkoltun/ --- 0:00 Sneak peek and intro 1:20 "Intelligent Systems" vs "AI" 3:02 Legged locomotion 9:26 The power of simulation 14:32 Privileged learning 18:19 Drone acrobatics 20:19 Using abstraction to transfer simulations to reality 25:35 Sample Factory for reinforcement learning 34:30 What inspired CARLA and what keeps it going 41:43 The challenges of and for robotics Links Discussed Learning quadrupedal locomotion over challenging terrain (Lee et al., 2020): https://robotics.sciencemag.org/content/5/47/eabc5986.abstract Deep Drone Acrobatics (Kaufmann et al., 2020): https://arxiv.org/abs/2006.05768 Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning (Petrenko et al., 2020): https://arxiv.org/abs/2006.11751 CARLA: https://carla.org/ --- Check out the transcription and discover more awesome ML projects: http://wandb.me/vladlen-koltun-podcast Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud --- Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery
1 Huhti 202149min
