Chip Huyen — ML Research and Production Pipelines

Wojciech joins us to talk the principles behind OpenAI, the Fermi Paradox, and the future stages of developments in AGI. --- Wojciech Zaremba is a co-founder of OpenAI, a research company dedicated to discovering and enacting the path to safe artificial general intelligence. He was also Head of Robotics, where his team developed general-purpose robots through new approaches to transfer learning, and taught robots complex behaviors. Connect with Wojciech: Personal website: https://wojzaremba.com// Twitter: https://twitter.com/woj_zaremba --- Topics Discussed: 0:00 Sneak peek and intro 1:03 The people and principles behind OpenAI 6:31 The stages of future AI developments 13:42 The Fermi paradox 16:18 What drives Wojciech? 19:17 Thoughts on robotics 24:58 Dota and other projects at OpenAI 33:42 What would make an AI conscious? 41:31 How to be succeed in robotics Transcript: http://wandb.me/gd-wojciech-zaremba Links: Fermi paradox: https://en.wikipedia.org/wiki/Fermi_paradox OpenAI and Dota: https://openai.com/projects/five/ --- 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

3 Kesä 202144min

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

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 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 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 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

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 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