Gradient Dissent: Conversations on AI

Jeremy Howard is a founding researcher at fast.ai, a research institute dedicated to making Deep Learning more accessible. Previously, he was the CEO and Founder at Enlitic, an advanced machine learning company in San Francisco, California. Howard is a faculty member at Singularity University, where he teaches data science. He is also a Young Global Leader with the World Economic Forum, and spoke at the World Economic Forum Annual Meeting 2014 on "Jobs For The Machines." Howard advised Khosla Ventures as their Data Strategist, identifying the biggest opportunities for investing in data-driven startups and mentoring their portfolio companies to build data-driven businesses. Howard was the founding CEO of two successful Australian startups, FastMail and Optimal Decisions Group. Before that, he spent eight years in management consulting, at McKinsey & Company and AT Kearney. TOPICS COVERED: 0:00 Introduction 0:52 Dad things 2:40 The story of Fast.ai 4:57 How the courses have evolved over time 9:24 Jeremy’s top down approach to teaching 13:02 From Fast.ai the course to Fast.ai the library 15:08 Designing V2 of the library from the ground up 21:44 The ingenious type dispatch system that powers Fast.ai 25:52 Were you able to realize the vision behind v2 of the library 28:05 Is it important to you that Fast.ai is used by everyone in the world, beyond the context of learning 29:37 Real world applications of Fast.ai, including animal husbandry 35:08 Staying ahead of the new developments in the field 38:50 A bias towards learning by doing 40:02 What’s next for Fast.ai 40.35 Python is not the future of Machine Learning 43:58 One underrated aspect of machine learning 45:25 Biggest challenge of machine learning in the real world Follow Jeremy on Twitter: https://twitter.com/jeremyphoward Links: Deep learning R&D & education: http://fast.ai Software: http://docs.fast.ai Book: http://up.fm/book Course: http://course.fast.ai Papers: The business impact of deep learning https://dl.acm.org/doi/10.1145/2487575.2491127 De-identification Methods for Open Health Data https://www.jmir.org/2012/1/e33/ Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Soundcloud, Apple, and Spotify! YouTube: https://www.youtube.com/c/WeightsBiases Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

25 Aug 202051min

As Lyft’s VP of Engineering, Software at Level 5, Autonomous Vehicle Program, Anantha Kancherla has a birds-eye view on what it takes to make self-driving cars work in the real world. He previously worked on Windows at Microsoft focusing on DirectX, Graphics and UI; Facebook’s mobile Newsfeed and core mobile experiences; and led the Collaboration efforts at Dropbox involving launching Dropbox Paper as well as improving core collaboration functionality in Dropbox. He and Lukas dive into the challenges of working on large projects and how to approach breaking down a major project into pieces, tracking progress and addressing bugs. Check out Lyft’s Self-Driving Website: https://self-driving.lyft.com/ And this article on building the self-driving team at Lyft: https://medium.com/lyftlevel5/going-from-zero-to-sixty-building-lyfts-self-driving-software-team-1ac693800588 Follow Lyft Level 5 on Twitter: https://twitter.com/LyftLevel5 Topics covered: 0:00 Sharp Knives 0:44 Introduction 1:07 Breaking down a big goal 8:15 Breaking down Metrics 10:50 Allocating Resources 12:40 Interventions 13:27 What part still has lots ofroom for improvement? 14:25 Various ways of deploying models 15:30 Rideshare 15:57 Infrastructure, updates 17:28 Model versioning 19:16 Model improvement goals 22:42 Unit testing 25:12 Interactions of models 26:30 Improvements in data vs models 29:50 finding the right data 30:38 Deploying models into production 32:17 Feature drift 34:20 When to file bug tickets 37:25 Processes and growth 40:56 Underrated aspect 42:34 Biggest challenges Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF

12 Aug 202044min

Bharath created the deepchem.io open-source project to grow the deep drug discovery open source community, co-created the moleculenet.ai benchmark suite to facilitate development of molecular algorithms, and more. Bharath’s graduate education was supported by a Hertz Fellowship, the most selective graduate fellowship in the sciences. Bharath is the lead author of “TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning”, a developer’s introduction to modern machine learning, with O’Reilly Media. Today, Bharath is focused on designing the decentralized protocols that will unlock data and AI to create the next stage of the internet. He received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He did his PhD in computer science at Stanford University where he studied the application of deep-learning to problems in drug-discovery. Follow Bharath on Twitter and Github https://twitter.com/rbhar90 rbharath.github.io Check out some of his projects: https://deepchem.io/ https://moleculenet.ai/ https://scholar.google.com/citations?user=LOdVDNYAAAAJ&hl=en&oi=ao Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

5 Aug 202055min

Chip Huyen is a writer and computer scientist currently working at a startup that focuses on machine learning production pipelines. Previously, she’s worked at NVIDIA, Netflix, and Primer. She helped launch Coc Coc - Vietnam’s second most popular web browser with 20+ million monthly active users. Before all of that, she was a best selling author and traveled the world. Chip graduated from Stanford, where she created and taught the course on TensorFlow for Deep Learning Research. Check out Chip's recent article on ML Tools: https://huyenchip.com/2020/06/22/mlops.html Follow Chip on Twitter: https://twitter.com/chipro And on her Website: https://huyenchip.com/ Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

29 Jul 202043min

👨🏻‍💻Our guest on this episode of Gradient Dissent is Peter Skomoroch! Peter is the former head of data products at Workday and LinkedIn. Previously, he was the cofounder and CEO of venture-backed deep learning startup SkipFlag, which was acquired by Workday, and a principal data scientist at LinkedIn. Check out his recent publication: What you need to know about product management for AI https://www.oreilly.com/radar/what-you-need-to-know-about-product-management-for-ai/ Follow Peter on Twitter: https://twitter.com/peteskomoroch And read some of his other work: Pangloss: Fast Entity Linking in Noisy Text Environments Large-Scale Hierarchical Topic Models Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Soundcloud, Apple, and Spotify! YouTube: https://bit.ly/32NzZvI Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

21 Jul 20201h 27min

Josh Tobin is a researcher working at the intersection of machine learning and robotics. His research focuses on applying deep reinforcement learning, generative models, and synthetic data to problems in robotic perception and control. Additionally, he co-organizes a machine learning training program for engineers to learn about production-ready deep learning called Full Stack Deep Learning. https://fullstackdeeplearning.com/ Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel and was a research scientist at OpenAI for 3 years during his PhD. Finally, Josh created this amazing field guide on troubleshooting deep neural networks: http://josh-tobin.com/assets/pdf/troubleshooting-deep-neural-networks-01-19.pdf Follow Josh on twitter: https://twitter.com/josh_tobin And on his website:http://josh-tobin.com/ Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Youtube, Apple, and Spotify! Youtube: https://www.youtube.com/playlist?list=PLD80i8An1OEEb1jP0sjEyiLG8ULRXFob_ Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

8 Jul 202048min

Miles Brundage researches the societal impacts of artificial intelligence and how to make sure they go well. In 2018, he joined OpenAI, as a Research Scientist on the Policy team. Previously, he was a Research Fellow at the University of Oxford's Future of Humanity Institute and served as a member of Axon's AI and Policing Technology Ethics Board. Keep up with Miles on his website: https://www.milesbrundage.com/ and on Twitter: https://twitter.com/miles_brundage Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Soundcloud, Apple, and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

1 Jul 20201h 2min

Hamel Husain is a Staff Machine Learning Engineer at Github. He has extensive experience building data analytics and predictive modeling solutions for a wide range of industries, including: hospitality, telecom, retail, restaurant, entertainment and finance. He has built large data science teams (50+) from the ground up and have extensive experience building solutions as an individual contributor. Follow Hamel on Twitter: https://twitter.com/HamelHusain And on his website: http://hamel.io/ Learn more about Github Actions: https://github.com/features/actions and the CodeSearchNet Challenge: https://github.blog/2019-09-26-introducing-the-codesearchnet-challenge/ Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple, and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

24 Jun 202036min