Jeremy Howard — The Story of fast.ai and Why Python Is Not the Future of ML

Jeremy Howard — The Story of fast.ai and Why Python Is Not the Future of ML

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/

Jaksot(131)

Jehan Wickramasuriya — AI in High-Stress Scenarios

Jehan Wickramasuriya — AI in High-Stress Scenarios

Jehan Wickramasuriya is the Vice President of AI, Platform & Data Services at Motorola Solutions, a global leader in public safety and enterprise security.In this episode, Jehan discusses how Motorola Solutions uses AI to simplify data streams to help maximize human potential in high-stress situations. He also shares his thoughts on augmenting synthetic data with real data and the challenges posed in partnering with startups.Show notes (transcript and links): http://wandb.me/gd-jehan-wickramasuriya-⏳ Timestamps: 00:00 Intro00:42 How AI fits into the safety/security industry 09:33 Event matching and object detection14:47 Running models on the right hardware17:46 Scaling model evaluation23:58 Monitoring and evaluation challenges26:30 Identifying and sorting issues30:27 Bridging vision and language domains39:25 Challenges and promises of natural language technology41:35 Production environment43:15 Using synthetic data49:59 Working with startups53:55 Multi-task learning, meta-learning, and user experience56:44 Optimization and testing across multiple platforms59:36 Outro-Connect with Jehan and Motorola Solutions:📍 Jehan on LinkedIn: https://www.linkedin.com/in/jehanw/📍 Jehan on Twitter: https://twitter.com/jehan/📍 Motorola Solutions on Twitter: https://twitter.com/MotoSolutions/📍 Careers at Motorola Solutions: https://www.motorolasolutions.com/en_us/about/careers.html-💬 Host: Lukas Biewald📹 Producers: Riley Fields, Cayla Sharp, Angelica Pan, Lavanya Shukla-Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

6 Loka 20221h

Will Falcon — Making Lightning the Apple of ML

Will Falcon — Making Lightning the Apple of ML

Will Falcon is the CEO and co-founder of Lightning AI, a platform that enables users to quickly build and publish ML models.In this episode, Will explains how Lightning addresses the challenges of a fragmented AI ecosystem and reveals which framework PyTorch Lightning was originally built upon (hint: not PyTorch!) He also shares lessons he took from his experience serving in the military and offers a recommendation to veterans who want to work in tech.Show notes (transcript and links): http://wandb.me/gd-will-falcon---⏳ Timestamps: 00:00 Intro01:00 From SEAL training to FAIR04:17 Stress-testing Lightning07:55 Choosing PyTorch over TensorFlow and other frameworks13:16 Components of the Lightning platform17:01 Launching Lightning from Facebook19:09 Similarities between leadership and research22:08 Lessons from the military26:56 Scaling PyTorch Lightning to Lightning AI33:21 Hiring the right people35:21 The future of Lightning39:53 Reducing algorithm complexity in self-supervised learning42:19 A fragmented ML landscape44:35 Outro---Connect with Lightning📍 Website: https://lightning.ai📍 Twitter: https://twitter.com/LightningAI📍 LinkedIn: https://www.linkedin.com/company/pytorch-lightning/📍 Careers: https://boards.greenhouse.io/lightningai---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Anish Shah, Cayla Sharp, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

15 Syys 202245min

Aaron Colak — ML and NLP in Experience Management

Aaron Colak — ML and NLP in Experience Management

Aaron Colak is the Leader of Core Machine Learning at Qualtrics, an experiment management company that takes large language models and applies them to real-world, B2B use cases.In this episode, Aaron describes mixing classical linguistic analysis with deep learning models and how Qualtrics organized their machine learning organizations and model to leverage the best of these techniques. He also explains how advances in NLP have invited new opportunities in low-resource languages.Show notes (transcript and links): http://wandb.me/gd-aaron-colak---⏳ Timestamps: 00:00 Intro00:57 Evolving from surveys to experience management04:56 Detecting sentiment with ML10:57 Working with large language models and rule-based systems14:50 Zero-shot learning, NLP, and low-resource languages20:11 Letting customers control data25:13 Deep learning and tabular data28:40 Hyperscalers and performance monitoring34:54 Combining deep learning with linguistics40:03 A sense of accomplishment42:52 Causality and observational data in healthcare45:09 Challenges of interdisciplinary collaboration49:27 Outro---Connect with Aaron and Qualtrics📍 Aaron on LinkedIn: https://www.linkedin.com/in/aaron-r-colak-3522308/📍 Qualtrics on Twitter: https://twitter.com/qualtrics/📍 Careers at Qualtrics: https://www.qualtrics.com/careers/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Cayla Sharp, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

26 Elo 202250min

Jordan Fisher — Skipping the Line with Autonomous Checkout

Jordan Fisher — Skipping the Line with Autonomous Checkout

Jordan Fisher is the CEO and co-founder of Standard AI, an autonomous checkout company that’s pushing the boundaries of computer vision.In this episode, Jordan discusses “the Wild West” of the MLOps stack and tells Lukas why Rust beats Python. He also explains why AutoML shouldn't be overlooked and uses a bag of chips to help explain the Manifold Hypothesis.Show notes (transcript and links): http://wandb.me/gd-jordan-fisher---⏳ Timestamps: 00:00 Intro00:40 The origins of Standard AI08:30 Getting Standard into stores18:00 Supervised learning, the advent of synthetic data, and the manifold hypothesis24:23 What's important in a MLOps stack27:32 The merits of AutoML30:00 Deep learning frameworks33:02 Python versus Rust39:32 Raw camera data versus video42:47 The future of autonomous checkout48:02 Sharing the StandardSim data set52:30 Picking the right tools54:30 Overcoming dynamic data set challenges57:35 Outro---Connect with Jordan and Standard AI📍 Jordan on LinkedIn: https://www.linkedin.com/in/jordan-fisher-81145025/📍 Standard AI on Twitter: https://twitter.com/StandardAi📍 Careers at Standard AI: https://careers.standard.ai/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Cayla Sharp, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

4 Elo 202257min

Drago Anguelov — Robustness, Safety, and Scalability at Waymo

Drago Anguelov — Robustness, Safety, and Scalability at Waymo

Drago Anguelov is a Distinguished Scientist and Head of Research at Waymo, an autonomous driving technology company and subsidiary of Alphabet Inc.We begin by discussing Drago's work on the original Inception architecture, winner of the 2014 ImageNet challenge and introduction of the inception module. Then, we explore milestones and current trends in autonomous driving, from Waymo's release of the Open Dataset to the trade-offs between modular and end-to-end systems.Drago also shares his thoughts on finding rare examples, and the challenges of creating scalable and robust systems.Show notes (transcript and links): http://wandb.me/gd-drago-anguelov---⏳ Timestamps: 0:00 Intro0:45 The story behind the Inception architecture13:51 Trends and milestones in autonomous vehicles23:52 The challenges of scalability and simulation30:19 Why LiDar and mapping are useful35:31 Waymo Via and autonomous trucking37:31 Robustness and unsupervised domain adaptation40:44 Why Waymo released the Waymo Open Dataset49:02 The domain gap between simulation and the real world56:40 Finding rare examples1:04:34 The challenges of production requirements1:08:36 Outro---Connect with Drago & Waymo📍 Drago on LinkedIn: https://www.linkedin.com/in/dragomiranguelov/📍 Waymo on Twitter: https://twitter.com/waymo/📍 Careers at Waymo: https://waymo.com/careers/---Links:📍 Inception v1: https://arxiv.org/abs/1409.4842📍 "SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation", Qiangeng Xu et al. (2021), https://arxiv.org/abs/2108.06709📍 "GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting", Zhao Chen et al. (2022), https://arxiv.org/abs/2201.05938---💬 Host: Lukas Biewald📹 Producers: Cayla Sharp, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

14 Heinä 20221h 9min

James Cham — Investing in the Intersection of Business and Technology

James Cham — Investing in the Intersection of Business and Technology

James Cham is a co-founder and partner at Bloomberg Beta, an early-stage venture firm that invests in machine learning and the future of work, the intersection between business and technology.James explains how his approach to investing in AI has developed over the last decade, which signals of success he looks for in the ever-adapting world of venture startups (tip: look for the "gradient of admiration"), and why it's so important to demystify ML for executives and decision-makers.Lukas and James also discuss how new technologies create new business models, and what the ethical considerations of a world where machine learning is accepted to be possibly fallible would be like.Show notes (transcript and links): http://wandb.me/gd-james-cham---⏳ Timestamps: 0:00 Intro0:46 How investment in AI has changed and developed7:08 Creating the first MI landscape infographics10:30 The impact of ML on organizations and management17:40 Demystifying ML for executives21:40 Why signals of successful startups change over time27:07 ML and the emergence of new business models37:58 New technology vs new consumer goods39:50 What James considers when investing44:19 Ethical considerations of accepting that ML models are fallible50:30 Reflecting on past investment decisions52:56 Thoughts on consciousness and Theseus' paradox59:08 Why it's important to increase general ML literacy1:03:09 Outro1:03:30 Bonus: How James' faith informs his thoughts on ML---Connect with James:📍 Twitter: https://twitter.com/jamescham📍 Bloomberg Beta: https://github.com/Bloomberg-Beta/Manual---Links:📍 "Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions" by Ali Alkhatib and Michael Bernstein (2019): https://doi.org/10.1145/3290605.3300760---💬 Host: Lukas Biewald📹 Producers: Cayla Sharp, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

7 Heinä 20221h 6min

Boris Dayma — The Story Behind DALL·E mini, the Viral Phenomenon

Boris Dayma — The Story Behind DALL·E mini, the Viral Phenomenon

Check out this report by Boris about DALL-E mini:https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAyhttps://wandb.ai/_scott/wandb_example/reports/Collaboration-in-ML-made-easy-with-W-B-Teams--VmlldzoxMjcwMDU5https://twitter.com/weirddalleConnect with Boris:📍 Twitter: https://twitter.com/borisdayma---💬 Host: Lukas Biewald📹 Producers: Cayla Sharp, Angelica Pan, Sanyam Bhutani, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

17 Kesä 202235min

Tristan Handy — The Work Behind the Data Work

Tristan Handy — The Work Behind the Data Work

Tristan Handy is CEO and founder of dbt Labs. dbt (data build tool) simplifies the data transformation workflow and helps organizations make better decisions.Lukas and Tristan dive into the history of the modern data stack and the subsequent challenges that dbt was created to address; communities of identity and product-led growth; and thoughts on why SQL has survived and thrived for so long. Tristan also shares his hopes for the future of BI tools and the data stack.Show notes (transcript and links): http://wandb.me/gd-tristan-handy---⏳ Timestamps: 0:00 Intro0:40 How dbt makes data transformation easier4:52 dbt and avoiding bad data habits14:23 Agreeing on organizational ground truths19:04 Staying current while running a company22:15 The origin story of dbt26:08 Why dbt is conceptually simple but hard to execute 34:47 The dbt community and the bottom-up mindset41:50 The future of data and operations47:41 dbt and machine learning49:17 Why SQL is so ubiquitous55:20 Bridging the gap between the ML and data worlds1:00:22 Outro---Connect with Tristan:📍 Twitter: https://twitter.com/jthandy📍 The Analytics Engineering Roundup: https://roundup.getdbt.com/---💬 Host: Lukas Biewald📹 Producers: Cayla Sharp, Angelica Pan, Sanyam Bhutani, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

9 Kesä 20221h

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