72 episodes

Gradient Dissent is a machine learning podcast hosted by Lukas Biewald that takes you behind-the-scenes to learn how industry leaders are putting deep learning models in production at Facebook, Google, Lyft, OpenAI, and more.

Gradient Dissent Lukas Biewald

    • Technology
    • 4.8 • 40 Ratings

Gradient Dissent is a machine learning podcast hosted by Lukas Biewald that takes you behind-the-scenes to learn how industry leaders are putting deep learning models in production at Facebook, Google, Lyft, OpenAI, and more.

    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 Intro
    00:40 The origins of Standard AI
    08:30 Getting Standard into stores
    18:00 Supervised learning, the advent of synthetic data, and the manifold hypothesis
    24:23 What's important in a MLOps stack
    27:32 The merits of AutoML
    30:00 Deep learning frameworks
    33:02 Python versus Rust
    39:32 Raw camera data versus video
    42:47 The future of autonomous checkout
    48:02 Sharing the StandardSim data set
    52:30 Picking the right tools
    54:30 Overcoming dynamic data set challenges
    57:35 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​

    • 57 min
    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 Intro
    0:45 The story behind the Inception architecture
    13:51 Trends and milestones in autonomous vehicles
    23:52 The challenges of scalability and simulation
    30:19 Why LiDar and mapping are useful
    35:31 Waymo Via and autonomous trucking
    37:31 Robustness and unsupervised domain adaptation
    40:44 Why Waymo released the Waymo Open Dataset
    49:02 The domain gap between simulation and the real world
    56:40 Finding rare examples
    1:04:34 The challenges of production requirements
    1:08:36 Outro
    ---
    Connect with Drago and 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​

    • 1 hr 9 min
    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 Intro
    0:46 How investment in AI has changed and developed
    7:08 Creating the first MI landscape infographics
    10:30 The impact of ML on organizations and management
    17:40 Demystifying ML for executives
    21:40 Why signals of successful startups change over time
    27:07 ML and the emergence of new business models
    37:58 New technology vs new consumer goods
    39:50 What James considers when investing
    44:19 Ethical considerations of accepting that ML models are fallible
    50:30 Reflecting on past investment decisions
    52:56 Thoughts on consciousness and Theseus' paradox
    59:08 Why it's important to increase general ML literacy
    1:03:09 Outro
    1:03:30 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​

    • 1 hr 6 min
    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--VmlldzoyMDE4NDAy (https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy)
    https://wandb.ai/_scott/wandb_example/reports/Collaboration-in-ML-made-easy-with-W-B-Teams--VmlldzoxMjcwMDU5 (https://wandb.ai/_scott/wandb_example/reports/Collaboration-in-ML-made-easy-with-W-B-Teams--VmlldzoxMjcwMDU5)
    https://twitter.com/weirddalle
    Connect 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​

    • 35 min
    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 Intro
    0:40 How dbt makes data transformation easier
    4:52 dbt and avoiding bad data habits
    14:23 Agreeing on organizational ground truths
    19:04 Staying current while running a company
    22:15 The origin story of dbt
    26:08 Why dbt is conceptually simple but hard to execute
    34:47 The dbt community and the bottom-up mindset
    41:50 The future of data and operations
    47:41 dbt and machine learning
    49:17 Why SQL is so ubiquitous
    55:20 Bridging the gap between the ML and data worlds
    1:00:22 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​

    • 1 hr
    Johannes Otterbach — Unlocking ML for Traditional Companies

    Johannes Otterbach — Unlocking ML for Traditional Companies

    Johannes Otterbach is VP of Machine Learning Research at Merantix Momentum, an ML consulting studio that helps their clients build AI solutions.
    Johannes and Lukas talk about Johannes' background in physics and applications of ML to quantum computing, why Merantix is investing in creating a cloud-agnostic tech stack, and the unique challenges of developing and deploying models for different customers. They also discuss some of Johannes' articles on the impact of NLP models and the future of AI regulations.
    Show notes (transcript and links): http://wandb.me/gd-johannes-otterbach
    ---
    ⏳ Timestamps:
    0:00 Intro
    1:04 Quantum computing and ML applications
    9:21 Merantix, Ventures, and ML consulting
    19:09 Building a cloud-agnostic tech stack
    24:40 The open source tooling ecosystem
    30:28 Handing off models to customers
    31:42 The impact of NLP models on the real world
    35:40 Thoughts on AI and regulation
    40:10 Statistical physics and optimization problems
    42:50 The challenges of getting high-quality data
    44:30 Outro
    ---
    Connect with Johannes:
    📍 LinkedIn: https://twitter.com/jsotterbach
    📍 Personal website: http://jotterbach.github.io/
    📍 Careers at Merantix Momentum: https://merantix-momentum.com/about#jobs
    ---
    💬 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​

    • 44 min

Customer Reviews

4.8 out of 5
40 Ratings

40 Ratings

Kyler Bruno ,

Hidden knowledge

This podcast is a window into ML of today and the future

Showeropera ,

Thought-provoking, not hype-y

This show brings on a fascinating array of guests for a diverse range of ML topics. I enjoy hearing ideas from practitioners and innovators in a friendly conversational style.

LisaIsHereForIt ,

💥Incredible interviews with AI innovators

It’s obvious Lukas puts extraordinary effort in covering salient topics and finding guests that are authentic and on the cutting edge of ML and AI - the insights they bring to bear are mind-blowing every. single. time.

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