Jordan Fisher — Skipping the Line with Autonomous Checkout

Jordan Fisher — Skipping the Line with Autonomous Checkout

Author: Lukas Biewald August 4, 2022 Duration: 57:58

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

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⏳ 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

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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/

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💬 Host: Lukas Biewald

📹 Producers: Riley Fields, Cayla Sharp, Angelica Pan, Lavanya Shukla

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Lukas Biewald hosts Gradient Dissent: Conversations on AI, a series that moves beyond theoretical discussions to examine how artificial intelligence is actually built and deployed. Each episode features a direct, unscripted talk with a leading practitioner-you’ll hear from engineers and researchers at places like NVIDIA, Meta, Google, Lyft, and OpenAI. The focus is on the tangible challenges and breakthroughs they encounter, from initial research to the complex reality of putting models into production. This isn't about abstract futures; it's a grounded look at the decisions shaping the field right now. Biewald, bringing his perspective from Weights & Biases, steers conversations toward the practical trade-offs and collaborative efforts that define modern AI work. For anyone in technology or business who wants to understand the mechanics behind the headlines, this podcast offers a rare, candid window into the process. You’ll come away with a clearer sense of how ideas become functional systems and what it really takes to operate at the cutting edge.
Author: Language: English Episodes: 100

Gradient Dissent: Conversations on AI
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