Today we’re joined by Kyle Story, computer vision engineer at Descartes Labs.
Kyle and I caught up after his recent talk at the Google Cloud Next Conference titled “How Computers See the Earth: A Machine Learning Approach to Understanding Satellite Imagery at Scale.” We discuss some of the interesting computer vision problems he’s worked on at Descartes, including custom object detectors and the company’s geovisual search engine, covering everything from the models they’ve developed and platform they’ve built, to the key challenges they’ve had to overcome in scaling them.
For the complete show notes, visit twimlai.com/talk/173.
Multi-Device, Multi-Use-Case Optimization with Jeff Gehlhaar - #587
Causal Conceptions of Fairness and their Consequences with Sharad Goel - #586
Brain-Inspired Hardware and Algorithm Co-Design with Melika Payvand - #585
Equivariant Priors for Compressed Sensing with Arash Behboodi - #584
Managing Data Labeling Ops for Success with Audrey Smith - #583
Engineering an ML-Powered Developer-First Search Engine with Richard Socher - #582
On The Path Towards Robot Vision with Aljosa Osep - #581
More Language, Less Labeling with Kate Saenko - #580
Optical Flow Estimation, Panoptic Segmentation, and Vision Transformers with Fatih Porikli - #579
Data Governance for Data Science with Adam Wood - #578
Feature Platforms for Data-Centric AI with Mike Del Balso - #577
The Fallacy of "Ground Truth" with Shayan Mohanty - #576
Principle-centric AI with Adrien Gaidon - #575
Data Debt in Machine Learning with D. Sculley - #574
AI for Enterprise Decisioning at Scale with Rob Walker - #573
Data Rights, Quantification and Governance for Ethical AI with Margaret Mitchell - #572
Studying Machine Intelligence with Been Kim - #571
Advances in Neural Compression with Auke Wiggers - #570
Mixture-of-Experts and Trends in Large-Scale Language Modeling with Irwan Bello - #569
Daring to DAIR: Distributed AI Research with Timnit Gebru - #568
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