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.
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Explainable AI for Biology and Medicine with Su-In Lee - #642
Transformers On Large-Scale Graphs with Bayan Bruss - #641
The Enterprise LLM Landscape with Atul Deo - #640
BloombergGPT - an LLM for Finance with David Rosenberg - #639
Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness - #638
Privacy vs Fairness in Computer Vision with Alice Xiang - #637
Unifying Vision and Language Models with Mohit Bansal - #636
Data Augmentation and Optimized Architectures for Computer Vision with Fatih Porikli - #635
Mojo: A Supercharged Python for AI with Chris Lattner - #634
Stable Diffusion and LLMs at the Edge with Jilei Hou - #633
Modeling Human Behavior with Generative Agents with Joon Sung Park - #632
Towards Improved Transfer Learning with Hugo Larochelle - #631
Language Modeling With State Space Models with Dan Fu - #630
Building Maps and Spatial Awareness in Blind AI Agents with Dhruv Batra - #629
AI Agents and Data Integration with GPT and LLaMa with Jerry Liu - #628
Hyperparameter Optimization through Neural Network Partitioning with Christos Louizos - #627
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