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Computer scientists as rogue art historians
Publisher |
Changelog Media
Media Type |
audio
Categories Via RSS |
Software How-To
Tech News
Technology
Publication Date |
Apr 12, 2023
Episode Duration |
00:43:17
What can art historians and computer scientists learn from one another? Actually, a lot! Amanda Wasielewski joins us to talk about how she discovered that computer scientists working on computer vision were actually acting like rogue art historians and how art historians have found machine learning to be a valuable tool for research, fraud detection, and cataloguing. We also discuss the rise of generative AI and how we this technology might cause us to ask new questions like: “What makes a photograph a photograph?”
What can art historians and computer scientists learn from one another? Actually, a lot! Amanda Wasielewski joins us to talk about how she discovered that computer scientists working on computer vision were actually acting like rogue art historians and how art historians have found machine learning to be a valuable tool for research, fraud detection, and cataloguing. We also discuss the rise of generative AI and how we this technology might cause us to ask new questions like: “What makes a photograph a photograph?”

What can art historians and computer scientists learn from one another? Actually, a lot! Amanda Wasielewski joins us to talk about how she discovered that computer scientists working on computer vision were actually acting like rogue art historians and how art historians have found machine learning to be a valuable tool for research, fraud detection, and cataloguing. We also discuss the rise of generative AI and how we this technology might cause us to ask new questions like: “What makes a photograph a photograph?”

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Featuring:

Show Notes:

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Timestamps:

(00:00) - Welcome to Practical AI(00:42) - Amanda Wasielewski(04:28) - What is art history?(10:00) - Integrating artworks for ML?(13:47) - How are art historians adaption to ML?(19:16) - Art models and the Tank Classifier(24:27) - What ML devs can learn from art history(32:06) - Deep learning paradoxes(38:12) - Where is the field going?(42:21) - Outro

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