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Suhail Doshi: The Future of Computer Vision

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Manage episode 418564085 series 2975159
内容由Daniel Bashir提供。所有播客内容(包括剧集、图形和播客描述)均由 Daniel Bashir 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Episode 123

I spoke with Suhail Doshi about:

* Why benchmarks aren’t prepared for tomorrow’s AI models

* How he thinks about artists in a world with advanced AI tools

* Building a unified computer vision model that can generate, edit, and understand pixels.

Suhail is a software engineer and entrepreneur known for founding Mixpanel, Mighty Computing, and Playground AI (they’re hiring!).

Reach me at editor@thegradient.pub for feedback, ideas, guest suggestions.

Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

Outline:

* (00:00) Intro

* (00:54) Ad read — MLOps conference

* (01:30) Suhail is *not* in pivot hell but he *is* all-in on 50% AI-generated music

* (03:45) AI and music, similarities to Playground

* (07:50) Skill vs. creative capacity in art

* (12:43) What we look for in music and art

* (15:30) Enabling creative expression

* (18:22) Building a unified computer vision model, underinvestment in computer vision

* (23:14) Enhancing the aesthetic quality of images: color and contrast, benchmarks vs user desires

* (29:05) “Benchmarks are not prepared for how powerful these models will become”

* (31:56) Personalized models and personalized benchmarks

* (36:39) Engaging users and benchmark development

* (39:27) What a foundation model for graphics requires

* (45:33) Text-to-image is insufficient

* (46:38) DALL-E 2 and Imagen comparisons, FID

* (49:40) Compositionality

* (50:37) Why Playground focuses on images vs. 3d, video, etc.

* (54:11) Open source and Playground’s strategy

* (57:18) When to stop open-sourcing?

* (1:03:38) Suhail’s thoughts on AGI discourse

* (1:07:56) Outro

Links:

* Playground homepage

* Suhail on Twitter


Get full access to The Gradient at thegradientpub.substack.com/subscribe
  continue reading

150集单集

Artwork
icon分享
 
Manage episode 418564085 series 2975159
内容由Daniel Bashir提供。所有播客内容(包括剧集、图形和播客描述)均由 Daniel Bashir 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Episode 123

I spoke with Suhail Doshi about:

* Why benchmarks aren’t prepared for tomorrow’s AI models

* How he thinks about artists in a world with advanced AI tools

* Building a unified computer vision model that can generate, edit, and understand pixels.

Suhail is a software engineer and entrepreneur known for founding Mixpanel, Mighty Computing, and Playground AI (they’re hiring!).

Reach me at editor@thegradient.pub for feedback, ideas, guest suggestions.

Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

Outline:

* (00:00) Intro

* (00:54) Ad read — MLOps conference

* (01:30) Suhail is *not* in pivot hell but he *is* all-in on 50% AI-generated music

* (03:45) AI and music, similarities to Playground

* (07:50) Skill vs. creative capacity in art

* (12:43) What we look for in music and art

* (15:30) Enabling creative expression

* (18:22) Building a unified computer vision model, underinvestment in computer vision

* (23:14) Enhancing the aesthetic quality of images: color and contrast, benchmarks vs user desires

* (29:05) “Benchmarks are not prepared for how powerful these models will become”

* (31:56) Personalized models and personalized benchmarks

* (36:39) Engaging users and benchmark development

* (39:27) What a foundation model for graphics requires

* (45:33) Text-to-image is insufficient

* (46:38) DALL-E 2 and Imagen comparisons, FID

* (49:40) Compositionality

* (50:37) Why Playground focuses on images vs. 3d, video, etc.

* (54:11) Open source and Playground’s strategy

* (57:18) When to stop open-sourcing?

* (1:03:38) Suhail’s thoughts on AGI discourse

* (1:07:56) Outro

Links:

* Playground homepage

* Suhail on Twitter


Get full access to The Gradient at thegradientpub.substack.com/subscribe
  continue reading

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