How to cultivate a high-signal AI feed
Basic tips on how to assess inbound ML content and cultivate your news feed. This is AI generated audio with Python and 11Labs. Music generated by Meta's MusicGen. Source code: https://github.com/natolambert/interconnects-tools Original post: https://www.interconnects.ai/p/making-a-ml-feed 00:00 How I assess all these AI releases 01:22 1. Model access and demos are king of credibility 02:31 2. Focus your feed on depth or breadth 03:09 3. Examples of using the model normally show its usable, shockingly 04:10 4. Leaderboards as the single leading claim is often anti-signal 05:00 5. Basic deep learning conceptual checks will often save you 06:13 6. If it's not even remotely reproducible or verifiable, it's not science 07:10 7. Don't over-index on Twitter 08:32 8. Data sharing, licenses, communication clarity, and small things add up 08:58 9. Research papers, technical reports, blog posts, and Tweets all serve different purposes 09:49 10. Socialize your information and build relationships
Basic tips on how to assess inbound ML content and cultivate your news feed. This is AI generated audio with Python and 11Labs. Music generated by Meta's MusicGen. Source code: https://github.com/natolambert/interconnects-tools Original post: https://www.interconnects.ai/p/making-a-ml-feed 00:00 How I assess all these AI releases 01:22 1. Model access and demos are king of credibility 02:31 2. Focus your feed on depth or breadth 03:09 3. Examples of using the model normally show its usable, shockingly 04:10 4. Leaderboards as the single leading claim is often anti-signal 05:00 5. Basic deep learning conceptual checks will often save you 06:13 6. If it's not even remotely reproducible or verifiable, it's not science 07:10 7. Don't over-index on Twitter 08:32 8. Data sharing, licenses, communication clarity, and small things add up 08:58 9. Research papers, technical reports, blog posts, and Tweets all serve different purposes 09:49 10. Socialize your information and build relationships