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
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