How to cultivate a high-signal AI feed

<p>Basic tips on how to assess inbound ML content and cultivate your news feed.<br />This is AI generated audio with Python and 11Labs. Music generated by Meta's MusicGen.<br />Source code: <a class="linkified" href="https://github.com/natolambert/interconnects-tools" target="_blank">https://github.com/natolambert/interconnects-tools</a><br />Original post: <a class="linkified" href="https://www.interconnects.ai/p/making-a-ml-feed" target="_blank">https://www.interconnects.ai/p/making-a-ml-feed</a></p><p>00:00 How I assess all these AI releases<br />01:22 1. Model access and demos are king of credibility<br />02:31 2. Focus your feed on depth or breadth<br />03:09 3. Examples of using the model normally show its usable, shockingly<br />04:10 4. Leaderboards as the single leading claim is often anti-signal<br />05:00 5. Basic deep learning conceptual checks will often save you<br />06:13 6. If it's not even remotely reproducible or verifiable, it's not science<br />07:10 7. Don't over-index on Twitter<br />08:32 8. Data sharing, licenses, communication clarity, and small things add up<br />08:58 9. Research papers, technical reports, blog posts, and Tweets all serve different purposes<br />09:49 10. Socialize your information and build relationships</p> <br /><br />This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://www.interconnects.ai/subscribe?utm_medium=podcast&#38;utm_campaign=CTA_2">www.interconnects.ai/subscribe</a>