Towards high-quality (maybe synthetic) datasets (Practical AI #290)

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As Argilla puts it: “Data quality is what makes or breaks AI.” However, what exactly does this mean and how can AI team probably collaborate with domain experts towards improved data quality? David Berenstein & Ben Burtenshaw, who are building Argilla & Distilabel at Hugging Face, join us to dig into these topics along with synthetic data generation & AI-generated labeling / feedback.

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Featuring:

• Ben Burtenshaw – Twitter, GitHub, LinkedIn
• David Berenstein – Twitter, GitHub, LinkedIn
• Chris Benson – Twitter, GitHub, LinkedIn, Website
• Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

• Argilla
• Distilabel
• Synthetic Data Generator UI
• Hugging Face and Argilla meetups

Something missing or broken? PRs welcome!

章节

1. Welcome to Practical AI (00:00:00)

2. Sponsor: Fly (00:00:44)

https://fly.io/

3. What does data collaboration mean? (00:03:56)

4. Understanding your data (00:07:18)

5. How to start curating data (00:09:58)

6. Practical steps to scale (00:13:12)

7. Sponsor: WorkOS (00:16:52)

https://workos.com

8. Traditional & new usecases (00:20:23)

9. Virtues of smaller models (00:24:51)

10. What Argilla looks like (00:27:04)

11. User backgrounds (00:30:55)

12. The non-technical POV (00:34:21)

13. Sponsor: Eight Sleep (00:38:23)

https://www.eightsleep.com/practicalai

14. AI feedback (00:41:09)

15. Hallucination issues (00:44:50)

16. What is Distilabel (00:46:10)

17. Usage & adoption (00:50:08)

18. Where things are going (00:52:55)

19. This is muy bueno (00:55:34)

20. Outro (00:56:15)