Hyperventilating over the Gartner AI Hype Cycle

Practical AI: Machine Learning, Data Science, LLM

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7M ago 55:11

MP3•Головна епізоду

This week Daniel & Chris hang with repeat guest and good friend Demetrios Brinkmann of the MLOps Community. Together they review, debate, and poke fun at the 2024 Gartner Hype Cycle chart for Artificial Intelligence. You are invited to join them in this light-hearted fun conversation about the state of hype in artificial intelligence.

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Sponsors:

Intel Innovation 2024 – Early bird registration is now open for Intel Innovation 2024 in San Jose, CA! Learn more OR register
Motific – Accelerate your GenAI adoption journey. Rapidly deliver trustworthy GenAI assistants. Learn more at motific.ai

Featuring:

Demetrios Brinkmann – X
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X

Show Notes:

Something missing or broken? PRs welcome!

Розділи

1. Welcome to Practical AI (00:00:00)

2. Sponsor: Intel Innovation 2024 (00:00:35)

https://intel.com/innovation?regcode=CMCCHL&utm_campaign=Changelog

3. Bad news for Daniel (00:02:44)

4. Gartner hype cycle (00:05:41)

5. !sexy AI services (00:09:55)

6. ML to AI Engineers (00:11:55)

7. LLMs are not a product (00:13:33)

8. Reading off the cycle (00:18:11)

9. Fighting confusion (00:19:47)

10. Drawing boundries (00:21:37)

11. AI vs ML engineer (00:23:21)

12. Prompt engineering fad (00:23:45)

13. AI TRiSM (00:24:10)

14. Synthetic data (00:25:28)

15. What's missing (00:26:48)

16. Demetrios' contribution (00:28:16)

17. Where is multi-modal AI? (00:30:00)

18. Transformers and SLMs (00:31:34)

19. Sponsor: Motific (00:34:22)

https://www.motific.ai/

20. DB AI tools (00:36:24)

21. Where's RAG? (00:38:11)

22. Demetrios' AI hyped items (00:39:56)

23. Fighting AI nepotism (00:44:10)

24. Broccoli AI (00:45:54)

25. Unsustainable AI (00:49:17)

26. Neighborly AI (00:50:30)

27. No vectors (00:51:15)

28. Thanks for joining us! (00:52:04)

29. Outro (00:54:23)

306 епізодів

#Artificial Intelligence #Changelog #Machine Learning #Deep Learning #Neural Networks #Computer Vision #Changelog Media #Tech