Unlocking the Potential of AI

on YouTube ↗01h 01m

" summary

Microsoft and GitHub demonstrate practical stacks: GPT-3 175B / 9 days / $4.6M and models with 32k token context windows, Azure OpenAI with Azure Cognitive Search vector embeddings and hybrid search, plus function calling and content safety filtering. A live demo builds a RAG app by uploading markdown to blob storage, indexing with 1536‑dim embeddings, deploying an AAD‑protected co‑pilot, and returning grounded answers. GitHub Copilot Chat edits code, fixes bugs and generates unit tests; internal metrics report 75% happier developers and 96% faster completions.

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" chapters (27)

1. 0:00Introduction to AI
2. 1:34AI birth in 1955
3. 3:04Shaky robot and AI evolution
4. 4:05Machine learning and generative AI
5. 5:31Traditional vs foundation models
6. 7:17GPT-3 scale and training costs
7. 9:04AI via natural language prompts
8. 11:18System prompts and guardrails
9. 13:49Structured data from unstructured input
10. 15:13Context windows in AI models
11. 15:58GitHub Copilot Chat demo
12. 20:59Using GitHub Copilot to fix bugs and generate tests
13. 23:26Responsible AI and limitations
14. 24:27Copilot and developer productivity
15. 27:14Copilot features and future plans
16. 30:25Custom models and GitHub Universe
17. 31:12How LLMs work
18. 34:46AI apps with your data
19. 37:15Azure OpenAI with custom data demo
20. 43:04Retrieval augmented generation
21. 45:35Vector search and semantic similarity
22. 49:09Vector search demo
23. 51:40Building retrieval app in Python
24. 56:28Prompt flow and productionizing AI
25. 57:33AI safety with Azure Content Safety
26. 58:15Azure Content Safety filters demo
27. 1:00:04Closing remarks and resources

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