Keynote: Transformers: The Rise of ChatGPT
summary
Presentation explains the Transformer architecture and the GPT family timeline with concrete numbers — GPT‑1 17M, GPT‑2 1.5B, GPT‑3 175 billion parameters, GPT‑3.5 fine‑tuned for dialogue and GPT‑4 multimodal — and describes RLHF as the finetuning method. Live demos claim image recognition (dog breed), translation (good morning → bom dia), website summarization, Excel upload that generates Python and charts, DALL·E‑3 image generation via the OpenAI API and Postman, and code optimization. It also shows a fabricated “Kesha Air” example to illustrate hallucinations, notes cutoff dates, privacy settings and prompt engineering techniques including zero/few‑shot, chain‑of‑thought and RAG.
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chapters (14)
- 0:00Introduction
- 4:37Transformers in AI and ChatGPT
- 7:09Origins of natural language processing
- 11:10Generative AI and foundation models
- 14:28ChatGPT multi-use demo
- 19:48Transformer language processing
- 23:46GPT models evolution
- 27:01Fine-tuning ChatGPT
- 29:33ChatGPT business use cases
- 34:19Data analysis and code demos
- 40:30LLM limitations and risks
- 45:13Prompt engineering strategies
- 49:40Future of AI
- 55:04Q&A
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