Introduction to Large Language Models

This episode provides a concise overview of Large Language Models (LLMs), AI systems that generate human-like text using transformer-based neural networks. It explores how LLMs learn from large datasets and highlights real-world applications such as conversational AI, document summarization, healthcare diagnostics, personalized education, and creative tasks like writing and coding. The episode also covers customization through prompt tuning and emphasizes responsible development to address biases and ensure transparency.

Reference -

1. Introduction to Large Language Models: https://developers.google.com/machine-learning/resources/intro-llms
2. Getting Started with LangChain + Vertex AI PaLM API: https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/orchestration/langchain/intro_langchain_palm_api.ipynb  
3. Learn about LLMs, PaLM models, and Vertex AI: https://cloud.google.com/vertex-ai/docs/generative-ai/learn-resources  
4. Training Large Language Models on Google Cloud: https://github.com/GoogleCloudPlatform/llm-pipeline-examples
5. Prompt Engineering for Generative AI: https://developers.google.com/machine-learning/resources/prompt-eng  
6. PaLM-E: An embodied multimodal language model: https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html  
7. Parameter-efficient fine-tuning of large-scale pre-trained language models: https://www.nature.com/articles/s42256-023-00626-4  
8. Parameter-Efficient Fine-Tuning of Large Language Models with LoRA and QLORA: https://www.analyticsvidhya.com/blog/2023/08/lora-and-glora/  
9. Solving a machine-learning mystery: https://news.mit.edu/2023/large-language-models-in-context-learning-0207
10. Background: What is a Generative Model?: https://developers.google.com/machine-learning/gan/generative
11. Gen AI for Developers: https://cloud.google.com/ai/generative-ai#section-3  
12. Ask a Techspert: What is generative AI?: https://blog.google/inside-google/googlers/ask-a-techspert/what-is-generative-ai/
13. What is generative AI?: https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai
14. Building the most open and innovative AI ecosystem: https://cloud.google.com/blog/products/ai-machine-learning/building-an-open-generative-ai-partner-ecosystem
15. Stanford U & Google's Generative Agents Produce Believable Proxies of Human Behaviors: https://syncedreview.com/2023/04/12/stanford-u-googles-generative-agents-produce-believable-proxies-of-human-behaviours/
16. Generative AI: Perspectives from Stanford HAI: https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_HAI_Perspectives.pdf
17. Generative AI at Work: https://www.nber.org/system/files/working-papers/w31161/w31161.pdf
18. The implications of Generative AI for businesses: https://www2.deloitte.com/us/en/pages/consulting/articles/generative-artificial-intelligence.html
19. How Generative AI Is Changing Creative Work: https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work
20. Attention is All You Need: https://research.google/pubs/pub46201/
21. Transformer: A Novel Neural Network Architecture for Language Understanding: https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html
22. What is Temperature in NLP?: https://lukesalamone.github.io/posts/what-is-temperature/
23. Model Garden: https://cloud.google.com/model-garden
24. Auto-generated Summaries in Google Docs: https://ai.googleblog.com/2022/03/auto-generated-summaries-in-google-docs.html
25. Few-shot learning: https://www.digitalocean.com/community/tutorials/few-shot-learning
26. Few-shot learning: https://www.ibm.com/think/topics/few-shot-learning
27. Few-shot prompting: https://www.promptingguide.ai/techniques/fewshot
28. Zero-shot prompting: https://www.promptingguide.ai/techniques/zeroshot
29. What are zero-shot prompting and few-shot prompting?: https://machinelearningmastery.com/what-are-zero-shot-prompting-and-few-shot-prompting/
30. NotebookLM - https://notebooklm.google.com/