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/