This educational podcast episode is designed to explain the complex topic of generative AI to beginners. The episode covers the fundamentals of generative AI, including how it differs from traditional machine learning, explores specific types of generative AI like diffusion models (for images) and large language models (LLMs, for text), and discusses the potential benefits and challenges associated with this technology. It also delves into a real-world study of generative AI's impact on customer service agents, highlighting how AI can augment human capabilities and potentially lead to new job creation. Finally, the episode touches on future trends in generative AI, such as multimodal AI and increasing accessibility, emphasizing the importance of ethical development and responsible use. It's suitable for anyone interested in learning about AI, particularly those without a strong technical background.

Reference -

1. Ask a Techspert: What is generative AI? https://blog.google/inside-google/googlers/ask-a-techspert/what-is-generative-ai/  


2. What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai  


3. Google Research, 2022 & beyond: Generative models: https://research.google/blog/google-research-2022-beyond-language-vision-and-generative-models/  


4. Building the most open and innovative AI ecosystem: https://cloud.google.com/blog/products/ai-machine-learning/building-an-open-generative-ai-partner-ecosystem


5. 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/  


6. Generative AI: Perspectives from Stanford HAI: https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_HAI_Perspectives.pdf  


7. Generative AI at Work: https://www.nber.org/system/files/working_papers/w31161/w31161.pdf  


8. How Generative AI Is Changing Creative Work: https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work  


9. NLP's ImageNet moment has arrived: https://thegradient.pub/nlp-imagenet/


10. LaMDA: our breakthrough conversation technology: https://blog.google/technology/ai/lamda/


11. PaLM-E: An embodied multimodal language model: https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html


12. PaLM API & MakerSuite: an approachable way to start prototyping and building generative AI applications: https://developers.googleblog.com/2023/03/announcing-palm-api-and-makersuite.html  


13. The Power of Scale for Parameter-Efficient Prompt Tuning: https://arxiv.org/pdf/2104.08691.pdf


14. Solving a machine-learning mystery: https://news.mit.edu/2023/large-language-models-in-context-learning-0207


15. Attention is All You Need: https://research.google/pubs/pub46201/


16. Transformer: A Novel Neural Network Architecture for Language Understanding: https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html


17. What is Temperature in NLP? https://lukesalamone.github.io/posts/what-is-temperature/  


18. Model Garden: https://cloud.google.com/model-garden


19. Auto-generated Summaries in Google Docs: https://ai.googleblog.com/2022/03/auto-generated-summaries-in-google-docs.html


20. NotebookLM