In this episode, Nathan sits down with Paige Bailey, Lead Product Manager of Generative Models at Google Deepmind. They discuss what it's like to be a PM for an LLM, defining ideal LLM behavior, distinguishing real abilities vs pattern matching, and the exciting projects at Google Deepmind.

Paige Bailey, Lead product manager at Google DeepMind and previously working at Microsoft GitHub, talks about the democratization of AI, advancements in AI-assisted coding, and the ethics of innovation. They also discuss the challenges of training large foundational models, the importance of model size for language models, and the potential impact of generative models on the future of work and education.

Paige Bailey, who began coding at age 9 and now leads the GenAI developer experience at Google, shares insights on the future of software development. She discusses how generative AI is democratizing coding, allowing developers to focus on creativity instead of routine tasks. Bailey also emphasizes that critical thinking and problem-solving have become more vital than traditional coding skills. She sees AI not as a replacement for developers, but as a tool to enhance their work, underlining the importance of effective application in tech.

This week features Paige Bailey, Product Lead for Generative Models at Google DeepMind, who has an impressive background leading AI efforts at Alphabet, GitHub, and Microsoft. She discusses the groundbreaking partnership between Replit and Google, focusing on how tools like Bard and Ghostwriter are reshaping the coding landscape. Topics include tackling remote work challenges for junior engineers, the power of open-source AI in democratizing tech, and the exciting potential of AI in creating personalized user experiences. The future of coding has never looked brighter!

Paige Bailey, lead product manager for generative models at Google DeepMind, discusses her work with machine learning techniques and tools, including the development of large language models like Bard. She explores the benefits and capabilities of Palm V2, the upgraded model for BARD in code, math, reasoning, and multilingual tasks. The chapter also delves into the trade-offs and accessibility of large language models, the exciting use cases of multimodal models, and the impact and importance of large language models in the field of machine learning.