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Learning from Machine Learning

Paige Bailey: Google Deepmind, LLMs, Power of ML to improve code | Learning from Machine Learning #5

May 19, 2023
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.
01:08:01

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Podcast summary created with Snipd AI

Quick takeaways

  • Machine learning models have evolved from simple statistics to highly capable, general-purpose models that are revolutionizing various domains.
  • Fine-tuning and instruction-tuning play a crucial role in improving the performance of machine learning models.

Deep dives

The Power of Generative Models and Advancements in AI

Machine learning practitioners discuss the challenges, advancements, and opportunities in the field. Paige Bailey, the lead product manager for generative models at Google DeepMind, shares her journey and excitement about the current state of machine learning. She highlights the evolution of machine learning models from simple statistics to highly capable, general-purpose models that are revolutionizing various domains. Bailey also discusses the impact of pre-training data mixtures on model performance and emphasizes the importance of fine-tuning and instruction-tuning. She explores the role of generative models in software development and the potential for multimodal models. Finally, she offers insights on navigating the hype and reality of AI, staying up-to-date with the latest advancements, and appreciating the unique qualities of being human.

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