In this podcast episode, Jeremy Howard, the founder of Fast AI, discusses the mission of Fast AI which aims to make deep learning accessible to everyone, regardless of their educational background. The conversation explores the development and potential usefulness of language models in various domains, with a focus on vision, tables of data, collaborative filtering, and text. Jeremy also discusses the journey of language models, the concept of fine-tuning, and the challenges faced in promoting transfer learning. Throughout the episode, Jeremy's commitment to democratizing technology and creating solutions for a wider population is highlighted. The impact of Fast AI's deep learning library and the importance of accessible software are also emphasized. Furthermore, the episode addresses the challenges of finding meaningful discussions in AI-related discords and the need for more open environments for intellectual exchange. The potential of Swift for Tensorflow and the significance of investing time in language frameworks are also touched upon. Overall, this episode provides insights into the work of Fast AI and its mission to make deep learning accessible to all.
Main points
• Jeremy Howard's background and the mission of Fast AI.
• The motivation behind the development of language models and their potential usefulness in different domains.
• The focus areas for language models: vision, tables of data, collaborative filtering, and text.
• The importance of making deep learning accessible to everyone regardless of educational background.
• The journey of language models and the concept of fine-tuning.
• Challenges faced in promoting transfer learning and fine-tuning.
• Jeremy Howard's dedication to democratizing technology and creating solutions for a larger population.
• The impact of Fast AI's deep learning library and courses.
• The challenges of accessing meaningful discussions in AI-related discords.
• The importance of more open and accessible environments for intellectual exchange.
• Jeremy Howard's involvement in the development of Swift for Tensorflow.
• The potential and challenges of small models and the importance of fine-tuning in deep learning.
• The evolving landscape of deep learning for coders and the need for exploration and experimentation.
• The importance of flash attention and the barriers to easy building in the AI field.
• The exploration of AI's capabilities and the unanswered questions in the field.
