How to Learn AI from First Principles?

The conversation started with Person 1 suggesting a unique approach to learning about neural networks. They encouraged readers to think about how a neural network should function from scratch, without reading about existing implementations. This approach, they argued, could inspire new ideas and potentially lead to improvements over current AI technologies. Person 1 emphasized the importance of starting with a blank slate and building a neural network from the ground up, without being influenced by existing architectures or techniques. They suggested that this approach could lead to novel solutions and a deeper understanding of neural networks.

Person 2 chimed in, discussing the concept of "first principles" and how it has evolved over time. They noted that in machine learning, there aren't many absolute truths, and most research involves building and testing new approaches. Person 2 provided a nuanced discussion of the term "first principles," tracing its origins to philosophical and scientific traditions. They argued that in the context of machine learning, "first principles" is often used loosely to refer to a focus on fundamental concepts and techniques, rather than a strict adherence to absolute truths.

Person 3 recommended a resource called Neural Networks From Scratch, which they found helpful for learning about neural networks. They also suggested exploring statistics and machine learning outside of deep learning, highlighting the importance of understanding the broader context of machine learning. Person 3 shared their personal experience with Neural Networks From Scratch, praising its clarity and effectiveness in explaining complex concepts.

Person 4 recommended watching Karpathy's "Zero to Hero" videos on YouTube, which provide a historical perspective on neural networks. They also suggested reading about the work of McCullough, Pitts, Minsky, and Papert, which laid the foundation for modern neural networks. Person 4 provided a detailed overview of the historical development of neural networks, highlighting key milestones and contributions from pioneering researchers.

Person 5 recommended 3blue1brown's series on neural networks, which provides a high-level overview of the topic. They praised the series for its clarity and visual explanations, suggesting that it would be helpful for readers looking for a comprehensive introduction to neural networks.

Person 6 shared their experience taking Andrew Ng's machine learning course on Coursera, which they found helpful for learning the basics of machine learning. They praised the course for its structured approach and practical exercises, suggesting that it would be a valuable resource for readers looking to gain a solid foundation in machine learning.

Person 7 jokingly suggested brushing up on linear algebra and then "drawing the rest of the fucking owl." This humorous comment was met with laughter and appreciation from other participants in the conversation.

Person 8 emphasized the importance of having a solid background in math, particularly calculus, linear algebra, discrete math, probability theory, and information theory. They argued that a strong foundation in these subjects is essential for understanding machine learning concepts and techniques.

Person 9 recommended Ian Goodfellow's textbook "Deep Learning." They praised the book for its comprehensive coverage of deep learning techniques and its clear explanations of complex concepts.

Person 10 suggested using the a16z AI canon, a free and open-source resource that provides a historical and structured approach to learning about AI. They praised the canon for its breadth and depth, suggesting that it would be a valuable resource for readers looking to gain a comprehensive understanding of AI.

Here's a question: If you all are so smart and know so much about neural networks and machine learning, why are you still arguing about the best way to learn about it?