Hi, Dan! Just want to say thanks for your work on this topic. I really loved your book so I wanted to share it with the HN community. Supervised learning always seemed to rub me the wrong way, both when I learned it in college and when I saw it used in practice in industry.

I was led to your book by recent research in self-supervised learning by LeCun et al [1] [2]. Since reading your book, I have been digging into the work by Rissanen [3], Grunwald [4], and Hinton [5], among many others. I'm trying to build up my knowledge so that I can apply it to TinyML [6] (e.g. running a neural network on a microcontroller with 256kb of RAM). In a TinyML context, power usage must be low and labeled data is non-existent. I have a vague intuition of how MDL can be used to guide the engineering constraints of TinyML, and I'm hoping to formalize this in my research.

Dan, if you know of any papers or research groups that would be related to this area, I'd love to read more about it.

[1] https://ai.meta.com/blog/self-supervised-learning-the-dark-m...

[2] https://ai.meta.com/blog/yann-lecun-ai-model-i-jepa/

[3] https://link.springer.com/book/10.1007/978-0-387-68812-1

[4] https://mitpress.mit.edu/9780262529631/the-minimum-descripti...

[5] https://www.researchgate.net/publication/5920308_To_recogniz...

[6] https://sites.google.com/g.harvard.edu/tinyml/home