On Knowing Everything

I want to make ‘Knowing Everything’ exceptionally concrete.

My plan is to create a comprehensive map of all of the knowledge that I want stored in long term memory (a simple example would be the equivalent of an incoming PhD’s knowledge across each of the 10 technical topics in Fields).

Everybody said they wanted to know everything, but never said what they meant by ‘everything’ or by ‘know’. I’m going to dump technical consilience into a spaced repetition system and have it for life.

A Beginning

I’m going to read and deeply understand (in implementation and full problem solving level detail) the first useful technical chapter to me of every one of the texts below. (Texts I’ve read but can’t claim full understanding of are highlighted).

What do I mean by deeply understand?

I will rewrite the entire chapter in my own words, without referring back to the text.
I will complete all problems and exercises in a format that can be reviewed externally.
I will complete some creative implementation or project based on the technical content of the chapter, which will be written up and made externally visible.
Information Geometry and Its Applications (Amari)
Functional and Shape Data Analysis (Srivastava / Klassen)
Information Theory (Cover / Thomas)
Statistical Mechanics (Talman)
Algorithmic Game Theory (Nisan)
Nonlinear Dynamics and Chaos (Strogatz)
Mechanism Design (Borgers)
Algorithms (CLRS)
Neuroscience (Principles of Neural Science)
Electromagnetics (Haliday / Resnick)
Quantum Mechanics (Griffiths)
Nuclear Physics (Krane)
Chemistry (Brown)
Intro Proof (How to Prove It)
Analysis (Abbott)
Set Theory (Halmus)
Abstract Algebra (Dummit, Foote)
Topology (Munkres)
Category Theory (Pierce, then Awodey)
Probability Theory: The Logic of Science (Jaynes)
Machine Learning: A Probabilistic Perspective
Computational Learning Theory (Kearns)
Learning Invariant Representations (Poggio)
Causality (Pearl)
Computability and Logic (Boolos)

Source: Original Google Doc