# Study plan

I want to be stronger. This is true in general, but in this case specifically I want to save the world and so need the resources to do so. The current main candidate for X-risk seems to be AI, so it would be rational to power up in order to help in that epic battle. So this is a list of skills that would be useful, along with sources from which to learn more. Sort of a skill tree?

# Want to learn

## AI Safety

## Calculus

- Optimization problems. Pity I didn’t listen during the simulation classes in university…
- Multivariate differentials

## Deep learning

## Entropy

## Game theory

## Linear Algebra

## Probability

- Probability Theory: The Logic of Science - this explains why Bayes was right, starting from a few core assumptions, all the way to showing how to do science properly

## Probabalistic programming

## Proofs

- What would be a good place to learn how to do mathematical prooving?
- TLA?

## Reinforcement learning

- Maybe go through these?

## Transformers

- Find a good explanation for them

## Writing skilz

- Probably mainly a matter of practice?

# Want to make

## AI Safety

- A good god - obviously
- A dependency graph of what is required for alignment?
- A general overview of what won’t work and why
- Reviews/summaries of AI/alignment papers

## Deep learning

- Anime recommendation system - input your ratings for various anime, get a list of predicted ratings
- Edible plant recognizer - input a picture of a plant, get a predicted species name
- bird song / bat sound recognizer - input a recording of a flying critter, get the species name out
- Working neural nets from scratch - various architectures to get a good grasp on them

## Game theory

- Run a load of MCMC experiments to really grok competition
- The Darwin game

## Reinforcement learning

- Blackjack player - David Silver’s assignment from the RL lectures

## Probabilistic programming

- Analyze what effects animal mortality on roads
- Simulate how resinging effects song rates, i.e. rather than manually simulating agents, do it via e.g. Pyro

## Transformers

- Implement GTP2?

## Writing

- More blog posts
- Paper summaries

# Learned

## AI safety

## Ecology

- A bachelor degree in environmental biology should suffice for the basics

## Calculus

- University over 10 years ago - the basics are there, but only enough to know where to look for more
- Better explained - gives an intuitive understanding of the basic ideas. Doesn’t go into the nitty gritty of how to solve derivatives, but what it does explain is enough to actually understand what’s going on. It also helps that it makes the quotient rule an obvious and easily derived equation

## Game theory

## Linear Algebra

- University over 10 years ago - the basics are there, but only enough to know where to look for more

## Programming

## Neural networks

- CS231n - a Stanford class for NNs - a very good primer to introduce the basic ideas
- Neural Networks and Deep Learning - a whole book going into NN details from scratch

## Rationality

## Reinforcement learning

- David Silver - Reinforcement Learning - a basic overview of what it’s all about