Cool, thanks! I didn't know about the third and the fourth.

After checking everything from comments here, I have enough material that it'll take me months to work through it all.

Also if you don't know it, Meaningness has some interesting remarks about formulating concepts and problem descriptions: http://meaningness.com/metablog/how-to-think#feynman-objects

I have also started a pursuit of learning useful concepts/models explicitly.

Some useful resources:

• Model Thinking course on Coursera

• Mental models by Gabriel Weinberg

• Creating a Latticework of Mental Models: An Introduction

• And, of course, the Useful Concepts Repository @ LessWrong

Pareto fellowship

Hi Victoria,

I am highly interested in AI safety research. Unfortunately, I do not have a strong math background and I live in an area distant from AI research. After spending some time thinking about my future I have come to the decision to go for a math intensive PhD in some area not far from MIRI or FLI. I have only the bachelor degree in Engineering with major in Computer Science and Software Engineering. Currently, I spend most of my time working full time as a software developer, preparing for a GRE general exam and thinking about PhD and FAI.

Andrew Critch from MIRI and Berkeley is very enthusiastic about pursuing the PhD. He suggested the Statistics. I would be glad to know your opinions about PhD/AI & FAI research. Here is a list of some questions, which are bothering me.

• What do you think would be more relevant for AI safety research - CS, Statistics or something else?
• What areas of research are the most promising for AI safety, in your opinion?
• Is it better to pick the research area close to what MIRI working on, or a more general AI research one (such as a ML).
• Is it possible to increase the chances of successful admission by gaining some research experience before the admissions in this year? Or is it better to spend the time in some other way?
• Does the Math GRE subject test increase the chance of admission?

Hi,

I have two areas I'd like to study: deep learning, and anything on the MIRI research guide. Lots of material is available on both topics, but I'd like to pair up with someone to build a good learning strategy (for lack of a better expression).

I have some knowledge of algebra, probability theory, logic, game theory, machine learning (Master's Degree in Computer Science).

Regarding deep learning, I have a small collection of links, Udacity, and I'm positive learning materials abound now that the field is really popular.

Regarding MIRI's research guide, well, the guide itself provides a lot of links and pointers.

My timezone is CET (UTC+1).

Although this may not be for everyone, I'd recommend listening to audiobooks. The main advantage is that you can easily listen to them while walking or taking public transport, while cooking, while exercising, etc., which I personally find makes these activities a lot less boring.

I've also found that my personal rate of reading is faster with audiobooks (using RockBox with an mp3 player to speed up playback to 3-3.5x) than with normal reading, at something like ~450 words/min or ~1.3 pages/min. Most of the speed increase comes from me being really slow at reading normally due to getting distracted, focusing too much on thinking through one part, or just forgetting to read quickly, but still.