It worth to note one more thing - I'm not really skilled Bayesian and rationalist, but I do my best and I'm currently studying. So far I've finished HPMOR, An Abridged Introduction to Less Wrong and now I'm working on core sequences. There I've just finished Map and Territory.

So please post (1) the one article that you think newcomers should read, to maximize the chance that they read more; and (2) articles you think should be in the first ten articles that a newcomer reads.

The order in which I would have liked to be introduced to Less Wrong (introduction, wisdom, insight, education, excitement, novelty, fun):

Introduction

• Twelve Virtues of Rationality
• An interview with Eliezer Yudkowsky (Parts 1, 2 and 3)

Wisdom & Insight

• Diseased thinking: dissolving questions about disease
• Self-fulfilling correlations
• Probability is in the Mind
• You're Entitled to Arguments, But Not (That Particular) Proof
• Efficient Charity: Do Unto Others...
• Newcomb's Problem and Regret of Rationality
• Occam's Razor
• Confidence levels inside and outside an argument

Education

• An Intuitive Explanation of Bayes’ Theorem

Excitement, Novelty, Fun

• The Apologist and the Revolutionary
• Beyond the Reach of God
• The mathematical universe: the map that is the territory
• That Alien Message
• A Much Better Life?

More

• List of all articles from Less Wrong (In chronological order.)
• References & Resources for LessWrong

Hello.

I was searching more about my interests and I've found a opportunity has a nice Bachelor's topic in maths/informatics/neuroscience. I was offered two topics:

• Analyse properties of correlation matrix graphs (e.g small-world property)
• Conditional mutual information - how to detect synergy and which values it can takes when there is restriction on cardinality of given variables.

Both are connected with neuroscience (e.g the correlation matrix is created by brain activity, variables are activities of different parts of brain etc.)

Does anyone have any informations or advices to this?

By the first two bullet points, do you mean you want to form formalized models of naturalized induction?

I've written an essay on the effects of interactive computation as an improvement for Solomonoff-like induction. (It was written in two all-nighters for an English class, so it probably still needs proofreading. It isn't well-sourced, either.) Do you mean things like that? I want to form a better formalization of naturalized induction than Solomonoff induction, one designed to be usable by space-, time-, and rate-limited agents, and interactive computation was a necessary first step. AIXI is by no means an ideal inductive agent.

I very much want to be hired for work like this.

You'll probably want to be either the sort of good at math who regularly leaves "is good at math" people in the dust or be prepared to work quite hard.

At least if you're going by the "AGI is all about math" route. If one takes the "AGI is more about cognitive science and psychology" approach, then they don't necessarily need to be quite that good at math, though a basic competence is still an absolute must.

You could look at the papers published in past AGI conferences for people/topics that seem relevant or worthwhile. E.g. AGI 2013, AGI 2012. There's also a Journal of Artificial General Intelligence whose past issues you could browse.

For something more "mainstream-friendly", there are the various computational cognitive architectures that have been developed in cognitive psychology, such as ACT-R and LIDA. Stan Franklin, one of the people behind LIDA, has also had some involvement with the AGI community.

Am I naive to hope that I can do anything useful and fulfilling (based on the given data) in this area ("strong AI")?

I don't know what the requirements for a Bachelor's thesis at your university are, but at mine, you could do a Bachelor's thesis that was basically just a literature review and didn't even try to produce any new information. Even if you couldn't actually contribute anything new at this point, even taking this opportunity to familiarize yourself with the existing work in the field would probably be useful for your future efforts.