# The AGI Landscape **The AGI Landscape** $$\Omega$$ is going to push the boundary of artificial general intelligence. $$ \mathbf{\Omega} = \underset{\theta}{\arg\max}\ \mathcal{AGI}(\theta) $$ ## Concepts * [Kolmogorov complexity](https://agi.university/master) ## Frameworks ## [Important Papers](https://agi.university/papers-1) * [Universal Transformers ](https://agi.university/universal-transformers) * [The Forget-me-not Process](https://agi.university/forget-me-not-process) * [AGI Safety Literature Review](https://arxiv.org/pdf/1805.01109.pdf) : summary of general safety research in agi * [Out-of-sample extension of graph adjacency spectral embedding](https://www.stat.berkeley.edu/~mmahoney/pubs/levin18a.pdf): consider the problem of obtaining an out-of-sample extension for the adjacency spectral embedding, a procedure for embedding the vertices of a graph into Euclidean space. * [Alignment for Advanced Machine Learning Systems](https://intelligence.org/files/AlignmentMachineLearning.pdf) * [Measuring and avoiding side effects using relative reachability](https://arxiv.org/pdf/1806.01186.pdf): introduces a general definition of side effects, based on relative reachability of states compared to a default state, that avoids these undesirable incentives. Nov 1. [The Importance of Sampling in Meta-Reinforcement Learning](http://papers.nips.cc/paper/8140-the-importance-of-sampling-inmeta-reinforcement-learning.pdf) 2. [Inequity aversion improves cooperation in intertemporal social dilemmas](http://papers.nips.cc/paper/7593-inequity-aversion-improves-cooperation-in-intertemporal-social-dilemmas.pdf) ## Books ### Probability * **R. Durrett** [Probability: Theory and Examples (4th edition)](http://www.amazon.com/Probability-Cambridge-Statistical-Probabilistic-Mathematics/dp/0521765390). * **P. Billingsley** Probability and Measure (3rd Edition). Chapters 1-30 contain a more careful and detailed treatment of some of the topics of this semester, in particular the measure-theory background. Recommended for students who have not done measure theory. * **R. Leadbetter et al** [A Basic Course in Measure and Probability: Theory for Applications ](http://www.amazon.com/gp/product/1107652529/)is a new book giving a careful treatment of the measure-theory background. There are many other books at roughly the same \`\`first year graduate" level. Here are my personal comments on some. * **D. Khoshnevisan** [Probability ](http://www.amazon.com/gp/product/1107652529/)is a well-written concise account of the key topics in 205AB. * **R. Bhattacharya and E. C. Waymire** [A Basic Course in Probability Theory](https://www.amazon.com/Basic-Course-Probability-Theory-Universitext/dp/3319479725) is another well-written account, mostly on the 205A topics. * **K.L. Chung**[ A Course in Probability Theory](http://www.amazon.com/gp/product/1107652529/) covers many of the topics of 205A: more leisurely than Durrett and more focused than Billingsley. * **D. Williams** [Probability with Martingales ](http://www.amazon.com/Probability-Martingales-Cambridge-Mathematical-Textbooks/dp/0521406056/)has a uniquely enthusiastic style; concise treatment emphasizes usefulness of martingales. * **Y.S. Chow and H. Teicher**[ Probability Theory: Independence, Interchangeability, Martingales ](http://www.amazon.com/Probability-Theory-Independence-Interchangeability-Martingales/dp/0387406077/). Uninspired exposition, but has useful variations on technical topics such as inequalities for sums and for martingales. * **R.M. Dudley**[ Real Analysis and Probability](http://www.amazon.com/Analysis-Probability-Cambridge-Advanced-Mathematics/dp/0521007542). Best account of the functional analysis and metric space background relevant for research in theoretical probability. * **B. Fristedt and L. Gray**[ A Modern Approach to Probability Theory](http://www.amazon.com/Modern-Approach-Probability-Theory-Applications/dp/0817638075/). 700 pages allow coverage of broad range of topics in probability and stochastic processes. * **L. Breiman**[ Probability](http://www.amazon.com/Probability-Classics-Applied-Mathematics-Breiman/dp/0898712963/). Classical; concise and broad coverage. * **O. Kallenberg** [Foundations of Modern Probability](http://www.amazon.com/Foundations-Modern-Probability-Its-Applications/dp/0387953132). Quoting an amazon.com reviewer: \`\`.... a compendium of all the relevant results of probability ..... similar in breadth and depth to Loeve's classical text of the mid 70's. It is not suited as a textbook, as it lacks the many examples that are needed to absorb the theory at a first pass. It works best as a reference book or a "second pass" textbook." * **John B. Walsh** [Knowing the Odds: An Introduction to Probability](http://www.amazon.com/Knowing-Odds-Introduction-Probability-Mathematics/dp/0821885324). New in 2012. Looks very nice -- concise treatment with quite challenging exercises developing part of theory. * **George Roussas** [An Introduction to Measure-Theoretic Probability](http://www.amazon.com/Introduction-Measure-Theoretic-Probability-Second/dp/0128000422/ref=asap_B00JALV1Z8_1_1?s=books\&ie=UTF8\&qid=1412368880\&sr=1-1). Recent treatment of classical content. * **Santosh Venkatesh** [The Theory of Probability: Explorations and Applications](http://www.amazon.com/Theory-Probability-Explorations-Applications/dp/1107024471). Unique new book, intertwining a broad range of undergraduate and graduate-level topics for an applied audience. * **I. Florescu** [Probability and Stochastic Processes](http://www.amazon.com/Probability-Stochastic-Processes-Ionut-Florescu/dp/0470624558/ref=sr_1_2?s=books\&ie=UTF8\&qid=1427746625\&sr=1-2\&keywords=florescu). Very clearly written, and with 550 pages gives a broad coverage of topics including intro to SDEs. * Jim Pitman has his [very useful lecture notes](http://bibserver.berkeley.edu/205/WorkInProgress/DurrettTOC.html) linked to the Durrett text; these notes cover more ground than my course will! Also some [lecture notes by Amir Dembo ](http://www-stat.stanford.edu/~amir/stat-310b/lnotes.pdf)for the Stanford courses equivalent to our 205AB. ## Reference 1. The `Books`: , by Professor David Aldous from UC Berkeley. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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