AGI University
  • The AGI Landscape
  • 我们的愿景 Our vision
  • Papers
  • Rationality and intelligence
  • AI safety gridworlds
  • Modeling Friends and Foes
  • Forget-me-not-Process
  • Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study
  • Universal Transformers
  • Graph Convolutional Policy Network
  • Thermodynamics as a theory of decision-making with informationprocessing costs
  • Concrete Problems in AI Safety
  • A course in game theory
  • Theory of games and economic behavior
  • Reinforcement learning: An introduction 1e
  • Regret analysis of stochastic and nonstochastic multi-armed bandit problems
  • The nonstochastic multiarmed bandit problem
  • Information theory of decisions and actions
  • Clustering with bregman divergences
  • Quantal Response Equilibria for Normal Form Games
  • The numerics of gans
  • The Mechanics of n-Player Differentiable Games
  • Reactive bandits with attitude
  • Data clustering by markovian relaxation and the information bottleneck method
  • Information bottleneck for Gaussian variables
  • Bounded Rationality, Abstraction, and Hierarchical Decision-Making: An Information-Theoretic Optimal
  • Risk sensitive path integral control
  • Information, utility and bounded rationality
  • Hysteresis effects of changing the parameters of noncooperative games
  • The best of both worlds: stochastic and adversarial bandits
  • One practical algorithm for both stochastic and adversarial bandits
  • An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits
  • Friend-or-Foe Q-Learning in General-Sum Games
  • New criteria and a new algorithm for learning in multi-agent systems
  • Correlated Q-Learning
  • Learning to compete, coordinate, and cooperate in repeated games using reinforcement learning
  • Learning against sequential opponents in repeated stochastic games
  • On the likelihood that one unknown probability exceeds another in view of the evidence of two sample
  • An empirical evaluation of Thompson Sampling
  • What game are we playing? end-to-end learning in normal and extensive form games
  • Intriguing properties of neural networks
    • Untitled
  • Explaining and harnessing adversarial examples
  • go-explore
  • The Landscape of Deep Reinforcement Learning
  • 用因果影响图建模通用人工智能安全框架
  • Papers
    • test
    • Measuring and avoiding side effects using relative reachability
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  • Concepts
  • Frameworks
  • Important Papers
  • Books
  • Probability
  • Reference

The AGI Landscape

Next我们的愿景 Our vision

Last updated 5 years ago

The AGI Landscape Ω\OmegaΩ is going to push the boundary of artificial general intelligence.

Ω=arg⁡max⁡θ AGI(θ)\mathbf{\Omega} = \underset{\theta}{\arg\max}\ \mathcal{AGI}(\theta)Ω=θargmax​ AGI(θ)

Concepts

  • Kolmogorov complexity

Frameworks

https://github.com/deepmind/pysc2

https://pythonprogramming.net/starcraft-ii-ai-python-sc2-tutorial/

Important Papers

  • Universal Transformers

  • The Forget-me-not Process

  • AGI Safety Literature Review : summary of general safety research in agi

  • Out-of-sample extension of graph adjacency spectral embedding: 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

  • Measuring and avoiding side effects using relative reachability: 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

  2. Inequity aversion improves cooperation in intertemporal social dilemmas

Books

Probability

  • R. Durrett Probability: Theory and Examples (4th edition).

  • 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 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 is a well-written concise account of the key topics in 205AB.

  • R. Bhattacharya and E. C. Waymire A Basic Course in Probability Theory is another well-written account, mostly on the 205A topics.

  • K.L. Chung A Course in Probability Theory covers many of the topics of 205A: more leisurely than Durrett and more focused than Billingsley.

  • D. Williams Probability with Martingales has a uniquely enthusiastic style; concise treatment emphasizes usefulness of martingales.

  • Y.S. Chow and H. Teicher Probability Theory: Independence, Interchangeability, Martingales . Uninspired exposition, but has useful variations on technical topics such as inequalities for sums and for martingales.

  • R.M. Dudley Real Analysis and Probability. 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. 700 pages allow coverage of broad range of topics in probability and stochastic processes.

  • L. Breiman Probability. Classical; concise and broad coverage.

  • O. Kallenberg Foundations of Modern Probability. 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. New in 2012. Looks very nice -- concise treatment with quite challenging exercises developing part of theory.

  • George Roussas An Introduction to Measure-Theoretic Probability. Recent treatment of classical content.

  • Santosh Venkatesh The Theory of Probability: Explorations and Applications. Unique new book, intertwining a broad range of undergraduate and graduate-level topics for an applied audience.

  • I. Florescu Probability and Stochastic Processes. 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 linked to the Durrett text; these notes cover more ground than my course will! Also some lecture notes by Amir Dembo for the Stanford courses equivalent to our 205AB.

Reference

  1. The Books: https://www.stat.berkeley.edu/~aldous/205B/index.html, by Professor David Aldous from UC Berkeley.