Inspiration.md

• http://www.nvidia.com/docs/IO/116711/sc11-multi-gpu.pdf

• http://dl.acm.org/citation.cfm?id=2002026

  • Evolving CUDA PTX programs by quantum inspired linear genetic programming

• On Intelligence - Hawkins (Numenta)

  • in \Literature

• http://www.wolframscience.com/nksonline/toc.html

  • New Kind of Science by the creator of mathematica/wolfram on the automation of pure mathematics (theorems and so on)

• http://www.scholarpedia.org/article/Echo_state_network

• http://homepages.cwi.nl/~sbohte/publication/paugam_moisy_bohte_SNNChapter.pdf

  • starting from page 32 reservoir computing

• http://lcn.epfl.ch/~gerstner/

  • presentations books etc on neural networks

• http://research.microsoft.com/apps/pubs/?id=209355

  • microsoft book about deep learning
  • also in \AI\Education\DeepLearning-NowPublishing-Vol7-SIG-039.pdf

• http://www.eecg.toronto.edu/~jayar/pubs/kuon/foundtrend08.pdf

  • about FPGA/ASIC? programming

• http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf

  • A Few Useful Things to Know about Machine Learning
  • also in literature folder

• Elements of Information - Cover, T. M., and Thomas, J. A. (1991) , Theory, Wiley, New York.

  • contains also: Data Compression: Examples of codes, Kraft inequality, Optimal codes, Bounds on the optimal codelength, Huffman codes, Shannon-Fano-Elias coding, Arithmetic coding, and so on..
  • Information Theory and the Stock Market: Kuhn-Tucker characterization of the log-optimal portfolio, Investment in stationary markets,

• The Mindful brain - Edelman, Mountcastle - 1978

  • in literature folder, or * http://homes.mpimf-heidelberg.mpg.de/~mhelmsta/pdf/1978%20Mountcastle%20book.pdf
  • velikost cca 40x A5

• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773171/

  • od Rada v souvislosti s intrinsic plasticity
  • popisuji samoorganizujici rekurentni sit s 3 druhy plasticity, vsechny jsou pry nutne pro funkcnost
  • "n the following, we present a RNN of threshold units combining three different forms of plasticity that learns to efficiently represent and “understand” the spatio-temporal patterns in its input."

• Principles of Synthetic Intelligence - Dorner 1999, Bach 2003, 2009

  • they talk about this book in the MicroPsi project * http://www.micropsi.com/

• https://www.youtube.com/watch?v=EK61htlw8hY

  • Geoffrey Hinton: how to increase accuracy of classifiers? -> make ensembles -> but low ratio of information/parameter -> learn new model to approximate the probabilities of the ensemble

• http://dippl.org/

  • probabilistic programming langagues

• http://plato.stanford.edu/entries/computer-science/

  • Stanford: The Philosophy of Computre Science

• http://www.nature.com/news/nature-makes-all-articles-free-to-view-1.16460

  • magazine Nature makes all its articles free online

• https://scottlocklin.wordpress.com/2014/07/22/neglected-machine-learning-ideas/

  • fields in Machine Learning, which are not very well covered in articles/books -> needs research / review

• https://github.com/prakhar1989/awesome-courses

  • list of awesome university courses (online?)

• http://www.catb.org/~esr/faqs/hacker-howto.html

  • what is a (white hat) hacker and how to become one

• http://aurellem.org/society-of-mind/

  • The Society of Mind by Marvin Minsky

• A Framework for Scalable Cognition

  • "Propagation of challenges, towards the implementation of Global Brain models"

• Coding is not the new literacy

• How to write a 21st century proof

  • motivational article A Computer Scientist Tells Mathematicians How To Write Proofs
  • by Leslie Lamport, a 2013 Turing award winner, who introduces a more concise and hierarchical method for writing mathematical proofs

• Neural Networks and Intellect: Using Model-Based Concepts

  • by Leonid I. Perlovsky (2001)

• Why Philosophers Should Care About Computational Complexity

  • concept of computability has helped philosophy, can computational complexity bring something new?
  • when reading abstract and conclusion parts it reminded me of Probably Approximately Correct by Leslie Valiant, the author of PAC learning theory
  • other articles from this author seem interesting as well

• Unsupervised Feature Learning and Deep Learning

  • list of recommended readings

• Is Parallel Programming Hard, And, If So, What Can You Do About It?

• MIT's Prof. Sussman's Reading List

• Pei Wang's Reading List

  • The following list is a partial education plan for students interested in the research of Artificial General Intelligence. It includes materials for roughly 30 one-semester courses.

• List of Unsolved Scientific Problems with Large Monetary Prizes

• Information geometry

  • "This subject, usually considered a branch of statistics, has important applications to machine learning and somewhat unexpected connections to evolutionary biology."

• The Hacker Shelf

  • Collection of free books for the intellectually curious

• Clever Algorithms: Nature-Inspired Programming Recipes

  • found thanks to The Hacker Shelf

• Street-Fighting Mathematics

  • The Art of Educated Guessing and Opportunistic Problem Solving
  • found thanks to The Hacker Shelf

• Polychronous neuronal groups

  • by Izhikevich
  • relevant to Neural Group Selection (Edelman), Liquid state machines / Echo state networks

• The Many-Worlds Interpretation of Quantum Mechanics

• State, Anarchy and Collective Decisions

  • Some Applications of Game Theory to Political Economy
  • Abram Demski recommended this to me

• Principles of synthetic intelligence

  • by Joscha Bach

• Classifier Systems and Genetic Algorithms

  • by Booker, Goldberg, Holland
  • Abram Demski recommended this to me as a "pattern based theory of mind"

• GitXiv

  • ArXiv + Git together