Thursday, April 8, 2010

Universal Artificial Intelligence (Artificial General Intelligence/AGI) - Course Program, Plovdiv University | Универсален Изкуствен Разум - Програма

Todor Arnaudov (aka Tosh, The Twenkid)
researcher, software engineer, versatile artist, (wanna-be) entrepreneur

Age: 25. M.S. Software Engineering 2008 (Second average grade in alumni), B.S. Computer Science 2007 (Best average grade in the alumni) in Plovdiv University. Intern in RIILP Wolverhampton – Computational Linguistics/Natural Language Processing. A polymath, independent researcher and versatile artist, working on establishment of a private company for R&D in Artificial General Intelligence and other fields, as well as a professional film and multimedia studio.

More Info:                  Български  
An interview in a popular-science magazine:
Twenkid Research:
Todor Arnaudov's Researches Blog: http://artificial-mind.blogspot.comTwenkid Studio (mostly in Bulgarian yet):

Universal Artificial Intelligence(Artificial General Intelligence/AGI)

Program as of April 8-th 2010

It will be taught in Faculty of Mathematics and Informatics of Plovdiv University, Bulgaria, starting from 9-th of April.

The course is appropriate for undergraduate students who want to work in the very advanced interdisciplinary field of the Artificial General Intelligence/Universal Artificial Intelligence, also known as Strong AI. It would be interesting also for those who just want to learn what are the current theories of intelligence and how the brains and mind work, according to current theories and speculations. The course tries to give the direction of how to design a thinking machine, eventually self-improving, and gives the directions for deeper study and research.

This course is original and it seems it's one of the first worldwide, if not the first* as systematic and interdisciplinary one, including:

- Futurology (Singularity, Transhumanism)
- Neuroscience  - How Brain works.
- Developmental Psychology
- Behaviorism/Reinforcement learning in living beings/humans and machines

Even Love as a form of reinforcement learning “bug”. ;) The course gives insights and theories of intelligence/Universe and meta evolution – higher level principles that cause and drive Universe evolution - which seem not to be popular yet – Boris Kazachenko's and mine. 

I will give students also some ideas of how and why to do practical research and engineering and high-tech entrepreneurship, something I'm trying to start-up myself and have some experience as an engineer (employee) in a fabless semiconductor start-up. The course is appropriate for all undergraduate students, because the special preliminary knowledge is taught in the course. A graduate student barely have an  advantage over a second year student.

Lectures will be in Bulgarian, but knowledge of English is highly recommended, especially if you wish to deepen your knowledge in the field. The exam will be a test.

Probably; eventually I will translate the course to English. Maybe teaching it abroad if reach to that point.

Universal Artificial Intelligence

  1. Introduction to the course. Why should one does research, especially so brave? PhD – Pro & Con. Is it possible to do a private research/science and scientific business? What is a Start-up? True stories and advices.

  2. What is Artificial Intelligence? Is it possible? Criticism and scepticists – Searle's Chinese room. Summary of the classical AI and why it failed. Review of the results and successes in current Weak AI (specialized/narrow AI) – why do they have limitations. Cognitive Science and cognitive architectures.

  3. What is Universal Artificial Intelligence (UAI, AGI, Strong AI). Technological Singularity and Singularity Institute. Transhumanism. Expected computing power of human brains. Attempts for literal simulation of brains. Universality paradox of the brains. Ethical issues, related to AGI.

  4. Complexity and Theory of Information. Probability Theory – statistical (empirical) probability. Chaos Theory. Systems Theory. Emergent functions and behavior. Universe as a computer – digital physics. Algorithmic Probability. Kolmogorov's Complexity and Minimum Message Length. Occam's Razor.

  5. Design of Mammalian and Human brains.

  6. What drives mammals and humans? Behaviorism – Reinforcement Learning as a Universal learning method. Supervised learning and imitation.

  7. Developmental Psychology (Child Psychology). Stages in cognitive development. First language acquisition.

  8. Machine Learning. Markov's Chains. Hidden Markov Models (HMM). Bayes Networks. Hierarchical Bayes' Networks and Hierarchical HMM. Principles of the algorithms of Viterbi and Baum-Welch (Expectation-Maximization).

  9. Tests for human and machine intelligence. Definition of a reinforcement learning Intelligent agent, according to Marcus Hutter. Beauty according to Jurgen Schmidhuber and Todor Arnaudov. Schmidhuber's Godel Machine.

  10. Theory of Mind and Universe by Todor Arnaudov – mind as a hierarchical system of interacting universal simulators of virtual universes. Universe as a computer and trends in Universe evolution. 

  11. Theory of Intelligence by Jeff Hawkins. How the neocortex in mammals and human works. Memory-Prediction Framework and Hierarchical Temporal Memory.

  12. Theory of Intelligence and Universe Meta-evolution by Boris Kazachenko. Cognition: hierarchically selective pattern recognition & projection. Мeta-evolution as Abstraction of a conserved core from its environment, via mediation of impacts & responses by increasingly differentiated adaptive interface hierarchy. Boris' prize for ideas.

  13. Summary of the principles of intelligence according to Todor Arnaudov, Marcus Hutter, Jeff Hawkins, Boris Kazachenko and others. Prediction, hierarchy, bottom-up development, scalability, complexity growth. Seed AI. Sample architecture of an AGI/UAI agent.

  14. Ben Goertzel's cognitive architecture Novamente.

  15. Other researchers and private companies in the field of AGI. Bibliography, references, links and directions. Discussion and Questions.

  16. Exam.

    See you in class! ;)

    * I know about the summer school of Ben Goertzel, Hugo De Garis and their colleagues in Xiamen 2009, but according to their program, it lacks significant parts, e.g. Jeff Hawkins is not even mentioned.
    ** I spotted one interesting interdisciplinary course, called “Psychology, Anthropology, Neuroscience, and Zoology 619” or “Biology of Mind” by Deric Bownds. It's taught to graduate biology students, so it's too much into Biology and lacks technical/mathematical parts.


    Boris Kazachenko said...

    Thanks for covering me Todor!

    A minor correction on "my" meta-evolution: "adaptive core" sounds misleading. By a "core" I mean what's conserved in a system, adaptive is its environmental interface. I used "adaptive" core to mean a core that defines adaptive interface, but is not adaptive itself. I'd describe meta-evolution as "Abstraction of a conserved core from its environment, via mediation of impacts & responses by increasingly differentiated adaptive interface hierarchy".
    Also, I added this to my "Intelligence" knol:

    A prize for ideas:

    Lots of people think that a major problem with AI is the lack of funding. I disagree, Einstein didn't need to be paid to work on his theory of relativity. Lots more think that AI is an engineering or mathematical problem. I think that's a "man with a hammer" syndrome. AI is the most theoretical problem ever, - cognitive algorithm is a meta-theory, more abstract than anything yet discovered.

    Anyway, I've been working on this theory most of my life, without funding or collaboration. Along the way, I managed to make a few bucks on my investments & want to put the money to a good use.
    If you generally agree with my approach & think you can contribute, here's an incentive:
    I offer prizes from $100 to $100,000 for ideas that correct or further develop the principles explained above. $100 could be a "consolation prize" for ideas that I already discovered but did not publish, or that are largely cosmetic in nature. $100,000 would be for a major advance, - I can only afford a few of those. A winner will have an option to convert his prize into an interest in all commercial applications of a final algorithm, at the rate of $10,000 per 1% share (this would be an informal commitment, -there're no specific plans for products or incorporation).

    Again, I don't believe money can work as a primary motivation, but it has a way of attracting attention. You can post feedback here or e-mail me at boris(.)k(at)verizon(.)net. It might lead to collaboration, stranger things happened.

    Good luck with your course!

    Todor "Tosh" Arnaudov said...

    You're welcome! :)

    OK, corrected!

    BTW, I agree about funding - it can't fix mistakes in methodology on its own.

    I believe that simulations and tests of draft versions of the theories would be helpful on the road to completion, though, and it wouldn't be that expensive as is to design a new CPU or a supercomputer, where they spend $ hundreds of millions or more.

    Also development of tools for speeding up idea generation/reading/writing/editing - "augmented intelligence/cognitive accelerators" would be helpful, I believe.

    Thanks and Best Regards,

    Boris Kazachenko said...

    Hate to repeat myself, but:
    - methodology is not separable from content,
    - the best simulation is in your own mind,
    - the best "cognitive accellerator" is collaboration, if you can get it, & block on distractions if you can't.

    Todor "Tosh" Arnaudov said...

    Comments on the AGI email list of AGIRI:

    John G. Rose ... via to AGI
    show details Nov 24 (5 days ago)

    Great course programs covering AGI summary/introduction, I like the selection of topics discussed. You might consider opening these up online via streaming/collaboration in the future…



    Ben Goertzel ... via to AGI, AGI
    show details Nov 28 (2 days ago)


    Looks like a great course you're offering!n

    FYI ... On your page you note that our 2009 AGI summer school didn't cover jeff Hawkins' work...

    I can't remember if any speaker mentioned hawkins, but, Allan combs gave some great lectures on neuroscience, which covered hierarchical processing in visual cortex among other topics ;)

    That AGI summer school presented a variety of perspectives, it wasn't just about open cog and my own views ... But it wasn't heavy on perception-centered AGI...


    ... ... ...
    ... ... ...

    Todor Arnaudov's answers:

    Thanks, John.

    There are materials from the course online (on the blog and on the site); most of the lecture slides and details are only in Bulgarian yet, though. As of collaboration - maybe, as long as I manage to create a team, for the moment I prefer keeping the authorship for myself.


    Thanks Ben!

    And thanks for the notes. :)

    Ben>That AGI summer school presented a variety of perspectives, it wasn't just about open cog and
    Ben>my own views ... But it wasn't heavy on perception-centered AGI...

    All I knew about the summer school was from the brief web page on your site:

    Hawkins wasn't mentioned in the program, and it sounded reasonable not to be, as he seemed from a distant "school of thought" compared to the lecturers' ones - as far as I knew or assumed theirs.

    Ben>I can't remember if any speaker mentioned hawkins, but, Allan combs gave some great lectures on
    Ben>neuroscience, which covered hierarchical processing in visual cortex among other topics ;)

    That's nice (I've noticed neuroscience in the program), but anyway I think HTM and the other sensorimotor topics are more general - memory-prediction framework and the other similar models are supposed/aiming to explain virtually all kinds of cognitive processes with an integral paradigm, and vision is just an example/case. In a POV of schools, there's a distinction whether it's suggested that vision is an example of a general framework, or it's one of the sub-architectures/sub-frameworks for an AGI.

    Todor "Tosh" Arnaudov said...

    Check the course program of the following course in AGI/SIGI (self-improving general intelligence):

    Mathematical Theory of Intelligence - Second Course in AGI/UAI at Plovdiv University by Todor Arnaudov (Course Program in English), taught during the winter of 2011.