Friday, January 4, 2019

CogAlg News - Boris Declares a Lead Developer

The "Cognitive Algorithm" (CogAlg) AGI project of Boris Kazachenko has found a new talent, which for a month of work is listed as "lead developer", according to the Contributing list in github

Good luck and looking forward for results!

I'm attached to this project in many ways, although I don't like some aspects of it.

AFAIK I was the first researcher-developer with credentials to acknowledge Boris' ideas back in time (since I found they matched "my theory").

Then I tried to promote him, his theory was suggested and presented in the world first University courses in AGI in 2010 and 2011*.

His prizes were first announced in the comments section of a publication of the AGI-2010 course program.

I've been the first and the only recurring prize winner so far with the biggest total prize for more than 8 years.


* In comparison, the artificial neural networks were given 3 slides in the lecture "Narrow AI and Why it failed". :) Now DNN achieved a lot, but it's still "narrow AI" without understanding and structure (not a "XAI", explainable/interpretable), poor transfer learning, shallow etc. Other authors have already defined extensively ANN's faults, such as Gary Marcus and in a more concise and his-theory-related form - Boris himself.

My slides:

The 2010 and 2011 course had only three slides specifically about ANN, only in one of the introductory lectures about “Narrow AI” and why it failed.

See slides 34–35:

http://research.twenkid.com/agi/2010/Narrow_AI_Review_Why_Failed_MTR.pdf

Translated from Bulgarian it says that ANNs:

* Pretend to be universal, but it is not working so far
* Represent TLU — threshold logic units
* They are graphs with no cycles, having vertices with different weights
* Input, output and hidden layers
* They are trained with samples (e.g. photos), which are classified by altering the weights
* Then when a new photo is fed, it’s attributed to a particular class
* Computationally heavy, holistic, chaotic
* Can’t work with events and time relations
* Static input

Slide 36, Recurrent NN, a more positive review:

Рекурентни невронни мрежи

● Мрежи на Хопфийлд. Асоциативна памет.
● Има цикли в графа – биологично по-верни.
● Започват да работят с понятие за време и събития.
● Long Short-Term Memory (LSTM) – Jurgen Schmidhuber – представител на Силното направление, опити за универсален ИР.
● Приложения: Разпознаване на ръчен почерк; робот, учещ с подкрепление в частично наблюдаема среда; композиция на музика и импровизация и др.