Creating “infant” AI: Natural thinking mimicking

The two waves of AI and the winter between them
Wave 1: knowledge base + production rules
similar to the real brain machinery
no automatic learning from data and natural texts
knowledge processing is based on first order logic – unlike the natural brain
Wave 2: deep learning
differentiability => automatic learning from data
fitting a curve through the backprop is pretty far from what the brain does – not able to implement real cognition

My approach: biologically plausible knowledge formation and processing
Main principles of an “AI infant” system:
Declarative (semantic) memory - analogous to LTM
Generalization mechanics
Stochastic reinforcement learning
Thinking by analogy mechanics
Declarative memory
reply
Grammar synthesizer
Sensory input

Declarative (semantic) memory
Knowledge is an undirected cyclic graph of ensembles
Basic entity ensembles are formed from “infant” sensory input, visual and audial
v:duck a:duck
Episodes in the life of the “infant” form ensembles, connected with entities
v:Mom a:duck v:duck – Mom is showing a Duck toy saying “Duck”

Generalization mechanism
Generalization is based on Hebbian learning with frequent patterns
Frequently activated ensembles capture adjacent neurons and form “twin” ensembles. They reconnect with the same ensembles becoming a “hub ensemble”

Stochastic reinforcement learning
Knowledge is just a pile of chaotic ensembles until you ask the “infant” questions
A question ignites an urge to be satisfied by a dopamine injection (hedonistic synapse learning)
Ensebles consist of many circuts – each circut corresponds to a combination of input ensembles and an output ensemble
The goal is to find and engrave the optimal pathway from input circuits to output circuits – the way to dopamine
Input circuits
output circuits

Thinking by analogy
While being reinforce trained not only entity circuits learn the right pathway
Their “hub” counterparts are ignited along the way and engrave the right path on “abstract” level
Reinforcement learning on episodes “Cow say moo”, “Duck say quack”, “Cat say miau” turns into the ‘Animal say animal sound” engram
Which will produce a correct answer for “What dog say?” if “Dog” is correctly attached to the “Animals” hub ensemble
moo
Cow
episode
say
Animals say hub
animals
say twin

Where is it all going?
Basic milestones of developement:
Ability to answer any complex question
Ability to gain knowledge from real texts, say from Wikipedia. Tons of algorithms needed
Ability to solve math tasks.
….
AGI