Alexander Borzenko

Language Processing in Human Brain

Proceedings of the First AGI Conference Volume 171 Frontiers in Artificial Intelligence and Applications

Edited by: P. Wang, B. Goertzel and S. FranklinFebruary 2008, 520 pp. ISBN: 978-1-58603-833-5

IOS Press

Presentation at

Artificial General IntelligenceAGI-2008

Language Processing in Human Brain

Human is the most efficient general “intellectual machine” in the world.

My aim is to show that nowadays we know everything to explain how brain works, and

then we can use the human brain’s algorithms to build AGI.

In order to reach the goal we use conventional common features of natural

brain in our model

Research Starting Points1. Natural language is naturally formed symbolic

system completely independent of these symbols’ physical nature

2. Intellect is a direct property of brain cerebral activity

3. Model elementary unit (formal neuron) has many input signals and only one consolidated output signal that can be passed to many other model units (like neurons in a natural brain do)

4. A group of mutually connected neurons makes what we call a column which has a consolidated output as well (optional)

5. Column system is united by innumerous mutual ties of chaotic type

6. Pyramidal neurons collect and summarise signals of other neurons and columns

7. Peripheral neural system randomly spreads input data to the model units

8. Efferent nerves have their corresponding afferent partners that helps us to accelerate pyramidal neuron learning (optional)

Abstract Neural Tissue

Text

OutputText Input

Main Principles

Principle of activity maintenance: if there is a configuration of dendrite activity (regardless of its type) and the neuron is active (e.g. firing) it modifies itself in such a way that the neuron should fire on this particular or similar dendrite activity configuration in future.

Principle of neuron spontaneous activity: there is a finite probability that any brain neuron fires independently of its current external configuration, history of firing, and the current states of its dendrite inputs.

Neuron’s Main Equation and Features

Let Etk be an event when some neuron has its synapse of number k activated at

the time t. Then, supposing that zero time corresponds to the last time when function Ψ has been changed, a probability P of the neuron firing can be symbolically described as the following

P = Ψ (Et, E0, U, M, N), N < M, where Et and E0 are vectors of synapse activity at time t and 0 respectively, U is

a probability that a neuron changes its reaction during one second due to the phenomenon of spontaneous activity, N and M are active and total synapse numbers

1. Neuron has two states: active when it fires and passive when it does nothing visible from the outside,

2. Neuron can recognize the similarity of current synapse activity of other neurons connected to it and can react with firing on similar configurations of current synapse activity,

3. Whenever active the neuron has a power to share its efforts with other neurons in order to activate or to suspend the activity of some other neurons and itself (actually knowing nothing about possible consequences of this activity),

4. Neuron can become active spontaneously.

Reliability of Column Neuron’s Memory

xtp=(Πt-1

r=1(1-xrp)) ∙{U+(1-U)[1-ΠiЄQ(p)(1-xt-1

i)]},

where U is a probability that a column neuron spontaneously changes its reaction during one second, Q(p) – the set of numbers of neurons which are connected to p-neuron.

Column summarizes its neurons’ reactions making reactions even more stable.

Probability of keeping the data in the Model

Column size 182

ProbabilityU = 10-7

of spontaneous changing the

neuron reaction in a

second

Summarizing the ‘columns’ Data: Probability of “Pyramidal Neuron”

Firing

Σ kЄM A (Etk, E0

k) * Wk > λ,

where A( ∙ , ∙ ) is a function of two arguments that returns 1 if its arguments are similar, and 0 otherwise; λ is some threshold, and Wk are some weights associated with corresponding columns. These weights are not constants, they vary according to the theory’s principles.

General Schema of Natural Language Processing in the Model

• Generation

Lt-1 → Lt

• Learning

Kt → Lt

until

Lt = Kt

Column 1

Column 2 Column 3

St1

“Pyramidal neurons” for A,B,C, … , X,Y,Z

Lt (output)

Kt (input)

St2 St

3

Tt1 Tt

2Tt

3

Stimulus to activate “pyramidal neuron” of a current symbol from Kt

in learning mode(optional)

Model behavior: Primary Stages

(1,000,000 columns)

• Q: What’s your name?• A: Alex• Q: How old are you?• A: I am a young man• Q: Who are you?• A: I’m a scientist

Leaning material:

Questions & answers

Model behavior: Intermediate Stages (3,500,000 columns)

• G: He took no notice of this• G: He looked across of my

companion who was also an elderly

• G: I was away in a cab and down the platform, with what you had better give me much more clever than average man

• G: I think that was to details of glance into the fire

Leaning material:

Fragments of chats,

Fragments of stories,

Chunks of novels

Model behavior: Final Stages (6,100,000 columns)

• Q: What is a sea floor?

• A: A floor is generally a lower horizon

• Q: What do you think about the mystery island colonists?

• A: Poor wreckers! They were in a great danger

Leaning material:

Dialogs,Internet chats,

Novels,Articles

Active Column Dynamics:Accumulated knowledge reduces the

average amount of involving columns per input phrase symbol

Stage Amount of columns

Relative Duration

Average Intensity

Primary 1,000,000 1 1.00

Intermediate 3,500,000 5 0.70

Final 6,100,000 14 0.43

Column Involving Trend

The relative number of involved

columns (per sentence) statistically

decreases with every new sentence

processed by model

Results

We have proved that human intellect is an aggregative reaction of independent

neurons which are connected to each other in a chaotic manner

We have the algorithm that reproduces a human brain function in the part of natural language comprehension and generation

(Abstract Neural Tissue)

English-speaking Wearable Military Assistant (PMM MA)

1. MA uses unlimited English to deliver and perceive information through speech, printed messages and video camera (optional).

2. MA can be instructed and guided using English language 3. MA learns and improves its functionality through

accumulation of experience 4. MA can schedule time- or situation-related events to

inform Commander about necessary actions on these events.

5. MA works as a “secretary” processing messages from different sources, estimating message importance and delivering them at proper time.

6. MA manages other computer applications allowing Commander to concentrate attention on really important tasks.

http://www.proto-mind.com

Thank you for your attention