1. Structural Emergence in Partially Ordered Sets
is the Key to Intelligence
Sergio Pissanetzky
AGI-11 Conference

2. Overview of my research 2005 - 2011
► Initial motivation: Refactoring.
► Scope: Refactoring is universal.
► Approach: Computational experiments.
► Current motivation: AGI.
► Discoveries:
● Partially ordered sets as a knowledge base.
● Emergent inference.
● Emergence in complex dynamical systems.
● Indications of EI in the brain.

3. Computational experiments
senses and
afferent nerves
natural
structures
brain
feedback
knowledge
compare
partially
ordered set
emergent
inference
predicted
structures
feedback

4. The first experiment PROGRAM CANONICAL MATRIX (SCRAMBLED) a = x1 * x2
b = x3 * x4
c = x1 * x5
d = x3 * x6
e = x7 * x8
f = x7 * x2
g = x7 * x5
h = x1 * x8
i = x3 * x9
j = x9 + e
k = h + i
l = a + b
m = x4 + f
n = c + d
p = x10 + n
q = x6 + g
r = x11 + l
s = x12 + k a b c d e f g h i A j k l m n p q r s

5. The result from the first experiment
REFACTORED
PROGRAM
CANONICAL MATRIX (STRUCTURED) d c A n p f m b a l r e j i h k s g q
d = x3 * x6
c = x1 * x5
n = c + d
p = x10 + n
f = x7 * x2
m = x4 + f
b = x3 * x4
a = x1 * x2
l = a + b
r = x11 + l
e = x7 * x8
j = x9 + e
i = x3 * x9
h = x1 * x8
k = h + i
s = x12 + k
g = x7 * x5
q = x6 + g
This process is emergent inference

6. Claim
● Any dynamical system has a natural hierarchical
structure that can be found by emergent inference.
Conjectures
● Emergent inference explains emergence and
self-organization in complex dynamical systems.
● Emergent inference in the brain explains intelligence.

7. The representation of systems by partially ordered sets
Any system
• z = f(x, y) Set = {x, y, z} Partial Order = {x < z, y < z}
A computer program.
• Parser.
CFS brain model = neural network + resource preservation.
• C = connect  memory
• F = fire  behavior
• S = shorten  intelligence, emotions, creativity
• Clustering takes place. Iteration forms clusters of clusters.
• Clusters are neural cliques, cortical columns, cortical modules.

8. EI is “the” key to intelligence vs. EI is “a” key to intelligence ?
Any “other” system can also be represented as a partially ordered set.

9. There is no integration, no refactoring, and no self-programming.
Traditional AI and AGI
car position
sensors
car driving
program
car
controls
stage sensors,
actors
stage control
program
stage
controls
chess
sensors
chess playing
program
chess
controls
There is no integration, no refactoring,
and no self-programming.

10. The brain integrates and refactors naturally
car
drive car
human
brain
stage
senses
manage stage
chess
play chess
The brain integrates and refactors naturally

11. The EI system integrates and refactors naturally
Emergent inference
problem
of Physics
law of Physics
raw image
image
recognition
emergent
inference
token ring
OO program
classes, objects
interdependent tasks
parallel program
The EI system integrates and refactors naturally

12. Do we need a principle for intelligence?
Aeronautical Engineering.
- 1800: lift force identified as the principle of flight.
Software engineering.
1980’s: the automation of objects.
1990’s: the automation of refactoring.
Artificial intelligence.
2000’s: the automation of integration.
2010’s: the automation of self-programming.
Neuroscience.
- “the exact way in which the brain enables thought is one of the great mysteries of science.” (Russell-Norvig).
- “we are still a long way from understanding how cognitive processes actually work.” (Russell-Norvig) .
Emergent inference is the principle for intelligence

13. Final message
● EI is the principle for intelligence and AGI.