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

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.

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

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

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

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.

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.

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.

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.

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

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

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

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