Epstein Secrets

f intel- ligence and mind. It then sketches the broad outlines of a novel, integrative architecture for Artificial General Intelligence (AGI) called CogPrime ... and describes an approach for giving a young AGI system (CogPrime or otherwise) appropriate experience, so that it can develop its own smarts,

n V A Path to Human-Level AGI 16 AGI Preschool 289 16.1 Introduction 289 16.1.1 Contrast to Standard AI Evaluation Methodologies 290 16.2 Elements of Preschool Design 291 16.3 Elements of Preschool Curriculum 292 16.3.1 Preschool in the Light of Intelligence Theory 293 16.4 Task-Based Assessment in AGI P

concepts in one of Douglas HoEstadter's books. Naturally, real concept heterarchies are far more large, complex and tangled than even this one. In CogPrime, ECAN enforces heterarchy via building SymmetricHebbianLinks, and PLN by building SimilarityLinks, IntensionalSimilarityLinks and ExtensionalSimilar

d "transfer learning," where the goal for an AI is to learn one task quicker given another task solved previously 10%117, TM95, EFTA00624066 16.2 Elements of Preschool Design 291 )1C05j. This is one of the capabilities an AI agent will need to simultaneously lear❑ different types of tasks as proposed in the Preschool sc

of intel- ligence and mind. It then sketches the broad outlines of a novel, integrative architecture for Artificial General Intelligence (AGT) called CogPrime ... and describes an approach for giving a young AGI system (CogPrime or otherwise) appropriate experience, so that it can develop its own smarts, cr

een eens 289 16.1 Introduction .......... 0.0000 teen teens 289 16.1.1 Contrast to Standard AI Evaluation Methodologies ................... 290 16.2 Elements of Preschool Design ...........00 0.00 e eee eee eee 291 16.3 Elements of Preschool Curriculum ........0... 00000 c eee eee eee 292 16.3.1 Preschool in the Light of Inte

n concepts in one of Douglas Hofstadter’s books. Naturally, real concept heterarchies are far more large, complex and tangled than even this one. In CogPrime, ECAN enforces heterarchy via building SymmetricHebbianLinks, and PLN by building SimilarityLinks, IntensionalSimilarityLinks and ExtensionalSimilari

fer learning,” where the goal for an AT is to learn one task quicker given another task solved previously [Car97, TM95, HOUSE_OVERSIGHT_013206 16.2 Elements of Preschool Design 291 BDS03, TSO07, RZDKO5]. This is one of the capabilities an AI agent will need to simultaneously learn different types of tasks as proposed in the