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Supercompilation 319 18.3.1 Three Aspects of Supercompilation 321 18.3.2 Supercompilation for Goal-Directed Program Modification 322 18.4 Self-Modification via Theorem-Proving 323 A Glossary 325 A.1 List of Specialized Acronyms 325 A.2 Glossary of Specialized Terms 326 References 343 EFTA006
Networks 282 15.3.3 Dual Networks 284 Section 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
cts of Supercompilation .............0..00 000022 eee eee ee 321 18.3.2 Supercompilation for Goal-Directed Program Modification............. 322 18.4 Self-Modification via Theorem-Proving ........... 0.00 eee eee eee 323 A Glossary ..... 0.00000 tne teen eee 325 A.1 List of Specialized Acronyms.........0.. 00 0c ce
on V A Path to Human-Level AGI 16 AGI Preschool.......0.... 0... teen 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... 00
nt decision graphs. The decision graph leading to maximal optimization, may not be the one that leads CogPrime cognition in optimal directions. 18.4 Self-Modification via Theorem-Proving Supercompilation is a potentially very valuable tool for self-modification. If one wants to take an existing schema and graduall
irtual or robotic preschool is an excellent way — perhaps the best way — to foster the development of human-level human-like AGI. 16.1.1 Contrast to Standard AI Evaluation Methodologies The reader steeped in the current AI literature may wonder why it’s necessary to introduce a new methodology and environment for evaluating AGI syst
Entities connected to both Self-Modification and Standard AI Evaluation Methodologies
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George W. Bush
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Joscha Bach
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Marc Rich
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