## Artificial General IntelligenceBen Goertzel, Cassio Pennachin “Only a small community has concentratedon general intelligence. No one has tried to make a thinking machine . . . The bottom line is that we really haven’t progressed too far toward a truly intelligent machine. We have collections of dumb specialists in small domains; the true majesty of general intelligence still awaits our attack. . . . We have got to get back to the deepest questions of AI and general intelligence. . . ” –MarvinMinsky as interviewed in Hal’s Legacy, edited by David Stork, 2000. Our goal in creating this edited volume has been to ?ll an apparent gap in the scienti?c literature, by providing a coherent presentation of a body of contemporary research that, in spite of its integral importance, has hitherto kept a very low pro?le within the scienti?c and intellectual community. This body of work has not been given a name before; in this book we christen it “Arti?cial General Intelligence” (AGI). What distinguishes AGI work from run-of-the-mill “arti?cial intelligence” research is that it is explicitly focused on engineering general intelligence in the short term. We have been active researchers in the AGI ?eld for many years, and it has been a pleasure to gather together papers from our colleagues working on related ideas from their own perspectives. In the Introduction we give a conceptual overview of the AGI ?eld, and also summarize and interrelate the key ideas of the papers in the subsequent chapters. |

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### Contents

CXX | 204 |

CXXI | 205 |

CXXII | 206 |

CXXIV | 212 |

CXXVI | 213 |

CXXVII | 214 |

CXXVIII | 215 |

CXXX | 217 |

XI | 19 |

XII | 22 |

XIII | 24 |

XIV | 26 |

XV | 27 |

XVI | 28 |

XVIII | 31 |

XIX | 33 |

XX | 35 |

XXI | 40 |

XXII | 43 |

XXIV | 45 |

XXV | 47 |

XXVI | 48 |

XXVII | 50 |

XXVIII | 52 |

XXX | 53 |

XXXI | 54 |

XXXII | 56 |

XXXIII | 57 |

XXXIV | 58 |

XXXV | 60 |

XXXVII | 63 |

XXXVIII | 64 |

XXXIX | 65 |

XL | 67 |

XLI | 68 |

XLII | 70 |

XLIII | 72 |

XLIV | 73 |

XLV | 75 |

XLVI | 77 |

XLVII | 80 |

XLVIII | 83 |

XLIX | 84 |

L | 88 |

LI | 91 |

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LIII | 95 |

LIV | 96 |

LV | 99 |

LVI | 100 |

LVII | 102 |

LVIII | 104 |

LIX | 105 |

LX | 106 |

LXI | 107 |

LXII | 108 |

LXIII | 109 |

LXV | 110 |

LXVII | 112 |

LXVIII | 115 |

LXIX | 116 |

LXX | 117 |

LXXI | 120 |

LXXII | 122 |

LXXIII | 124 |

LXXIV | 125 |

LXXV | 127 |

LXXVI | 130 |

LXXIX | 133 |

LXXX | 134 |

LXXXI | 135 |

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LXXXVI | 148 |

LXXXVII | 149 |

LXXXVIII | 150 |

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XC | 155 |

XCI | 156 |

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XCIV | 162 |

XCV | 166 |

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CCX | 352 |

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CCXXXIX | 484 |

CCXL | 489 |

CCXLI | 493 |

CCXLII | 496 |

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### Common terms and phrases

ability abstract action adaptive agent AI’s AIXI AIĩ algorithm algorithmic information theory approach Artiﬁcial Intelligence Atoms behavior Ben Goertzel brain cognitive complex concept conﬂict context create cycle deﬁned deﬁnition diﬀerent diﬃcult distribution dynamics eﬀects eﬃcient eﬀort environment evolution evolutionary evolved example feature ﬁeld ﬁnd ﬁnite ﬁrst formal function GĻodel machine goal hardware heuristics holonic IDSIA implemented induction inference input integration interaction internal knowledge Kolmogorov complexity language levels of organization logic Machine Learning memory mental imagery mind MindAgents NARS neural network neurons nodes Novamente objects optimal output patterns perception possible prediction probabilistic probability problem reasoning reﬂectance reinforcement learning reward rewrite rules Schmidhuber script seed AI semantic sensory modalities sequence signiﬁcant simulation solve speciﬁc structure suﬃcient supersystem task term theorem theory thought Turing machine universal variables visual Webmind