Document Text Content
Ben Goertzel with Cassio Pennachin & Nil Geisweiller &
the OpenCog Team
Engineering General Intelligence, Part 1:
A Path to Advanced AGI via Embodied Learning and
Cognitive Synergy
September 19, 2013
This book is dedicated by Ben Goertzel to his beloved,
departed grandfather, Leo Zwell – an amazingly
warm-hearted, giving human being who was also a deep
thinker and excellent scientist, who got Ben started on the
path of science. As a careful experimentalist, Leo would
have been properly skeptical of the big hypotheses made
here – but he would have been eager to see them put to the
test!
Preface
This is a large, two-part book with an even larger goal: To outline a practical approach to
engineering software systems with general intelligence at the human level and ultimately beyond.
Machines with flexible problem-solving ability, open-ended learning capability, creativity and
eventually, their own kind of genius.
Part 1, this volume, reviews various critical conceptual issues related to the nature of intelligence
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, creativity and wisdom through its own experience. Along the way a formal theory
of general intelligence is sketched, and a broad roadmap leading from here to human-level artificial
intelligence. Hints are also given regarding how to eventually, potentially create machines
advancing beyond human level – including some frankly futuristic speculations about strongly
self-modifying AGI architectures with flexibility far exceeding that of the human brain.
Part 2 then digs far deeper into the details of CogPrime’s multiple structures, processes and
functions, culminating in a general argument as to why we believe CogPrime will be able to
achieve general intelligence at the level of the smartest humans (and potentially greater), and
a detailed discussion of how a CogPrime-powered virtual agent or robot would handle some
simple practical tasks such as social play with blocks in a preschool context. It first describes
the CogPrime software architecture and knowledge representation in detail; then reviews the
cognitive cycle via which CogPrime perceives and acts in the world and reflects on itself; and
next turns to various forms of learning: procedural, declarative (e.g. inference), simulative and
integrative. Methods of enabling natural language functionality in CogPrime are then discussed;
and then the volume concludes with a chapter summarizing the argument that CogPrime can
lead to human-level (and eventually perhaps greater) AGI, and a chapter giving a thought
experiment describing the internal dynamics via which a completed CogPrime system might
solve the problem of obeying the request “Build me something with blocks that I haven’t seen
before.”
The chapters here are written to be read in linear order – and if consumed thus, they tell
a coherent story about how to get from here to advanced AGI. However, the impatient reader
may be forgiven for proceeding a bit nonlinearly. An alternate reading path for the impatient
reader would be to start with the first few chapters of Part 1, then skim the final two chapters of
Part 2, and then return to reading in linear order. The final two chapters of Part 2 give a broad
overview of why we think the CogPrime design will work, in a way that depends on the technical
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details of the previous chapters, but (we believe) not so sensitively as to be incomprehensible
without them.
This is admittedly an unusual sort of book, mixing demonstrated conclusions with unproved
conjectures in a complex way, all oriented toward an extraordinarily ambitious goal. Further,
the chapters are somewhat variant in their levels of detail – some very nitty-gritty, some more
high level, with much of the variation due to how much concrete work has been done on the
topic of the chapter at time of writing. However, it is important to understand that the ideas
presented here are not mere armchair speculation – they are currently being used as the basis
for an open-source software project called OpenCog, which is being worked on by software
developers around the world. Right now OpenCog embodies only a percentage of the overall
CogPrime design as described here. But if OpenCog continues to attract sufficient funding
or volunteer interest, then the ideas presented in these volumes will be validated or refuted
via practice. (As a related note: here and there in this book, we will refer to the "current"
CogPrime implementation (in the OpenCog framework); in all cases this refers to OpenCog as
of late 2013.)
To state one believes one knows a workable path to creating a human-level (and potentially
greater) general intelligence is to make a dramatic statement, given the conventional way of
thinking about the topic in the contemporary scientific community. However, we feel that once
a little more time has passed, the topic will lose its drama (if not its interest and importance),
and it will be widely accepted that there are many ways to create intelligent machines – some
simpler and some more complicated; some more brain-like or human-like and some less so; some
more efficient and some more wasteful of resources; etc. We have little doubt that, from the
perspective of AGI science 50 or 100 years hence (and probably even 10-20 years hence), the
specific designs presented here will seem awkward, messy, inefficient and circuitous in various
respects. But that is how science and engineering progress. Given the current state of knowledge
and understanding, having any concrete, comprehensive design and plan for creating AGI is
a significant step forward; and it is in this spirit that we present here our thinking about the
CogPrime architecture and the nature of general intelligence.
In the words of Sir Edmund Hillary, the first to scale Everest: “Nothing Venture, Nothing
Win.”
Prehistory of the Book
The writing of this book began in earnest in 2001, at which point it was informally referred to
as “The Novamente Book.” The original “Novamente Book” manuscript ultimately got too big
for its own britches, and subdivided into a number of different works – The Hidden Pattern
[Goe06a], a philosophy of mind book published in 2006; Probabilistic Logic Networks [GIGH08],
a more technical work published in 2008; Real World Reasoning [GGC + 11], a sequel to Probabilistic
Logic Networks published in 2011; and the two parts of this book.
The ideas described in this book have been the collaborative creation of multiple overlapping
communities of people over a long period of time. The vast bulk of the writing here was done by
Ben Goertzel; but Cassio Pennachin and Nil Geisweiller made sufficient writing, thinking and
editing contributions over the years to more than merit their inclusion of co-authors. Further,
many of the chapters here have co-authors beyond the three main co-authors of the book; and
the set of chapter co-authors does not exhaust the set of significant contributors to the ideas
presented.
The core concepts of the CogPrime design and the underlying theory were conceived by Ben
Goertzel in the period 1995-1996 when he was a Research Fellow at the University of Western
Australia; but those early ideas have been elaborated and improved by many more people than
can be listed here (as well as by Ben’s ongoing thinking and research). The collaborative design
process ultimately resulting in CogPrime started in 1997 when Intelligenesis Corp. was formed
– the Webmind AI Engine created in Intelligenesis’s research group during 1997-2001 was the
predecessor to the Novamente Cognition Engine created at Novamente LLC during 2001-2008,
which was the predecessor to CogPrime.
ix
Acknowledgements
For sake of simplicity, this acknowledgements section is presented from the perspective of the
primary author, Ben Goertzel. Ben will thus begin by expressing his thanks to his primary
co-authors, Cassio Pennachin (collaborator since 1998) and Nil Geisweiller (collaborator since
2005). Without outstandingly insightful, deep-thinking colleagues like you, the ideas presented
here – let alone the book itself– would not have developed nearly as effectively as what has
happened. Similar thanks also go to the other OpenCog collaborators who have co-authored
various chapters of the book.
Beyond the co-authors, huge gratitude must also be extended to everyone who has been
involved with the OpenCog project, and/or was involved in Novamente LLC and Webmind Inc.
before that. We are grateful to all of you for your collaboration and intellectual companionship!
Building a thinking machine is a huge project, too big for any one human; it will take a team
and I’m happy to be part of a great one. It is through the genius of human collectives, going
beyond any individual human mind, that genius machines are going to be created.
A tiny, incomplete sample from the long list of those others deserving thanks is:
• Ken Silverman and Gwendalin Qi Aranya (formerly Gwen Goertzel), both of whom listened
to me talk at inordinate length about many of the ideas presented here a long, long time
before anyone else was interested in listening. Ken and I schemed some AGI designs at
Simon’s Rock College in 1983, years before we worked together on the Webmind AI Engine.
• Allan Combs, who got me thinking about consciousness in various different ways, at a very
early point in my career. I’m very pleased to still count Allan as a friend and sometime
collaborator! Fred Abraham as well, for introducing me to the intersection of chaos theory
and cognition, with a wonderful flair. George Christos, a deep AI/math/physics thinker from
Perth, for re-awakening my interest in attractor neural nets and their cognitive implications,
in the mid-1990s.
• All of the 130 staff of Webmind Inc. during 1998-2001 while that remarkable, ambitious,
peculiar AGI-oriented firm existed. Special shout-outs to the "Voice of Reason" Pei Wang
and the "Siberian Madmind" Anton Kolonin, Mike Ross, Cate Hartley, Karin Verspoor and
the tragically prematurely deceased Jeff Pressing (compared to whom we are all mental
midgets), who all made serious conceptual contributions to my thinking about AGI. Lisa
Pazer and Andy Siciliano who made Webmind happen on the business side. And of course
Cassio Pennachin, a co-author of this book; and Ken Silverman, who co-architected the
whole Webmind system and vision with me from the start.
x
• The Webmind Diehards, who helped begin the Novamente project that succeeded Webmind
beginning in 2001: Cassio Pennachin, Stephan Vladimir Bugaj, Takuo Henmi, Matthew
Ikle’, Thiago Maia, Andre Senna, Guilherme Lamacie and Saulo Pinto
• Those who helped get the Novamente project off the ground and keep it progressing over the
years, including some of the Webmind Diehards and also Moshe Looks, Bruce Klein, Izabela
Lyon Freire, Chris Poulin, Murilo Queiroz, Predrag Janicic, David Hart, Ari Heljakka, Hugo
Pinto, Deborah Duong, Paul Prueitt, Glenn Tarbox, Nil Geisweiller and Cassio Pennachin
(the co-authors of this book), Sibley Verbeck, Jeff Reed, Pejman Makhfi, Welter Silva,
Lukasz Kaiser and more
• All those who have helped with the OpenCog system, including Linas Vepstas, Joel Pitt,
Jared Wigmore / Jade O’Neill, Zhenhua Cai, Deheng Huang, Shujing Ke, Lake Watkins,
Alex van der Peet, Samir Araujo, Fabricio Silva, Yang Ye, Shuo Chen, Michel Drenthe, Ted
Sanders, Gustavo Gama and of course Nil and Cassio again. Tyler Emerson and Eliezer
Yudkowsky, for choosing to have the Singularity Institute for AI (now MIRI) provide seed
funding for OpenCog.
• The numerous members of the AGI community who have tossed around AGI ideas with me
since the first AGI conference in 2006, including but definitely not limited to: Stan Franklin,
Juergen Schmidhuber, Marcus Hutter, Kai-Uwe Kuehnberger, Stephen Reed, Blerim Enruli,
Kristinn Thorisson, Joscha Bach, Abram Demski, Itamar Arel, Mark Waser, Randal Koene,
Paul Rosenbloom, Zhongzhi Shi, Steve Omohundro, Bill Hibbard, Eray Ozkural, Brandon
Rohrer, Ben Johnston, John Laird, Shane Legg, Selmer Bringsjord, Anders Sandberg, Alexei
Samsonovich, Wlodek Duch, and more
• The inimitable "Artilect Warrior" Hugo de Garis, who (when he was working at Xiamen
University) got me started working on AGI in the Orient (and introduced me to my wife
Ruiting in the process). And Changle Zhou, who brought Hugo to Xiamen and generously
shared his brilliant research students with Hugo and me. And Min Jiang, collaborator of
Hugo and Changle, a deep AGI thinker who is helping with OpenCog theory and practice
at time of writing.
• Gino Yu, who got me started working on AGI here in Hong Kong, where I am living at time
of writing. As of 2013 the bulk of OpenCog work is occurring in Hong Kong via a research
grant that Gino and I obtained together
• Dan Stoicescu, whose funding helped Novamente through some tough times.
• Jeffrey Epstein, whose visionary funding of my AGI research has helped me through a
number of tight spots over the years. At time of writing, Jeffrey is helping support the
OpenCog Hong Kong project.
• Zeger Karssen, founder of Atlantis Press, who conceived the Thinking Machines book series
in which this book appears, and who has been a strong supporter of the AGI conference
series from the beginning
• My wonderful wife Ruiting Lian, a source of fantastic amounts of positive energy for me
since we became involved four years ago. Ruiting has listened to me discuss the ideas
contained here time and time again, often with judicious and insightful feedback (as she
is an excellent AI researcher in her own right); and has been wonderfully tolerant of me
diverting numerous evenings and weekends to getting this book finished (as well as to other
AGI-related pursuits). And my parents Ted and Carol and kids Zar, Zeb and Zade, who
have also indulged me in discussions on many of the themes discussed here on countless
occasions! And my dear, departed grandfather Leo Zwell, for getting me started in science.
• Crunchkin and Pumpkin, for regularly getting me away from the desk to stroll around the
village where we live; many of my best ideas about AGI and other topics have emerged
while walking with my furry four-legged family members
xi
September 2013
Ben Goertzel
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 AI Returns to Its Roots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 AGI versus Narrow AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 The Secret Sauce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.5 Extraordinary Proof? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.6 Potential Approaches to AGI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6.1 Build AGI from Narrow AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6.2 Enhancing Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6.3 Emulating the Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6.4 Evolve an AGI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.6.5 Derive an AGI design mathematically . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.6.6 Use heuristic computer science methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6.7 Integrative Cognitive Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6.8 Can Digital Computers Really Be Intelligent? . . . . . . . . . . . . . . . . . . . . . . . . 8
1.7 Five Key Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.7.1 Memory and Cognition in CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.8 Virtually and Robotically Embodied AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.9 Language Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.10 AGI Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.11 Structure of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.12 Key Claims of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Section I Artificial and Natural General Intelligence
2 What Is Human-Like General Intelligence? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.1 What Is General Intelligence? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.2 What Is Human-like General Intelligence? . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2 Commonly Recognized Aspects of Human-like Intelligence . . . . . . . . . . . . . . . . . . . 20
2.3 Further Characterizations of Humanlike Intelligence . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3.1 Competencies Characterizing Human-like Intelligence . . . . . . . . . . . . . . . . . 24
2.3.2 Gardner’s Theory of Multiple Intelligences . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
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Contents
2.3.3 Newell’s Criteria for a Human Cognitive Architecture . . . . . . . . . . . . . . . . . 26
2.3.4 intelligence and Creativity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4 Preschool as a View into Human-like General Intelligence . . . . . . . . . . . . . . . . . . . . 27
2.4.1 Design for an AGI Preschool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.5 Integrative and Synergetic Approaches to Artificial General Intelligence . . . . . . . 29
2.5.1 Achieving Humanlike Intelligence via Cognitive Synergy . . . . . . . . . . . . . . . 30
3 A Patternist Philosophy of Mind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2 Some Patternist Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3 Cognitive Synergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4 The General Structure of Cognitive Dynamics: Analysis and Synthesis . . . . . . . . 42
3.4.1 Component-Systems and Self-Generating Systems . . . . . . . . . . . . . . . . . . . . 42
3.4.2 Analysis and Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.4.3 The Dynamic of Iterative Analysis and Synthesis . . . . . . . . . . . . . . . . . . . . 46
3.4.4 Self and Focused Attention as Approximate Attractors of the Dynamic
of Iterated Forward-Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.5 Perspectives on Machine Consciousness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.6 Postscript: Formalizing Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Brief Survey of Cognitive Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 Symbolic Cognitive Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2.1 SOAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.2.2 ACT-R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2.3 Cyc and Texai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.2.4 NARS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2.5 GLAIR and SNePS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 Emergentist Cognitive Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.3.1 DeSTIN: A Deep Reinforcement Learning Approach to AGI . . . . . . . . . . . 66
4.3.2 Developmental Robotics Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.4 Hybrid Cognitive Architectures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.4.1 Neural versus Symbolic; Global versus Local . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.5 Globalist versus Localist Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.5.1 CLARION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.5.2 The Society of Mind and the Emotion Machine . . . . . . . . . . . . . . . . . . . . . . 80
4.5.3 DUAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.5.4 4D/RCS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.5.5 PolyScheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.5.6 Joshua Blue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.5.7 LIDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.5.8 The Global Workspace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.5.9 The LIDA Cognitive Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.5.10 Psi and MicroPsi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.5.11 The Emergence of Emotion in the Psi Model . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.5.12 Knowledge Representation, Action Selection and Planning in Psi . . . . . . . 93
Contents
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4.5.13 Psi versus CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5 A Generic Architecture of Human-Like Cognition . . . . . . . . . . . . . . . . . . . . . . . . 95
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.2 Key Ingredients of the Integrative Human-Like Cognitive Architecture Diagram 96
5.3 An Architecture Diagram for Human-Like General Intelligence . . . . . . . . . . . . . . . 97
5.4 Interpretation and Application of the Integrative Diagram . . . . . . . . . . . . . . . . . . . 104
6 A Brief Overview of CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.2 High-Level Architecture of CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.3 Current and Prior Applications of OpenCog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
6.3.1 Transitioning from Virtual Agents to a Physical Robot . . . . . . . . . . . . . . . . 110
6.4 Memory Types and Associated Cognitive Processes in CogPrime . . . . . . . . . . . . . 110
6.4.1 Cognitive Synergy in PLN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.5 Goal-Oriented Dynamics in CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6.6 Analysis and Synthesis Processes in CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
6.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Section II Toward a General Theory of General Intelligence
7 A Formal Model of Intelligent Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
7.2 A Simple Formal Agents Model (SRAM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
7.2.1 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
7.2.2 Memory Stores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
7.2.3 The Cognitive Schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
7.3 Toward a Formal Characterization of Real-World General Intelligence . . . . . . . . . 135
7.3.1 Biased Universal Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.3.2 Connecting Legg and Hutter’s Model of Intelligent Agents to the Real
World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
7.3.3 Pragmatic General Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
7.3.4 Incorporating Computational Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
7.3.5 Assessing the Intelligence of Real-World Agents . . . . . . . . . . . . . . . . . . . . . . 139
7.4 Intellectual Breadth: Quantifying the Generality of an Agent’s Intelligence . . . . . 141
7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
8 Cognitive Synergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
8.1 Cognitive Synergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
8.2 Cognitive Synergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
8.3 Cognitive Synergy in CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
8.3.1 Cognitive Processes in CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
8.4 Some Critical Synergies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
8.5 The Cognitive Schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
8.6 Cognitive Synergy for Procedural and Declarative Learning . . . . . . . . . . . . . . . . . . 153
8.6.1 Cognitive Synergy in MOSES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
8.6.2 Cognitive Synergy in PLN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
8.7 Is Cognitive Synergy Tricky? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
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8.7.1 The Puzzle: Why Is It So Hard to Measure Partial Progress Toward
Human-Level AGI? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
8.7.2 A Possible Answer: Cognitive Synergy is Tricky! . . . . . . . . . . . . . . . . . . . . . . 158
8.7.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
9 General Intelligence in the Everyday Human World . . . . . . . . . . . . . . . . . . . . . . 161
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
9.2 Some Broad Properties of the Everyday World That Help Structure Intelligence 162
9.3 Embodied Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
9.3.1 Generalizing the Embodied Communication Prior . . . . . . . . . . . . . . . . . . . . 166
9.4 Naive Physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
9.4.1 Objects, Natural Units and Natural Kinds . . . . . . . . . . . . . . . . . . . . . . . . . . 167
9.4.2 Events, Processes and Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
9.4.3 Stuffs, States of Matter, Qualities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
9.4.4 Surfaces, Limits, Boundaries, Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
9.4.5 What Kind of Physics Is Needed to Foster Human-like Intelligence? . . . . . 169
9.5 Folk Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
9.5.1 Motivation, Requiredness, Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
9.6 Body and Mind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
9.6.1 The Human Sensorium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
9.6.2 The Human Body’s Multiple Intelligences . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
9.7 The Extended Mind and Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
9.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
10 A Mind-World Correspondence Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
10.2 What Might a General Theory of General Intelligence Look Like? . . . . . . . . . . . . 178
10.3 Steps Toward A (Formal) General Theory of General Intelligence . . . . . . . . . . . . . 179
10.4 The Mind-World Correspondence Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
10.5 How Might the Mind-World Correspondence Principle Be Useful? . . . . . . . . . . . . 181
10.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
Section III Cognitive and Ethical Development
11 Stages of Cognitive Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
11.2 Piagetan Stages in the Context of a General Systems Theory of Development . . 188
11.3 Piaget’s Theory of Cognitive Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
11.3.1 Perry’s Stages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
11.3.2 Keeping Continuity in Mind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
11.4 Piaget’s Stages in the Context of Uncertain Inference . . . . . . . . . . . . . . . . . . . . . . . 193
11.4.1 The Infantile Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
11.4.2 The Concrete Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
11.4.3 The Formal Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
11.4.4 The Reflexive Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
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12 The Engineering and Development of Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
12.2 Review of Current Thinking on the Risks of AGI . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
12.3 The Value of an Explicit Goal System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
12.4 Ethical Synergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
12.4.1 Stages of Development of Declarative Ethics . . . . . . . . . . . . . . . . . . . . . . . . . 211
12.4.2 Stages of Development of Empathic Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . 214
12.4.3 An Integrative Approach to Ethical Development . . . . . . . . . . . . . . . . . . . . . 215
12.4.4 Integrative Ethics and Integrative AGI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
12.5 Clarifying the Ethics of Justice: Extending the Golden Rule in to a
Multifactorial Ethical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
12.5.1 The Golden Rule and the Stages of Ethical Development . . . . . . . . . . . . . 222
12.5.2 The Need for Context-Sensitivity and Adaptiveness in Deploying
Ethical Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
12.6 The Ethical Treatment of AGIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
12.6.1 Possible Consequences of Depriving AGIs of Freedom . . . . . . . . . . . . . . . . . 228
12.6.2 AGI Ethics as Boundaries Between Humans and AGIs Become Blurred . 229
12.7 Possible Benefits of Closely Linking AGIs to the Global Brain . . . . . . . . . . . . . . . . 230
12.7.1 The Importance of Fostering Deep, Consensus-Building Interactions
Between People with Divergent Views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
12.8 Possible Benefits of Creating Societies of AGIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
12.9 AGI Ethics As Related to Various Future Scenarios . . . . . . . . . . . . . . . . . . . . . . . . 234
12.9.1 Capped Intelligence Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
12.9.2 Superintelligent AI: Soft-Takeoff Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
12.9.3 Superintelligent AI: Hard-Takeoff Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . 235
12.9.4 Global Brain Mindplex Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
12.10Conclusion: Eight Ways to Bias AGI Toward Friendliness . . . . . . . . . . . . . . . . . . . . 239
12.10.1Encourage Measured Co-Advancement of AGI Software and AGI Ethics
Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
12.10.2Develop Advanced AGI Sooner Not Later . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Section IV Networks for Explicit and Implicit Knowledge Representation
13 Local, Global and Glocal Knowledge Representation . . . . . . . . . . . . . . . . . . . . . . 245
13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
13.2 Localized Knowledge Representation using Weighted, Labeled Hypergraphs . . . . 246
13.2.1 Weighted, Labeled Hypergraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
13.3 Atoms: Their Types and Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
13.3.1 Some Basic Atom Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
13.3.2 Variable Atoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249
13.3.3 Logical Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
13.3.4 Temporal Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
13.3.5 Associative Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
13.3.6 Procedure Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
13.3.7 Links for Special External Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
13.3.8 Truth Values and Attention Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
13.4 Knowledge Representation via Attractor Neural Networks . . . . . . . . . . . . . . . . . . . 256
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13.4.1 The Hopfield neural net model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256
13.4.2 Knowledge Representation via Cell Assemblies . . . . . . . . . . . . . . . . . . . . . . 257
13.5 Neural Foundations of Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258
13.5.1 Hebbian Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258
13.5.2 Virtual Synapses and Hebbian Learning Between Assemblies . . . . . . . . . . 258
13.5.3 Neural Darwinism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
13.6 Glocal Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260
13.6.1 A Semi-Formal Model of Glocal Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262
13.6.2 Glocal Memory in the Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
13.6.3 Glocal Hopfield Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
13.6.4 Neural-Symbolic Glocality in CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
14 Representing Implicit Knowledge via Hypergraphs . . . . . . . . . . . . . . . . . . . . . . . 271
14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
14.2 Key Vertex and Edge Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
14.3 Derived Hypergraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
14.3.1 SMEPH Vertices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
14.3.2 SMEPH Edges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
14.4 Implications of Patternist Philosophy for Derived Hypergraphs of Intelligent
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
14.4.1 SMEPH Principles in CogPrime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
15 Emergent Networks of Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
15.2 Small World Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
15.3 Dual Network Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
15.3.1 Hierarchical Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
15.3.2 Associative, Heterarchical 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 . . . . . . . . . . . . . . . . . . . . . . . . 293
16.4 Task-Based Assessment in AGI Preschool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
16.5 Beyond Preschool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298
16.6 Issues with Virtual Preschool Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298
16.6.1 Integrating Virtual Worlds with Robot Simulators . . . . . . . . . . . . . . . . . . . . 301
16.6.2 BlocksNBeads World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
17 A Preschool-Based Roadmap to Advanced AGI . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
17.2 Measuring Incremental Progress Toward Human-Level AGI . . . . . . . . . . . . . . . . . . 308
17.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
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18 Advanced Self-Modification: A Possible Path to Superhuman AGI . . . . . . . . 317
18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
18.2 Cognitive Schema Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318
18.3 Self-Modification via 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
Chapter 1
Introduction
1.1 AI Returns to Its Roots
Our goal in this book is straightforward, albeit ambitious: to present a conceptual and technical
design for a thinking machine, a software program capable of the same qualitative sort of general
intelligence as human beings. It’s not certain exactly how far the design outlined here will be
able to take us, but it seems plausible that once fully implemented, tuned and tested, it will be
able to achieve general intelligence at the human level and in some respects beyond.
Our ultimate aim is Artificial General Intelligence construed in the broadest sense, including
artificial creativity and artificial genius. We feel it is important to emphasize the extremely
broad potential of Artificial General Intelligence systems. The human brain is not built to be
modified, except via the slow process of evolution. Engineered AGI systems, built according to
designs like the one outlined here, will be much more susceptible to rapid improvement from
their initial state. It seems reasonable to us to expect that, relatively shortly after achieving the
first roughly human-level AGI system, AGI systems with various sorts of beyond-human-level
capabilities will be achieved.
Though these long-term goals are core to our motivations, we will spend much of our time here
explaining how we think we can make AGI systems do relatively simple things, like the things
human children do in preschool. The penultimate chapter of (Part 2 of) the book describes a
thought-experiment involving a robot playing with blocks, responding to the request "Build me
something I haven’t seen before." We believe that preschool creativity contains the seeds of,
and the core structures and dynamics underlying, adult human level genius ... and new, as yet
unforeseen forms of artificial innovation.
Much of the book focuses on a specific AGI architecture, which we call CogPrime, and which
is currently in the midst of implementation using the OpenCog software framework. CogPrime
is large and complex and embodies a host of specific decisions regarding the various aspects of
intelligence. We don’t view CogPrime as the unique path to advanced AGI, nor as the ultimate
end-all of AGI research. We feel confident there are multiple possible paths to advanced AGI,
and that in following any of these paths, multiple theoretical and practical lessons will be
learned, leading to modifications of the ideas possessed while along the early stages of the path.
But our goal here is to articulate one path that we believe makes sense to follow, one overall
design that we believe can work.
1
2 1 Introduction
1.2 AGI versus Narrow AI
An outsider to the AI field might think this sort of book commonplace in the research literature,
but insiders know that’s far from the truth. The field of Artificial Intelligence (AI) was founded
in the mid 1950s with the aim of constructing “thinking machines” - that is, computer systems