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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 EFTA00623759 EFTA00623760 This book is dedicated by Ben Goertzel to his beloved, departed grandfather, Leo Ztuell - 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! EFTA00623761 EFTA00623762 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 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, 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 arti- ficial 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 "Ii EFTA00623763 vu' 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 Ls 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 roential, a philosophy of mind book published in 2006; Probabilistic Logic Networks IGIGHOSI, a more technical work published in 2008; Real World Reasoning IGGC lib, a sequel to Proba- bilistic 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 EFTA00623764  ix 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 Al 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. 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 Ls 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 Christca, a deep Al/math/physim 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 Sicilian 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. EFTA00623765 x • The Webmind Diehards, who helped begin the Novamente project that succeeded Webmind beginning in 2001: Cassio Pennachin, Stephan Vladimir Bugaj, Takuo Henmi, Matthew lkle', Thiago Maia, Andre Senna, Guilhenne 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 theme who have helped with the OpenCog system, including Linas Vepstas, Joel Pitt, Jared Wigmore / Jade O'Neill, Zhenhua Cal, Deheng Huang, Shujing Ke, Lake Watkins, Alex van der Peet, Samir Araujo, Fabricio Silva, Yang Ye, Shuo Chen, Michel Drenthe, Ted Sanders, Gustavo Gain and of course Nil and Cassio again. Tyler Emerson and Eliezer Yudkowsky, for choosing to have the Singularity Institute for Al (now MIR1) 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 Rutter, Kai-Uwe Kuehnberger, Stephen Reed, Blerim Enruli, Kristinn Thorisson, Joscha Bach, Abram Demski, hamar Arel, Mark Waser, Randal Koene, Paul Rosenbloom, Zhongzhi Shi, Steve Omohundro, Bill Hibbard, Eray Ozkural, Brandon Rohrer, Ben Johnston, John Laird, Shane Legg, Selmer Brin&sjord, Anders Sandberg, Alexei Samsonovich, Wlodek Duch, and more • The inimitable "Artilect Warrior" Hugo de Gans, 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 Mb 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. EFTA00623766  xi • 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 September 2013 Ben Goertzet EFTA00623767 EFTA00623768 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 Al 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 EFTA00623769 xiv 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 EFTA00623770 Contents xv 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 EFTA00623771 xvi Contents 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 EFTA00623772 Contents xvii 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 Al: 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 EFTA00623773 xviii Contents 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 EFTA00623774 Contents xix 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 EFTA00623775 EFTA00623776 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 EFTA00623777 2 I 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 with human-like general intelligence, including humanoid robots that not only look but act and think with intelligence equal to and ultimately greater than human beings. But in the intervening years, the field has drifted far from its ambitious roots, and this book represents part of a movement aimed at restoring the initial goals of the AI field, but in a manner powered by new tools and new ideas far beyond those available half a century ago. After the first generation of Al researchers found the task of creating human-level AGI very, difficult given the technology, of their time, the Al field shifted focus toward what Ray Kurzweil has called "narrow AI" - the understanding of particular specialized aspects of intelligence; and the creation of AI systems displaying intelligence regarding specific tasks in relatively narrow domains. In recent years, however, the situation has been changing. More and more researchers have recognized the necessity - and feasibility - of returning to the original goals of the field. In the decades since the 1950s, cognitive science and neuroscience have taught us a lot about what a cognitive architecture needs to look like to support roughly human-like general intelli- gence. Computer hardware has advanced to the point where we can build distributed systems containing large amounts of RAM and large numbers of processors., carrying out complex tasks in real time. The AI field has spawned a host of ingenious algorithms and data structures, which have been successfully deployed for a huge variety of purposes. Due to all this progress, increasingly, there has been a call for a transition from the current focus on highly specialized "narrow AI" problem solving systems, back to confronting the more difficult issues of "human level intelligence" and more broadly "artificial general intelligence (AGI)." Recent years have seen a growing number of special sessions, workshops and confer- ences devoted specifically to AGI, including the annual BICA (Biologically Inspired Cognitive Architectures) AAAI Symposium, and the international AGI conference series (one in 2006, and annual since 2008). And, even more exciting, as reviewed in Chapter 4, there are a number of contemporary, projects focused directly and explicitly on AGI (sometimes under the name "AGI", sometimes using related terms such as "Human Level Intelligence"). In spite of all this progress, however, we feel that no one has yet clearly articulated a detailed, systematic design for an AGI, with potential to yield general intelligence at the human level and ultimately beyond. In this spirit, our main goal in this lengthy two-part book is to outline a novel design for a thinking machine - an AGI design which we believe has the capability to produce software systems with intelligence at the human adult level and ultimately beyond. Many of the technical details of this design have been previously presented online in a wikibook V;Oel06J; and the basic ideas of the design have been presented briefly in a series of conference papers IGPS1,03, CPPGOU, G00%1. But the overall design has not been presented in a coherent and systematic way before this book. In order to frame this design properly, we also present a considerable number of broader theoretical and conceptual ideas here, some more and some less technical in nature. EFTA00623778 1.4 The Secret Sauce 3 1.3 CogPrime The AGI design presented here has not previously been granted a name independently of its particular software implementations, but for the purposes of this book it needs one, so we've christened it CogPrime . This fits with the name "OpenCogPrime" that has already been used to describe the software implementation of CogPrime within the open-source OpenCog AGI software framework. The OpenCogPrime software, right now, implements only a small fraction of the CogPrime design as described here. However, OpenCog was designed specifically to enable efficient, scalable implementation of the full CogPrime design (as well as to serve as a more general framework for AGI R&D); and work currently proceeds in this direction, though there is a lot of work still to be done and many challenges remain. The CogPrime design is more comprehensive and thorough than anything that has been presented in the literature previously, including the work of others reviewed in Chapter 4. It covers all the key aspects of human intelligence, and explains how they interoperate and how they can be implemented in digital computer software. Part 1 of this work outlines CogPrime at a high level, and makes a number of more general points about artificial general intelligence and the path thereto; then Part 2 digs deeply into the technical particulars of CogPrime. Even Part 2, however, doesn't explain all the details of CogPrime that have been worked out so far, and it definitely doesn't explain all the implementation details that have gone into designing and building OpenCogPrime. Creating a thinking machine is a large task, and even the intermediate level of detail takes up a lot of pages. 1.4 The Secret Sauce There is no consensus on why all the related technological and scientific progress mentioned above has not yet yielded AI software systems with human-like general intelligence (or even greater levels of brilliance!). However, we hypothesize that the core reason boils down to the following three points: • Intelligence depends on the emergence of certain high-level structures and dynamics across a system's whole knowledge base; • We have not discovered any one algorithm or approach capable of yielding the emergence of these structures; • Achieving the emergence of these structures within a system formed by integrating a number of different AI algorithms and structures requires careful attention to the manner in which I This brings up a terminological note: At several places in this Volume and the next we will refer to the current CogPrime or OpenCog implementation; in all cases this refers to OpenCog as of late 2013. We realize the risk of mentioning the state of our software system at time of writing: for future readers this may give the wrong impression, because if our project goes well, more and more of CogPrime will get implemented and tested as time goes on (e.g. within the OpenCog framework, under active development at time of writing). However, not mentioning the current implementation at all seems an even worse course to us, since we feel readers will be interested to know which of our ideas - at time of writing - have been honed via practice