Ben Goertzel and Matthew Ikle’, with numerous colleagues Introduction to Artificial General Intelligence (incomplete, very preliminary draft) May 4, 2015 Preface Why a Text on AGI? AI has always included what we would now call AGI as a significant component – in fact, arguably the founders of the AI field were more interested in human-like general intelligence than in narrow application-specific functionality. Since the early aughts, though, the urge has gradually risen among a community of researchers in the AI community to explicitly distinguish pursuits such as AGI and Human-Level AI from the application-specific or problem-specific AI that has come to dominate the AI field. A vibrant collection of intersecting research communities has arisen, dealing with various aspects of AGI theory and practice. For the student or researcher interested in getting up to speed on ideas and developments related to AGI, however, there has not previously been anywhere really great to turn. There are several edited volumes covering AGI topics [? ], [? ], and then the AGI conference proceedings volumes; but while this is interesting and often high quality material, it is somewhat haphazard in its coverage. There seemed a need for a more systematic introduction to the issues and concepts that preoccupy AGI researchers. Thus the motivation to put this book together. Intended Audience While there is not that much highly technical material here, the discussion is generally pitched at the reader who is already familiar with the basics of data structures and algorithms, and mainstream “narrow AI” as is taught in a typical undergraduate AI course. Review of neu- roscience and cognitive science is provided (specifically targeted toward AGI), but review of undergrad computer science is not. Thus, a natural audience for the book would be • Master students or final-year undergraduate students in computer science • Grad students or final-year undergrads in allied disciplines like neuroscience, cognitive sci- ence, mathematics or engineering – who have some basic familiarity with computer science and programming concepts • Professional programmers or scientists who are experienced reading advanced technical material in their own fields, and have seen narrow-AI applications here and there, and v vi would like to find out what AGI is all about at a level above popularizations, but without having to dig deeply into the research literature Utilization in University Courses The present book is intended to be useful in university courses, for instance it can serve as a key part of the curriculum for a semester or year course at the grad or final-year undergrad level focused on Artificial General Intelligence specifically. Such a course would naturally be placed after a standard “narrow AI” focused AI course, in a computer science or cognitive science curriculum. This book does not constitute a complete curriculum for such a class, however, because it lacks practical exercises. To form a compelling course on AGI, one option would be to couple the present text, which gives a broad overview of the concepts related to AGI, with appropriate tutorial material related to specific AGI-related software systems. This could be done in many different ways, and this is an area where different instructors will likely want to exercise their own creativity. One specific option, for instance, would be to couple this book with: • Hands-on work with the tutorials supplied with a few different AGI-related systems. LIDA, ACT-R and Soar have fairly advanced tutorials, and OpenCog’s tutorials are coming along. • Guided implementation of simple AGI-oriented learning agents, for instance as done in Olivier Georgeon’s Developmental AI MOOC http://liris.cnrs.fr/ideal/mooc/ Thoroughly going through the tutorials of any of the above-mentioned systems would take students a substantial amount of time (a couple dozen hours per system, at least). So for instance a one-year course on AGI, with 4 hours of instruction per week, could potentially have 2 hours/week of theoretical instruction based on the present text, and 2 hours/week of lab sessions involving hands-on work. The Origins of this Book, and the Wonderful and Frustrating Diversity of the AGI Field This is a multi-author textbook and hence owes a lot to a lot of people. However the original idea of putting it together was due to Ben Goertzel, the lead editor, and the rest of this Preface is written from Ben’s point of view... I got the idea to write or edit a textbook of some sort on AGI in 2012 or so. At that point, I got as far as making a table of contents, and emailing a bunch of colleagues soliciting chapters. I got some nice material back via email, but didn’t get much further, due to being overwhelmed with other projects. The project smoldered in the background though, and in spare moments here and there I put together the material that eventually became Chapters2,1 and ??. What stimulated me to finally bite the bullet and put together a draft of the book was the decision to accept an opportunity to help out with a new MS program in AI, to be offered at the Addis Ababa Institute of Technology, in Ethiopia. I wanted to teach a course of AGI, as part of vii this MS program. And, while I could do an AGI course using a hodgepodge of articles as course materials, this was a definite near-term use-case for the long-germinating AGI textbook. Over the years the idea was germinating, when I mentioned the “AGI textbook” idea to other AGI researchers, the most common reaction was a mixture of interest, with acknowledgement of what a huge challenge such an undertaking would be. The most challenging aspect commonly remarked upon was the sheer diversity of perspectives and ideas in the AGI field. As one friend and fellow researcher put it, "No two AGI researchers could possibly agree on what an AGI textbook should contain." Of course, while this isn’t quite literally true, it does contain a semblance of reality. The AGI field is somewhat disparate and disorganized, as befits its early stage of development. But nevertheless, it seems important to ease the path for students and young researchers to get into the field – so they can contribute their own perspectives to the mix, as well as push new perspectives forward. My hope, and that of my co-editor Matt Ikle’ and the other contributors, is that this book can be useful in this regard. Ben Goertzel Hong Kong, 2015 Contents Section I The Past, Present and Future of AGI 1 Overview of the AGI Field ............................................... 3 1.1 Introduction...........................................................3 1.2 AGI versus Narrow AI.................................................3 1.3 The Emergence of an AGI Community....................................4 1.3.1 AGI and Related Concepts.......................................5 1.4 Perspectives on General Intelligence.....................................5 1.4.1 The Pragmatic Approach to Characterizing General Intelligence.......6 1.4.2 Psychological Characterizations of General Intelligence...............6 1.4.3 A Mathematical Approach to Characterizing General Intelligence.......7 1.4.4 The Adaptationist Approach to Characterizing General Intelligence.....7 1.4.5 Broadly Suspected Aspects of General Intelligence..................7 1.5 Current Scope of the AGI Field.........................................8 1.5.1 Universal AI...................................................8 1.5.2 Symbolic AGI.................................................. 10 1.5.3 Emergentist AGI................................................ 10 1.5.4 Hybrid AGI.................................................... 12 1.6 Future of the AGI Field................................................ 13 2 A Brief History of AI and AGI ........................................... 15 2.1 Introduction........................................................... 15 2.2 The Prehistory of AI.................................................... 17 2.3 1600s-1800s: Mechanical Calculators, and Models of Thought as Calculation... 18 2.4 Turn of the 20th Century: Maturation of the View of Human and Artificial Thought as Complex Mechanical Operations............................... 21 2.5 Mid 20th Century: The Birth of Electronic Computing and Modern AI Technology............................................................ 22 2.6 Late 1950s - Early 1970s: Emergence and Flourishing of AI as a Discipline.... 26 2.7 Mid 1970s - early 80s: Having Failed to Progress as Fast as Hoped, AI Experiences a Funding Winter, but also a Host of New Ideas................ 29 2.8 Mid 1980s - early 90s: AI Funding for Expert Systems Rises and Falls; Connectionist, Probabilistic and Subsumption Approaches Surge............. 30 ix x Contents 2.9 Fueled by Powerful Computers and Big Data, Narrow AI Technology Finds Broad Practical Applications............................................ 33 2.9.1 Dramatic Progress in Neuroscience, with Limited Implications for AI... 34 2.10 2004-2012: While Narrow AI Tech Further Pervades Industry, a Trend back toward AGI / Human Level AI R&D Emerges............................. 35 3 Artificial General Intelligence: Concept, State of the Art and Future Prospects ................................................................. 47 3.1 Introduction........................................................... 47 3.1.1 What is General Intelligence?...................................... 48 3.1.2 The Core AGI Hypothesis......................................... 49 3.1.3 The Scope of the AGI Field.......................................