1. Achieving Advanced Machine Consciousness via Artificial General Intelligence in Virtual Worlds Ben Goertzel, PhD

2. Contents 1. The Nature of Consciousness 2. Artificial General Intelligence versus Narrow AI 3. The Novamente and OpenCog AGI Projects 4. The Marriage of AGI and Virtual Worlds 5. Initial Application: Virtual Pet Brain

3. A Useful Philosophical Perspective On Consciousness In

5. Metaphysical Foundation: Peircean/Jungian Categories First: raw, unprocessed being … e.g. qualia Second: reaction … e.g. pure physical reaction Third: relationship (beyond Peirce … “Fourth: synergy”, etc.)

6. Archetypal Perspectives First person: firstness of X … the world as directly experienced … the stream of qualia … Third person: thirdness of X … the world as an objective relational structure, a network of patterns Fourth person (normally called “second person”): fourthness of X … the synergy of relationships … the Buber-ian I-You The real second person: secondness of X … experiencing the world as an automaton?

7. Inter-perspective correlations Example of a hypothesis spanning perspectives: The more intense qualia experienced by a system, correspond to the more informationally significant patterns detectable in that system by an intelligent, well-informed observer

8. Reflective consciousness and other emergent constructs Hypothesis: Among the more informationally significant patterns in generally intelligent systems are: The phenomenal self Reflective consciousness The illusion of will

9. Modeling Reflective Consciousness, Self and Will Using Hypersets Hypothesis: The qualia we humans describe as “reflective awareness”, “self” and “will” correspond to patterns in our brains that are conveniently expressible in terms of hypersets (non-well-founded sets)

10. Modeling Reflective Consciousness, Self and Will Using Hypersets “S is conscious of X" is defined as: The declarative content that {"S is conscious of X" correlates with "X is a pattern in S"}, where S is an intelligent system’s phenomenal self "S wills X" is defined as: The declarative content that {"S wills X" causally implies "S does X”}, where S is an intelligent system’s phenomenal self "X is part of S's self" is defined as: The declarative content that {"X is a part of S's self" correlates with "X is a persistent pattern in S over time"}

11. Evaluating Hypersets as Patterns in Dynamical Systems The hyperset defined by X = F(X) may be evaluated as a pattern in a system by comparing the iterates A F(A) F(F(A)) … to the system’s trajectory at various times for various A

12. Summary There are multiple archetypal perspectives: First, Second, Third, Fourth person,… There are correlations between the different perspectives (e.g. intense qualia correspond to informational patterns) There are specific emergent structures (self, will, reflection) that correlate with intense patterns/qualia in generally intelligent systems It may be interesting to model these emergent structures using hypersets

13. Artificial General Intelligence versus Narrow AI In

14. Artificial General Intelligence (AGI) “The ability to achieve complex goals in complex environments using limited computational resources” Autonomy Practical understanding of self and others Understanding “what the problem is” as opposed to just solving problems posed explicitly by programmers Solving problems that were not known to the programmers

15. Narrow AI The vast majority of AI research practiced in academia and industry today fits into the “Narrow AI” category Each “Narrow AI” program is (in the ideal case) highly competent at carrying out certain complex goals in certain environments Chess-playing, medical diagnosis, car-driving, etc.

16. Today, Narrow AI Dominates the AI Field (in both academia and applications) Deep Blue - whoops us pesky humans at chess - but can’t learn to play a new game based on a description of the game rules DARPA Grand Challenge - a great leap forward -- but it can’t learn to drive different types of vehicles besides cars (trucks, boats, motorcycles) Google - fantastic service: but can’t answer complex questions. Whatever happened to AskJeeves?

17. 2001

19. Artificial General Intelligence (AGI) Hypothesis: Human-level general intelligence naturally comes along with the emergence of Phenomenal self Reflective consciousness Illusion of free will

20. A Pragmatic, Integrative Approach to Advanced AGI In

21. Novamente Cognition Engine The Novamente Cognition Engine (NCE) represents a serious scientific/engineering effort to create powerful artificial general intelligence, via an integrative, computer science based approach While the NCE may be applied in many different domains, the most natural way to develop and apply it, at the current stage, is in the context of controlling physically and/or virtually embodied intelligent agents For more detail on the NCE, see novamente.net/papers

22. Open Cognition Framework The OpenCog project (opencog.org) is an open-source offshoot of the Novamente project, which has been seeded in 2008 with significant AGI code donated by Novamente LLC It includes the RelEx NL comprehension system, founded on the CMU link parser plus additional rule-based and statistical NLP methods

23. The essential dynamics of these AGI systems follows the basic logic of animal behavior: Enact a procedure so that Context & Procedure ==> Goals i.e. at each moment, based on its observations and memories, the system chooses to enact procedures that it estimates (based on the properties of the current context) will enable it to achieve its goals, over the time-scales these goals refer to

24. There is an important distinction between explicit goals and implicit goals  Explicit goals: the objective-functions the system explicitly chooses actions in order to maximize  Implicit goals: the objective-functions the system actually does habitually maximize, in practice For a system that is both rational, and capable with respect to its goals in its environment, these will be basically the same. But in many real cases, they may be radically different

25. Goal Dynamics  A sufficiently intelligent system is continually creating new subgoals of its current goals  Some intelligent systems may be able to replace their top- level supergoals with new ones, based on various dynamics  Goals may operate on radically different time-scales  Humans habitually experience “subgoal alienation” -- what was once a subgoal of some other goal, becomes a top-level goal in itself. AI’s need not be so prone to this phenomenon

26. 1.Knowledge Representation 2.Cognitive Architecture 3.Knowledge Creation 4.Environment / Education (incl. physical & virtual robotics) 5.Emergent Structures and Dynamics There is no single, mechanism-level “magic trick” at the heart of general intelligence … rather, intelligence arises in appropriately-constructed complex systems as an emergent phenomenon. The trick is to figure out what sorts of complex systems will give rise to general intelligence as an emergent property. There is unlikely to be “one correct answer” to this question … but all we need to build the first thinking machine is one of the many correct answers. Five key aspects of AGI design:

27. The Novamente/OpenCog high-level cognitive architecture is based on the state of the art in cognitive psychology and cognitive neuroscience. Most cognitive functions are distributed across the whole system, yet principally guided by some particular module.

28. Unique hypergraph knowledge representation bridges the gap between subsymbolic (neural net) and symbolic (logic / semantic net) representations, achieving the advantages of both, and synergies resulting from their combination.

29. Each cognitive processing machine, within each unit, contains an “Atom Space” full of nodes and links representing knowledge, plus a set of cognitive processes acting on this Atom Space, encapsulated in software objects called MindAgents and scheduled by a Scheduler object.

30. Each box in the cognitive architecture diagram, corresponds at the software level to a cluster of machines called a “unit”, containing a local persistent DB plus one or more cognitive processing machines.

31. MOSES Probabilistic Evolutionary Learning (for gaining procedural knowledge directly) Combines the power of two leading AI paradigms: evolutionary and probabilistic learning Extremely broad applicability. Successful track record in bioinformatics, text and data mining, and virtual agent control. Probabilistic Logic Networks (for gaining declarative knowledge directly) The first general, practical integration of probability theory and symbolic logic. Extremely broad applicability. Successful track record in bio text mining, virtual agent control. Based on mathematics described in Probabilistic Logic Networks, published by Springer in 2008 Algorithms for Procedural and Declarative Knowledge Creation

32. Economic Attention Allocation Each node or link in the knowledge network is tagged with a probabilistic truth value, and also with an “attention value”, containing Short-Term Importance and Long-Term Importance components. An artificial-economics-based process is used to update these attention values dynamically -- a complex, adaptive nonlinear process.

33. The system contains multiple heuristics for Atom creation, including “blending” of existing Atoms

34. Atoms associated in a dynamic “map” may be grouped to form new Atoms: the Atomspace hence explicitly representing patterns in itself

35. Hypothesis: Integrative Design Can Allow Multiple AI Algorithms to Quell Each Others’ Combinatorial Explosions Probabilistic Evolutionary Program Learning Probabilistic Logical Inference Economic Attention Allocation Pattern Mining

36. Overall Philosophy Algorithms for declarative and procedural knowledge creation and attention allocation … integrated with appropriate synergy and acting on an appropriately powerful knoweldge representation … used to control a system pursuing complex goals … may lead to the emergence of system structures characteristic of general intelligence

37. Why Do I Believe I Can Succeed When So Many Others Have Failed? Approach is based on a well-reasoned, comprehensive theory of mind, which dictates a unified approach to the five key aspects mentioned above  Knowledge representation  Learning/reasoning  Cognitive architecture  Embodiment / interaction  Emergent structures / dynamics  Cognitive Theory summarized in The Hidden Pattern (Ben Goertzel, Brown Walker Press, 2006) The specific algorithms and data structures chosen to implement this theory of mind are efficient, robust and scalable and, so is the software implementation

38. The Marriage of AGI and Virtual Worlds In

39. How Important Is Embodiment?  Some AI theorists believe that robotic embodiment is necessary for the achievement of powerful AGI  Others believe embodiment is entirely unnecessary  We believe embodiment is extremely convenient for AGI though perhaps not strictly necessary; and that virtual-world embodiment is an important, pragmatic and scalable approach to pursue alongside physical-robot embodiment

40. Public virtual worlds provide a wonderful opportunity for teaching baby AI’s: not only the experience of embodiment, but the massive plus of having hundreds of thousands or millions of teachers helping the AI to learn

41. Current virtual world platforms have some fairly severe limitations, which fortunately are fairly easily remedied Object-object interactions are oversimplified, making tool use difficult Agent control relies on animations and other simplified mechanisms, rather than having virtual servomotors associated with each joint of an agent’s skeleton

42. Example solution: Integration of a robot simulator with a virtual world engine Player / Gazebo: 3D robot control + simulation framework RealXTend/OpenSim: open-source virtual world It seems feasible to replace OpenSim’s physics engine with appropriate components of Player/Gazebo, and make coordinated OpenSim client modifications +

43. Cognition Engine non-parametrized behavior signals e.g. “take one step forward” high-level perceptual data Coordinates of objects, Labeled with type Cognitive Control of agents in current virtual worlds -- e.g. Second Life, Multiverse, HiPiHi

44. action signals raw perceptions Perceptual preprocessor Behavioral postprocessor behavior signals mid-level perceptual data Cognition Engine e.g. ”Force F exerted by servomotor M in direction D” e.g. video output of camera eyes e.g. 3D polygonal mesh, marked up with limited object Identification information e.g. “take one step forward, using gait parameter vector V” Neural net module evolver Behavioral modules Object classification modules Hybrid Generally-Intelligent Robot Brain Architecture, version 1

48. Application: Novamente Pet Brain In

49. Novamente Pet Brain The Pet Brain incorporates MOSES learning to allow pets to learn tricks, and Probabilistic Logic Networks (PLN) inference regulates emotion-behavior interactions, and allows generalization based on experience. The Pet Brain utilizes a specialized version of the Novamente Cognition Engine to provide unprecedentedly intelligent virtual pets with individual personalities, and the ability to learn spontaneously and through training. Pets understand simple English; and future versions to include language generation

50. Demo Screenshots: Training Novamente-powered smart pets can be taught to do simple or complex tricks - from sitting to playing soccer or learning a dance - by learning from a combination of encouragement, reinforcement and demonstration. give “sit” command… reinforce and/or correct. show example…successful sit, great… ReinforceImitateTeachCorrect

51. Teaching with a Partner In partner-based teaching, the pet understands that one avatar is the teacher and the other is the student, whose interactions with the teacher the pet is supposed to understand, abstract, and imitate

52. Next Step: Language Learning Our initial virtual pets have robust but simplistic language understanding, sufficient to learn an unlimited variety of commands In the next version, integration of Novamente’s RelEx language processing system with the Novamente Pet Brain will provide a more powerful approach to embodied language learning With human-controlled avatars as language teachers, Novamente-controlled virtual agents will be able to rapidly improve their language comprehension and generation via adaptive learning

53. Next-Gen Pet/Baby Brain Architecture The next generation of the Avatar Brain will incorporate additional modules allowing language processing and more advanced inference -- the next step on the path from virtual dogs to human-level virtually-embodied AGIs

54. Deep understanding and control of self structures and dynamics Full Self-Modification Reflexive Formal Concrete Infantile Abstract reasoning and hypothesizing. Objective detachment from phenomenal self. Rich variety of learned mental representations and operations thereon. Emergence of phenomenal self. Making sense of and achieving simple goals in sensorimotor reality. No self yet. Stages of Development of an AGI

55. Intelligence…

58. The Coming Technological Singularity Verner Vinge (1993) “Within thirty years, we will have the technological means to create superhuman intelligence. Shortly thereafter, the human era will be ended… When greater-than-human intelligence drives progress, that progress will be much more rapid”

59. “I set the date for the Singularity- representing a profound and disruptive transformation in human capability- as 2045. The nonbiological intelligence created in that year will be one billion times more powerful than all human intelligence today." The Singularity is Near, When Humans Transcend Biology - Ray Kurzweil (2005)