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From Narrow AI to Artificial General Intelligence (AGI)

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Keynote from Intellifest 2012 addressing the differences between narrow (classical) Artificial Intelligence and Artificial General Intelligence. Implications of cloud computing for AGI are also discussed.

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From Narrow AI to Artificial General Intelligence (AGI)

  1. 1. IntelliFest 2012 International Conference on Reasoning Technologies INTELLIGENCE IN THE CLOUD From Classical (Narrow) AI to AGI Helgi Helgason Perseptio
  2. 2. Helgi Páll Helgason helgi@perseptio.com In collaboration with: Dr. Kristinn R. Thórisson & Eric Nivel Intellifest 2012 AI researcher, Ph.D. candidate Center for Analysis and Design of Intelligent Agents, Reykjavik University Founder / CEO, Perseptio
  3. 3. Narrow AI AGI Constructivist AI HUMANOBS Project AGI & Cloud Computing Intellifest 2012
  4. 4.  Displaying human-like behavior?  Solving computationally complex problems (in some unspecified amount of time)?  Performing isolated tasks that have conventionally required humans?  Adapting to a complex, dynamic environment with insufficient knowledge and resources? Intellifest 2012
  5. 5. Intellifest 2012 Nature’s way of dealing with complexity under resource and time constraints No real-world intelligence exists that does not address the passage of time head on
  6. 6. Intellifest 2012 “Intelligence is the capacity of a system to adapt to its environment while operating with insufficient knowledge and resources.” - Pei Wang (Rigid flexibility: The Logic of Intelligence. Springer 2006)
  7. 7. Intellifest 2012 “If either time or computational resources are infinite, intelligence is irrelevant .” - Dr. Kristinn R. Thórisson
  8. 8. Intellifest 2012 Time constraints Abundant information Limited resources ATTENTION
  9. 9. Intellifest 2012 Time constraints Abundant information Limited resources INTELLIGENCE
  10. 10. “Narrow” (classical) AI: • Systems explicitly designed to solve specific, reasonably well-defined problems  E.g. Deep Blue, Watson, etc. Artificial General Intelligence: • Systems designed to autonomously learn new tasks and adapt to changing environments Intellifest 2012
  11. 11.  Neural-network based virus detection system  Personalized movie recommendation system  Software for prediction, classification and pattern recognition  NLP-based personalized news delivery software  Development and implementation of algorithmic trading strategies using AI techniques Intellifest 2012
  12. 12.  Constitutes vast majority of work in field of AI to-date  While proven useful in industry beyond doubt, by definition unlikely to lead to human-level intelligence • Highly unlikely that isolated bits and pieces of the “intelligence puzzle” can somehow be fused into holistic intelligence  Difficult to generalize for new problems  Task environments are typically limited or simplified representations of the real world  Substantial adaption to tasks not required Intellifest 2012
  13. 13.  AGI (Artificial General Intelligence): • Relatively small group of researchers (so far) refocusing on an original idea behind AI: human-level AI • First AGI conference: 2008 • Systems explicitly designed to autonomously learn novel tasks and adapt to changing environments • Ultimately targets human-level intelligence (and beyond) in real world environments Intellifest 2012
  14. 14. AGI (Artificial General Intelligence): • Long-term research effort • Challenging funding situation • Results are not guaranteed Intellifest 2012
  15. 15. Realworld environment Intellifest 2012 Sensors Actuators Data Processes
  16. 16. Intellifest 2012 Sensors Actuators Data Processes
  17. 17. Realworld environment Intellifest 2012 Sensors Actuators Data Processes Sensors Actuators Data Processes
  18. 18.  Benefits • Versatile, highly adaptive and autonomous systems • Reduced design/development cost due to high reusability  Downsides • Predictability and determinism are sacrificed to some degree • Require learning phase before coming practically useful Intellifest 2012
  19. 19. Intellifest 2012  Software systems approaching human-level intelligence will be massive and complex • Significantly greater complexity than exists in current software systems • Cognitive limitations of designers/programmers  Realistic to believe we will build such systems using current software methodologies? • Manual construction • Coarse grained modular systems • Divide-and-conquer
  20. 20. Intellifest 2012 Con - struct - ionist A.I.: Manually-built artificial intelligence systems; learning restricted to combining predefined situations and tasks, from detailed specifications provided by a human programmer. “The programmer as construction worker”
  21. 21. Intellifest 2012 Underlying assumptions: • Systems of reasonable intelligence can be built with an architecture of a few thousand manually constructed modules • Such a system could automatically:  Tune parameters as needed  Route information and control among the modules
  22. 22. Intellifest 2012 Reality: • Intelligent systems are (functionally)  Heterogeneous  Large  Densely-coupled  Self-reflective • Intelligence is the product of the operation of a system
  23. 23. Intellifest 2012 Reality: • Massive, complex dependencies exclude:  manual construction of modules  modular construction  piecewise composition where each piece built in isolation  Exceedingly large functional state-space  Subdivision hides important interconnections • Divide-and-conquer fails
  24. 24. Intellifest 2012
  25. 25. Intellifest 2012
  26. 26. Intellifest 2012 Available evidence strongly indicates that the power of general intelligence, arising from a high degree of architectural plasticity, is of a complexity well beyond the maximum reach of traditional software methodologies.
  27. 27. Intellifest 2012 “We’ve still got a couple of years to go before we’re ready for the moon.”
  28. 28. Intellifest 2012 Con - struct - ivist A.I.: Self-constructive artificial intelligence systems with general knowledge acquisition skills; systems develop from a seed specification; capable of learning to perceive and act in a wide range of novel tasks, situations and domains. Thórisson, K. R. (2009). From Constructionist to Constructivist A.I. Keynote, AAAI Fall Symposium Series: Biologically Inspired Cognitive Architectures, Washington D.C., Nov. 5- 7, 175-183. AAAI Tech Report FS-09-01, AAAI press, Menlo Park, CA.
  29. 29. Intellifest 2012  Developmental approach  Targets a ratio of hand-coded to auto- generated programs of magnitude 1:1000000 and up  Requires: • Small building blocks (peewee-size)  Facilitates transfer and re-use between tasks and domains  Fast, (temporally) predictable execution
  30. 30. Intellifest 2012  Standard programming languages • Designed for humans • Complex operational semantics • Not suited for automatic self-programming • No (explicit) temporal grounding  Constructivist AI needs a new paradigm: • Transparent operational semantics • Machine-understandable language • Explicit temporal grounding • Self-organizing management • Uniform representation
  31. 31. Intellifest 2012  Fundamental to AGI systems • All cognitive actions take time, impacting the system’s place in the context of the real world  Resource management • Constant operating scenario: abundant information, limited resources • Range of time-constraints  Many posed by the environment  Temporal dimension of knowledge is important • Past events • (Expected) future events
  32. 32. Intellifest 2012 System must be able to • Predict the effects and side-effects of its actions in the world • Predict the effects and side-effects of its own internal operations Requires uniform representation • That includes time at atomic operation level
  33. 33. Intellifest 2012 Humanoids that Learn Socio-Comunnicative Skills Through Observation Funded by European Union 7th Framework Programme Coordinator / Principal Investigator: Dr. Kristinn R. Thórisson
  34. 34. Intellifest 2012 Target domain: TV-style interview Two roles: Interviewee, Interviewer Scenario: • Two humanoid avatars and props in a virtual 3D environment • Multiple modalities involved in perception and control of an avatar  Speech, intonation, gaze, head movements, hand movements
  35. 35. Intellifest 2012 Developmental operation: • 1. Motor babbling phase  System tries out its actuators and builds models of how it can impact the environment • 2. Observation phase:  Humans control both avatars and perform interview while system observes • 3. Operation phase:  System takes over one of the roles
  36. 36. Intellifest 2012  New AGI architecture developed for this project: Autocatalytic Endogenous Reflective Architecture
  37. 37. Intellifest 2012  Broad-scope, general-purpose architecture addressing: • Perception, decision, motor control • General-purpose action learning in dynamic worlds  Tiny construction components • Relative to size of architecture  Support of transversal cognitive skills, at multiple levels of granularity and abstraction • System-wide learning • Temporal grounding • Observation and imitation of complex realtime events • Inference, abstraction, prediction, simulation ... and more
  38. 38. Intellifest 2012
  39. 39. Intellifest 2012 Recursive • Meta-control
  40. 40. Intellifest 2012 Replicode: New programming language • Developed specifically for HUMANOBS/AERA  Open source • Designed to build model-based, model-driven production systems that can modify their own code  Containing a (very) large number of concurrent, interacting programs
  41. 41. Intellifest 2012  New programming language • Encodes short parallel programs and executable models • Explicit temporal grounding  Soft realtime • Data-driven execution model  Computation based on pattern matching • No explicit conditional statements (if-then) or loops • All executable code runs concurrently
  42. 42. Intellifest 2012  New programming language • Code can be active or inactive • Code can be input for some other code • Dynamic code production • Execution feedback • Supports distribution of computation and knowledge across clusters of computing nodes
  43. 43. Intellifest 2012 [FACT@T0: BOX AT POS (0,0)] [CMD@T0: MOVE BOX (1, 0)] [PRED@T1: BOX AT POS (1,0)] [GOAL@T1: BOX AT POS (0,0)] [FACT@T0: BOX AT POS (1,0)] [CMD@T1: MOVE BOX (-1,0)] PREDICTION Model PRESCRIBE ACTION
  44. 44. Intellifest 2012 _start:(pgm |[] |[] [] (inj [] p:(pgm |[] |[] [] (inj [] (mk.val self position (vec3 1 2 3) 1) [SYNC_FRONT ( (+ now 10000)) 1 forever root nil] ) (mod [this.vw.act -1]) 1 ) [SYNC_FRONT now 1 forever root nil] ) (inj [] (ins p |[] RUN_ALWAYS 50000us NOTIFY) [SYNC_FRONT now 1 forever root nil 1] ) 1 ) |[] i_start:(ipgm _start |[] RUN_ONCE 90000us NOTIFY 1) [] [SYNC_FRONT now 1 1 root nil 1]
  45. 45. Intellifest 2012
  46. 46. Intellifest 2012 PROTOTYPE DEMO Verbally directed object manipultaion
  47. 47. Intellifest 2012 In the domain of (generally) intelligent systems, the management of system resources is typically called “attention” Critical (and neglected) issue for AGI • Systems constantly working with limited resources under time constraints in environments providing abundant information
  48. 48. Intellifest 2012  Design of AGI systems needs to address practical limitations from the outset  AGI systems will face time-constraints and need to be reactive and interruptible, yet capable of planning  Retrofitting AGI systems with resource management highly challenging • Duration of atomic operations becomes important
  49. 49. Intellifest 2012  Control mechanism responsible for prioritizing data and processess  Targets equally • External information (from the environment) • Internal information (from within the system)  General, no assumptions about • Tasks • Environments • Modalities / Embodiment  Adaptive • Learns to improve itself based on experience
  50. 50. Attentional patterns Matching Data items Processes Top-down Bottom-up Contextualized process performance history Contextual process evaluation Experience-based process activation Sensory devices Environment (Real world) Actuation devices Commands Sampled data Data biasing Goals / Predictions Derived Bottom-up attentional processess Evaluation Process biasing Data -> Process mapping
  51. 51. Intellifest 2012  Potential: • Knowledge sharing  Systems learning not just from their own experience, but from the experience of other identical (or similar) systems  On-demand access to knowledge bases & services • Distributed resources  Systems using computational resources they do not physically contain • Remote on-demand sensing
  52. 52. Intellifest 2012  Possible limitations: • Communication latency  Operations involving network communication may introduce time delays, which may be significant in terms of operation • Communication bandwidth  Sensory information can be a significant amount of data (100 MB+/sec) • Cloud server load  Response times for cloud-based operations less predictable
  53. 53. Intellifest 2012  Interesting directions: • Augment system knowledge from cloud during idle time • Compress and/or pre-process sensory information and run cognitive processes in the cloud  Latency still an issue • Cloud-based services used as specialized tools  When allowed for by temporal constraints
  54. 54. Intellifest 2012 Practically difficult: • Distributing cognitive processes between system’s own hardware and the cloud  AGI systems likely to have a very large number of components with rich, complex interconnections and interactions  Communication latency becomes a major issue
  55. 55. Intellifest 2012 Onboard cognitive resources Cloud cognitive resources Network barrier
  56. 56. Intellifest 2012 Perception • Computer vision • Image processing • Feature detection • Speech recognition
  57. 57. Intellifest 2012 System design • More general & reusable systems • Temporal issues • Resource management
  58. 58. Intellifest 2012
  59. 59. Intellifest 2012  HUMANOBS project • http://www.humanobs.org  Replicode • http://wiki.humanobs.org/public:replicode:replicode-main  Publications • From Constructionist to Constructivist A.I.  Kristinn R. Thórisson (2009) • Cognitive Architecture and Autonomy: A Comparative Review  Kristinn R. Thórisson, Helgi Páll Helgason (2011)  AGI 2012 • The Fifth Conference on AGI, Oxford, UK, Dec 8-11 2012 • http://agi-conference.org/2012/

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