1.IntelliFest 2012 International Conference on Reasoning Technologies INTELLIGENCE IN THE CLOUD From Classical (Narrow) AI to AGI Helgi Helgason Perseptio

2. Helgi Pll Helgason helgi@perseptio.com In collaboration with: Dr. Kristinn R. Thrisson & Eric Nivel Intellifest 2012 AI researcher, Ph.D. candidate Center for Analysis and Design of Intelligent Agents, Reykjavik University Founder / CEO, Perseptio 3. Narrow AI AGI Constructivist AI HUMANOBS Project AGI & Cloud Computing Intellifest 2012 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. Intellifest 2012 Natures 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. 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. Intellifest 2012 If either time or computational resources are infinite, intelligence is irrelevant . - Dr. Kristinn R. Thrisson 8. Intellifest 2012 Time constraints Abundant information Limited resources ATTENTION 9. Intellifest 2012 Time constraints Abundant information Limited resources INTELLIGENCE 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. 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. 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. 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. AGI (Artificial General Intelligence): Long-term research effort Challenging funding situation Results are not guaranteed Intellifest 2012 15. Realworld environment Intellifest 2012 Sensors Actuators Data Processes 16. Intellifest 2012 Sensors Actuators Data Processes 17. Realworld environment Intellifest 2012 Sensors Actuators Data Processes Sensors Actuators Data Processes 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. 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. Intellifest 2012 Con - struct - ionist A.I.: Manually-built articial intelligence systems; learning restricted to combining predened situations and tasks, from detailed specications provided by a human programmer. The programmer as construction worker 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. Intellifest 2012 Reality: Intelligent systems are (functionally) Heterogeneous Large Densely-coupled Self-reflective Intelligence is the product of the operation of a system 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. Intellifest 2012 25. Intellifest 2012 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. Intellifest 2012 Weve still got a couple of years to go before were ready for the moon. 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. Thrisson, 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. 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. 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. Intellifest 2012 Fundamental to AGI systems All cognitive actions take time, impacting the systems 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. 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. Intellifest 2012 Humanoids that Learn Socio-Comunnicative Skills Through Observation Funded by European Union 7th Framework Programme Coordinator / Principal Investigator: Dr. Kristinn R. Thrisson 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. 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. Intellifest 2012 New AGI architecture developed for this project: Autocatalytic Endogenous Reflective Architecture 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. Intellifest 2012 39. Intellifest 2012 Recursive Meta-control 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. 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. 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. 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. 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. Intellifest 2012 46. Intellifest 2012 PROTOTYPE DEMO Verbally directed object manipultaion 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. 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. 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. 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. 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. 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. 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. Intellifest 2012 Practically difficult: Distributing cognitive processes between systems 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. Intellifest 2012 Onboard cognitive resources Cloud cognitive resources Network barrier 56. Intellifest 2012 Perception Computer vision Image processing Feature detection Speech recognition 57. Intellifest 2012 System design More general & reusable systems Temporal issues Resource management 58. Intellifest 2012 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. Thrisson (2009) Cognitive Architecture and Autonomy: A Comparative Review Kristinn R. Thrisson, Helgi Pll Helgason (2011) AGI 2012 The Fifth Conference on AGI, Oxford, UK, Dec 8-11 2012 http://agi-conference.org/2012/