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P rinciples of S ynthetic I ntelligence

March 2008. 7. P rinciples of S ynthetic I ntelligence. Joscha Bach, University of Osnabrück, Cognitive Science. What is Artificial General Intelligence up to?. Perception, and what depends on it, is inexplicable in a mechanical way, that is, using figures and motions.

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P rinciples of S ynthetic I ntelligence

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  1. March2008 7 Principles ofSynthetic Intelligence Joscha Bach, University of Osnabrück, Cognitive Science

  2. What is Artificial General Intelligence up to? Perception, and what depends on it, is inexplicable in a mechanical way, that is, using figures and motions. Suppose there would be a machine, so arranged as to bring forth thoughts, experiences and perceptions; it would then certainly be possible to imagine it to be proportionally enlarged, in such a way as to allow entering it, like into a mill. This presupposed, one will not find anything upon its examination besides individual parts, pushing each other— and never anything by which a perception could be explained. (Gottfried Wilhelm Leibniz 1714)

  3. What is Artificial General Intelligence up to? Perception, and what depends on it, is inexplicable in a mechanical way, that is, using figures and motions. Suppose there would be a machine, so arranged as to bring forth thoughts, experiences and perceptions; it would then certainly be possible to imagine it to be proportionally enlarged, in such a way as to allow entering it, like into a mill. This presupposed, one will not find anything upon its examination besides individual parts, pushing each other— and never anything by which a perception could be explained. (Gottfried Wilhelm Leibniz 1714)

  4. AI Scepticism: G. W. Leibniz Perception, and what depends on it, is inexplicable in a mechanical way, that is, using figures and motions.

  5. AI Scepticism: Roger Penrose Perception, and what depends on it, is inexplicable in a mechanical way, that is, using figures and motions. The quality of understanding and feeling possessed by human beings is not something that can be simulated computationally.

  6. AI Scepticism: John R. Searle Perception, and what depends on it, is inexplicable in a mechanical way, that is, using figures and motions. Syntax by itself is neither constitutive of nor sufficient for semantics. Computers only do syntax, so they can never understand anything. The quality of understanding and feeling possessed by human beings is not something that can be simulated computationally.

  7. AI Scepticism: Joseph Weizenbaum Perception, and what depends on it, is inexplicable in a mechanical way, that is, using figures and motions. Syntax by itself is neither constitutive of nor sufficient for semantics. Computers only do syntax, so they can never understand anything. The quality of understanding and feeling possessed by human beings is not something that can be simulated computationally. Human experience is not transferable. (…) Computers can not be creative.

  8. Perception, and what depends on it, is inexplicable in a mechanical way, that is, using figures and motions. AI Scepticism: General Consensus… Syntax by itself is neither constitutive of nor sufficient for semantics. Computers only do syntax, so they can never understand anything. The quality of understanding and feeling possessed by human beings is not something that can be simulated computationally. Computers can not, because they should not. The “Winter of AI” is far from over. Human experience is not transferable. (…) Computers can not be creative.

  9. AI is not only trapped by cultural opposition AI suffers from • paradigmatic fog • methodologism • lack of unified architectures • too much ungrounded, symbolic modeling • too much non-intelligent, robotic programming • lack of integration of motivation and representation • lack of conviction

  10. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures

  11. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures (infrared) imaging of combustion engine

  12. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures (infrared) imaging of combustion engine

  13. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures

  14. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architecturesRequirement: Dissection of system into partsand relationshipsbetween them

  15. #1: Build functionalist architectures Requirement: Dissection of system into partsand relationshipsbetween them

  16. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method

  17. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method – not vice versa! AI‘s specialized sub-disciplines will not be re-integrated into a whole.

  18. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method 3. Aim for the Big Picture, not narrow solutions

  19. Conceptual Analysis: HCogAff (Sloman 2001)

  20. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method 3. Aim for the Big Picture, not narrow solutions 4. Build grounded systems

  21. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method 3. Aim for the Big Picture, not narrow solutions 4. Build grounded systems –but do not get entangled in the „Symbol Grounding Problem“ The meaning of a concept is equivalent to anadequate encoding over environmental patterns.

  22. Modal vs. amodal representation (Barsalou 99)

  23. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method 3. Aim for the Big Picture, not narrow solutions 4. Build grounded systems 5. Do not wait for robots to provide embodiment

  24. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method 3. Aim for the Big Picture, not narrow solutions 4. Build grounded systems 5. Do not wait for robots to provide embodiment – Robotic embodiment is costly, but not necessarily more “real” than virtual embodiment.

  25. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method 3. Aim for the Big Picture, not narrow solutions 4. Build grounded systems 5. Do not wait for robots to provide embodiment 6. Build autonomous systems

  26. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method 3. Aim for the Big Picture, not narrow solutions 4. Build grounded systems 5. Do not wait for robots to provide embodiment 6. Build autonomous systemsIntelligence is an answer to serving polythematic goals, by unspecified means, in an open environment.  Integrate motivation and emotion into the model.

  27. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method 3. Aim for the Big Picture, not narrow solutions 4. Build grounded systems 5. Do not wait for robots to provide embodiment 6. Build autonomous systems 7. Intelligence is not going to simply “emerge”

  28. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method 3. Aim for the Big Picture, not narrow solutions 4. Build grounded systems 5. Do not wait for robots to provide embodiment 6. Build autonomous systems 7. Intelligence is not going to simply “emerge”:Sociality, personhood, experience, consciousness, emotion, motivation will have to be conceptually decomposed and their components and functional mechanisms realized.

  29. Integrated architecture, based on a theory originating in psychology Unified neuro-symbolic representation (hierarchical spreading activation networks) Functional modeling of emotion: Emotion as cognitive configuration Emotional moderators Functional modeling of motivation: Modeling autonomous behavior Cognitive and Physiological drives Integrating motivational relevance with perception/memory Taking the Lessons: MicroPsi

  30. Implementation: MicroPsi (Bach 03, 05, 04, 06)

  31. Implementation: MicroPsi (Bach 03, 05, 04, 06) Low-level perception

  32. Implementation: MicroPsi (Bach 03, 05, 04, 06) Low-level perception Control and simulation

  33. Implementation: MicroPsi (Bach 03, 05, 04, 06) Low-level perception Multi-agent interaction Control and simulation

  34. Implementation: MicroPsi (Bach 03, 04, 05, 06) Robot control Low-level perception Multi-agent interaction Control and simulation

  35. Foundation of MicroPsi: PSI theory (Dörner 99, 02) How can the different aspects of cognition be realized?

  36. PSI theory (Dörner 99, 02)

  37. PSI theory (Dörner 99, 02)

  38. PSI theory (Dörner 99, 02)

  39. PSI theory (Dörner 99, 02)

  40. Motivation in PSI/MicroPsi

  41. Integrated representation

  42. Goal of MicroPsi: broad model of cognition Aim at • Perceptual symbol system approach • Integrating goal-setting • Use motivational and emotional system as integral part of addressing mental representation • Physiological, physical and social demands and affordances • Modulation/moderation of cognition

  43. Lessons for Synthesizing Intelligence 1. Build whole, functionalist architectures 2. Let the question define the method 3. Aim for the Big Picture, not narrow solutions 4. Build grounded systems 5. Do not wait for robots to provide embodiment 6. Build autonomous systems 7. Intelligence is not going to simply “emerge” Website: www.cognitive-agents.org • Publications, • Download of Agent, • Information for Developers

  44. … and this is where it starts. Thank you! Website: www.cognitive-agents.org • Publications, • Download of Agent, • Information for Developers

  45. Many thanks to… • the Institute for Cognitive Science at the University of Osnabrück and the AI department at Humboldt-University of Berlin for making this work possible • Ronnie Vuine, David Salz, Matthias Füssel, Daniel Küstner, Colin Bauer, Julia Böttcher, Markus Dietzsch, Caryn Hein, Priska Herger, Stan James, Mario Negrello, Svetlana Polushkina, Stefan Schneider, Frank Schumann, Nora Toussaint, Cliodhna Quigley, Hagen Zahn, Henning Zahn and Yufan Zhao for contributions

  46. Motivation in PSI/MicroPsi

  47. Modulation in PSI/MicroPsi

  48. Motivation in PSI/MicroPsi Urges/drives: • Finite set of primary, pre-defined urges (drives) • All goals of the system are associated with the satisfaction of an urgeincluding abstract problem solving, aesthetics, social relationships and altruistic behavior • Urges reflect demands • Categories: • physiological urges (food, water, integrity) • social urges (affiliation, internal legitimacy) • cognitive urges (reduction of uncertainty, and competence)

  49. Emotion in PSI/MicroPsi Lower emotional level (affects): • Not independent sub-system, but aspect of cognition • Emotions are emergent property of the modulation of perception, behavior and cognitive processing • Phenomenal qualities of emotion are due to • effect of modulatory settings on perception on cognitive functioning • experience of accompanying physical sensations (Higher level) emotions: • Directed affects • Objects of affects are given by motivational system

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