7 principles of synthetic intelligence joscha bach, university of osnabrück, cognitive science...
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7 Principles ofSynthetic Intelligence
Joscha Bach, University of Osnabrück,Cognitive Science
March 2008
March 1st, 2008 2
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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)
March 1st, 2008 3
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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)
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AI Scepticism: G. W. Leibniz
Perception, and what depends on it, is inexplicable in a mechanical way, that is, using figures and motions.
March 1st, 2008 5
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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.
March 1st, 2008 6
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AI Scepticism: John R. Searle
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.
Syntax by itself is neither constitutive of nor sufficient for semantics. Computers only do syntax, so they can never understand anything.
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AI Scepticism: Joseph Weizenbaum
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.
Syntax by itself is neither constitutive of nor sufficient for semantics. Computers only do syntax, so they can never understand anything.
Human experience is not transferable. (…) Computers can not be creative.
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AI Scepticism: General Consensus…
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.
Syntax by itself is neither constitutive of nor sufficient for semantics. Computers only do syntax, so they can never understand anything.
Human experience is not transferable. (…) Computers can not be creative.
Computers can not, because they should not.
The “Winter of AI” is far from over.
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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
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures
(infrared) imaging of combustion engine
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures
(infrared) imaging of combustion engine
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures
Requirement:Dissection of system into partsand relationshipsbetween them
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#1: Build functionalist architectures
Requirement:Dissection of system into partsand relationshipsbetween them
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method –
not vice versa!
AI‘s specialized sub-disciplines will not be re-integrated into a whole.
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method3. Aim for the Big Picture, not narrow solutions
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method3. Aim for the Big Picture, not narrow solutions4. Build grounded systems
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method3. Aim for the Big Picture, not narrow solutions4. 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.
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method3. Aim for the Big Picture, not narrow solutions4. Build grounded systems5. Do not wait for robots to provide embodiment
March 1st, 2008 24
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method3. Aim for the Big Picture, not narrow solutions4. Build grounded systems5. Do not wait for robots to provide embodiment –
Robotic embodiment is costly, but not necessarily more “real” than virtual embodiment.
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method3. Aim for the Big Picture, not narrow solutions4. Build grounded systems5. Do not wait for robots to provide embodiment6. Build autonomous systems
March 1st, 2008 27
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method3. Aim for the Big Picture, not narrow solutions4. Build grounded systems5. Do not wait for robots to provide embodiment6. Build autonomous systems
Intelligence is an answer to serving polythematic goals, by unspecified means, in an open environment.
Integrate motivation and emotion into the model.
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method3. Aim for the Big Picture, not narrow solutions4. Build grounded systems5. Do not wait for robots to provide embodiment6. Build autonomous systems7. Intelligence is not going to simply “emerge”
March 1st, 2008 29
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method3. Aim for the Big Picture, not narrow solutions4. Build grounded systems5. Do not wait for robots to provide embodiment6. Build autonomous systems7. 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.
March 1st, 2008 30
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Taking the Lessons: MicroPsi
• 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
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Implementation: MicroPsi (Bach 03, 05, 04, 06)
Node Net Editor
Net Simulator/Agent
Execution
World Editor
MonitoringConsole
Application
World Simulator
3D DisplayServer
3D DisplayClient
Eclipse Environment
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Node Net Editor
Net Simulator/Agent
Execution
World Editor
MonitoringConsole
Application
World Simulator
3D DisplayServer
3D DisplayClient
Eclipse Environment
Implementation: MicroPsi (Bach 03, 05, 04, 06)
Low-level perception
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Node Net Editor
Net Simulator/Agent
Execution
World Editor
MonitoringConsole
Application
World Simulator
3D DisplayServer
3D DisplayClient
Eclipse Environment
Implementation: MicroPsi (Bach 03, 05, 04, 06)
Low-level perception
Control and simulation
March 1st, 2008 34
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Node Net Editor
Net Simulator/Agent
Execution
World Editor
MonitoringConsole
Application
World Simulator
3D DisplayServer
3D DisplayClient
Eclipse Environment
Implementation: MicroPsi (Bach 03, 05, 04, 06)
Low-level perception
Control and simulationMulti-agent interaction
March 1st, 2008 35
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Node Net Editor
Net Simulator/Agent
Execution
World Editor
MonitoringConsole
Application
World Simulator
3D DisplayServer
3D DisplayClient
Eclipse Environment
Implementation: MicroPsi (Bach 03, 04, 05, 06)
Low-level perception
Control and simulationMulti-agent interaction
Robot control
March 1st, 2008 36
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Foundation of MicroPsi: PSI theory (Dörner 99, 02)
How can the different aspects of cognition be realized?
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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
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Lessons for Synthesizing Intelligence
1. Build whole, functionalist architectures2. Let the question define the method3. Aim for the Big Picture, not narrow solutions4. Build grounded systems5. Do not wait for robots to provide embodiment6. Build autonomous systems7. Intelligence is not going to simply “emerge”
Website: www.cognitive-agents.org• Publications, • Download of Agent, • Information for Developers
March 1st, 2008 45
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… and this is where it starts.
Thank you!
Website: www.cognitive-agents.org• Publications, • Download of Agent, • Information for Developers
March 1st, 2008 46
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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
March 1st, 2008 49
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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)
March 1st, 2008 50
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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