7 Principles ofSynthetic Intelligence

Joscha Bach, University of Osnabrück,Cognitive Science

March 2008

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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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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.

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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.

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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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Conceptual Analysis: HCogAff (Sloman 2001)

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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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Modal vs. amodal representation (Barsalou 99)

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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

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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

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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”

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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.

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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

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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

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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

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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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PSI theory (Dörner 99, 02)

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PSI theory (Dörner 99, 02)

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PSI theory (Dörner 99, 02)

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PSI theory (Dörner 99, 02)

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Motivation in PSI/MicroPsi

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Integrated representation

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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

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… and this is where it starts.

Thank you!

Website: www.cognitive-agents.org• Publications, • Download of Agent, • Information for Developers

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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

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Motivation in PSI/MicroPsi

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Modulation in PSI/MicroPsi

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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)

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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