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AGI versus Narrow AI
Ben Goertzel, PhD
Novamente LLC
Biomind LLC
Artificial General Intelligence Research Institute
Virginia Tech, Applied Research Lab for National and Homeland Security
Artificial General
Intelligence (AGI)
“The ability to achieve complex goals in complex
environments using limited computational resources”
• Autonomy
• Practical understanding of self and others
• Understanding “what the problem is” as opposed to
just solving problems posed explicitly by
programmers
2006
2007
Post-proceedings of
2006 AGI Workshop
(North Bethesda MD, May 2006)
To be published by IOS
Press
Narrow AI
The vast majority of AI research practiced in academia
and industry fits into the “Narrow AI” category
Each “Narrow AI” program is (in the ideal case) highly
competent at carrying out certain complex goals in
certain environments
•chess-playing, medical diagnosis, car-driving, etc.
A major lesson from the history of AI is: Narrow AI
success is nearly always of minimal use as a steppingstone toward AGI success
Some Existing AI Paradigms
Paradigm
Strengths
Weaknesses
GOFAI
Representation of abstract
knowledge
Reasoning (short proofs)
Pattern recognition
Learning
Autonomy
Neural nets
Pattern recognition
Learning
Associative memory
Perception/action/cognition
integration
Representation of abstract
knowledge
Abstract reasoning
Learning
Autonomy
Evolutionary
Programming
Pattern recognition
Learning of complex procedures
Representation of abstract
knowledge
Abstract reasoning
Autonomy
Probabilistic
Reasoning
Representation of abstract
uncertain knowledge
Reasoning (short proofs)
Hypothesis formation
Autonomy
Pattern recognition
Subsumption
Robotics
Autonomy
Learning
Perception-action integration
Cognition
Representation of abstract
knowledge
Examples of Narrow AI
Deep Blue
Can’t learn to play a new game based on a
description of the game rules
DARPA Grand Challenge
Software can’t even learn to drive different types of
vehicles besides cars (trucks, boats, motorcycles)
Google
Can’t answer questions. Whatever happened to
AskJeeves?
Examples of Narrow AI
From My Own Recent Work
Biomind ArrayGenius software for recognizing
patterns in gene expression data
Doesn’t interpret the patterns it finds in the context
of the literature and the totality of relevant online
quantitative data
RelEx software for mapping English sentences
into semantic structures
Doesn’t do reasoning to resolve semantic
ambiguity in a context-appropriate way
How to Create AGI?
AGI will not be achieved by incrementally
“general-izing” narrow-AI apps
To achieve AGI, we need to go back to basics
We need to create an artificial baby of sorts
(though not necessarily a human-like baby … it may be a baby
with architecture and algorithms sculpted via computing,
cognitive and systems science rather than neuroscience)
We need to teach the baby well
And then, the practical applications will come
(and oh boy, will they come!!)