working toward pragmatic convergence: agi roadmap and a unified roadmap itamar arel, machine...

WORKING TOWARD PRAGMATIC CONVERGENCE: AGI ROADMAP AND A UNIFIED ROADMAPItamar Arel, Machine Intelligence Lab Itamar Arel, Machine Intelligence Lab (http://mil.engr.utk.edu)The University of TennesseeThe University of Tennessee

AGI 2009 AGI 2009 UT Machine Intelligence Lab UT Machine Intelligence Lab http://mil.engr.utk.eduAGI 2009 AGI 2009 UT Machine Intelligence Lab UT Machine Intelligence Lab http://mil.engr.utk.edu

Reality Check

Despite 60 years of hard work no AGI

Funding situation is dire Reputation is poor Bad news: things seem to

continue along the same trajectory

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Can convergence happen?

AGI researchers diverge on disparate trajectories

No consensus on what AGI really is Claim: we need a unified view of

overarching goals Proposition: an initial framework to

facilitate consensus of short-term research focus

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The case for AGI Axioms

Def: core functional attributes without which an AGI system cannot be considered one

Necessary but not sufficient set Advantages

Help unify terminology Promote an eventual roadmap Discard non-AGI propositions

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Axiom #1: Observability

AGI system must have the ability to continuously receive observations from its environment

Appears obvious, but critical The particular nature of observations

irrelevant May be partial with respect to

environment state

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Axiom #2: Actuation Capability

Ability to impact environment in some desired manner

Without this – no control loop – no AGI

“Thinking” by itself is insufficient Implies physical actuators (or virtual

ones)

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Axiom #3: Process High-Dimensional Data

Mammal brains concurrently exposed to high-dimensional sensory information

A system with limitation on that is not AGI

Typically multi-modal sensory information

Sensory data fusion will take place

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Axiom #4: Capturing Spatiotemporal Dependencies

Core human brain capability Representing wide time scale is

critical Anticipating events as

consequence of other events Tied to pattern recognition

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Axiom #5: Utility Function

Existence of functional goal Drives action selection Not necessary reinforcement

learning like Intrinsic feedback in addition to

external Credit assignment problem –

“strategic thinking”

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Avoiding the 10 IQ Fallacy

AGI is hard to demonstrate on small-scale problems

Narrow “AI” can always step in (sometimes do better)

Axioms should reflect “true” AGI attributes

… more during the AGI Roadmap discussion

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Thank you11

working toward pragmatic convergence: agi roadmap and a unified roadmap itamar arel, machine...

Documents

agi researchers

agi axiomsdef

true agi system

true agi attributes

agi roadmap discussion

observabilityagi system

core functional attributes

eduthe case