Critiques of the Turing Test

hesitantdoubtfulΤεχνίτη Νοημοσύνη και Ρομποτική

29 Οκτ 2013 (πριν από 4 χρόνια και 6 μήνες)

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Prominent AI

Colleague of Alan Turing at Bletchley Park

1992 Paper:

Turing’s Test and Conscious Thought

Provides a critique of the test

Solipsism and the “Charmed Circle”

“…Turing underestimated the appeal of a more
subtle form of solipsism generalized to groups.”

The argument can be stated as: “the only way by
which one could be sure that a machine thinks is to
be a member of a charmed circle which has
accepted that machine into its ranks and can
collectively feel itself thinking.”



“The test can only detect only those processes that
are susceptible to introspective verbal report.”

Many thought processes that cannot be articulated by

A machine might be able to articulate them , even
when a human cannot.

Most highly developed mental skills are of the
verbally inaccessible kind (Hutchins)

“Expert Systems” famously failed in knowledge
extraction through dialog

Consciousness and Human

What story is assigned to a sequence of events?

Cutaneous Rabbit

5 taps on the wrist

2 near the elbow

3 at the upper arm

Chinese Room

Consider a program that can appear intelligent in
conversation in Chinese

Suppose that someone who doesn’t speak Chinese
executes the program “by hand”

The non
Chinese speaker does not understand the
conversation, just as a computer does not
understand the conversation.

A successful Turing Test could be
accomplished through table lookup (given a
large enough memory)

Is this really intelligence?

Turing’s test might not be passed in the
foreseeable future, but that doesn’t really

Let machines make progress without the
requirement that they imitate people

Computers will provide their own
contributions without the need for imitation.

Weak AI

How the task is accomplished doesn’t matter

We can use a mechanism vastly different than what
humans do

Success is based strictly on performance

Strong AI

Tasks should use the same mechanisms used by

We want to duplicate human intelligence

We want machines to be conscious of what they are

Defined by a set of problems that are
generally considered to require intelligence in

Knowledge Processing

Natural Language Understanding

Game Players

Diagnostic/Classification Problems

Machine Learning

“Rules of Thumb”

Methods that tend to work, but don’t guarantee

Find a simpler problem you know how to solve and try
to generalize to the larger problem

Work backwards from the goal state

In the 1970’s and 1980’s many people
believed “expert systems” would replace
many if not most experts

“Knowledge Engineers” were tasked with
extracting and encoding knowledge from

It didn’t work very well, largely because much
if not most expertise is

Puzzle solving

Finding the best of a set of possible




Chinese Chess

Dots and Boxes

Given a set of facts, deduce “useful”

Representation of facts

Method used for deduction

Identification of “useful” facts

If (some criteria) then some fact

If (some criteria) then perform some action

Expert Systems were often produced using
production rules.

Simplified model of basic building blocks of
the brain

Much smaller number of neurons

Much simpler model of how neurons work

Neural Networks are used in many pattern

Simulate evolution

Natural selection used as a form of search

Genetic Algorithms

A population of simulated genes evolves in an attempt
to solve a problem

Genetic Programming

A population of programs evolves in an attempt to
a problem