Turing’s Legacy
Minds & Machines
Alan Turing
“I believe that in about fifty years’ time it will
be possible to programme computers, with a
storage capacity of about 109, to make them play
the imitation game so well that an average
interrogator will not have more than 70 per cent
chance of making the right identification after
5 minutes of questioning”
-Alan Turing (1950)
Alan Turing
• Alan Turing was a British mathematician who was most
famous for his work in theoretical computer science
• During World War II, Turing helped break German codes
using mechanical computers
• In 1952, the British government considered Turing’s
homosexuality to be a crime, and forced him to go
through hormonal treatment.
• In 1954, age 41, Turing died from eating an apple laced
with cyanide; most likely suicide
• In 1999, Turing was listed as one on the top 100 most
important people of the 20th century
• On September 10, 2009, the British government
apologized for their treatment of Alan Turing.
Turing’s Legacy
• Turing’s legacy consists of 2 parts:
– Turing Machines (1936)
– Turing Test (1950)
Turing Test
• “I propose to consider the question, 'Can
machines think?' This should begin with
definitions of the meaning of the terms
'machine 'and 'think'. … [But] Instead of
attempting such a definition I shall replace
the question by another... The new form of
the problem can be described in terms of a
game which we call the 'imitation game'.“
-Alan Turing, “Computing Machinery and Intelligence”, 1950
The Imitation Game
Machine
Interrogator
Human
Some Initial Observations on the
Turing Test
• The Turing Test attributes intelligence purely on
verbal interactions. Is that ok?
• Well, physical characteristics (size, weight,
agility, etc) don’t seem to be relevant as far as
intelligence goes, so that seems right.
• However, shouldn’t we have to open up the
computer program and see how it works to make
this kind of determination?
• Then again, do we ever open up other human
beings to determine whether they are intelligent?
• Hmm, maybe Turing has a point.
The Turing Test:
Can Machines Think?
Premise 1: Machines can pass the Turing Test
Premise 2: Anything that passes the Turing Test
is intelligent
Conclusion: Machines can be intelligent
Can Machines pass the
Turing Test?
Computationalism
• Cognition can be defined in terms of informationprocessing:
–
–
–
–
–
–
Perception is taking in information
Memory/Beliefs/Knowledge is storing information
Reasoning is inferring new information
Learning is updating information
Planning is using information to make decisions
Etc.
• Information-processing can be done through
computations
• Therefore, cognition is computation.
Computationalism and the Brain
• Notice that the argument on the previous
slide is a purely conceptual one in that it is
not based on any empirical evidence.
• Indeed, it predicts the existence of some
kind of brain (computer) in any cognitive
being.
• So, the fact that we have a brain, which is
in many ways a computer, can be seen as
empirical confirmation of the view of
computationalism.
Computationalism and the Brain,
Part I
• The brain fits with computationalism:
– The brain is unlike any other organ; the heart, lungs,
liver, etc. all do something very much physical
(collect, filter, pump, etc.)
– The brain, however, is quite different: Its function
seems to be to take in signals, and send out signals,
in communication with the nervous system.
– Thus, the brain seems to be an informationprocessor: a computer of sorts.
– Indeed, we know that the nature of the mind changes
when the brain changes: thus, maybe:
• brain = ‘hardware’
• mind = ‘software’
Computers
• A ‘computer’ is something that computes, i.e.
something that performs a computation.
• Between the 17th and 20th century, a ‘computer’
was understood to be a human being; humans
who computed things!
• It was only by automating (mechanizing) this
process, that we obtained ‘computers’ as we
now think of them.
Computations
• A computation is a symbol-manipulation
algorithm.
– The symbols represent something
– Hence, the computation is about that
something: “we compute something”
Example: Long Division
Components for Computation
• In a famous 1936 paper, Turing argued
that all computations can be reduced to
the following basic components:
– One symbol string of arbitrary size
– An ability to move along this symbol string
– An ability to read and write symbols
• We now call this: a Turing-machine
Turing Machines Demo
Computable Functions
• We can use a Turing-machine to compute the
sum, and product, of any two numbers.
• These functions are therefore Turingcomputable
• Lots of other functions are Turing-computable
• E.g. all functions needed to run Microsoft Word
are Turing-computable (i.e. you can run
Microsoft Word on a Turing-machine)
The Church-Turing Thesis
• If a computer of type X can compute a
function f, we say that f is X-computable
• The Church-Turing Thesis:
– No matter what type of computer X you have:
All functions that are X-computable are
Turing-computable.
• In short: Turing-machines can compute
anything that is computable.
Universal Turing Machines
Turing proved that there exists a Turing-machine
that can simulate any other Turing-machine
TM, I
UTM
Description of
machine TM
and input I
The Universal Turing Machine
TM(I)
The output that
machine TM would
give if I would be its
input
Programmable Computers
• Turing’s insight led to the notion of universally
programmable computer:
• A single computer (the UTM) that can act like
any other computer by being given a description
of that computer (a computer program), and act
like that computer by following the instructions of
that program.
• Thus:
– Hardware (UTM)
– Software (Computer Program)
• Now: Operating System functions like UTM
A Note on
Hardware and Software
• Often proponents of Computationalism (and
Materialism) make the following analogy:
– Brain = Hardware
– Mind = Software
• This is actually not a good analogy to make:
– Software specifies how the hardware is to behave
• But nothing is telling the brain how to behave.
• There is no program, no set of instructions being read and
executed by the brain.
– Software is at the level of step-by-step instructions
• Materialists want to see minds as an abstract high-level
perspective on the functioning brain
0’s and 1’s
• Turing showed how all computation can be done
using a limited number of simple processes
manipulating a small number of symbols.
• In fact, it turns out you only need 2 symbols!
• You do need lots of these symbols, and you do
need to perform lots of these simple operations.
• But this is exactly how the modern ‘digital
computer’ does things. That is, at the ‘machine
level’, it’s all simple manipulations of 0’s and 1’s.
Physical Dichotomies
• The 0’s and 1’s are just abstractions
though; they need to be physically
implemented.
• Thus, you need some kind of physical
dichotomy, e.g. hole in punch card or not,
voltage high or low, quantum spin up or
down, penny on piece of toilet paper or
not, etc.
Computationalism and the Brain,
Part II
• Again, the brain fits with what we saw:
– Lots of simple devices, all organized together
to perform lots of simple operations
• Our brain has 1011 neurons, and 1014 neural
connections
• Early views on the brain supposed that neurons
firing or not would constitute 0’s and 1’s.
Causal Topology
• A physical system implements a computational
system if and only if that system implements a
certain causal topology.
• This topology is highly abstract. As long as you
retain the functionality of the parts, and the
connections between the parts, you can:
– Move parts
– Stretch parts
– Replace parts
• This is why there can be mechanical computers,
electronic computers, DNA computers, optical
computers, quantum computers, etc!
Computationalism and the Brain,
Part III
• So are our brains organic, carbon-based,
‘meat-computers’?!
• Again, it seems to fit:
– Implements a complex causal topology where
the only thing that seems to matter is how the
neurons are connected.
Summary
• Two independent arguments for
computationalism:
– One conceptual: cognition is informationprocessing, and that’s exactly what computers
do
– One empirical: the mind seems dependent on
the brain, where the brain seems to be:
• an information-processing device,
• made of large numbers of simple devices,
• that implement a complex causal topology to
support various information-processing capacities
Back to the Turing Test:
Can Machines Think?
Premise 1: Machines can pass the Turing Test
Premise 2: Anything that passes the Turing Test
is intelligent
Conclusion: Machines can be intelligent
Is Anything that Passes the
Turing Test Intelligent?
Cheap Tricks? Eliza
• A psychotherapist program developed by
Joseph Weizenbaum in 1966.
• Eliza used a number of simple strategies:
– Keywords and pre-canned responses
• “Perhaps I could learn to get along with my
mother”
-> “Can you tell me more about your family?”
– Parroting
• “My boyfriend made me come here”
-> “Your boyfriend made you come here?”
– Highly general questions
• “In what way?”
• “Can you give a specific example?”
Eliza and the Turing Test
• Many people conversing with Eliza had no idea
that they weren’t talking to a human.
• So did Eliza pass the Turing Test?
• (Or is it just easy being a psychotherapist?!)
• Eliza wasn’t really tested in the format that
Turing proposed.
• Still, it is interesting that humans were quick to
attribute human-level intelligence to such a
simple program.
• Maybe in a real Turing Test a relatively simple
computer program can ‘trick’ the interrogator as
well?
The Test is Sloppy
• The Turing Test seems to be a real sloppy
way to get at intelligence or at least it is
severely lacking in detail:
– Who is the interrogator?
– How long is the conversation?
– What is the conversation about?
– How does the interrogator decide?
– What are the metrics used?
The Loebner Competition
• Modern day version of the Turing Test
• Multiple judges rank-order multiple humans and
multiple computer programs from ‘most likely to
be human’ to ‘least likely to be human’.
• Loebner has promised $100,000 for the first
computer program to be ‘indistinguishable from
a human’.
• Thus far, Loebner is still a rich man: occasionally
a judge will rank a program above a human, but
on the whole the judges systematically rank the
humans above the computer programs.
An OK Test After All?
• Apparently it is quite difficult to pass the test!
– When put to the real test, interrogators can see
through superficial trickery
• So it seems we could say that if something does
pass the test, then there is at least a good
chance for it to be intelligent.
• In fact, if we are turning this into an inductive
argument anyway, the sloppiness of the test isn’t
a huge concern either: we can now simply adjust
our confidence in our claim in accordance to the
nature of the conversation.
• So is this maybe what Turing was saying?
“A Computer is Merely
Crunching Numbers”
• As we saw, a computer is ‘crunching’
symbols, not numbers.
• OK, but the objection still stands: does the
computer know what those symbols even
mean? -> The Chinese Room Objection
• Response: Just because the UTM (OS) is
‘merely’ crunching symbols without
understanding what they are doesn’t mean
that a working computer doesn’t
understand these symbols.
“Contrary Views”
• In his paper Turing goes over a list of
“Contrary Views on the Main Question”:
• Machines:
– can’t make mistakes
– can’t be creative
– can’t learn
– can’t do other than what they’re told
A Puzzle
• That’s weird: if Turing proposed the Turing
Test as some kind of practical test for
machine intelligence, you would think that
Turing would address objections of the
previous kind, i.e. that maybe something
can pass the test without being intelligent.
• Instead, it seems like Turing addresses
objections to the claim that machines can
pass the test.
• Why?
Another Question
• Why the strange set-up of the TuringTest? Why did Turing ‘pit’ a machine
against a human in some kind of contest?
Why not have the interrogator simply
interact with a machine and judge whether
or not the machine is intelligent based on
those interactions?
The Super-Simplified Turing Test
Interrogator
Machine
Answer: Bias
• The mere knowledge that we are dealing
with a machine will bias our judgment as
to whether that machine can think or not,
as we may bring certain preconceptions
about machines to the table.
• Moreover, knowing that we are dealing
with a machine will most likely lead us to
raise the bar for intelligence: it can’t write a
sonnet? Ha, I knew it!
• By shielding the interrogator from the
interrogated, such a bias and bar-raising is
eliminated in the Turing-Test.
The Simplified Turing Test
Interrogator
Machine or Human
Level the Playing Field
• Since we know we might be dealing with a
machine, we still raise the bar for the entity
on the other side being intelligent.
• Through his set-up of the test, Turing
made sure that the bar for being intelligent
wouldn’t be raised any higher for
machines than we do for fellow humans.
• Still, this leaves the earlier puzzle.
My Answer
• I propose that the convoluted set-up wasn’t merely a
practical consideration to eliminate bias in some strange
game, but rather to confront us with our the very
prejudices that, at Turing’s time, many people had
against machine intelligence.
• Thus, the ‘Turing Test’ isn’t at all meant like practical
test, but rather a thought experiment meant to make us
think differently about machines and machine
intelligence.
• Indeed, the ‘Objections’ that Turing addresses aren’t so
much objections to machines being able to pass the
Turing Test, but rather objections that go straight to the
issue of machine intelligence.
Language
• Another way of looking at the Turing Test is that
if we put a label ‘intelligent being’ on other
human beings based on their behavior then, just
to be fair, we should do the same for machines,
whether we are correct or precise in any such
attributions or not.
• In other words, Turing’s point was that we don’t
have a precise definition of ‘intelligence’, but that
we do have a fuzzy concept of it, and that our
use of slapping this label onto things (human or
otherwise) should at least be consistent.
‘Imitation Game’ vs ‘Turing Test’
• In other words, I think it is likely that Turing
never intended to propose any kind of test
for machine intelligence (let alone propose
a definition!).
– Interesting fact: In his original article Turing
uses the word ‘pass’ or ‘passing’ 0 times,
‘test’ 4 times, and ‘game’ 37 times.
The Turing ‘Test’ as Harmful!
• Moreover, I believe that regarding Turing’s contribution
as laying out a test is harmful.
• The harm is that we have been thinking about the goal of
AI in these terms, and that has been, and still is,
detrimental to the field of AI.
• E.g. In “Essentials of Artificial Intelligence”, Ginsberg
defines AI as “the enterprise of constructing a physical
symbol system that can reliably pass the Turing Test”
• But trying to pass the test encourages building cheap
tricks to convince the interrogator, which is exactly what
we have seen with Eliza, Parry, and pretty much any
entry in the Loebner competition.
• This kind of work has advanced the field of AI, and our
understanding of intelligence … exactly zilch!
Grand Challenges
• Maybe the Turing Test (and the Loebner
competition) is a kind of Grand Challenge?
– Landing people on moon
– Chess (Deep Blue)
– Urban Challenge
– Jeopardy (Watson)
• But at this point in time, I feel that trying to
create human-level intelligence in a
computer is a ridiculously-grand challenge,
and hence a ridiculous Grand Challenge
How to Read Turing’s Paper
• So what did Turing really mean? Taken literally, this is
an issue of history, not philosophy.
• A better question to ask is: What, if anything, can we
learn from Turing’s paper?
• Well, there are many interesting parts of the paper,
especially in Turing’s responses to the ‘Contrary Views’.
• But I believe the most important reading of his paper is
to see the Turing ‘Test’ as a statement about the use of
the word ‘intelligence’.
• That is, rather than an actual, practical, test, I believe we
should look at the Turing Test as a thought experiment
that forces us to examine our preconceptions (and
prejudices!) regarding the concept of intelligence.
• In fact, I propose that we no longer refer to the Imitation
Game as the Turing ‘Test’!!
Pluto and Planets
• Asking how many planets there are in our solar
system seems to be a factual matter:
– We believe there is a straightforward fact of the
matter to this issue.
• If I say: “There are X planets in our solar system” then this
statement is either true or false.
– How many planets there are is an empirical issue:
observations will tell us how many there are
• However, as the case of Pluto demonstrated,
things aren’t that easy. This issue isn’t just an
empirical issue, but also one of interpretation.
• Maybe the same is true for machine intelligence!
Artificial Flight and
Artificial Intelligence
• Imagine going back 100 years when the Wright
Brothers had their first flight.
• We can imagine people say: “Well, but that’s not
real flight. There is no flapping of the wings!”
• But over time, we realized that, from the
standpoint of using concepts that help us think,
explain, predict, and otherwise make sense of
the world around us, it is a good idea to consider
airplanes as really flying.
• Again, maybe the same is true for intelligence!
The original question, “Can machines think?”, I believe
to be too meaningless to deserve discussion. Nevertheless
I believe that at the end of the century the use of words and
general educated opinion will have altered so much that one
will be able to speak of machines thinking without expecting
to be contradicted.
-Alan Turing (1950)