Welcome to this course on A I, as we mentioned
let me first start, today with the syllabus
that we are going to cover, and within the
little bit of this in the last class, towards
the
end, and I am just repeating this for continuity.
So, will spend, the first few weeks, not
two or three lectures, on the first part of
the course, which is the history and philosophy
of A I. And we as we will see, goes back quite
a bit in time, and this is qualitatively
going to be, very different from the rest
of the course, which is going to be mostly
algorithms.
And will start with the simplest algorithm
like, depth first search, breadth first search
and
so on, move on to heuristic search, in which
we look at how search can be guided,
towards the solution that we are trying to
find, and we look at algorithm like hill
climbing, and tabu search, and ..We will find
that even that is not
going to be good enough, so we will try some
randomized approaches like simulated
annealing, genetic algorithms, and ant colony
optimization.
.These are basically optimization techniques,
but we will try to see them from the search
perspective, when we will look at, very well
known algorithm called A star and it is
variations, which we will see. Then as I mentioned
earlier that we will look at, something
called goal trees or problem decomposition
that if you want to solve a problem, and you
want to break it up into parts, and solve
each parts separately, that technique is called
problem decomposition.
Let to an area called rule based systems which
we will look at, will also do game
playing, may perhaps not as late as this,
may be somewhere here. So that, I can give
you
one assignment, to start off with which is
to implement the game playing program? And
finally, depending on how much time we have
left, we should have, something on
planning and constraint satisfaction, which
is kind of preview of the course that we offer
next semester.
In which we will study this algorithm like
alphabeta algorithm, minimax algorithm, and
a
heuristic version called S S S star. And then
depending upon how much time we have
will spend some time on, these two topics
planning and constraint satisfaction, in which
we look at, general algorithms for planning.
And we will see, by planning essentially we
mean finding a sequence of actions, which
does something useful for you, and we will
also look at logic and inferences. Because
it is not that we are just solving problems,
of
how to do things, but we also making inferences,
that if we know something, then we
know something else.
So, that is a process of making inferences,
and the language that we use for
representation is logic, and we will spend
some time that. So, these two topics are
actually covered independently, and completely
in two different courses that we offer
next semester. One is called planning and
constraint satisfaction, and the other one
is
called knowledge representation reasoning,
which is not the title we are using here.
..
So, the text book that we will follow is,
the book which I have just published, it is
just
about come out and, there are some text books
in A I which have been, very popular and,
earlier I was using a lot of a material from
here. So, rich and knight book on A I, Russell
and Norvig, which is probably the most, well
known text book at this point of time, and
a
book by Winston which was written earlier.
Then there are certain specialized books,
so these two books by Fogel and Michalewicz
is on certain aspects that we will cover,
and this book by Judea pearl is something
we
will use while game playing essentially. And
these two books which I will just mention
again, deal with the history and the philosophy
part of A I essentially.
..
So, these two books that I mentioned, and
this is going to be the subject matter of
the
first few lectures, the historical and the
philosophical perspectives to A I. And you
can
see that, is a topic, because we use this
word intelligence here, and that is something
which has concerned people over a lot of time
essentially. And we want to see, what has
been the thought, behind what is A I essentially.
So, these two books are, and I would
recommend that you, read at least portions
of this, there is the book called A I the
very
idea.
And we will discuss shortly, why this book
is different from the rest, John Haugeland
is
a philosopher by profession, not computer
scientist, and he is looking at the
philosophical side of things that, one of
the key questions we will ask. And today we
will
start doing that is, can machines think, I
wanted to start already thinking about this
question. And today we should discuss some
of these basic concepts, what is intelligence
for example, and Haugeland looks into the
philosophy behind this, Pamela McCorduck is
also from the social sciences, and she wrote
this book quite, long time ago, actually 1974
or something like that.
And I hope you will notice that the title
is, if nothing else at least a little provocative,
because she uses the pronoun, who for machines.
So, she has machines who think, and
who is something that we normally, use for
peoples essentially, human beings and so on
and so forth. So, she is talking about machines,
who think not machines which think for
.example, and therefore, already there is
a suggestion, that her own intimations is
to
believe that, yes it is possible that machines
can think.
And these two books, we will follow in the
slides that I have prepared, are mostly from
these two books and a little bit from Wikipedia,
so I will give you all those sources, from
the rest of the course I will not use slides
very much, I we will just discuss things on
the
board essentially. So, I want today’s class
to be little bit interactive, well not just
today’s
class, but today’s class will be more interactive.
And I wanted to start thinking about
question of what is intelligence, and we will
discuss that, but before we do that, let us
just look at, what are the classical definitions
that people have given, for this field of
artificial intelligence.
.
So, let us see first, what Herbert Simon has
to say, Herbert Simon was one of the
founding persons in this area of A I, starting
in the 1950s, he and his collaborator Allen
Newell, they founded the school at Carnegie
Mellon university. And we will see, their
contribution as we go along, Simon also one
of the few people, who works in A I, whose
got a Nobel prize. As you know, we do not
get Nobel prize in computer science, but
Simon got one for economics, and he was the
multifaceted person, he did many things,
as people used to be earlier.
So, his definition is we call programs intelligent,
if they exhibit behaviors that would be
regarded intelligent, if they were done by
human beings. So, this is the most common
.definition of A I that people use, that it
is concerned with lighting programs or making
machines do things, which should be considered
intelligent by, if they were done by
human beings essentially. So, what are the
first things that A I people got into was,
things like chess playing essentially, because
chess playing was always considered to be
a hallmark of intelligent behaviors essentially.
It is only the bright, and the intelligent
people who could play good chess.
.
There is a long story of chess playing, the
first programs were written in 1950’s, one
of
the first outline of the game was given by
pone Neumann in the 60’s grand master called
David levy, I do not know whether I have it
in my history, but may be it will come later.
So, let us write it here, around 1968 also,
he wagered the bet that to chess program,
cannot beat him for the next ten years. Because,
chess was considered to be something
which is very intellectual in nature, well
luckily for him, he won his bet, which is
because it ended in 1978.
But, many of you would know that, in the mid
90s, late 90s the then world champion
Garry Kasparov was beaten by chess playing
program essentially. Chess in fact, it is
not
so, intellectual in the sense that we tend
to, talk about you know, philosophical sense.
Yes, it requires lot of computing machinery,
and we will see that, if you have a lot of
computing machinery, you can play good chess.
..
Let us look at another old definition; this
is by Barr and Feigenbaum, also two old timers
in A I, so his frictions says that, physicist
ask what kind of place is universe is, and
seek
to characterize the behavior systematically.
Biologists ask, what it means to be a physical
system to be living, and he says we in A I
wonder, what kind of information processing
system can ask this such questions essentially.
So, in other words, he asking about,
talking about intelligence, that physicists
are asking questions about the physical world,
biologists are asking questions about the
living creatures, what kind of information
processing system, could ask such questions.
So, essentially saying what kind of system
would be intelligent, in that sense of the
world
essentially, when Elaine Rich as I mentioned
one of the popular books in A I, she wrote
one in eighty three or something or eighty
six. And she gives a computer science flavor
to the definition; she says that, A I is the
study of techniques for solving exponentially
hard problems in polynomial time essentially,
by exploiting knowledge, about the
problem domain.
Of course, those of you, who are diehard theory
people, would immediately object,
saying that you cannot solve a hard problem
in polynomial time, because by definition,
it
is a hard problem. But there are two counters
to this, one is that, we may not necessarily,
be looking for solving them in polynomial
time in the worst case. In certain situations
like, we will see travelling salesman problem,
is one of the hardest problems, that people
.have encountered. But given some constraints
on the problem, of how the edges are
connected, what are the weights on the edges,
you can have much faster solutions.
The second counter to this, objection that
you cannot sign, that you cannot solve
problems in polynomial time is that we are
not seeking to find optimal solutions. And
this is something, which many, many people
have observed, that human beings are not
optimizers, we do not necessarily find, what
solutions? The solution that we considered
to be optimal, we are what some people called
as satisfiers, satisfiers essentially, which
says, that you are happy with the good solutions
essentially, you does not have to be
optimal essentially.
So, just an example, with sort of strikes
me once in a while, living in Chennai, that
if you
have walking along one of the roads in I I
T, may be one thing that you want to optimize
on, the amount of shade that you walk through,
but we do not have such dense ((Refer
Time.) at everywhere there is shades, so you
have to choose a path essentially.
And even if, one is conscious of the fact,
that one wants to walk through shade and with
one does not mind walking a little bit longer.
So that, our objective function is to
maximize shade, and not worry too much about
the length of our path, even then, we do
not go into zigzag path that we would, if
we want to really follow the shade essentially.
So, we do not optimize in that sense, even
when you want to be away from the sun, you
are happy that if the path that we are following,
as enough lot of shade, not necessarily
the maximum amount of shade essentially. So,
in that sense we do not solve, hard
problems completely, we do not find optimal
solutions, but we tend to find good
solutions essentially, and that is what we
do all the time. We go shopping; you do not,
check in ten places then find the minimal
cost price, and then buy your product. Even
though on the web nowadays you can do that
sort of a thing, but in general if you think
that the price is reasonable, we go and buy
this stuff essentially.
And one more definition, which is due to Charniak
and McDermott, who also wrote a
very famous book, on A I very popular book
which, I use for part of my session, I do
not
think I mentioned it may be I should added
to the list there. They talk about A I, being
the study of mental faculties, through the
use of computational models. So, we had said
earlier that, there are two approaches to
A I, one is the cognitive approach which says,
which I, we are trying to understand intelligence.
.And the other is the engineering approach
which says that, we want to build smart
systems or smart apps if you want to say nowadays
essentially. So, what this definitions
says is that, we want to study mental faculties,
and to do that we will be computational
model, and use them for the studies actually,
where definition, which I like most, before
I
come to that, look at these definitions. They
are saying, if a human being does this, then
it is intelligent, and we want to sort of
do something similar, so we want to mimic
human
intelligence.
.
So, the definition which I like most is come
from not a computer scientist, but from a
philosopher, that we mentioned John Haugeland
in the book A I, the very idea. He says,
that the fundamental goal of A I, is not merely
to mimic intelligence or produce some
cleaver fake of intelligence, he says that
not the goal at all. A I wants the genuine
article,
machines with minds, of their own in the full
and the literal sense. Now, it is a very
interesting question, and we would debate
it today a little bit, in the class, as to
what we
mean by intelligence and can machine have
machines have it.
And then you goes on to say, and all this
is in this book here, that this is not science
friction, but real science based on the theoretical
conception, as deep and daring, namely
that we are at the root computers ourselves,
essentially. So, if you are at the root,
computers ourselves, which means if you are
at the root machines ourselves, then to
answer the questions can machine think has
been . solve essentially,
.because yes, human beings can think and therefore,
machines can think essentially. But
the idea that we want to pursue, is that the
idea that thinking and computing are radically
the same, is idea in his book, which is A
I the very idea, it is very interesting book.
And for those of you of philosophically inclined,
should go and have look at it, and this
idea, that thinking and computing are kind
of tied up together, goes back much before
Haugeland. And we will see, either in today’s
class or in the next class, that the British
philosophers Thomas Hobbes, was one of the
first person through, put forward this idea.
Hobbes of course, was not a computer scientist,
in those days, there was no computer
science, he was a political scientist, and
this kind of stuff.
.
So, let us, get to the fundamental questions,
and this is the part that, I want you to,
give
answers to or what do you think about this
question? So, I have not written any answers
for this. I have just written the questions,
and I will write the answers on the board
as an
when, they come out from, the class essentially.
So, the question you want to ask is, what
is intelligence? I mean if there is going
to be ever a debate about whether machines
can
be intelligent or not machines can think on
are, first we should be clear is to, what
do we
mean by intelligent, I mean if I write a program
is, let say the singular value
decomposition of a matrix, would that is a
program intelligent, well I do not know.
So, can I have some responses from the class,
what is intelligence, what is let us forget
about what is thinking? Let us say, because
thinking is this thing, but when is, when
.would something be call intelligent, what
is intelligence, what would you require in
a
system or in agent, for you to call it intelligent,
what are the fundamental characteristic
of intelligent behavior?
. Ability to take decisions.
.
That is very generic, yes definitely a part
of intelligence, but may be if you could expand
on that, from a little bit. Example is you
know you have a small program, which says
if
something, then something else, it is also
doing taking some decision, by looking at
some
data; obviously, you are looking at something
and taking a decision.
. Use of knowledge to respond to new situations.
Use of knowledge of course, you will have
to tell me, what do you mean by knowledge.
And this definition has a little bit of inconsistency,
built into it, in the sense that, most of
the time when you use knowledge or experience,
exploit experience we use them in
situations, which are similar, which are not
entirely new in that sense. Well if by new
situation, you mean a new problem, then one
has to ask the question, what do you mean
by that essentially? You know there is the
. saying which says that,
you can never step into the same river twice,
essentially, that is never the same thing.
But of course, nevertheless I will, I am not
disputing, what you are saying, I am just
trying to get people to respond more, we do
as human beings, you make extensive use of
.knowledge, and we spend close to, what should
I say, twenty two years, twenty five
years, acquiring knowledge . will later use,
essentially in our lives
essentially. Human being, humans have a very
different kind of a species I think, I mean
we are the only species, which has schools
up to twelfth standard, and then college four
years after that and then, masters and may
be you know p h d in some cases. No other
species spend so much time, acquiring knowledge
essentially.
. Sir, we able to make inductive inferences,
and something which others senses
just follow from your input, but to be able
to make some new.
Ok
Assumptions
So, I will just use a term inductive inferences
or in other words to generalize, ability to
generalize. So, you go to the some hotel and
you ate masala dosa, and you are happy,
you come back. Next time you go there, and
you have something else, let us say
oottapam and you come back, and then you generalize,
that this hotel, gives you good
food or you might say that, you know, south
Indian food is very good.
These kind of inferences that we come to,
is making inductive inferences, you we look
at
a few instances of something, and then from
where, we generalize, that you know, it
holds for a certain class of things essentially.
I see, a few leaves, and all of them are
green, then I conclude that all leaves are
green essentially, which of course, does not
true
at least not all the time, may be in Chennai
yes, when they, when we have leaves, but not
in the rest of the world.
. Basically, spending that definition applicable,
generalize and classify.
Classify would come in this making decisions,
what else have is that all that we do as
human beings, is that all we lay our claim
to for being intelligent.
. Choosing the best available of .
Well when that comes here, choosing best options.
. Ability to learn.
.Ability to learn yes, which is a little bit
difference from here, and we can say by learn
we
mean acquire knowledge, one can learn from
once own experience, you do to something
with gives a little bit of a pain. So, maybe
you touch a hot stove or something like that,
two, three times, and then you learn, that
is again inductive inferences essentially,
but to
learn all kinds of things to learn facts.
.
To learn relations between things, is something
that, we do quite effectively. So, what do
you mean by this? Communication.
. not
So, but there is a more fundamental thing,
to I mean, expressing well something,
incidentally is something which is a feedback,
we get from all the companies which
come to higher people here. Let us say that
our students are not good at communications
essentially, but that is not the idea, even
that is not about your talking about I think,
the
very fact that we can, communicate something.
So, let me go to the fundamental thing,
what does this lie on, something which if
is specific to the human species.
. speech
Speech, the speech, before speech use of language,
language is something which is
unique to; at least we think it is unique
to our species. There are doubts that you
know
may be, whales communicate over long distances,
and dolphins can communicate, and
.that cannot stop, but we are not quite sure.
And we do see that, there are other creatures
which make sounds, which are; obviously, aimed
or directed at least towards their own
species, but it is not clear to us, what they
are proving actually.
So, it is a use of language, which us enabled
us to carry forward knowledge. So, if you
have a brilliant scientist like Newton, whose
thinking about the universe, and the world
around him, and coming to conclusions, and
arriving at some understanding of how the
world operates, the fruit of his effort is
available to us, and it is available to us,
only
through the medium of language essentially.
Because, we can talk to other people,
because we can write books, so printing of
course, was another invention which help this
process, but this simply be able to communicate,
to tell stories, this whole idea ((Refer
Time.)
You know that is stories are passed on from
one person to the next, like all the stories
that we hear in our subcontinent, the Ramayan
the Mahabharat and so on, where sort of
overly conveyed from generation to generation.
And all that is possible, entirely through
the use of language, it is language, which
has allowed us, to hold down to whatever
knowledge we get from, our interactions with
the world, and pass it on to other people
essentially, anything else, can one think
of. So, will take this, as part of thing,
and then
we will see, whether machines can be intelligent.
.
.So, let me move on, a little bit and ask
the next question, this is not the very complicated
question; I just want to be sure that we are
all on the same page, because I need talk
of
machines thinking and so on. So, what do you
mean by a machine, otherwise we will be
stuck with trying to answer a question, that
can machines think, without knowing what
we mean by thinking, and without knowing what
exactly we mean by machines
essentially. So, both these terms we should
know, that is what do we mean by that
essentially.
. Why which does a particular task repeated.
A device which does the particular task repeatedly.
. However.
I am not going to write this here, is that
complete enough definition of a machine.
. Device that has reduces human effort.
A device that reduces human effort, what about
an exercising machine? Treadmill or
something.
. Computations
Something that there is computation, but computation
is only, one kind of activity that
we consider, we have a machine which grains
coffee beans for you, I do not know that is
doing computation. Now more fundamentally,
when will I call something a machine that
is what I mean by the questions essentially?
So, if it is not a machine, what can it be?
. It follows the cable instructions; you instructed
and do the work for you. Does
not think on it is own.
He says, does not think on it is own says
. get the answer to the
question that can machines think. So, machines
are thinks, which cannot think on their
own. Now, this bit about following instructions,
I do not know, I mean there are, of
course at some stage, in the life of the machine,
there are instructions given to a machine.
So, but if I have a air conditional like in
this room or thermostats somewhere, it is
not
really following instructions.
.But . some coding or something.
Yes some that is what I say, that some stages
it is life, some instructions were given to
it,
but then I can say the same thing about you
as a person, that you are following
instructions, your parents said go and attend
lectures, do not bunk classes, that is why
you are sitting here in this class . more
fundamentally, what is this,
when would I call something on machine.
.
So, let me give a circular definition, something
which acts mechanically; of course, as
. it is a circular definition, that is using
the term machine and
mechanical, they are related to each other.
So, it is not really a good definition in
that
sense, but it gives us an idea, what I am
trying to convey essentially. Because, we
can
express this more easily, when do you say
that something is acting mechanically, and
I
do not want the answer that without thinking,
because thinking is a something, which
happens at a different level all together
as we will see.
Basically in a well-defined manner, according
to certain rules, let us say laws of physics,
if it is a physical machine or some other
mathematical laws, if it is some computing
machine, something which operates, according
to fixed set of rules. So, the question that
one ask is, and will come to that in a moment,
so this is the question which has raised.
So, just to be cleared is a computer a machine,
it does operate according to some very
well defined laws and so on.
.Of course, a computer is a, very special
kind of a machine, it is a very flexible kind
of a
machine, which says, so this whole idea store
program, which we discovered? It is
discovered, not quite discovered, but at least,
brought forward by Charles Babbage which
says that, you can have a same machine, and
you can put in a different program. And it
will do something different for you essentially,
make it a very flexible machine, but
nevertheless, it is the machine, because at
the base, there is something which is very
repetitive which is going on.
And whenever we say, machine in the rest of
this course, basically we will mean a
program in computer. So, when we say, can
a machine think; then it means can we
program a computer, so that it appears to
be thinking or is thinking, as this. So, this
is a
question that is fundamental in the sense,
there was a edging debate as we will see some
arguments against thinking, in the next slide.
In the last fifty years, sixty years people
have been talking about, whether machines
can think or not.
So, what does, so does anyone here, have a
strong opinion either side. So, when I say,
by
this time, I mean a computer program, can
I program a computers, so it is a thinking
machine, is that possible at all. And we try
to find some aspects of what we call
intelligent behavior or is there something
missing that we have not mentioned here, we
forgot to mention here, which the computer
cannot do, can never do, is there something
like the halting problem . situation here.
So, does anyone have a
opinion either ways, there is anyone strongly
feel that yes machines can think, there is
nothing fundamentally against it or there
anyone have a opinion which says, no machines
cannot think, only we human beings can think
essentially.
. . did not tell what is thinking?
Well, I that is the first question I started
asking you will. So, we wrote all this stuff
by
saying that if you are using this.
. intelligence.
So, we sort of say that they are closely correlated,
thinking is the process how to of
which intelligence arises, we might say. So,
no one has the strong opinion I take it
essentially. So, that is fine, there is nothing
either ways, and finally, as Haugeland said,
I
mean, I that the and to what Haugeland thinks
about this question, that are we machines
.is already here, in his answers essentially,
he thinks that we are machines. But, is anyone
here, who feels that strongly about this,
that yes we are machines or no we are not
machines, we are flesh and blood creatures
of carbon, we are not made of silicon, any
strong views.
So, supposing I would to say, let us try and
put forward the idea that we are machines,
what is the argument that you would give,
to say that yes, we are also machines. So,
one
of the fundamental objections, the people
ask, there is that, you know machines versus
whatever it is, which is called as free will.
So, when I asked you little while ago as to
what would be, if you were not a machine,
then the answer that some people give is that,
you have a own free will. So, in some sense,
a machine does not have it any free will
essentially, a machine operates according
to fix set of instructions, and fix set of
laws,
and always obeys those instructions and laws
essentially.
Whereas, free will, which you do not understand,
we do not know whether we have free
will or not, I mean people claim that human
beings have free will, but they all go and
vote for some congress and b j p all the time
essentially. So, but anyway, what is this
thing called free will, basically says that
we make choices, that we have, the ones who
decide, how our lives will be, how what we
will do in the next instant, and thinks like
that. You know, your open philosophy is like
existentialism dealt quite a bit, in the post
what period, about this notion of free will,
and you know making choices think ((Refer
Time.)
So, if you want machines, then we would not
have something called free will; or is that
a
contradiction; or if we are machines do we
like, some of the Indian thought says, that
everything is free decided, like this say,
whatever have to happen will happen essentially.
Of course, then we are all machines, and then,
there is no second thought about it, but if
I
want to sort of deconstruct say, we are machines,
because of this reason, I could sort of
give you an argument, which says that. We
grow out of a single cell, to start with
instructions written in our genetic code,
about how to will our bodies, what color to
of
eyes to have, all kind of things.
And then essentially, we build ourselves using
this thing and therefore, we become
human beings and, just like computers are
flexible, and they can do different things,
at
different times. We also flexible, may be
a little bit more than the current day computers,
.but we are in the end, we are machines essentially
or I could give you an argument which
says that, see our brain is made up of a,
ten to hundred billion neurons, all of them
operate in by a very simple mechanical procedure.
So, our brains are mechanical in
nature, and therefore, since a brains control
us we are mechanical in nature, I could give
argument like this. So, what will you say
against it, I mean if you were to say anything
against it.
. We have something called emotion that is
not in machines.
We have something called emotion that is not
in machine essentially.
. We are biased to our emotion.
But how do you know, it is not in the machine.
. Suppose, I turnoff my computer.
Suppose you are system patches, can we say
it is angry with you, I mean it may not
display it in other ways I think, no more
seriously, why should we say that, machines
cannot have emotions. So, I will pointed to
a book, it is called the emotion machine,
and
it is written by a guy called Marvin Minsky,
was also one of the founders of A I. As we
will see the history of A I, as we go along,
he founded the m i t A I lab, along with John
McCarthy, and he has it is in the last five,
six years odd, he is written this book called,
the emotion machine essentially.
So, it actually goes . again the slightly
longer divide as so what do
you mean by emotion and so on and so forth.
I could try to characterize emotion by
saying that, you have memories, and then you
have some value, labels attached to
memories, that some memories are good; some
memories are bad. And then you have
states, which are attached to those value
labels, so you are happy or you are sad. So,
one
could talk about things like that, but is
it something, which is exclusive to us, I
do not
know, and do creatures like dogs and cats
have emotions.
. Yes
.They have, but are they also intelligent
or that is another question, is intelligence
the
prerogative of human beings, only or do we
allow dogs and cats, and deer and monkey,
to be intelligent or not.
. Yes
But, if you go down this, ladder of life,
so to speak, then you have dogs and cats,
then
you have mosquitoes somewhere here, then you
have bacteria, then you have virus. So,
at which point, you stop essentially. We will
we are not here to answer this question, we
are here to keep in mind, that these questions
have been asked by many people, and this
is not the goal, our goal to you know, it
is not a course on philosophy, but still we
should
be aware of it. So, here small cartoon I got
from, so our, if we were machines yes then,
I
suppose our admiration would be mutual happy
or if you want to call as admiration.
.
So, let me give you some arguments, which
are well known in literature, which claim
that machines can, the question we asking
is, can the machine think, can machine think.
So, what are the objections, the first . guy
call Herbert Dreyfus says
that, intelligence depends upon unconscious
instincts, that can never be captured in
formal rules essentially. So, you cannot read
this, I did not know how to make this a bit
stronger, darker, whether basically a Wikipedia
page, which is critiques of A I,
essentially.
.Dreyfus spent, he has made a carrier out
of saying that A I is not possible essentially.
So,
at least he is made a carrier out of it, what
you think about these unconscious instincts
that can never be captured in formal rules.
So, this is one of the arguments which people
say these kinds of arguments which say that
we often do not know what we are doing?
Why we are doing something? I did this, but
I did not know why I did this, but does this
say that, I was doing something really mysterious,
which I cannot reproduce in a
machine. Let us together argument by philosopher
John Searle, it is called the Chinese
room argument, he says can an agent locked
in a room processing questions in Chinese,
based on a set of syntactic rules, be said
to understand Chinese. So, is an, it is a
thought
experiment which John Searle proposes, it
is a very famous argument, just lookup the
Chinese, whom argument on the web, when you
will get all these descriptions.
So, the idea is that, supposing you as a English
speaking person; or whatever Hindi; or
Tamil speaking person, you all locked up in
the room. And you are full of these slips
of
paper, which have these syntactic rules, which
says if you see this pattern, then send out
this response, if you see this pattern, then
send out this response. You do not know, what
that thing is about, you see some patterns,
and you have an instructed, to loop match
a
pattern, and send out a response based on
that. And you are there somebody; from
outside below the door slipping, sending you
slip of paper, with some patterns, then you
make some other patterns on slips of paper,
and send them back essentially.
You do not know, what is happening? What it
turns out apparently at the end of this, is
that somebody is asking questions in Chinese,
and you are giving them answers in
Chinese. So, John Searle says, and this is
the Chinese room experiment, thought
experiment, says that supposing this were
to happen, would you say that, the person
whose answering you, those Chinese. And he
says no, because the way that experiment
has been described, and he says that therefore,
but his behavior looks like intelligent
behavior, because he is giving you all the
answers, but he said really intelligence,
he says
no essentially.
And of course, there is a little bit of an
operational trap there, which is what I written
here, how many rules will an agent need to
have, for the thought experiment to be
convincing essentially. And we will see this
idea, again in a different form, as we go
along, one more objection from the celebrated
mathematical physicist John Roger
Penrose, you must have heard about him, those
who have Nobel laureate, he wrote this
.book with, which became quite a hit essentially,
it was called the emperors you mind
essentially.
.
If you write the name, you know, so parading
the emperor’s new clothes, and he is also
asking this question about, can one we can
machines think or not, his answer is that,
no
machines cannot think. We are the only thinking
creatures, and he says that there
something happening in our brains, which current
day physics cannot understand, cannot
explain essentially. And that is something
he says respective quantum mechanical, if
you
want to go into the details, you should look
up the web, and read his book essentially,
which is not so easy to read.
But still, he wrote a later book, I forgot
it is name, which is the shorter version of
this
book. So, that is another argument, then there
are arguments like, he mentioned emotion,
intuition, consciousness, ethics. So, some
people say, it would not be ethical to have
intelligent machines, so they cannot be intelligent.
Now, this is kind of round about
argument which says, it would be bad for,
I do not know who, so we cannot have
intelligent machines essentially. Of course,
we are very ethical people, and we go around
suspending twenty eight year old IAS officers,
because of some small residues that we
have against them.
..
So, there are many arguments, which a co ordination
and they have been many counters
to the argument which I have not talked about,
because he wants to get on, to what
Turing said. So, you all know Alan Turing,
he was very instrumental in tracking codes,
during world war, this thing, what he says,
that he would have been one hundred and one
years old. If he were alive today, what he
says last year was his birth centenary and
lots
of things were going on, he says that the
question whether machines can think is just
a
meaningless question.
Because we are not able to, even describe
with we made an attempt here, to say what
is
thinking, but it not very clear to say, what
is thinking I mean I keep ((Refer Time:
47:07)) and thinks like that are of course,
meaningless essentially. As his I guess g
e and
certain essentially, what he did, was that,
let us not get into this raising debate of,
can a
machine think or not. He says I will give
you a test, which is called as a imitation
game,
which we will see in the next slide, which
is now known as the turing test, then nothing
to do with turing machines, of this he says,
about this turing test, we will see in a
moment.
..
Let us first see the test, and then come back.
The turing test is like this, that there is
a
human judge, in this something has happened
to this anyway, there is a human judge
sitting on in those is a teletype, in current
they were in may be on a mobile phone
chatting with someone. So, you are chatting
with someone, you type in something, and
somebody else types backs something and so
on and so forth. So, he imagines that
teletype, connected to a machine on the other
side, but there is a wall in between, so you
do not know whether it is a machine or whether
it is a human being essentially.
And what turing said, was that if he gave
a figure like, seventy percent of the time,
the
machine can fool the judge into thinking that
the judge is talking to a human being; then
the machine is intelligent. We will come back
to, the test again, so what it turing feel,
he
felt and this was in 1950, when he wrote this
paper, called computer machinery and
intelligent, it is available on the web, if
you go to many places, you will just get the
paper
directly. He says that in about fifty years
of time, which is 2000 in year, 2000 will
be
possible to program computers with a storage
capacity of 10 is to 9, so 10 is to 9 was
considered to be a big number, and histories
repeat with these kind of example.
Bill gates apparently had one said that, who
on earth will need the memory more than
sixty four k essentially. So, he said that,
with the capacity of 10 is to 9 to make them,
play the imitation game, the game that we
do describe. So well, that an average
interrogator, will not have more than 70 percent
chance of making the right
.identification, after five minutes of questioning.
And many says, that I believe that the
end of the century, which is at the end of
fifty years, use of words and general educated
opinion will be altered so much, that one
will be able to speak of machines thinking
without expecting, to be contradicted.
Very difficult to make predictions, in these
kind of matters, David levy said that, he
no
machine can beat him, Alan Turing says that,
all machine the machines will ((Refer
Time.) pass turing test, both was wrong in
the sense, that we still cannot say that,
you know, we have machines which pass turing
test. So, currently there is something
called a Loebner prize, which has been instituted
by Agricola Loebner as a name
suggest, it is an annual competition, where
they are judged by for human like response.
So, it is not as here fooling something, but
for human like response, and there is a grand
prize of 100000 dollars, in case who interested
in some pocket money, I mean say it is
still open essentially. So, there are two,
so there is a question which I want to, ask
a few
week do not have too much time, will have
to stop soon. And we will start with this
in
the next class, is to what you think of the
turing test, as a test of intelligence remembers,
now he himself said that do not talk about
thinking and all, but just accept this test.
.
Let me just show you, some examples of this
year’s competition, which I got from the
web, so this year, there are four finalist,
who are going to compete on September 14,
this
finalist have been selected based on some
earlier rounds, and this is the transcript
from,
.the leader of those four best one. So, let
me just read it out, so there is a judge and
there
this program called izar. So, the judge says
hello, I am Ronan, that is the name of the
judge, what is your name, the program response,
I am just try to imagine, how would you
write a program, which would respond like
this.
This is the output of this program, which
is called izar, it is says Ronan, it is about
time
you contacted me, I was waiting for you, my
name is izar, pronounced I zar, but you can
call me izzy if you want, doing anything fun,
too much recursion in A I M l and so on
and so forth. Judge says, at some later point
I have just not I have not given you the full
essentially, I like bananas, which is your
favorite fruit. He says tell me more about,
your
unknown, this is the trick, which the program
is employing, to answer a question, which
is it cannot answer very easily, you are not
only one. So, you have to build in this kind
of
tricks, human being also do that, if you are
taking a viva about something, you know, I
do not have a problem with banana, is that
your favorite fruit the obvious one and so
on.
Then he says, I have been getting into hoomii,
a type of mongolian throat singing. So,
you have to put in certain amounts of knowledge
in your system, to be able to convince
the listen, so he is trying to impress.
.
So, let me leave you with a program which
was written in1960 or something, this
program is called eliza, you must might have
heard about it, it was named after eliza
Doolittle, who was a character in Bernard
Shaw’s play called Pygmalion, and we will
.visit Pygmalion again later, it was a very
simple n l p program written, at m i t by
weizenbaum in 1966. It use simple rules to
manipulate language, it would read what the
users written, manipulated little bit, and
throw it back.
So, it says, if you go and say for example,
somebody will say, so for example, if you
want to say, I like bananas, if it simply
say, why do like bananas. So, it just twist
that,
and send it back to you. And there popular
version called doctor, which I am sure you
might have seen, it runs a script which makes
it looks like psychotherapist essentially.
It
of course, makes it easy to ask questions,
it can always one of the standard questions
these program ask is, tell me more about your
family. You know, if they cannot say
anything else, . tell you more about your
family, and as a human
being, you would so this program is doing
some deep analysis, .
So, here is the Russian scientist, who was
visiting Stanford, who was running a version
of this, we just read this. So, I have colored
these things to show you that you know, it
just twisting that sentence, in this thing.
So, these are, this is, so there was a scientist
apparently, after this conversation he started
pouring out, all his words to this program
and so on and so forth. And Weizenbaum found
that his secretary was all the time talking
to this program, and apparently she was quite
furious, when she found out that
Weizenbaum had access to those conversations
essentially.
And nowadays of course, you know prism, and
everything, Weizenbaum actually found
that peoples responses, words are disturbing
that he wrote a book, which says that no,
no
computers cannot do all this kind of thing
essentially. So, we are gullible, and I think
we
will take it up, in the next class, with some
even older examples of how, we look at
something, and we believe that it is doing
something in the intelligence for us essentially.
Meanwhile I would like you to think about
this turing test, in the next class on
Wednesday, we will start discussing, what
we think about the turing test essentially.
.
