Last time we went through Alan Turing's 1950 paper on the Turing test.
And today we're going to be looking a bit more at that, but mainly at some related issues to do with some other thought experiments and a
kind of software that cast serious doubt on the Turing test in the last lecture.
Next time, I'm going to be trying to bring threads together, looking at how Turing and Cell match up in this lecture, we're going to see.
John Searle getting the upper hand, but I promise you that will be redressed.
So first of all, a thought experiment from Net Block, very aptly called Blockhead, though I don't think he gave it that name.
So the idea is that the Turing test cannot be a good test of intelligence because you can imagine you can
do a thought experiment of a system that would pass the Turing test and yet very clearly be mindless,
unintelligent. So let's suppose we imagine every possible sensible conversation of a given length being stored.
So humongous range of all the possible sensible conversations that you could have in English.
And imagine then that we have a system which goes through the ease.
And when it gets an input that presumably a sensible input from its interlocutor,
it looks through all the sensible conversations until it finds one that matches and then it gives the appropriate response.
Now, clearly, if that were to be done, then what comes from the machine will be a sensible conversation.
So in that case, it would pass the Turing test, but nevertheless, it clearly isn't an intelligent system because it's just a gigantic look up table.
There's no understanding there. We can't call it intelligent. All right.
Let's just, first of all, do a little reality check on this thought experiment.
One thing I'd advise you quite generally when you come across a thought experiment in philosophy and there are lots of them.
It's always worth asking, is this the least bit plausible?
Because if it isn't, that might cast doubt on it. Okay, so let's just so that we've got some numbers to play with.
Imagine that we're thinking of a conversation which has 10 sentences, each one from the of one from the respondent.
And imagine that on average, each of these sentences is 10 words.
Let's suppose that on average, each word can be chosen from a menu of about 100 choices.
Right? I mean, there are thousands, tens of thousands of words in English, so we're being pretty modest here.
But let's just get a ballpark figure. How many conversations does that generate?
Well, we've got 20 sentences, each involving 10 words and each of those words chosen from a menu of 100 choices.
So the number of conversations here is about 100 to the 200, which is 10 to the 400.
Now, the number of atoms in the universe is probably around about 10 to the 80.
So imagine replacing every atom in the main in the known universe with a complete universe.
How many atoms do we now have? Well, 10 to 160.
Imagine doing the same thing three more times, taking every atom and replacing it with a complete universe.
When you've done that, a total of four times you have 10 to the 400 atoms.
That's how many conversations we're talking about. OK.
So it's not that this thought experiment is just a little bit implausible.
It's absolutely outrageously implausible. And we're only talking here about a conversation that involves 10 questions and replies, OK.
Now, suppose you imagine a system that exhibits some impressive behaviour.
Is that enough to attribute it with intelligence? Well, we could mean two things here that there could be two different questions we're asking.
On the one hand, we might be asking whether that behaviour is definitive of intelligence.
So anything that behaves like that is correctly described as intelligent just in virtue of that behaviour.
That's one thing we could mean, but the other thing we could mean is that the behaviour provides strong evidence of intelligence
because the behaviour could only plausibly be generated by something that is intelligent.
And those are two different things, right? One says this behaviour is, by definition, intelligent.
This one says it's strong evidence of intelligence.
Now, when we come across thought experiments like Blockhead, I want to suggest that our intuitive reaction to it.
You know, we say, Oh, that clearly isn't intelligent. It's just a giant look up table.
All right. That reaction suggests that we're not thinking of intelligence as merely to be measured by the appropriate behaviour.
But actually what we want is behaviour that generated cleverly with limited resources, not just a look up table, but notice in any way.
But anyway, in practise, the two comes at the same thing. I thought experiments can float free of any plausible reality.
But if we look in practise, Blockhead just could not exist.
It's not a plausible thought experiment. So in practise,
in order to generate the kind of behaviour that we think of as if it's strong evidence of intelligence
that could only come about by having some pretty nifty processing underneath some clever processing,
at least it seems that way.
So I want to suggest that the bloc had thought experiment actually shouldn't be given too much weight and to press home that point,
I want to give you a different thought experiment.
Suppose I provide you with a chess playing programme and it plays chess at grandmaster level in real time.
Would you say that shows that it's at least intelligent at playing chess?
Well, you might think so, but here's a thought experiment on the other side.
Suppose someone were to write a computer programme of only about 50 lines of code in a standard general programming language?
All right, no packages that you can import or anything like that.
So 50 lines of code which could play chess at a Grandmaster 11 in real time.
Such a crude programme could not possibly count as genuinely intelligent.
I mean, I ask you 50 lines of code. All right. Hence, Grandmaster performance chess is not a reliable proof, even if intelligent chess play.
What I want to suggest is that's a rubbish argument. It's a rubbish argument because the hypothesis that it's based on.
But you could even have a computer programme that plays grandmaster chess in real time and only 50 lines long is plainly crazy.
So we should not be persuaded by this kind of argument. I mean, you can see you could make that argument.
I've chosen chess, but you could make it about any domain. So what I'm suggesting to you is we should be very sceptical about thought experiments
that are just completely beyond any bounds of plausibility that they come to cheaply.
You can invent one like this far too easily, and it appeals to our intuitions in certain ways.
But I'm suggesting that our intuitions shouldn't be allowed to be pulled too much by thought experiments that are so far from reality.
Very nice quote here from Dan Dennett talking about thought experiments.
If you look at the history of philosophy,
you see that all the great and influential stuff has been technically full of holes, but utterly memorable and vivid.
I think he's slightly exaggerating, but not all the great and influential stuff, but some of it.
They are what I call intuition pumps, lovely thought experiments like Plato's Cave and Descartes Evil Demon and Hobbes edition of the
State of Nature in the social contract and even Kemp's idea of the categorical imperative.
I don't know of any philosopher who thinks that any one of those is a logically sound argument for anything.
I suspect there are some who do, but their wonderful imagination grabbers, jungle gyms for our imagination.
They structure the way you think about a problem. These are the real legacy of the history of philosophy.
Okay. So I think there's a lot in this. I mean, the idea of a thought experiment is as pumping our intuitions is a very strong one.
But let's look a bit more closely at what that's doing. The thought experiments are trying to illuminate one thing, for example,
the nature of the world or the social order by harnessing our familiar understanding of something else.
So Plato's analogy of the cave, you've got shadows cast by a fire, and that's supposed to tell us something about the way the world is.
Social contract theories suggest that our interactions in the social order are somewhat similar to making an explicit contract.
So we're encouraged to see one thing as relevantly similar to the other.
And that can pull our judgements in particular directions regarding that thing.
The same would be true of computer analogies, so obviously comparing the mind to a computer programme.
Richard Dawkins gives another example he compares a religion to a computer virus.
He suggests that religious beliefs have certain characteristics which make
them spread in a very fertile way in the mind without a critical examination.
But the problem is that different analogies can easily suggest quite different conclusions.
So the problem with thought experiments is that they don't necessarily point as ambiguous
unambiguously in a single direction or a direction that is necessarily merited.
OK, we'll be coming back to this later. But now I want to introduce the thought experiment, which is nearly as famous as the Turing test.
John Sayles Chinese room OK, so we imagine a conversation conducted in written Chinese.
It's very like the Turing test, except that the participant in the Turing test, the one who's giving the responses, is somebody enclosed in a room.
All right. And like me, who understands English, knows no Chinese at all.
But the conversation is in fact conducted in Chinese,
so we have a Chinese speaker who writes questions on one side of a card and posts them into the room and then
the person in the room that's me has to consult various rulebooks to decide how to process what's written down.
And then I write down on the other side of the card some symbols which turn out to be Chinese symbols,
giving meaningful answers to the question that's been posted in.
Okay, so the guy in the room that's me has no knowledge, whatever of the Chinese language or the meaning,
the significance of the symbols he's reading or writing. So that's the word semantics here.
But as far as I'm concerned, when I see these symbols, they have no semantics, no meaning for me.
Instead, on generating my written answers by strictly applying rules based purely on the syntax that is the shape and
the structure of the character strings that come in and so gives a very helpful example here of what he means.
Take a squiggle squiggles sign out of basket number one and put it next to a squiggle squiggle sign from basket number two.
I've been unable to discern from Chinese friends exactly which symbol is the squiggle squiggle sign or the scroll scroll,
but maybe get his cartoon from Wicked.
Wikimedia Commons Commons showing a very simplified version of the Chinese room.
Notice that this is completely ridiculous.
We've got individual symbols coming in and going out, and the guy is consulting a single rulebook which looks extremely crude.
There's no way that's going to produce meaningful answers in a Turing test like situation, right?
When the questions coming in could be many and varied.
Vladimir, whose computer science philosophy student at Harvard, has kindly produced an illustration which is a little bit more realistic.
So here's the guy in the Chinese room income. The questions outgo his answers.
He's got a huge library of books. He's identifying the symbols.
This book is telling him right down to this other book page such and such, and you can see that he's got a box of counters there,
and he's constructing the symbols over here, and there are directions to other various rooms.
And here there is a map of the whole layout just making clear that we've got an absolutely huge building.
And maybe, maybe that's a little bit more plausible.
Well, it's got a lot more plausible than that. Whether it's actually plausible.
Well, maybe, maybe not.
But it's a it's a far better step of the kind of sophistication that would be required to fit the Chinese room to get off the ground.
Okay, so just a point. The original version of the Chinese room is different from from the well-known one, so actually started out in 1980,
giving a context in which the questions were limited to understanding of a story told in Chinese.
So a story is produced in Chinese, and then what the guy has to do is answer questions about it.
So far more limited than a Turing test, but in 1984, so emboldened went beyond that.
And basically we have a Turing test conducted through the Chinese room.
OK. What he sells conclusion from all this.
Well, clearly the man in the room that's me does not understand Chinese, OK?
Nobody's going to argue that I in the room operating or doing all these symbols without a clue what any of the symbols mean understands Chinese.
I clearly don't. But I am producing meaningful replies.
Moral understanding a language or indeed having mental states at all involves more than just having a bunch of formal symbols.
It involves having an interpretation or a meaning attached to those symbols.
Computer programmes like the rules, followed by the man in the room, are purely formally specified.
That is, they have no semantic content. But in fact, Seoul's conclusion,
or you know what it is that he's denying he's actually not so easy to pin down and
Searle is rather slippery on this will come back to this in the next lecture, we'll see.
There's a there's a reason why he's somewhat slippery. But here I want to draw your attention to it.
So most of the time, he expresses his thesis as a denial of intentionality or semantic content.
Intentionality, by the way, is and the way in which words and thoughts reach out to things in the world.
So suppose we take the word tree in Chinese.
If you're a Chinese speaker for you, that word has intentionality.
You see the word and you think of trees out there in the world.
But for me, if I see that symbol, it has no such intentionality.
I see the symbol. I see it shape, but I've no idea what it refers to.
OK, so Seoul denies intentionality to the Chinese room semantic content,
but he also denies that the digital machines can have a mind mental states, mental content, cognitive states, cognitive processes.
And he describes his argument as attacking the claim of strong artificial intelligence that digital machines can think or have consciousness.
Now that is a very wide range of claims that seem to me to be potentially very different.
I've given some citations there so you can follow them up.
His most cautious interpretation is to say that what cannot happen is that digital computers have semantic state,
meaningful state purely in virtue of following a symbolic algorithm.
So that seems a relatively modest claim.
OK, so that the claim is here I am in the Chinese room processing these symbols.
I am just following a symbolic algorithm there, and purely in virtue of that,
my the state that I am manipulating, whether in my mind or in the room, have no semantic significance.
If they have any semantic significance, they have going to have to acquire it from something more than the formal algorithm.
But he often goes beyond that as we'll see one point, by the way.
Just like Alan Turing in his paper, do you remember when he said we exclude men born in the usual way in exactly the same way?
So is does not want to say that machines can't think because we are machines and we can think,
at least in his book, but it's digital computers again that can't think.
And here he's clarifying in virtue of following an algorithm. OK, I'm not going to look at two main replies to cells thought experiment.
Say we'll be coming back to this next time and digging in a bit deeper.
But for now, I think these are the two most popular replies, and they're ones that I think particularly important.
So here's the system reply. OK. The man in the room, you know, I'm processing all these things.
I don't understand Chinese. That's uncontroversial.
But now think about the room itself, the room containing me containing the books containing the symbols containing the,
you know, all the rules that whole system enclosed in the room is actually handling Chinese in an intelligent way.
Questions are coming in. Intelligent responses are going out.
Maybe there's understanding that even if I in the room as if you like the central processor unit of the room, I don't have understanding of Chinese.
Nevertheless, the whole system does. I'm so, so rebuts that he says this is subject to exactly the same objection.
There is no way that the system can get from the syntax to the semantics.
I, as the central processing unit, have no way of figuring out what any of these symbols means, but then neither does the whole system.
Okay. For the moment, I want to just point out and I'm and this is a reference to Copeland's very useful book,
which I've mentioned before in these lectures. He points out that sells rebuttal here just begs the question, right?
As a matter of logic, the fact that the man in the room doesn't understand Chinese does not prove that the room doesn't understand Chinese.
He gives the the following argument bill the cleaner has never sold pyjamas to Korea.
Therefore, Bill's company has never sold pyjamas to Korea. OK, that's obviously a bad argument.
Okay. The point is that a component of a system can lack a property that the whole system has.
So the fact that the man doesn't understand Chinese as a matter of logic does not
imply that the whole system of which he's a part doesn't understand Chinese.
So if Sir wants to rebut the system reply, he has to give a more positive argument.
Of course, the Chinese room still remains false an intuition pump because you might be thinking,
you know, Okay, Copeland, you've pointed out this footlong response.
You know that the argument isn't strictly logically valid that come on,
we all know that a room can't understand anything, you know, in that thought experiment.
The only plausible understand is the man. So if he doesn't understand Chinese, then nothing there understands Chinese.
Yeah, OK.
But if that's the way you're going to argue, you are putting a lot of weight on the thought experiment, which, as we said, is a very implausible one.
OK. It seems very strange to invent a thought experiment which is so distant from reality
and then appeal ultimately just to rather basic intuitions about it in that way.
We want to actually have a more forceful argument. OK, the second reply very popular one is the robot reply.
So bear in mind that so is putting a lot of emphasis on what he calls semantic content or intentionality.
And I said intentionality is to do with symbols being able to, as it were,
reach out to the world to refer to things if we have intentional states like beliefs and desires.
These are beliefs about things, desires for things.
They're intentional in that they refer beyond us to things outside.
OK, now suppose then we take a system like the Chinese room and imagine that it's actually embedded in the world.
So instead of just having input and output through a through the slots, you know, with questions coming in and answers going back,
suppose the room were actually connected up to robotic sensors and effectors so that
the output from the room instead of just being some written symbols in Chinese.
Instead, the output actually is movements, actions and so forth.
Well. So it's essentially the same reply as long as we suppose that the robot has only a computer for a brain or,
you know, the man in the Chinese room being here, the analogue for the computer.
Then, even though it might behave exactly as if you'd understood Chinese,
it would still have no way of getting from the syntax to the semantics of Chinese.
You can see this if you imagine that I am the computer inside a room in the robot's skull.
I shuffle symbols without knowing that some of them come into me from television cameras
attached to the robot's head and others go out to move the robot's arms and legs.
As long as all I have is a formal computer programme. I have no way of attaching any meaning to any of the symbols.
OK. And again, that probably seems quite plausible. There I am in the Chinese room.
I'm still in the same position. I mean, I'm getting these symbols in.
I'm putting symbols out the fact that those are acting as inputs to some motors,
which are moving the robot around in the world and doing actual things, simulating a comprehending of Chinese.
You know, imagine the robot is actually interacting with Chinese people.
Where in the world? And imagine that the symbols that come into me are being taken from microphones and so forth involving Chinese people.
I there in the robot skull have no knowledge of that at all.
So again, it seems that the symbols have no semantic content, at least again, that's the claim.
So we're going to return to this in the last lecture for now.
I just want you to notice the intimate connexion between cells, Johnny's room and the Turing Test.
Both of them postulate an algorithmic system capable of generating conversation
that's indistinguishable in quality from that of an intelligent native speaker.
But they draw opposite conclusions. So Turing, in effect, is saying something like this.
Think of the the sonnet example in his paper.
Imagine a computer programme that's able to converse like this.
How could you possibly deny that it's genuinely intelligent? It seems quite plausible, right?
Which Chip Searle is saying? Imagine a computer programme that conducts its conversation using crudely
syntactic processes like this the man in the room with the baskets and the rolls.
How could you possibly claim that it's genuinely intelligent?
So here we've got, you know, two thought experiments, one of them pulling us in one direction,
one of us pulling in the other two contrary intuition pumps.
Well, just like with Blockhead, let's go back to reality.
As I say, we'll be coming back to this debate between them in the last lecture.
But what I want to do right now is think about the plausibility of the thought experiments.
Well, first of all, the Chinese room is completely and utterly implausible.
Sophisticated linguistic behaviour being generated in real time by manually consulting books of rules contained in a room.
No scope for sensory input, real time updating emotional reactions, let alone the complexity of the whole task.
Interestingly, Turing's predictions in his paper are far more reasonable.
So these are from section six of his paper. I drew your attention to them last time, but let's look in a bit more detail at them now.
I believe that in about 50 years time,
it will be possible to programme computers with a storage capacity of about a billion to make them play The Imitation Game so well that
an average interrogator will not have more than 70 percent chance of making the right identification after five minutes of questioning.
Second prediction 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.
OK, so I think the second prediction is actually quite reasonable.
Certainly now imagine I'm playing against a computer playing chess,
I'm looking at the screen and the screen is showing me the analysis that's currently going on within the computer.
It shows me the lines that are being considered and it shows me the balance of material.
And it shows me the overall verdict. So you come up and you say, Peter, why is the computer taking so long to respond?
And I say it's thinking hard because it's realised that if he tries to defend
against my attack by bringing its night over to protect the King Knight F6,
I'll be able to grab its pawn on the other side. The Queen takes 87 percent.
It's displaying now that it assesses the position is better for me.
Materially, there's that minus one that it's got, you know, I'm going to be a pawn up,
but it's predicting that it won't be too badly off it if it decides to let the pawn fall.
Overall assessment minus nought point one. It's pulled down, but only minus nought point one, so it must think that it's got 0.9 of activity.
So I think it must be expecting to get some activity to compensate. And what I want to claim is that conversation looks quite natural.
All right. All of those words in red that are psychological words.
Nowadays, we do apply them to computer systems without almost a second thought.
Certainly, it would be. I would be very surprised if someone was so pedantic as to say, Well, of course,
it's not literally thinking, it's not literally realise anything, it's not literally trying to do anything.
And so the language seems very natural. In a computational context these days.
So our thinking about these things has modified what about the other prediction that the Turing test by 2000?
And will at least have been passed to this rather modest extent.
There's an average interrogator will not have more than 70 percent chance of making the right identification after five minutes of questioning.
Well, I suggest that that was actually eminently plausible in retrospect.
I don't think it was actually achieved,
but I think it could have been achieved if that had been what artificial intelligence researchers were trying to achieve.
I think they'd have done it. But it's plausible for a bad reason.
And those researchers were entirely right not to devote their efforts to achieving it.
Let's see why. So I think this is one of the most unfortunate passages in Turing's entire paper because he gives the impression that a criterion
for progress towards machine intelligence can be based on how well the programme can fool an average interrogator and for how long.
And as a result, quite a lot of effort has gone in, not from serious A.I. researchers,
but from others to try to pass the Turing test, according to that kind of criteria.
But here's the problem. It turns out that fooling an average interrogator is relatively easy to achieve,
but not by techniques that plausibly involve genuine, genuinely intelligent information processing.
This, I suggest, is perhaps the fundamental problem with the Turing test.
Here is a piece from the BBC website.
I think it was in 2014. June 2014, the Turing test has finally been passed.
Well, it's a nice bit of publicity for Reading University, but it's really not very plausible, as we shall see.
But anyway, for the first time, we have Turing's criterion being met.
OK, humans had no more than 70 percent chance of identifying which was the human which and which was the computer after five minutes of questioning.
That's the claim to fame. What's going on here was revealed in 1966 by Joseph Eisenhower and Joseph Biden bound basically set
the pattern for the way in which these things have worked since he published the ELIZA programme,
together with a script showing how very simple text manipulation can generate a plausible conversation.
Ingeniously, he had his chat bot playing the role of a rich and psychotherapist, echoing what the human says,
expressing sympathy, asking gentle questions to elicit their feelings and so forth.
And the computer responses are generated by making small changes to the human inputs, exchanging first and second person and so on.
So here is some of the dialogue that he published.
I'll explain later about the rather dramatic slides, but what I want to do is illustrate this.
Using some software that I wrote called Elizabeth and Elizabeth is is a chat about creation system except negation of one.
It allows you to look inside and see what's happening. So let's suppose I've gone to Elizabeth and I want a bit of counselling.
Yes, like this, I hope, Will. Yeah. Sorry.
My mum is sad. Tell me more about your family.
OK. Hmm.
There's clear understanding there isn't that pretty good.
Oh, OK. Sorry.
I can't see it on the screen in front of me, that's the problem we are.
Yeah. Look at this, I'm very sceptical.
I'm going to ask, can you think? All right.
At that point, you will probably conclude this is all smoke and mirrors.
It is all smoke and mirrors and we can I think there we are there.
We can see what's going on within Elizabeth. You can actually see how the process is taking place.
This group doesn't actually use any memory, but there's oh, I didn't try that, just pressing return.
If I just press return, the response comes back. Can't you think of anything to say?
The mum gets translated into mother, dad, into father. You can have as many input transformations as you like.
Keyword transformations, you can see output right at the bottom.
If the word stupid or idiot occurs that I don't think language like that is going to help.
I think phrase why do you think phrase mother or father you get?
Tell me more about your family. Are you the youngest in your family? What do you remember most about your childhood?
Phrase one is younger than face to, so friends to is older than phrase one and so on.
I mean, it is basically the responses are just pre-packaged.
And of course, I've put in some ones about thinking and souls at the end just for a bit of a laugh.
And that is all run by this script. So basically, you just edit the script and off it goes.
One thing you can do with this, if you want to play with it, you can. I hope this will work if I open a same dialogue.
If we are over here, you can't see this. I'm loading the files and bound dialogue and looks right.
You can go. And now this is actually running the very dialogue that vies bound published, so I put it into Elizabeth, you can, as I say,
try this on the web, and if I press function key too, we can see his dialogue, so you have to take my word for it.
This is being produced in exactly the same way men are all alike, in what way they're always bugging us about something or other.
Can you think of a specific example? Well, my boyfriend made me come here.
Your boyfriend made you come here. He says, I'm depressed much of the time.
I am sorry to hear you are depressed. It's true.
I am unhappy. Do you think coming here will help you not to be unhappy?
I need some help. That much seems certain. What would it mean to you if you got some help?
Perhaps I could learn to get along with my mother. Tell me more about your family.
You see where I got that one from? My mother takes care of me.
Who else in your family takes care of you? My father.
Your father can't do anything else with it. Just my father.
Okay. Your father, sir, you are like my father in some ways.
What resemblance do you see? You're not very aggressive, but I think you don't want me to notice that.
What makes you think? I am not very aggressive? You don't argue with me.
Why do you think I don't argue with you? You are afraid of me.
Does it please you to believe I am afraid of you? My father is afraid of everybody.
What else comes to mind when you think of your father? Bullies?
Does that have anything to do with the fact that your boyfriend made you come here again?
It is all smoke and mirrors. Moreover, I can reveal that in-bound cheated because in implementing his script as published,
I had to reverse the order of a couple of the lines in order to get it to work.
Clearly, he was doing it in such a way that he ended up with that really nice coincidence where you get bullies and the response.
Does that have anything to do with the fact that your boyfriend made you come here?
I can tell you the fact that that leads up with bullies was pure coincidence.
So here's how it works. If you have the word alike in the input back comes the response.
In what way? Most sentences containing the word alike?
In what way is an appropriate response, something or other?
Can you give an example? My ex, my boyfriend, made me come here.
Your boyfriend made you come here. And by the way, remember the ex might be useful later.
OK, I need wine. What if you got wine?
What would it mean to you if you've got wine? So for my mother? All those questions about your family.
My mother does such and such. Who else in your family does such and such?
And then if there's no matching pattern, what you saw in my little script there was tell me what you liked doing here.
One of the responses is, does that have anything to do with the fact that your ex remembered from earlier?
OK? So it gives the impression of intelligence, but it isn't.
OK, so let's be quite clear, chat bots are not intelligent.
You can download my software, play around with it.
You will very, very soon come to the conclusion that these are rather limited even buys and bound script, which is clever.
I mean, it's extremely clever. It's got a lot of keywords in there.
But you know, if you try to conduct any sort of sustained conversation with it, you'll find it extremely frustrating.
They seem to confirm that Searle is right. You've got mere syntactic processing there.
No semantics at all. Even the bit of them that looks most clever, right?
The the switch you may have noticed and wondered about the switch between you and me.
Sorry, where is the Oh, that's interesting, isn't it?
That's very interesting. Let me go back to.
The initial script that I showed you before, if I can get to it, sorry, I'm going the wrong way.
There we are. Here we are.
You can see the simple change from I am to you, are you all to I am I to you, me, to you and so forth.
This is this is conducted. This happens in a very, very straightforward way.
So the way I make Elisabeth work, it's a bit different from vice and bounds.
That's why I was saying it's interesting vice and doesn't does everything through the key with transformations.
And it so happens that if you take something like my sister is younger than me, you say so you are older than your sister.
The change between me and you and my and your and so on happens very, very straightforwardly.
So even the part of the manipulation gives the impression of most understanding is extremely crude.
I mentioned the chatterbox that was supposed to pass the Turing test in 2014, or at least was claimed to be called Eugene Gusman.
It's a chatter bot that claims to be a, I think, a 13 year old Ukrainian boy.
It's a nice trick. Again, it's like having the Ruggieri and psychotherapist.
The good thing about a Ukrainian boy is if that if the grammar goes wrong.
All right. Well, what do you expect? Not a native speaker, right? So, you know, you can see here here.
This is obtainable from the web. It's very much the same kind of thing.
You've got simple patterns and responses. And this is part of a dialogue which a guy called Leonid Muszynski tried with the chat bot.
The chat bot claims to be from Ukraine.
Well, in that case, you want to know about what had happened in Odessa on May the 2nd that year, and it clearly showed no sign of knowing it at all.
So basically, Chatterbox, if you try to have any sort of sensible conversation with them,
a sustained conversation rather than just taking their rather vague outputs at face value, it quickly falls apart.
No, I'm not saying crackpots are completely useless.
They can actually be valuable precisely because we are so inclined to interpret their outputs as intelligent.
Most people prefer to interact conversationally.
And so you can have automated help systems and things like that that are based on chat bot technology.
And often, I mean, imagine you have had an automated help system for tourists to Oxford.
Most of the time, they will be asking questions that can be answered in a very straightforward way.
So if you see botanic gardens say in the query, the chances are they want to know where the botanic gardens are and how to get there.
So if you've got canned responses that give answers appropriately, that can be quite useful,
and chat bot methods can succeed in eliciting information quite effectively.
Just before you look at the next slide, I just want you to focus on the beautiful, low key design of that slide and compare it with this.
So much at both, Elizabeth was picked up by a guy in a market research consultancy in Africa,
and we did a pilot of a study whereby it was used to pick up information about people's mobile phone choices and why they change networks and so on.
So we put kind of questions in in order to elicit that kind of thing.
This is what the market research company did to my slide. So now you know why those earlier slides with Eliza?
And they looked rather dramatic. I found an interesting experience, and one of the funniest things for me was just seeing this.
OK, so let's let's look at the Turing test.
The problem with the Turing test, I've pointed out, is, well, we know that it's not a necessary condition for intelligence.
We've we've seen that Turing acknowledge that you can have things that are intelligent, that don't don't pass for humans.
But unless we interpret it pretty rigorously, it's not sufficient. And that's ironically because of human lack of critical judgement.
So it's our failure of intelligence that actually makes the Turing test an inappropriate test.
And much of our conversation is sloppy and careless in normal life.
You know, you go into a pub, listen to the conversation. A lot of it is very imprecise.
That means that when we come across conversation, that is sloppy and imprecise, we regard it as having been output by an intelligent system.
We read intelligence into it. We try to make sense of it.
So if we have an interlocutor who comes out with the kind of vague responses that Eliza does,
you know, vague, but somewhere in the right ballpark of relevant, we read more in.
We read them as being caused by something that's genuinely intelligent where there is genuine meaning behind it.
And in fact, there isn't. So it's a great shame,
I think the cheering gave the impression that better performance in his test that means fooling us for longer is actually a criterion of intelligence.
It just isn't. So it's a very implausible test if you interpret it in that way.
We've seen it's more plausible as a sufficient condition for intelligence when interpreted more stringently.
So if something produced conversation of the level of Turing's sonnet conversation across a wide range of fields,
that might be a reasonable criterion for intelligence,
at least in the sense of a sufficient criterion, you would think, yes, this is genuinely intelligent.
If it can match that without having its responses, you know, candid and so forth.
But then it seems inappropriate. Be demanding because if you end up with a conversation like that, it's going to be the more intelligence it reveals,
the more material there is which may push us away from seeing it as human.
And there's another problem as well. This is brought out in a paper by Robert French published in Mind in 1990.
You might think this is overkill, but it's fairly easy to devise questions which elicit our cultural understanding of things,
and it's hard to imagine that a computer could be programmed with any ease to do this.
So, for example, right flood as the name of a glamorous model or a cuddly toy?
Well, any native English speaker will see that fugly is a rather bad choice if you're a glamorous movie star, right?
But cuddly toy? Yeah, that's that's that that works quite well.
If you can imagine a baby having a flood, Lee, that's very dear to it.
And questions like this rating things pulls on, draws on a lot of our cultural understanding of things,
and it would be extremely difficult to programme a computer to be indistinguishable from a human in all those respects.
But that really seems a bit irrelevant. I mean, it's this, isn't you?
If you're testing for a computer being intelligent, these sorts of trick questions we feel shouldn't really be the main point.
Okay, so the Turing test is a failure. But can we do better?
Well, I think we can, actually. I'm just adding a couple of letters there and changing it to the tutoring test.
So the big problem with the Turing test, OK, is it is to get plausible performance in a Turing test,
at least, you know, five minutes of questioning, average interrogator and so on.
The best way to go is deceit. You pretend to be something you're not.
Now it's it's not at all desirable to have a test of intelligence, which depends on hiding things.
Rather, we want to test that actually involves revealing things, revealing understanding.
So suppose we imagine a system that's designed to tutor maybe in a limited domain will almost certainly in a limited domain, take chemistry.
So chemistry is a very sophisticated subject.
It's complicated, difficult. There are lots of things that linked together in order to learn chemistry of any sort of deep level,
you need to understand complex informational structures and how they fit together.
OK. Things to do with, you know, quantum schedules and how different elements and different molecules interact.
Now, imagine you had a system which was designed to tutor chemistry.
It's not going to pretend to be human. It's not going to try to be indistinguishable or anything like that.
The test is how well can it tutor? So can someone who doesn't understand the various complexities of chemistry,
go and interact with the system and then say at the end of an hour, it understands something.
The person understands something which he or she didn't before and maybe understands
it just as well as if they had been tutored by a competent human tutor.
So there's something in the spirit of the Turing test. You write the test is can cabinet achieve the competence that a human could?
But we've now got something which works by eliciting real understanding in a human and therefore the more
it has built into it in terms of complex informational structures linking together in appropriate ways,
the better it will do. It's revealing, understanding rather than pretending.
And it has the considerable virtue also potentially of producing seriously useful products.
I put this to Hugh Learner, who organises the LERVE No-Prize,
suggesting that he might wish to reconfigure it in the future, but I'm afraid he's not interested.
There we go. I tried. And that's it for today. See you for the last lecture next time.
Thank you.