Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey the everyone, and welcome to Forward Thinking,
Speaker 1: the podcast that looks at the future and says Marvin,
Speaker 1: I love you. I'm Jonathan Strickland and I'm Joe McCormick. Joe,
Speaker 1: I got a question for you, Okay, ask me a question.
Speaker 1: What did you have for lunch today? I don't want
Speaker 1: to talk about that. That's personal, Okay, fine, all right, Well,
Speaker 1: what was the last movie you saw? Movies are weird
Speaker 1: these days, you know. I'm going to ask one more question, Joe.
Speaker 1: Who's your favorite person to work with in the office?
Speaker 1: Obviously you Joe? One one? I lied one last question?
Speaker 1: Are you? Are you human? Why would you ask? Of course,
Speaker 1: your responses have left the question somewhat vague. I'm not
Speaker 1: really sure. So it could be that you are, in
Speaker 1: fact some sort of simulation that is drawing upon a
Speaker 1: massive data bank of pre determined answers to questions, and
Speaker 1: and you're just drawing from that, and you're not actually
Speaker 1: genuinely coming up with responses. Why would you say that?
Speaker 1: Tell me more about your feelings? Are doctor space Zo
Speaker 1: or Eliza? We wanted to talk today? About artificial intelligence
Speaker 1: and the Touring test and whether or not the Touring
Speaker 1: test is in fact a good test for artificial intelligence
Speaker 1: and what exactly is it and and it does it
Speaker 1: doesn't really do what we think it does, because there
Speaker 1: is that huge news story a couple of weeks back,
Speaker 1: a couple of weeks back, being in June of where
Speaker 1: in uh, Eugene Goostman, who is a chat butt, not
Speaker 1: a real human, not a real human, um it is
Speaker 1: a chat butt, I suppose it's more technically correct, was
Speaker 1: announced to have passed the Turing test for the first
Speaker 1: time kind of sort of, well, not for the first
Speaker 1: time for this program. A lot of the media coverage
Speaker 1: suggested like, for the first time a computer program has
Speaker 1: quote passed the Turing test or quote beaten the Turing test,
Speaker 1: which there are multiple issues that we need to address
Speaker 1: to to actually talk about that, But first let's let's
Speaker 1: go on and explain what what Goostman did. Well, what
Speaker 1: it definitely did was full ten of thirty judges, which
Speaker 1: is thirty three for anyone following along doing the math
Speaker 1: at home that we're participating in a celebration of turning
Speaker 1: on the sixtieth anniversary of his death. It was a
Speaker 1: celebrity guest judge panel. It was pretty fancy. There was
Speaker 1: one of the actors from Red Dwarf on there and
Speaker 1: by fool amument that the computer program tricked them into
Speaker 1: thinking it was a human, right right. This program has
Speaker 1: been around since about two thousand one, when a team
Speaker 1: out of the Ukraine began designing it, and it poses
Speaker 1: as a thirteen year old boy from Odessa, Ukraine. It's
Speaker 1: taken part in a few different competitions like this over
Speaker 1: the years, and you can even find a version online
Speaker 1: if you want to play around with it. But the
Speaker 1: news that this news that really brought it into the
Speaker 1: public eye was a press release again yes, claiming that
Speaker 1: it was the first chatbot to pass the Turing test
Speaker 1: in an open ended event. And open ended is kind
Speaker 1: of one of those keywords here, because a lot of
Speaker 1: competitions in which chatbots are put up against the Turing
Speaker 1: Test involve a closed series of questions that is predetermined.
Speaker 1: It's a specific set of parameters. Either you are only
Speaker 1: allowed to ask certain questions, or the whole interview took
Speaker 1: place before the event and you are reviewing a transcript
Speaker 1: and then have to determine whether or not the participant
Speaker 1: was a human or a machine. So those are generally speaking,
Speaker 1: the different variations on this. We'll we'll get more into
Speaker 1: the actual touring test in a second, but that's sort
Speaker 1: of how they are performed these days, right, and so
Speaker 1: this achievement is pretty cool. But it did get way
Speaker 1: crazy over hyped in the media, I think largely because
Speaker 1: the press release, which was mostly a quotation from one
Speaker 1: Dr Kevin Warwick, who was an engineer slash futurist, slash provocateur,
Speaker 1: slash cyborg right form, he designed it to be kind
Speaker 1: of overhyped, which means that it did drum up public
Speaker 1: interest in touring and chat bots and artificial intelligence and
Speaker 1: technology at large, which is totally awesome. But the fact
Speaker 1: that the news media wasn't quite sure what the turing
Speaker 1: test was, or how this chat bought was interacting with it,
Speaker 1: or the fact that this was a chat bought and not,
Speaker 1: for example, a supercomputer, which is a ludicrous concept. Lots
Speaker 1: of people supercomputer defeat touring test. No, it was a
Speaker 1: it was software, Yes it was. It was a program,
Speaker 1: was independent, independent of hardware, It could run on lots
Speaker 1: of different types of hardware. Probably your smartphone, for example.
Speaker 1: So so we might need a new Turing test, right,
Speaker 1: So before we talk about new Turing tests, let's talk
Speaker 1: about the old one. Shall we maybe make a distinction
Speaker 1: between what idea actually came from the early computer scientists,
Speaker 1: Alan Touring, and how that has evolved into what we
Speaker 1: popularly think of as the Turing test today. Was just
Speaker 1: talking about. Yeah, yeah, so Touring test in general is
Speaker 1: kind of a misnomer because Touring He wrote a paper
Speaker 1: in nineteen fifty in which he was discussing the idea
Speaker 1: of machine intelligence and whether machines could actually possess intelligence,
Speaker 1: And his philosophy was that machines could demonstrate behaviors that
Speaker 1: we associate we humans associate with human intelligence, right, and
Speaker 1: that if a machine could produce such behavior reliably, then
Speaker 1: it stands to reason that we call that machine intelligent,
Speaker 1: because it, to all appearances is intelligent. Therefore we might
Speaker 1: as well go ahead and extended the courtesy. So that
Speaker 1: was generally what he was saying, And he said that
Speaker 1: he believed that by a fifty years time, which would
Speaker 1: be the year two thousand, a typical computer of sufficient
Speaker 1: power and sophistication would be able to fool people thirty
Speaker 1: pc of the time that it was in fact a
Speaker 1: human and not a machine, that that was the level
Speaker 1: of sophistication he expected. And this was more of a prediction,
Speaker 1: not a test. People have since looked at it as
Speaker 1: being a kind of test to apply to computers to
Speaker 1: see whether or not they pass this bar for machine intelligence,
Speaker 1: which is sort of a reverse engineering. Look at what
Speaker 1: he was talking about now. He used a specific example
Speaker 1: in his in his uh explanation of fooling someone thinking
Speaker 1: it was human, and that was a variation on something
Speaker 1: he called the imitation game. And the imitation game is
Speaker 1: a game. It's a part the game. Yeah. The original
Speaker 1: idea would be that you have three players. You've got
Speaker 1: an interrogator who could be a man or a woman,
Speaker 1: and then you have a male player and a female
Speaker 1: player who will be interrogated. All three people are separated
Speaker 1: from one another. They are not able to see and
Speaker 1: preferably not be able to hear one another, right, communicating
Speaker 1: through right and preferably communicating through teletype or some other
Speaker 1: form of non handwriting communication, just in case handwriting would
Speaker 1: give away some form of gender identity. Right, because it's
Speaker 1: the interviewer's job to figure out which of the participants
Speaker 1: is the man and which is the woman, right, and
Speaker 1: it's the man player's job to try and trick the interrogator.
Speaker 1: It's the female player's job to try and get the
Speaker 1: interrogator to get the answer correct. So the man will
Speaker 1: attempt to throw doubt into the situation, and the interrogator
Speaker 1: has to figure out without any other like visual or
Speaker 1: audible cues, who is who, And Touring said, well, let's
Speaker 1: replay this with saying it's a human interrogator, and then
Speaker 1: you have a human respond it and a machine responded
Speaker 1: as the interrogator's job to figure out whether or not
Speaker 1: the entity he or she is talking to is in
Speaker 1: fact human or machine, and suggested that by the year
Speaker 1: two thousand, computers would be at a level sophisticate enough
Speaker 1: where thirty percent of the time you would not be
Speaker 1: able to tell you would you know, or thirty if
Speaker 1: you prefer, of the people who would interview said machine
Speaker 1: would be unable to tell that it was a machine.
Speaker 1: So Turing didn't really lay out a path to how
Speaker 1: this would be achieved, did he? More or less, he
Speaker 1: he talked about the principles of computer science and that
Speaker 1: they would become more and more sophisticated to the point
Speaker 1: where this would become possible, but did not, in fact
Speaker 1: layout like, here's my fifty year plan for making computers intelligence.
Speaker 1: I guess he probably he wasn't talking about natural language
Speaker 1: processing algorithms and such stuff. No, no, no, it was
Speaker 1: our general approach was talking about the idea of a
Speaker 1: machine possessing the general intelligence that would allow it to
Speaker 1: do something right. This is this was more of a
Speaker 1: philosophical perspective than anything else. And uh. On top of that,
Speaker 1: there were a lot of people who disagreed with Tourings philosophy.
Speaker 1: They said, you know, you're saying that because you're able
Speaker 1: to mimic intelligence that that means you have some form
Speaker 1: of possession of intelligence. You've asserted this without providing any
Speaker 1: actual proof. One of the criticisms I read specifically stated
Speaker 1: that one of the things I had said earlier was
Speaker 1: how we say, if a machine appears to be intelligent,
Speaker 1: we might as well say it is intelligent. And uh.
Speaker 1: And one interpretation of that is that touring would say, well,
Speaker 1: let's say two people are talking to one another. They
Speaker 1: each assumed the other person possesses intelligence, partly because of
Speaker 1: the conversation that's going on. Therefore, if a machine were
Speaker 1: able to hold a conversation with you, wouldn't it be
Speaker 1: just party is to go ahead? And a shouldn't it
Speaker 1: be entitled to write? Whereas the critic I read said, well,
Speaker 1: to be fair, humans pretty much assume that other humans
Speaker 1: possess intelligence. It has nothing to do with the conversation.
Speaker 1: It has everything to do with the human being a human,
Speaker 1: Which doesn't necessarily mean it's based upon any kind of
Speaker 1: science or any kind of rational approach, but that is
Speaker 1: how humans are. I mean humans are also a little
Speaker 1: bit on the irrational side. I mean we we kind
Speaker 1: of also extend that courtesy to like our pets, which
Speaker 1: you know, yeah, we we at least we at least
Speaker 1: in in ways that we write about and and verbalize,
Speaker 1: will anthropomorphize lots of stuff, sure, like like our toaster.
Speaker 1: Given enough capacity, we go like, that's a good smart toaster,
Speaker 1: or we'll say some things like this toaster hates me.
Speaker 1: It burns it either either just warms the bread or
Speaker 1: it burns it to a crisp. It just hates me.
Speaker 1: And that's when we're projecting things upon these objects that
Speaker 1: they don't inherently possess. But part of this philosophical argument
Speaker 1: is that we're really only assuming that other humans have
Speaker 1: this intelligence, right, I mean it's tied into sort of
Speaker 1: the philosophical problem of solipsism. It's a thing nobody wants
Speaker 1: to assume that they are the only mind in the universe,
Speaker 1: or some people might want to assume that, but hopefully
Speaker 1: nobody wants to don't want to assume it, but proved
Speaker 1: me wrong, is what I'm saying. It's one of those
Speaker 1: where you just operate day to day under the assumption
Speaker 1: that that is not the case, but you have no
Speaker 1: way to prove it's not the case. But philosophy aside,
Speaker 1: the turn test is being used as a very practical
Speaker 1: means of evaluating AI these days, right, at least as
Speaker 1: far as being able to mimic a humans ability to
Speaker 1: hold a conversation. Right. So, Yeah, it's sort of moved
Speaker 1: beyond the bounds of this philosophical discussion in which it
Speaker 1: was originally proposed, and now it has become a thing
Speaker 1: people try to do. Yeah, it's become something that has
Speaker 1: been uh a goal in of itself, which again has
Speaker 1: become part of a growing conversation around AI and whether
Speaker 1: or not that's a legitimate goal sure, which is partially
Speaker 1: cool because it's encouraging competition and a good kind of
Speaker 1: sense of go get them fun amongst a lot of
Speaker 1: of programmers and computer scientists and thinkers, whereas other people say, yeah,
Speaker 1: but it's kind of diverting attention away from real problems
Speaker 1: and machine intelligence that need to have more work put
Speaker 1: into them. But everyone's worried about making chat bought two
Speaker 1: thousand full of people that it's actually you know, Bob
Speaker 1: from Minnesota. Yeah. Whether or not those people were actors
Speaker 1: on Red Dwarf, yeah, it doesn't matter. Okay. So in
Speaker 1: the in the sort of popular thinking today, the popular
Speaker 1: tech thinking, what is the Turing test? Now? What? What
Speaker 1: does what does it mean when someone says I've beaten
Speaker 1: the Turing tests? The Turing test today is based again
Speaker 1: on that prediction that by two thousand a computer would
Speaker 1: be able a full thirty percent of the time, or
Speaker 1: in thirty percent of the cases, the computer would be
Speaker 1: able to fool someone that it itself was a human being.
Speaker 1: So now we've taken that on and turned it on
Speaker 1: its head and said, the Touring test is a test
Speaker 1: where if a chatbot is able to convince a human
Speaker 1: interviewer that it in fact is a human or more
Speaker 1: of the time it passes the touring test and it is,
Speaker 1: I guess, showing some form of intelligence. I mean, that's
Speaker 1: the that's the real problem where it all breaks down
Speaker 1: right as the definition of intelligence. And part of it
Speaker 1: was just let's see if we can create a program
Speaker 1: sophisticated enough so that it can respond to questions in
Speaker 1: a way that is natural and will fool folks into thinking, oh,
Speaker 1: it's another human being on the other end of this terminal.
Speaker 1: And as we've talked about before on this show, the
Speaker 1: problem of computers understanding natural language is a huge one.
Speaker 1: So it's not a terrible benchmark as benchmarks go. No,
Speaker 1: it can in fact improve lots of different areas and
Speaker 1: artificial and elligience. It's just a question of how do
Speaker 1: you implement it. If you're implementing it in a very
Speaker 1: superficial way, you might be able to get some some
Speaker 1: fairly interesting results from an actual quote unquote touring test
Speaker 1: where you submit this to judges and the judges believe
Speaker 1: in fact that it is a human. But you might
Speaker 1: be doing so using essentially smoke and mirrors away where
Speaker 1: the computer does not have any real natural language processing
Speaker 1: ability to it. It just responds in a way that
Speaker 1: convinces the judges that it, in fact is a human.
Speaker 1: It's got some clever tricks, right. And in fact, most
Speaker 1: of the chatbots that have passed the Turing test, and
Speaker 1: this has gone back to the nineteen seventies, is it's
Speaker 1: not a new thing that has just happened. Whoa you
Speaker 1: said past the Turing test? I mean, I thought based
Speaker 1: on the media reporting that the thirteen year old Ukrainian boy,
Speaker 1: the fake thirteen year old Ukrainian boy, Eugene Goostman was
Speaker 1: the first to pass. No, we we previously to that
Speaker 1: had a fake paranoid schizophrenic yeah, Perry r r y
Speaker 1: and a fake psychologist Eliza Eliza. Yeah, so Eliza was
Speaker 1: a program. In fact, Eliza is probably the first really
Speaker 1: well known chatbot that kind of I didn't necessary to
Speaker 1: probably heard me doing a little bit of Aliza earlier.
Speaker 1: It's a Rogerian psychiatrist personality. Actually, Eliza had several different
Speaker 1: personalities she could assume, but the doctor one is the
Speaker 1: most popular of the most well known of all the variations.
Speaker 1: And then you have Perry who mimic the behavior of
Speaker 1: a paranoid schizophrenic, including a backstory. The the programmer for
Speaker 1: Perry created basic facts about Perry that Perry would share,
Speaker 1: sometimes spontaneously or or if you just got quote unquote
Speaker 1: frustrated they were. The way this would work is that
Speaker 1: the program would analyze what was being said just by
Speaker 1: looking for certain keywords and then trying to create us.
Speaker 1: And it's based on those keywords. Not really a sophisticate analysis.
Speaker 1: It was just very basic, and Perry would occasionally throw
Speaker 1: in complete non sequadors, but in a paranoid schizophrenic kind
Speaker 1: of way, which was convincing enough to at least full
Speaker 1: some psychiatrists who were reading transcripts of interviews with Perry
Speaker 1: and saying, no, this actually sounds like it could be
Speaker 1: a human being. Okay, so it sounds like in all
Speaker 1: these cases we're dealing with a chat about that is
Speaker 1: this assuming the personality of sort of a specialized interlocutor, like, uh,
Speaker 1: you have a doctor who has a very particular way
Speaker 1: of talking, meaning not really responding to what you're saying
Speaker 1: and just saying how does that make you feel? Right?
Speaker 1: Or you have a paranoid schizophrenic who's sort of range
Speaker 1: of response is naturally very limited, especially a skewed a
Speaker 1: skewed view of reality, or or you have Eugene Goostman,
Speaker 1: even the more recent one, who has probably a limited
Speaker 1: command of English and a limited understanding of the popular
Speaker 1: culture outside of his own community. Just all the world
Speaker 1: works in general. If you're talking about a thirteen year old,
Speaker 1: you're not going to have the expectation of maturity and
Speaker 1: sophistication at the world. So so that's a big criticism
Speaker 1: year olds listening who are probably fabulous humans. You're probably you,
Speaker 1: You're You're the exception, okay, but all those other year olds,
Speaker 1: you're the future. And you know what Cheers was about.
Speaker 1: So we'll get into that. So the the the point
Speaker 1: here is that we have not seen really an example
Speaker 1: of a chat bot that is able to assume the
Speaker 1: role of a typical however you wanted to find that,
Speaker 1: you know, average human being who communicates and whatever the
Speaker 1: language of choice happens to be, usually as English. But
Speaker 1: let's say that we had a the ideal goal here
Speaker 1: would be have having a chat bot that communicates fluently
Speaker 1: in whatever language was picked for the competition and can
Speaker 1: answer questions that any person the age of said chatbot
Speaker 1: would be able to answer and uh and be able
Speaker 1: to to roll with any of those kind of of
Speaker 1: um conversational venues. Right. Basically, we're looking for Lieutenant Data. Yeah, yeah,
Speaker 1: Mr Data, Okay, I want to move us along here,
Speaker 1: please to ask the question. So, given that h this
Speaker 1: test is not actually necessarily a test, and that it
Speaker 1: has it has, depending on your interpretation, been bested in
Speaker 1: kind of limited ways throughout the years, why is it
Speaker 1: still popularly considered the bar for artificial intelligence? Well? I
Speaker 1: think mainly it's because, first of all, it mimics a
Speaker 1: part of intelligence that we all recognize, right, So that
Speaker 1: makes it easy for it's especially easy to sell it
Speaker 1: to a general public who may or may not be
Speaker 1: very savvy as far as what machine intelligence entails. So
Speaker 1: it's an easy story. So that has If you can
Speaker 1: have a conversation with the thing, it's clearly smart. Yeah,
Speaker 1: So that has that going for it. Uh. Secondly, this
Speaker 1: is a non trivial problem. I mean, creating some form
Speaker 1: of computer or machine intelligence that can respond to human
Speaker 1: language in a way that is natural, whether it is
Speaker 1: doing so in a way that makes you think it's
Speaker 1: human or not. That's a non trivial problem. I mean
Speaker 1: you're talking about humans have the ability to put the
Speaker 1: same kind of information into vastly different wording so that
Speaker 1: they're conveying the same meaning, but they could do it
Speaker 1: in multiple ways, and you have to be able to
Speaker 1: design a program and a machine that can roll with that,
Speaker 1: that can understand or at least respond in all these
Speaker 1: different variations with the appropriate information. So whether or not
Speaker 1: you're talking about a chatbot or you're talking about some
Speaker 1: form of interface. Let's say it's a call center where
Speaker 1: you're trying to get help for a problem, there could
Speaker 1: be a hundred different ways for you to phrase what
Speaker 1: has gone wrong with whatever the issue is, and the
Speaker 1: machine intelligence has to be able to interpret all one
Speaker 1: hundred of those ways and come back with the appropriate
Speaker 1: response that is the same for all one hundred different
Speaker 1: ways that you you could phrase it. These are really
Speaker 1: tough challenges. We've seen it uh demonstrated in lots of
Speaker 1: different types of technology, the IBM S Watson being a
Speaker 1: big one that we can point to, where it took
Speaker 1: the lots of different subtle things about the English language
Speaker 1: and was able to interpret them and respond in the
Speaker 1: format of a game show and do it successfully. And
Speaker 1: that was pretty tricky because it involved not just analyzing
Speaker 1: all right, well, this, this word here in this sentence
Speaker 1: is the object, this one is the verb, this one's
Speaker 1: the subject, and not just consulting Google and coming up
Speaker 1: with answers to questions, but understanding puns for example. Yeah,
Speaker 1: it had to be able to uh, to respond to
Speaker 1: word play things that that, so that could interpret what
Speaker 1: was actually being asked of it. That was a significant achievement.
Speaker 1: So it's something that you can point to and say,
Speaker 1: look how hard this problem is. It's something that's very
Speaker 1: easy for us humans. We we learn language, we even
Speaker 1: learn to uh you know how, it's flexible, We even
Speaker 1: change it ourselves. I mean there are words that exist
Speaker 1: now that didn't exist when I was a kid, that,
Speaker 1: you know, things that we entered the just the popular
Speaker 1: parlance and then became actual words. So it's something that
Speaker 1: is difficult to to program into a machine. It's probably
Speaker 1: one of the real reasons why this bar is seen
Speaker 1: as like the defining element of our artificial intelligence, because
Speaker 1: it's not an easy problem. Law zs there you go.
Speaker 1: But so okay, so so there there are good reasons
Speaker 1: for this to be the bar, and there are good
Speaker 1: reasons for it to no longer be the bar. Let's
Speaker 1: let's talk about a few of those, yes, please, Okay, Well,
Speaker 1: I would like to wing out that when we're making
Speaker 1: these sort of what you might read as criticisms of
Speaker 1: the Turing test, we're not criticizing Alan Turing and his
Speaker 1: vision for this. You know, he's one of the best
Speaker 1: human people qualitatively. Yeah, well, he certainly. He certainly was
Speaker 1: like the founding father of computer science. Like the reason
Speaker 1: computer science is the way it is is largely in
Speaker 1: part due to his work. So there's that. No, Yeah,
Speaker 1: I just more I think we should focus on sort
Speaker 1: of the natural limitations of this concept as it is
Speaker 1: most often envisioned, and why it doesn't necessarily apply to
Speaker 1: artificial intelligence in general. One of them is that, in
Speaker 1: the way people use this test, it's not designed to
Speaker 1: talk about artificial intelligence in general, because artificial intelligence spans
Speaker 1: a lot more than natural language processing. I mean, it's
Speaker 1: specifically focused on mimicking human behavior in a text based conversation. Now,
Speaker 1: that would have to be a feature of what people
Speaker 1: would call say, an artificial general intelligence, something that that
Speaker 1: a general intelligence should be capable of doing, but not
Speaker 1: the only thing it should be capable of. But AI
Speaker 1: research focuses on all kinds of other things. You have
Speaker 1: AI algorithms that recognize objects by sight, that's artificial intelligence.
Speaker 1: You can have AI algorithms that control physical movement that
Speaker 1: can be artificial intelligence. You might even say that you
Speaker 1: know the room BA that's going around on your floors.
Speaker 1: There there is a type of artificial intelligence that governs
Speaker 1: its movements. Well, let let's put it to you this way, Joe.
Speaker 1: Let's say that I I tell you, all right, I've
Speaker 1: got two people here that I want you to judge
Speaker 1: whether or not they're intelligence. One of them is able
Speaker 1: to tell you what happened in the very first episode
Speaker 1: of All in the Family, able to explain what happened
Speaker 1: in that episode. The other is able to navigate to
Speaker 1: a place it has never been before and be able
Speaker 1: to to get there by consulting various maps, Able to
Speaker 1: move through traffic, able to get to the destination and
Speaker 1: back safely, and do so in a reasonable amount of time.
Speaker 1: You would probably say, all right, well, both of those
Speaker 1: seem intelligent to me. Sure you know one it's more
Speaker 1: fun at parties, but one it's more likely to get
Speaker 1: to the party. One of those were likely to drop
Speaker 1: you off a minute. Which one is fun at the
Speaker 1: party depends on how much you like all in the family.
Speaker 1: I go to parties just to have people explain the
Speaker 1: plot I was going to go. I was gonna go
Speaker 1: with the Jersey Shore, but I figured that would have
Speaker 1: been a low blow. So the no, no, I know.
Speaker 1: It's somebody who explains to you the plot of transformers
Speaker 1: for which means they're not human. Joe, you explained to
Speaker 1: me the plot of transformers for the other day. I
Speaker 1: don't think I did. I think I just said product
Speaker 1: placement over and I think, actually, I think your explaination
Speaker 1: there was no plot. I think it's what your point was. Yeah. So,
Speaker 1: so a driver a driverless car Google car, for example,
Speaker 1: you would say, possesses many aspects of intelligence. It would
Speaker 1: be able to in its in its final implementation, the
Speaker 1: way it's been described, you would get into said car, essentially,
Speaker 1: turn it on and tell it where you wanted to go,
Speaker 1: and it would take you there. It would navigate potentially
Speaker 1: through very complex street layouts. I mean, you could be
Speaker 1: especially if you're going from one city into another city,
Speaker 1: respond to traffic and obstacles in real time. Exactly, it
Speaker 1: would be able to uh to anticipate if some sort
Speaker 1: of obstacle came into its pathway and be able to
Speaker 1: react to it. So that all of these things, being
Speaker 1: able to recognize obstacles, being able to react to them,
Speaker 1: being able to emerge into existing traffic, being able to
Speaker 1: go from one location to another, even if that car
Speaker 1: has never been there before. All these things are aspects
Speaker 1: of intelligence that would never come into play if you
Speaker 1: only concentrate on the touring test, as this is the
Speaker 1: example of whether or not a machine is intelligent. Yeah,
Speaker 1: totally true. Okay. I want to offer another limitation, which
Speaker 1: is that using this popular vision of the touring test
Speaker 1: might be encouraging the wrong kind of development of chat
Speaker 1: bots for natural language processing. I mean, obviously, anything that
Speaker 1: knows how to use words and sentences really well, that
Speaker 1: is a good step in the right direction, how to
Speaker 1: comprehend them, how to put them together. But chat thoughts
Speaker 1: that are designed to fool humans into thinking they're humans,
Speaker 1: it's all based on deception. Yeah, I've seen it suggested
Speaker 1: that we're not raising the bar for computer speech we're
Speaker 1: lowering the bar for human intelligence. That's also that's also
Speaker 1: a case. Actually, I read some great articles that talked
Speaker 1: about actual touring test procedures where humans were considered to
Speaker 1: probably be the machine and the machines were considered to
Speaker 1: be humans, partly because one of the things about this
Speaker 1: touring test is it doesn't say that the machine has
Speaker 1: to be honest at all. In fact, it can't be.
Speaker 1: It's got to deceive you into thinking it's humans. So
Speaker 1: by it's very nature, it has to be able to
Speaker 1: answer questions that it honestly would have to say, I
Speaker 1: have no answer for that. If it were just being
Speaker 1: a machine. For example, if you said what do your
Speaker 1: parents do? It would have to either invent something or
Speaker 1: say my parents died when I was I mean, it
Speaker 1: wouldn't be able to say I don't have parents because
Speaker 1: of my computer. That would that would end up eliminating
Speaker 1: it from the touring test unless you also assume that
Speaker 1: the human right and it's coming up with responses like
Speaker 1: Joe did at the top of the podcast saying like like, well,
Speaker 1: how would I know that I'm where? In that case,
Speaker 1: you might have the interrogators say, well, I assume that
Speaker 1: the humans are also being dishonest that they are also
Speaker 1: trying to trick me. So if something is coming across
Speaker 1: is obviously mechanic, well then that has to be a machine,
Speaker 1: or it has to be a human pertending to be
Speaker 1: a machine. Rather, it can't be a machine that's just
Speaker 1: a poorly designed chatbot. It could totally be a machine
Speaker 1: that's a poorly designed chatbot. So if you, if Joe,
Speaker 1: if you kept on saying like, I don't want to
Speaker 1: talk about that, or you know, let's change the subject,
Speaker 1: I might sit there and say, well, this is coming
Speaker 1: across as a chatbot, but I bet it's actually a
Speaker 1: person pretending to be a chatbot. And meanwhile, there were
Speaker 1: other examples they were The one I read was specifically,
Speaker 1: and this really appealed to me, was specifically an interview
Speaker 1: with a Shakespearean scholar where the interviewer was asking questions
Speaker 1: and the Shakespearean scholar, being a human and being smart
Speaker 1: and being a little snarky, was responding in kind of
Speaker 1: a snarky way to some of the questions, and they
Speaker 1: just assumed that because it was a snarky comment, that
Speaker 1: this was some clever programming, even though the person being
Speaker 1: interviewed was demonstrating an understanding of the subject matter, not
Speaker 1: just regurgitating things or or pulling together simple were sentences.
Speaker 1: They were actually being pretty complex. So for example, they
Speaker 1: might say, um, like, what's your favorite Shakespearean play? And
Speaker 1: the respondent said something along the lines of, you know,
Speaker 1: that's a really general question. I don't even really know
Speaker 1: how to answer that. If we if we want to
Speaker 1: go with just the comedies, then I guess it happens
Speaker 1: to be Twelfth Night or some thing along those lines.
Speaker 1: And so it was. It was actually a very thoughtful response,
Speaker 1: and it was and the person was also trying to
Speaker 1: engage the interrogator into a conversation, and the interrogator would
Speaker 1: end up resisting, and they ended up saying, well, this
Speaker 1: this Shakespeare scholar, one's clearly a robot. And this other
Speaker 1: one that kept on saying, like, trying to deflect the question,
Speaker 1: that's clearly a human pretending to be a robot. And
Speaker 1: they were They were wrong in both cases. They were wrong.
Speaker 1: I would I would really like to never be personally
Speaker 1: challenged on whether I'm a human or not. I feel
Speaker 1: like I would fail that game. Yeah, well, at any rate,
Speaker 1: so I'm a real person. So yes, it is lowering
Speaker 1: the bar for what we humans consider to be a
Speaker 1: passing grade. But it also is all based on deception.
Speaker 1: Like you said, Joe, it's not it's not based on
Speaker 1: any kind of let's improve the computer's ability to do
Speaker 1: any particular task other than to fool people into thinking
Speaker 1: it's human. That's not terribly useful. In fact, that could
Speaker 1: be really bad for us to really go down that
Speaker 1: road and just get really good at making computers think
Speaker 1: trick humans. Yeah, let's not do that. Yeah, that's the
Speaker 1: first thing we're teaching. It's like before they're even able
Speaker 1: to do all this other great stuff. For the main
Speaker 1: thing we're focused on is how to lie to us.
Speaker 1: At least we didn't teach them like how to hate us.
Speaker 1: That's their their line comes from an honest place. But
Speaker 1: then we have next. Another criticism is that leads to
Speaker 1: the idea that the machine is not doing any sort
Speaker 1: of thinking at all. It's doing, like we said, smoke
Speaker 1: and mirrors kind of approach to deflecting us verbal trickery
Speaker 1: to make us think that it's that it's thinking, but
Speaker 1: it's not actually thinking. And that is illustrated and in
Speaker 1: one aspect in the Chinese Room thought experiment. Yeah, so
Speaker 1: this is kind of a higher level criticism, is kind
Speaker 1: of back up and say, wait a minute, I sort
Speaker 1: of this criticism says, I disagree with Turing's initial premise
Speaker 1: about how you rep resent intelligence. Right. The idea here
Speaker 1: is is that even if you were to create a
Speaker 1: chatbot that one dent of the time could fool people
Speaker 1: into thinking that it was in fact human, you could
Speaker 1: not specifically state that the machine itself was intelligent. That
Speaker 1: the the whole touring test is based upon a premise
Speaker 1: that is faulty. Yeah, we've done a podcast about the
Speaker 1: Chinese Room experiment before, So if you want to go back,
Speaker 1: I believe it's called does your Computer No Chinese? Yeah,
Speaker 1: if you want to go back and listen to that,
Speaker 1: please check it out. It's cool one. But but it
Speaker 1: boils down to the idea that there are some philosophers
Speaker 1: and thinkers who say that UM, no programmatic system can
Speaker 1: never understand the meaning of words and symbols. No, it
Speaker 1: simply is following directions, predetermined directions, and that it cannot
Speaker 1: go beyond those predetermined directions. So it is it is
Speaker 1: by its own design limited in what it can do. So,
Speaker 1: in other words, the basic ideas. Imagine you've got a
Speaker 1: room that doesn't have any windows to it. It's got
Speaker 1: one doorway and under and you you are in the room,
Speaker 1: and the only other thing that's in the room is
Speaker 1: besides like basic furniture, is a big book of instructions.
Speaker 1: And occasionally a piece of paper gets shoved under the
Speaker 1: door that has a symbol written in a Chinese Chinese character,
Speaker 1: and you do not understand Chinese. That's important for this
Speaker 1: thought experiment. You don't understand Chinese. So you get this
Speaker 1: what is to you a meaningless symbol because you have
Speaker 1: no idea what it means, what it says, what means
Speaker 1: You open up the big book of directions and you
Speaker 1: find the symbol in the book of directions. The book
Speaker 1: of directions tells you what's symbol to draw. In response
Speaker 1: to that, you do, so you slide that under the door,
Speaker 1: and you just keep doing that over and over and
Speaker 1: over again. To anyone from outside that room, it appears
Speaker 1: that the room understands Chinese because they put a symbol underneath,
Speaker 1: and they get the proper response shoved back eventually, But you,
Speaker 1: as the person inside the room, do not understand Chinese.
Speaker 1: You are simply following a series of directions that are
Speaker 1: just predetermined, and you have no understanding of that. Now.
Speaker 1: Of course, there are a lot of also philosophical responses
Speaker 1: to this criticism. One of the main ones, and I'd
Speaker 1: say probably the most popular one and the one that
Speaker 1: makes the most sense to me, is the idea that, Okay, well,
Speaker 1: while the person in this analogy, basically it's a criticism
Speaker 1: of the scope of the analogy. It says that, Okay,
Speaker 1: in this analogy or in this thought experiment, the person
Speaker 1: in the room might not understand Chinese, but that it
Speaker 1: would make sense to talk about the system as a whole.
Speaker 1: So the room, the person, and the book put together
Speaker 1: as a thing understanding Chinese, you could say that the
Speaker 1: room system does understand what Chinese is, just as you
Speaker 1: could say that a particular region in your brain doesn't
Speaker 1: necessarily understand English, but you understand English. Yeah. So it's
Speaker 1: not that everything single part of your brain is dedicated
Speaker 1: to language processing. It's not. So you couldn't say that
Speaker 1: this one particular part of your brain understands English. That
Speaker 1: would just be untrue. But it also would would be
Speaker 1: untrue to say, well, the person therefore does not understand English.
Speaker 1: But you've got to look at the full system, right,
Speaker 1: So that is another counter argument to that particular objection. Okay,
Speaker 1: So the question might be, do we need an alternative
Speaker 1: to the touring toes something in the popular consciousness that
Speaker 1: represents the new bar that all artificial intelligence has to pass.
Speaker 1: I I would say, yes, what might that look like? Well,
Speaker 1: how about instead of trying to get it to ape
Speaker 1: a human through text, we actually see if we can
Speaker 1: create a program that can comprehend stuff, that can consume
Speaker 1: some form of media, whatever that might be, and then
Speaker 1: be able to actually answer meaningful questions about that media.
Speaker 1: So it could be that you have this program and
Speaker 1: you allow it, you know, you feed it the information
Speaker 1: about a book, and then you might ask what were
Speaker 1: the main character's motivations? Why did the main character do
Speaker 1: the actions he or she did in the context of
Speaker 1: that novel? Or you might have it watch a television
Speaker 1: program and then answer questions, So what happened first, what
Speaker 1: happened next, why did that happen? You know, that kind
Speaker 1: of thing, and that if a machine were able to
Speaker 1: comprehend something and answer these questions in a meaningful way.
Speaker 1: That would go much further toward establishing this machine as
Speaker 1: being intelligent in this in a similar way that we
Speaker 1: humans are intelligent. Yeah, that's interesting. I like that idea,
Speaker 1: especially talking about visual media, like giving it a movie
Speaker 1: to watch and then say, asking questions about them. So
Speaker 1: that would encompass quite a few things all at once.
Speaker 1: That would encompass, uh, visual data processing, recognizing the faces,
Speaker 1: being able to look at a strip of film and
Speaker 1: interpret all that is data out a narrative, understanding what's
Speaker 1: going on in the relationship of different characters and objects.
Speaker 1: Of course, it would include natural language processing. It would
Speaker 1: include understanding of cultural tropes and things like that, so
Speaker 1: sort of incorporating cultural knowledge into this engine, which is
Speaker 1: something that a lot of these chat abouts so far
Speaker 1: haven't been able to do. That's why they have to
Speaker 1: deflect and say, I know, I've never seen cheers. I
Speaker 1: don't know what it's about. So that would do a
Speaker 1: whole lot all in in one fell swoop. At the
Speaker 1: same time, I wonder if I wonder if that's setting
Speaker 1: the bar too high. See, I don't know if it's
Speaker 1: saying the bar too high. If you're talking about establishing
Speaker 1: strong AI. If you want truly strong AI, then you
Speaker 1: have to have that high bar set, right sure. Cognitive
Speaker 1: scientist Gary Marcus wrote a really great blog post for
Speaker 1: The New Yorker about this entire concept. He he pointed
Speaker 1: out that a true test of intelligence is learning new
Speaker 1: things at will, which this demonstrates. And also that sounds
Speaker 1: really simple, but that's just our human bias. Everything that
Speaker 1: we do is basically learning new things at will. But
Speaker 1: it's really hard for computers to do. Is we have
Speaker 1: talked many times about on the show before. He closed
Speaker 1: his blog post with with this excellent tidbit and I quote,
Speaker 1: no existing program, not Watson, not Gooseman, nut Siri can
Speaker 1: currently come close to doing what any bright, real teenager
Speaker 1: can do. Watch an episode of The Simpsons and tell
Speaker 1: us when to laugh. Yeah, I mean, that's a great point.
Speaker 1: I said that I won't. I won't assume a computer
Speaker 1: is intelligent until it is able to watch the Transformers
Speaker 1: movies and then ask you why you made it do
Speaker 1: that thing and then and it be hurt if it's
Speaker 1: if it's hurt, and it it considers you trade, Yeah,
Speaker 1: it feels that you are a cruel overmaster. Then you
Speaker 1: know you have created an intelligent machine, and you should
Speaker 1: listen to it and also probably apologize. Again. I think
Speaker 1: that's a really great way of framing the issue, and
Speaker 1: that's very interesting and very ambitious. I still wonder the
Speaker 1: same thing I was just thinking about, like, is that
Speaker 1: too high to set the bar? I don't know how
Speaker 1: high you should set the bar. I mean, on one hand,
Speaker 1: it's great to shoot for the moon, you know, to
Speaker 1: to encourage really really amazing leaps of innovation. But if
Speaker 1: you set the bar too high, it might be discouraging
Speaker 1: because there's almost nothing to aim for it. It's not
Speaker 1: even in sight. The other way to look at it
Speaker 1: is to say, if you don't set the bar too high,
Speaker 1: what are you saying about human intelligence? Because for the
Speaker 1: most part, what we're talking about here as machines still
Speaker 1: operating under an intelligence that is similar to human intelligence.
Speaker 1: That's another criticism, criticism that is not going to go
Speaker 1: away anytime soon, the idea that we're holding machines to
Speaker 1: a standard that applies to humans. We're not creating a
Speaker 1: standard that particularly applies to machines. And therefore, any any
Speaker 1: test we come up with, whether it is really hard
Speaker 1: or really easy, is ultimately, uh a a a fool's endeavor,
Speaker 1: like this is not the right pathway to to to
Speaker 1: go down. But still there are other alternatives we can
Speaker 1: talk about, and slightly more specific ones like that one
Speaker 1: reminds me of kind of the essay comprehension version or
Speaker 1: a bit of the s A T. Yeah and the
Speaker 1: verbal side yeah, well, not even the verbal but like
Speaker 1: the reading sure, sure, yeah, so they they Another similar
Speaker 1: one to that is the Wino Grad Schema Challenge. There's
Speaker 1: actually a great paper that's all written about this particular
Speaker 1: proposed challenge that again, it's it takes aim to replace
Speaker 1: the Touring test as the standard for testing this particular
Speaker 1: type of AI. It's still looking specifically at how a
Speaker 1: computer is able to to interpret and respond to text,
Speaker 1: but it is not looking at a way for a
Speaker 1: computer to uh pose as a human being, which they
Speaker 1: said was a fundamental flaw in the design of the
Speaker 1: Turing test. Well, I can respect that, So we can
Speaker 1: say it's narrowly focused on natural language processing maybe, but
Speaker 1: it doesn't go for those tricks, the little little games
Speaker 1: that these programs play, right, So it's built on the
Speaker 1: concept of recognizing textual entailment, which it was an earlier
Speaker 1: proposed alternative to the Turing test. Now, that specifically is
Speaker 1: where you have a pair of sentences and you detect
Speaker 1: whether or not they are logically paired together properly, and
Speaker 1: you answer with a yes or no. Yes those logically
Speaker 1: are paired together, or no they are not. So here's
Speaker 1: an example. William Shakespeare's best known plays Romeo and Juliet.
Speaker 1: I know it could be Hamlet. That's fine, Williams Shakespeare's
Speaker 1: best known plays Romeo and Juliet. And then the second
Speaker 1: sentence would be Shakespeare wrote Romeo and Juliet. So you
Speaker 1: ask any human being this thing, and they would say yes,
Speaker 1: those two fall together. Actually depends on how logical the
Speaker 1: human being is, because if you ask Spock and tell
Speaker 1: him he supposed to be operating out of a vacuum
Speaker 1: and not having any cultural knowledge, he would say no.
Speaker 1: Because Shakespeare might have been an actor. Interesting, that would
Speaker 1: be my answer, completely outside of it. But still I've
Speaker 1: seen it positive that Falcans would certainly fail the Turing test.
Speaker 1: So right, you can be too logical, you can be
Speaker 1: so logical that you're not play. Right. Shakespeare's best known
Speaker 1: work is Romeo and Juliet, and then Shakespeare wrote Romeo
Speaker 1: and Juliet. Then would in that case I would say yes,
Speaker 1: they definitely, But as you phrased it, I could say
Speaker 1: no because Shakespeare might not have been the writer. But
Speaker 1: that's fair. But you know that's ultimately the whole point
Speaker 1: is just trying to get two sentences that either are
Speaker 1: logically compatible or not. Okay, give me two that are
Speaker 1: definitely not. Um All right, let's see. How about Uh,
Speaker 1: some pets are dogs. I own a pet, so I
Speaker 1: have a dog. The no, definitely not, because those two
Speaker 1: things don't necessarily belong to each other. That would just
Speaker 1: be a very simple, basic example. But the one a
Speaker 1: grad scheme of challenges a little more of a variation
Speaker 1: on this. It's Uh, You're given a single sentence that's
Speaker 1: followed by a question and two choices for answers. So
Speaker 1: here's an example. The trophy would not fit in the
Speaker 1: brown suitcase because it was too small. What was too
Speaker 1: small the trophy or the suitcase? The trophy would not
Speaker 1: fit into the brown suitcase because it was too small.
Speaker 1: What was too small? Are you quizzing me? I'm asking you,
Speaker 1: I'm asking you. The suitcase was too small? Right, Because
Speaker 1: if the trophy were too small, that doesn't make sense.
Speaker 1: It wouldn't it wouldn't prevent it from into the suitcase. Correct.
Speaker 1: But here's the thing that pronoun It could refer to
Speaker 1: either the trophy or the suitcase. It's the context of
Speaker 1: the question. It's how we understand what the question is
Speaker 1: asking that we're able to give the answer. But if
Speaker 1: we were able to change one word in that sentence,
Speaker 1: we could completely change which answer was the correct one.
Speaker 1: So I said the trophy would not fit in the
Speaker 1: brown suitcase because it was too large. What was too large?
Speaker 1: The trophy? Right? So by changing one word in the sentence,
Speaker 1: I have changed like I've kept everything else the same,
Speaker 1: I've kept the same answers. I kept the same everything
Speaker 1: else except for the changing the small to large, and
Speaker 1: that changes which answer is the correct one. So if
Speaker 1: you were able to pose a series of these style
Speaker 1: of questions where by changing one word you could swap
Speaker 1: out which answer is the correct answer and otherwise you've
Speaker 1: got the same answers for each individual pair of questions,
Speaker 1: and the computer was able to reliably answer which one
Speaker 1: is the correct answer, then they would argue that is
Speaker 1: a better demonstration of machine intelligence because the computer is
Speaker 1: actually comprehending what was being asked and coming up with
Speaker 1: the correct answer. Yeah, so that actually is more general
Speaker 1: than just natural language processing. It's not just making good
Speaker 1: sense of the grammatical units in this sentence. It's actually
Speaker 1: drawing on a knowledge base, the kind of NOLED ledge
Speaker 1: base that would tell you, yes, a thing would have
Speaker 1: to fit inside a suitcase. It would. It's having to
Speaker 1: get rid of ambiguity, right. The ambiguity is the pronoun.
Speaker 1: The same thing, by the way, can be true if
Speaker 1: you use people's names and you just you make sure
Speaker 1: that the two names are are gender compatible. It's the
Speaker 1: same gender whether you know, it doesn't have to be
Speaker 1: one that's specific to a gender. But you could say,
Speaker 1: like Chris and Sam, and then you use the pronoun
Speaker 1: he and you don't you know, designate otherwise other than
Speaker 1: the context of the sentence who he is? And you
Speaker 1: asked that question like who is he? Is it Chris
Speaker 1: or is it Sam? Then if the computer is able
Speaker 1: to reliably answer those kind of questions, then again that's
Speaker 1: a good demonstration of machine intelligence. And it it all
Speaker 1: is about this ambiguous sentence being uh analyzed and understood
Speaker 1: and I think that this is a slightly easier problem
Speaker 1: for a machine to tackle, or for a program or
Speaker 1: to have a machine tackle rather because well Joe Joe
Speaker 1: used in our notes that this reminds you of the
Speaker 1: verbal section of the S A T. And I think
Speaker 1: that that in in contrast to the reading section, it's
Speaker 1: it's just a slightly more logic based umblem. Yeah. And
Speaker 1: see the issue here though, is that this test tests
Speaker 1: comprehension as opposed to just pulling up from a database
Speaker 1: of pre existing responses or even just patterns that a
Speaker 1: chatbot can pull from where there's no understanding there, right,
Speaker 1: there's no And you could in theory, present any new
Speaker 1: type of sentence that follows this pattern and then ask
Speaker 1: any type of question that follows this pattern to a
Speaker 1: computer capable of answering these things, and it would be
Speaker 1: able to handle it. It It wouldn't just be a preset
Speaker 1: all right, Well, there's these twenty questions that you know
Speaker 1: we're going to ask you, so make sure your computer
Speaker 1: can answer them, because that's just you know, you could
Speaker 1: just program a comp it or to say, yeah, when
Speaker 1: the sentences in this order, this is the answer you give. Um,
Speaker 1: it would have to be able to answer any question
Speaker 1: it had never seen before and be able to comprehend
Speaker 1: what the question was asking and give the proper answer.
Speaker 1: So that is a different type of intelligence than just
Speaker 1: natural language processing. It goes beyond that. But these these
Speaker 1: are not the only ideas that are out there. I mean,
Speaker 1: we all actually kind of have probably our own concepts
Speaker 1: of how this would go. Joe, you mentioned one, well,
Speaker 1: I was kind of joking, but kind of serious. Also
Speaker 1: my ideas you should have a computer program that has
Speaker 1: to write fan fiction. You so, you you give it
Speaker 1: a story about a character, and then it has to
Speaker 1: look at that and then itself come back and write
Speaker 1: a short story about the same character basically in keeping
Speaker 1: with what human judges would agree is that character's psychological profile,
Speaker 1: how how this character should behave So not only would
Speaker 1: it have to understand the the motivations and personality of
Speaker 1: the character, it would have to be able to construct
Speaker 1: a narrative, which let's let's hold to at least at
Speaker 1: least the standard that the narrative needs to make sense. Right,
Speaker 1: I'm not saying it has to be a real good story.
Speaker 1: I'm just saying it has to be the kind of
Speaker 1: story a human would write if they understood the character
Speaker 1: they were writing. Your standards are really slipping already. That
Speaker 1: is a tough one too. We had a whole episode
Speaker 1: where we talked about whether computers are capable of creating
Speaker 1: art and how difficult the problem of a computer writing
Speaker 1: in a author's voice can be. Let's be fair, though
Speaker 1: fan fiction authors, only a small number of them are
Speaker 1: are what I would consider really really good. There are
Speaker 1: some really really good ones, and that's only because there's
Speaker 1: so much fan fiction. I would argue that there are
Speaker 1: only a few really good ones that that that that
Speaker 1: the percentage. I mean, there's just there's a great deal
Speaker 1: of it on the internet. Okay, fair, fair enough, Like
Speaker 1: you would say, you know, there's only a few really
Speaker 1: good television shows on TV, but there's a lot of it.
Speaker 1: So now I would say that this would be truly
Speaker 1: challenging because not only would it have to analyze the
Speaker 1: existing work so it could get a handle on what
Speaker 1: this character is and what this character's uh motivations and
Speaker 1: behaviors tend to be, then create a brand new situation
Speaker 1: for this character to inhabit, have the character behave in
Speaker 1: a believable way within that situation and have the situation
Speaker 1: at least makes some sort of sense, so that you
Speaker 1: don't have random events following one another with no sense
Speaker 1: of causation. Right, So this wouldn't involve things like processing
Speaker 1: visual information and all that kind of stuff. I was
Speaker 1: trying to imagine something that would still be text based
Speaker 1: the way the Turing test is today, but it would
Speaker 1: require a much much deeper kind of idea of socialization
Speaker 1: and general knowledge and emotional intelligence that that these chatbots
Speaker 1: really can't come close to, right. And I think that
Speaker 1: that concept of emotion being part of the test is
Speaker 1: a really interesting one because it seems like a lot
Speaker 1: of these new propositions for Turing tests all kind of
Speaker 1: hinge on the question of a machine being able to
Speaker 1: understand human emotions. So I mean, like, are we are
Speaker 1: we working towards a clump test being the new Turing test?
Speaker 1: If you don't recognize what that is. It was the
Speaker 1: machine that determined whether replicants were replicants or humans in
Speaker 1: Blade runner Um or or yeah, just bringing Lieutenant data
Speaker 1: in here and having him talk to computers about whether
Speaker 1: or not their computers And well, I don't think the
Speaker 1: Winograd test would really involve that, but the other two
Speaker 1: definitely would. Yeah, yeah, And I'm not sure how I
Speaker 1: feel about that element. I do think that the original
Speaker 1: Turing test is about creating a computer that behaves like
Speaker 1: a human rather than thinks like a human. And I
Speaker 1: think that we need to ask ourselves whether human behavior
Speaker 1: is the gold standard of intelligence, which which we talked
Speaker 1: about earlier a little bit here. Human intelligence is not
Speaker 1: the only kind of intelligence, nor is it necessarily the
Speaker 1: one that we need to focus on to create machines
Speaker 1: that are truly useful to us. Right? And is that
Speaker 1: is that, in fact bio chauvinistic, That is that is
Speaker 1: a real word that I heard real researchers by saying,
Speaker 1: this machine isn't intelligent because it can't talk to me
Speaker 1: about the last television show I saw or the last
Speaker 1: mut song I listened to. And if it can't do that,
Speaker 1: then it can't be intelligent. That does seem to be
Speaker 1: pretty shortsighted, sure, I mean, especially if you think about it,
Speaker 1: There are plenty of human beings that couldn't pass the
Speaker 1: Tearing test. There are plenty of human beings who haven't
Speaker 1: passed the Teares right, right, Um, So, like she experienced
Speaker 1: scholars for example. Right, right, So, so maybe maybe we
Speaker 1: need to in fact revise our concept of intelligence here um,
Speaker 1: even beyond the point where a single test is useful
Speaker 1: in this question. Yeah, That's exactly what I was wondering
Speaker 1: when I was doing research for this. I started thinking,
Speaker 1: maybe there just isn't a test. Maybe there isn't one
Speaker 1: test that we should think of as the standard bar
Speaker 1: for artificial intelligence, something like the Turing test or one
Speaker 1: of our alternatives. It might be a really good sort
Speaker 1: of goal post in a narrowly focused part of artificial intelligence,
Speaker 1: but I can't imagine what the test would be for
Speaker 1: for the truly general intelligence question. Right. So. In other words,
Speaker 1: these tests might be useful in that they can give
Speaker 1: focus to people working in artificial intelligence specific goals to
Speaker 1: work toward. But even if you achieve that goal, that
Speaker 1: doesn't necessarily mean that you have achieved strong AI. It
Speaker 1: may be that we move the goal posts, which happens
Speaker 1: with us trying to define what human intelligence is, right,
Speaker 1: we we end up kind of it ends up being
Speaker 1: one of those arguments where you say, well I can't
Speaker 1: necessarily tell you what it is, I can start telling
Speaker 1: you what it isn't. And eventually we're gonna eliminate enough
Speaker 1: stuff so that we have whatever the Colonel is and
Speaker 1: that will be our definition. But we're not there yet.
Speaker 1: The same may be true with machine intelligence. In fact,
Speaker 1: I assume it what really will be. And one of
Speaker 1: the things I propose is that instead of creating actual tests,
Speaker 1: things that we think the machines need to overcome and saying, well,
Speaker 1: now we've reached the point where we can say this
Speaker 1: machine is truly intelligent, another thing we could do is
Speaker 1: just watch and look for examples of things that we
Speaker 1: consider to be part of intelligence. And again we're coming
Speaker 1: from it from a human perspective, because most of us
Speaker 1: are humans, you know, Yeah, then to some degree or another,
Speaker 1: And so we think of it as things like things
Speaker 1: that that humans exhibit, like curiosity, the desire to know
Speaker 1: how something works, and the the innovation to find out
Speaker 1: how it works by trying different things, experimentation. The Board
Speaker 1: had curiosity. The Board also as a work of fiction,
Speaker 1: and also I think you would agree the Board had intelligence,
Speaker 1: so that's not even relevant to this discussion in multiple ways, uhize,
Speaker 1: But yeah, this idea of having curiosity, experimentation, innovation invention
Speaker 1: a machine inventing something like you were saying with the
Speaker 1: creating a fan fiction, that's invention, right, That's not just regurgitation.
Speaker 1: It's actually being able to understand rules at least on
Speaker 1: some level. Maybe not human understanding, but it is able
Speaker 1: to apply these rules and create something new with it.
Speaker 1: This is something that is not easy to do, and
Speaker 1: I think until we have machines that are capable of
Speaker 1: doing all these things, we won't really say that we
Speaker 1: have a strong AI And in fact, we may never
Speaker 1: get there. And that's okay. If we never get there,
Speaker 1: that's fine. We are we are still going to be
Speaker 1: developing incredible machines that can do things that will make
Speaker 1: our lives much easier on us. That it may not
Speaker 1: mean that we're going to have a deep, meaningful conversation
Speaker 1: with our computer, but it may mean our computer is
Speaker 1: able to get us all the things that we need.
Speaker 1: At that time, I wanted to follow up on exactly
Speaker 1: that thing, because I was just thinking, maybe the real
Speaker 1: test isn't a test we come up with, but a
Speaker 1: test that's determined by utility. I mean, all these tests
Speaker 1: we've been talking about are just sort of exercises. They're not. Yeah,
Speaker 1: they're they're not what a machine can do for us.
Speaker 1: There what we can get a machine to do in
Speaker 1: front of us. That's a good way of putting it.
Speaker 1: So yeah, it'll be interesting. I think we'll see more
Speaker 1: debate about the UH, the usefulness of the touring test
Speaker 1: as any form of measurement of machine intelligence, will see UH,
Speaker 1: probably see other teams try to tackle these other harder,
Speaker 1: arguably problems, and who knows, maybe within our lifetimes will
Speaker 1: actually see machines that at least approach this this concept
Speaker 1: of strong AI, or maybe we'll get to a point
Speaker 1: where most people say, you know what, our our work
Speaker 1: would be better suited going towards some other part of
Speaker 1: machine intelligence that could directly benefit us, rather than drive
Speaker 1: for a goal that is looking increasingly difficult to achieve
Speaker 1: because every time we eliminate one thing, five more things
Speaker 1: pop up and then we say, oh, this this problem
Speaker 1: was more complex than I first anticipated. So um, I'm
Speaker 1: interested to see how it goes. Maybe our listeners are too, Hey, listeners,
Speaker 1: are you interested to see where artificial intelligence goes? Do
Speaker 1: you have any thoughts on the subject, You've got any
Speaker 1: opinions or or questions? Maybe you want us to clarify something,
Speaker 1: or maybe you just want us to talk about some
Speaker 1: entirely different subject, and you say enough with the AI
Speaker 1: already talk about. I don't know the future of lawnmowers.
Speaker 1: Let us know. We'll definitely give it strong consideration. You
Speaker 1: can drop us a line on Facebook, Twitter, or Google Plus.
Speaker 1: Our handle at all three is FW thinking, and we
Speaker 1: will talk to you again really soon. For more on
Speaker 1: this topic in the future of technology, visit forward thinking
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