Speaker 1: All Zone Media.
Speaker 2: Hello and welcome to Better Offline. I'm your host ed
Speaker 2: z trun. In early June, three researchers from the University
Speaker 2: of Glasgow published a paper in the Ethics of Information
Speaker 2: and Technology Journal called chat GBT is Bullshit. And I
Speaker 2: just want to be clear. This is a great and
Speaker 2: thoroughly researched and well argued paper. This is not silly
Speaker 2: at all. It's actually great academia. And today I'm joined
Speaker 2: by the men who wrote it, academics Michael Townsend Hicks,
Speaker 2: James Humphries and Joe Slater, to talk about chat gbt's
Speaker 2: mediocrity and how it's not really built to represent the
Speaker 2: world at all. So, for the sake of argument, could
Speaker 2: you define bullshit for me?
Speaker 3: So you are bullshitting if you are speaking without caring
Speaker 3: about the truth of what you say. So normally, if
Speaker 3: I'm telling you staff about the world in a good case,
Speaker 3: I'll be telling you something that's true, and like trying
Speaker 3: to tell you something that's true. If I'm lying to you,
Speaker 3: I'll be knowingly telling you something that's false or something
Speaker 3: I think is false. I'm bullshitting. I just don't care.
Speaker 3: I'm trying to get you to believe me. I don't
Speaker 3: really care about whether what I say is true. I
Speaker 3: might not have any particular view on whether it's true.
Speaker 2: Or not right. And you define between like soft and
Speaker 2: hard bullshit? Can you also get into that as well?
Speaker 2: Can you also identify yourselves as well to.
Speaker 4: R Yeah, yeah, and Joe. So the soft bullshit hard
Speaker 4: bullshit distinction is a very serious and technical distinction, right,
Speaker 4: So he came up with.
Speaker 3: This because bullshit is in the technical philosophical sense. Comes
Speaker 3: from Harry Frankfurt, recently diseased but a really great philosopher.
Speaker 4: And he talks about the about.
Speaker 3: Bullshit that there is in a popular culture and just
Speaker 3: in general discourse these days. Some of the ways he
Speaker 3: talks about bullshit seemed to suggest that it needs to
Speaker 3: be accompanied by a sort of malign intention.
Speaker 4: I'm doing something kind of bad.
Speaker 3: I'm intending to mislead you about the enterprise of what
Speaker 3: I'm doing.
Speaker 4: Yeah, maybe about who you are or what you know.
Speaker 5: So you may be trying to portray yourself as someone
Speaker 5: who is knowledgeable about a particular subject. Maybe you're a
Speaker 5: student who showed up to class without doing the work.
Speaker 5: Maybe you're trying to portray yourself as someone who's virtuous
Speaker 5: in ways you're not. Maybe you're a politician who wants
Speaker 5: to seem like you care about your constituents, but actually
Speaker 5: you don't. So you're not trying to mislead somebody about
Speaker 5: what you're saying the content of your utterance. You're trying
Speaker 5: to mislead them instead about like why you're saying it's
Speaker 5: that's what we call hard bullshit.
Speaker 4: Yeah, it's one of the things Frankfort talked about.
Speaker 3: Yeah, so FRANKFOT doesn't make this hard bullshit soft bullshit
Speaker 3: the station, but we do because sometimes it seems like
Speaker 3: Frank for has this particular kind of intention in mind,
Speaker 3: but sometimes he's just a bit looser with it. And
Speaker 3: we want to say that chat GPT and other large
Speaker 3: language models they don't really have this intention to deceive
Speaker 3: because they're not people that don't have these intentions. They're
Speaker 3: not trying to mess with us in that kind of way,
Speaker 3: but they do lack this kind of caring about truth.
Speaker 1: Well, I'm James, by the way, I suppose we strictly
Speaker 1: don't want to say that they aren't hard bullshitters, and
Speaker 1: we just think if you don't think that large language
Speaker 1: models are sapient. If you don't think they're kind of
Speaker 1: minds in any important way, then they're not hard bullshitters.
Speaker 1: So I think in the paper we don't take a
Speaker 1: position on whether or not they are. We just say
Speaker 1: if they are, this is the way in which they are.
Speaker 1: But minimally they're soft bullshitters. So they're kind of soft bullshites,
Speaker 1: as Joe says, doesn't require that speakers attempting to deceive
Speaker 1: the audience about the nature of the enterprise.
Speaker 6: Hard bullshit does.
Speaker 1: So if it turns out that large language models are sapient,
Speaker 1: which they're definitely not, like that's just technoproperty.
Speaker 2: Yeah, that's nonsense.
Speaker 1: Yeah, But if they are, then they're bullshitters and minimally
Speaker 1: their soft bullshitters or their bullshit machines.
Speaker 2: So you also make the distinction in there the intention,
Speaker 2: so the very fabric of hard bullshit that you intentionally
Speaker 2: are bullshitting to someone, you kind of make this distinction
Speaker 2: that the intention of the designer and the involved prompting
Speaker 2: could make this hard bullshit. Because with a lot of
Speaker 2: these models and someone recently jail broke chat GPT and
Speaker 2: it listed all of the things that's prompted to do
Speaker 2: could prompting be considered intentional? That Sam Altman, CEO of
Speaker 2: open AI, could be intentionally bullshitting? I think it is.
Speaker 1: Yeah, this again, I think it's something I don't know
Speaker 1: what the kind of hive mind consensus on this is.
Speaker 1: I'm sort of sympathetic to the idea that if you
Speaker 1: take this kind of purposive or teleological attitude towards right,
Speaker 1: what an intention is is an effort to do something,
Speaker 1: then maybe they do have intentions. But again, I think
Speaker 1: in the paper we just sort of wanted I mean,
Speaker 1: it's a standard philosophical move, right, just sort of go, look,
Speaker 1: here's all this controversial stuff as we can make it.
Speaker 1: Now we can hit you with the really controversial shit
Speaker 1: that we wanted to get to. So in the paper
Speaker 1: we sort of deliberately when maybe you might think it
Speaker 1: has intentions for it this reason, we kind of have
Speaker 1: no judgment on this efficiently, I'm sympathetic to the sort.
Speaker 6: Of view that you're putting.
Speaker 4: I think you're kind of sympathetic this as well.
Speaker 6: Right, Yeah, so.
Speaker 5: I Mike, there are a few ways that you can
Speaker 5: think of CHATCHYBT as having intentions.
Speaker 4: I think, and we talked about a few of them.
Speaker 5: One is by thinking of the designers who created it
Speaker 5: as kind of imbuing it with intentions. So they created
Speaker 5: it for a purpose and to do a particular task,
Speaker 5: and that task is to make people think that it's
Speaker 5: a normal sounding person.
Speaker 6: Right.
Speaker 5: It's to make people when they have a conversation with it,
Speaker 5: not be able to distinguish between what it's outputting and
Speaker 5: what a normal human would say.
Speaker 6: Right.
Speaker 4: Right, And that kind.
Speaker 5: Of goal, if it amounts to an intention, is the
Speaker 5: kind of intention we think a bullshitter has.
Speaker 4: Right.
Speaker 5: It's not trying to deceive you about anything it's saying.
Speaker 5: It doesn't care whether what it's saying is true. That's
Speaker 5: not part of the goal. But the goal is to
Speaker 5: do is to make it seem as if it's something
Speaker 5: that it's not, like specifically a human interlocutor. And one
Speaker 5: source for that goal is the programmers who designed it.
Speaker 5: Another is the training method. So it was trained by
Speaker 5: being given sort of positive and negative feedback in order
Speaker 5: to achieve.
Speaker 4: A specific thing, right, And that specific thing is just
Speaker 4: sounding normal.
Speaker 5: And that's so similar to what our students are doing
Speaker 5: when they try to pretend that they read something they
Speaker 5: haven't read.
Speaker 2: There is something very collegiate about the way it bullshits though.
Speaker 2: It reminds me of when I was in college when
Speaker 2: Penn State and Aporistwyth two very different institutes.
Speaker 4: Right, both kind of in the middle of nowhere.
Speaker 2: Yeah, both very sad. But the one thing you saw
Speaker 2: with like students who were doing like B plus homework
Speaker 2: is they were using words they didn't really understand. They
Speaker 2: were putting things together in a way that realists, like
Speaker 2: the intro body conclusion, there was a certain formula behind it,
Speaker 2: and it's just it feels exactly like it. But that
Speaker 2: kind of brings me to my next question, which is,
Speaker 2: how did you decide to write this? What inspired this?
Speaker 5: Right?
Speaker 1: I would feel this because whenever Mike tells the story,
Speaker 1: you get sort of sanitized version. We were in the
Speaker 1: pub whining about student essays. Perfect, yeah, like.
Speaker 4: You were not long here, right, you're not long yeah.
Speaker 1: Long in post of a bunch of us, or went
Speaker 1: to the pub on emotional let's let's welcome our new
Speaker 1: members of staff sort of event, and inevitably with hat
Speaker 1: about two points we were all pissing in moaning and
Speaker 1: do you think it might have been.
Speaker 4: Neiled that kind of prompted this. No, I don't think
Speaker 4: he was the.
Speaker 6: We talked about it after, right, Yeah, in a case
Speaker 6: like sort of came up.
Speaker 1: We were talking about this sort of prevalence of chat
Speaker 1: GPT generated stuff and kind of what it said about
Speaker 1: how at least some students were kind of approaching the assessments,
Speaker 1: and you know, I forget who. Someone sort of just
Speaker 1: went off handly, yeah, it was all just Frank thirteen
Speaker 1: bullshit though, isn't it. And we sort of collectively went hello,
Speaker 1: because obviously, all having a background in plosphy, we've all
Speaker 1: read on bullshit. You know, we all went he we
Speaker 1: get to say bullshit and allegively serious academic work. So
Speaker 1: the start it was we've had this experience of having
Speaker 1: to read all of this kind of uncanny Valley stuff,
Speaker 1: and when prompted, we all went, oh, it really is
Speaker 1: like Frank thirty and bullshit in a way that we
Speaker 1: can probably get paper outselof.
Speaker 5: Yeah, And at that point I think we were in
Speaker 5: the department meetings talking about how to deal with chat
Speaker 5: GPT written papers, and there were discussions going on all
Speaker 5: over the university, including kind of in high levels and
Speaker 5: the administration to come up with a policy, and we
Speaker 5: specifically wanted a stronger policy because our university is very
Speaker 5: interested in cutting into technology, right, and so they wanted
Speaker 5: the students to have an experience using and figuring out
Speaker 5: how to use whatever the freshest technology is.
Speaker 2: Which as they should as they should, but not for essays, right,
Speaker 2: That's the.
Speaker 4: Worry we had.
Speaker 5: We thought, you know, if we're not very clear about this,
Speaker 5: the students will be using it in a way that
Speaker 5: will detract from the educational experience.
Speaker 4: Right. And at the same time, it was becoming more.
Speaker 5: Widely known how these machines work, like specifically how they're
Speaker 5: doing next token or next word prediction in order to
Speaker 5: come up with a digestible string of text. And when
Speaker 5: you know that that's how you're they're doing it, I mean,
Speaker 5: it seems so similar to what humans do when they
Speaker 5: have no idea what they're talking about. And so when
Speaker 5: we were talking about it just seemed like an obvious
Speaker 5: paper that someone was going to write, and we thought
Speaker 5: that a better bs. You know, eventually people are going
Speaker 5: to see this connection and it's.
Speaker 2: A great papa.
Speaker 4: I think it worked very well.
Speaker 5: I mean, I think that it's the kind of thing
Speaker 5: where when I pitch this to other philosophers, it doesn't
Speaker 5: take them very long to just agree to be like, ah, yes.
Speaker 6: That's right.
Speaker 2: It's an interesting philosophical concept as well, because the way
Speaker 2: that people look at chat GPT in large language models
Speaker 2: is very much machine do this. But when you think
Speaker 2: about it, there are the ways where people are giving
Speaker 2: it consciousness. I just sort of tweet just now as
Speaker 2: someone talking about saying please with every request. It's like, no,
Speaker 2: I will abuse the computer in whatever manner I see fit.
Speaker 2: But it's it's curious because I think more people need
Speaker 2: to be having conversations like this. And one particular thing
Speaker 2: I like that you said, I actually would love you
Speaker 2: to go into more detail, is you said, the chat
Speaker 2: GPT in large language buddles, they're not designed to represent
Speaker 2: the world at all. They're not lying or misrepresenting. They're
Speaker 2: not designed to do that. What do you mean by that?
Speaker 5: I mean kind of as background, I do philosophy of science,
Speaker 5: but my thoughts about something like CHATCHEPT are largely inspired
Speaker 5: by the fact that I also teach a class called
Speaker 5: Understanding Philosophy through Science Fiction.
Speaker 4: Oh and and now we like talk.
Speaker 5: About whether computers could be conscious and I don't know
Speaker 5: what you guys think. Actually I think they could, right,
Speaker 5: I just don't think this one is. And part of
Speaker 5: the reason I think they could but this one isn't
Speaker 5: is that I think that in order to sort of
Speaker 5: represent the world or have the kinds of things we
Speaker 5: have that are like beliefs, desires, thoughts that are about
Speaker 5: external things, you have to have internal states that are
Speaker 5: connected in some way to the external world. Usually causation.
Speaker 5: We're perceiving things, information is coming in. Then we've got
Speaker 5: some kind of state in our brain that's designed just
Speaker 5: to track these things in the external world.
Speaker 4: Right.
Speaker 5: That's a huge part of our cognitive lives. It is
Speaker 5: just tracking external world things and it's a very important
Speaker 5: part of childhood development when you figure out how to
Speaker 5: interact them.
Speaker 2: It's semiotics, right, Daniel John Aberistwyth told me semiotics it's
Speaker 2: like an reception of the world.
Speaker 5: And this is like theory of meaning stuff. So, yeah,
Speaker 5: semiotics is like theory of science. How is it that assign.
Speaker 6: Can be folks, the representation of the thing and the
Speaker 6: thing itself.
Speaker 4: That can happen, yeah, but not always.
Speaker 5: Sometimes it's just the representation of the thing and there's
Speaker 5: a lot of philosophy as at figuring out how brain
Speaker 5: states or words on a page can be about external
Speaker 5: world things. And a big part of it, at least
Speaker 5: from my perspective, has to do with tracking those things,
Speaker 5: keeping tabs on them, changing as a result of seeing
Speaker 5: differences in the external world. And chatch ept is not
Speaker 5: doing any of that, right, That's not what it's designed
Speaker 5: to do. It's taking in a lot of data once
Speaker 5: and then using that to respond to text. But it's
Speaker 5: not remembering individuals, tracking things in the world in any
Speaker 5: way perceiving things in the world. It's just forming a
Speaker 5: sort of statistical model of what people say. And that's
Speaker 5: kind of so divorced from what most thinking beings do.
Speaker 2: It's divorced from experience.
Speaker 4: Yeah. I mean, as far as I can tell, it
Speaker 4: doesn't have anything like experience.
Speaker 6: Yeah.
Speaker 1: And that's one of the things that this I thinks of.
Speaker 1: In one way it comes down to is that if
Speaker 1: you sort of post this sufficiently far, someone is going
Speaker 1: to go, ah, isn't this just biochauvinism, right, Like, aren't
Speaker 1: you just assuming that unless something runs on like meat,
Speaker 1: it can't be sentient? And this isn't something we get
Speaker 1: into into paper part because we didn't really think it
Speaker 1: was worth addressing. But the sorts of things that seem
Speaker 1: like they're like never mind consciousness, right, but it seemed
Speaker 1: to be necessary in order for something to be trying
Speaker 1: to track the world, or in order to be corresponding
Speaker 1: the world, or to form beliefs about the world. Chat
Speaker 1: GPT just doesn't seem to meet any of them. Kind Of,
Speaker 1: it's if it does turn out that it's sapient, then
Speaker 1: chat GPT has got some profoundly serious executive function disorders, right, sapient, Right,
Speaker 1: so we don't have to worry about it. But kind
Speaker 1: of it's not the case that we've got some blundering
Speaker 1: proto general intelligence that's trying to figure out how to
Speaker 1: represent the world. It's not trying to represent the world
Speaker 1: at all. It's utterances are designed to look as if
Speaker 1: it's trying to represent the world, and then we just
Speaker 1: go that that's just bullshit.
Speaker 6: This is a classic case of bullshit.
Speaker 2: Yeah, it seems to be making stuff up, but making
Speaker 2: stuff up doesn't even seem to describe it. It's just
Speaker 2: throwing shit at a wall very accurately, but not accurately enough.
Speaker 1: Yeah, it's got various guidelines that allow it to throw
Speaker 1: shit at the wall where a sort of reasonably high
Speaker 1: degree of Actually, no, you're right. I mean, one of
Speaker 1: the things that a human bullshitter could at least be
Speaker 1: characterized as doing is they'd have to try and kind
Speaker 1: of judge their audience, right, They'd have to try and
Speaker 1: make the bullshit plausible to the audience that they're speaking to,
Speaker 1: and chat GPT can't do that, right. All it can
Speaker 1: do is go on a statistically large enough model and
Speaker 1: it looks.
Speaker 6: Like Z follows why follows X?
Speaker 1: Right, it's not kind of got any does have any
Speaker 1: consciousness at all, of course, but it doesn't have any
Speaker 1: sensitivity to the sorts of things that people are in
Speaker 1: fact likely to find plausible. It just does a kind
Speaker 1: of brute number crunch. So it's more complicated than that,
Speaker 1: but I think it boies down effectively to kind of
Speaker 1: number crunching, right, kind of data and context.
Speaker 2: In which probabilistic planning of what planning, it doesn't plan.
Speaker 2: That's the thing. It's interesting. There are these words you
Speaker 2: use to describe things that, when you think about it,
Speaker 2: are not accurate. You can't say it plans or things.
Speaker 4: I think one thing.
Speaker 5: Between when we came up with the idea and when
Speaker 5: we finish writing the paper, we spent some time reading
Speaker 5: about how it works and how it represents language and
Speaker 5: what the statistical.
Speaker 4: Model is like.
Speaker 5: And I was maybe more impressed than James about that,
Speaker 5: because it is like doing things that are similar to
Speaker 5: what we do when we understand language. It does seem
Speaker 5: to kind of locate words in a meaning space right right,
Speaker 5: and connect them to other words, and you know, show
Speaker 5: similarity and meaning, and it also does seem to be
Speaker 5: able to in some way understand context. But we don't
Speaker 5: know how similar that is for a variety of reasons,
Speaker 5: but mostly because it's too big of a system and
Speaker 5: we can only kind of probe it. And it's trained indirectly, right,
Speaker 5: so it's not programmed by individuals. And even though that's
Speaker 5: kind of a very impressive model of language and meaning
Speaker 5: and may some ways be similar to what we do
Speaker 5: when we understand language, we're doing a lot more things
Speaker 5: like planning, things like tracking things in the world. Just
Speaker 5: having desires and representing the way you want the world
Speaker 5: to be and thereby generating goals doesn't seem to be
Speaker 5: something that it has anything any.
Speaker 4: Room for in its architects.
Speaker 1: This is something of the time of it's just goods
Speaker 1: me and you were talking about the kind of in
Speaker 1: some ways it learns language the same way.
Speaker 6: That we do.
Speaker 1: I mean, it's got no grasp of expleted in fixation, right,
Speaker 1: this is one of chumps here.
Speaker 2: What does that mean? Just for not for me? I
Speaker 2: definitely know.
Speaker 1: Yeah, yeah, of course if I give you the sentence
Speaker 1: that's completely crazy, man, and tell you to put the
Speaker 1: word fucking into that sentence, there's a number of ways
Speaker 1: in which any language speaker is going to do it
Speaker 1: that they'll just go, yeah, of course.
Speaker 4: That where it goes, right.
Speaker 1: But we it seems that we've got a grasp on
Speaker 1: this incredibly early on in a way that doesn't look
Speaker 1: like it's the ways at least most of us that
Speaker 1: taught language, right, we get quite highly told off when
Speaker 1: we try.
Speaker 4: And do expleted in fixation.
Speaker 1: Yes, So this I think would be one of those
Speaker 1: cases where you could do a sort of disnalogy by cases. Right,
Speaker 1: you present chat GPT a sentence and say insert the
Speaker 1: word fucking correctly in this sentence.
Speaker 4: And I don't think it would be very good at.
Speaker 6: It I think it would be.
Speaker 4: I think you reckon.
Speaker 1: I mean, we probably couldn't tell we could, but we shouldn't.
Speaker 4: Yeah.
Speaker 5: One of the things that I thought was like kind
Speaker 5: of interesting about how it works is that it does
Speaker 5: learn language, probably differently from the way we do, but
Speaker 5: it does it all by examples, you know, So it's
Speaker 5: looking at all these pieces of text and thinking, ah,
Speaker 5: this is okay, that's okay. And one kind of interesting
Speaker 5: thing about how humans understand language is that we're able
Speaker 5: to kind of understand meaningless but grammatical sentences. It's not
Speaker 5: clear to me that chat GPT would understand those. That's
Speaker 5: another Chopski example. So you know, Chomsky has this example
Speaker 5: that's like colorless uss sleep furiously color it's colorless green ideas, right,
Speaker 5: And that's a meaningless sentence, but it's grammatically well formed,
Speaker 5: and we can understand that it's grammatically well formed, but
Speaker 5: also that it's meaningless. Because chat Gibt kind of combines
Speaker 5: different aspects of what philosophers of language, logicians, linguistics people
Speaker 5: see as like different components of meaning. It sees these
Speaker 5: as all kind of wrapped up.
Speaker 4: In the same thing. It puts them in the same.
Speaker 5: Big model I'm not sure it could differentiate between ungrammaticality
Speaker 5: and meaninglessness.
Speaker 2: So we're doing real time science right now.
Speaker 4: I just yeah, we should check the words get.
Speaker 2: Into the following sentence in the correct way. Man, that's crazy.
Speaker 2: And I did it six times, and I would say
Speaker 2: fifty percent of the time it got it right. And
Speaker 2: it did. Man, that's fucking crazy. Man, that's crazy. Fucking Man,
Speaker 2: that's fucking crazy. Man that's crazy. Fucking My favorite is man,
Speaker 2: comma that's crazy, Comma fucking.
Speaker 6: To be fair.
Speaker 4: Reliable.
Speaker 1: You take the commas out of that last one, and
Speaker 1: you've got a grammatical sentence.
Speaker 2: Yeah.
Speaker 1: Of course in Glasgow you can also start with fucking man,
Speaker 1: that's crazy.
Speaker 2: West London as well. But the thing is, though it
Speaker 2: doesn't know what correct means there.
Speaker 6: Yeah.
Speaker 2: Now, when it's trained on this language, when it's trained
Speaker 2: on thousands of Internet posts that stole, it's not like
Speaker 2: it reads them and says, oh, I get this, like
Speaker 2: I see what they're going for. It just learns structures
Speaker 2: by looking, which is kind of how we learn language.
Speaker 2: But it kind of reminds me of like when I
Speaker 2: was a kid and I'd hear someone say something funny
Speaker 2: I'd to repeat it, and my dad, who's wonderful, would
Speaker 2: just say that doesn't make any sense, and you'd have
Speaker 2: to explain, because if you're learning everything through copying, you're
Speaker 2: not learning, you're just memorizing.
Speaker 6: Yeah, yeah, exactly.
Speaker 5: There's a I don't know if you have already talked
Speaker 5: to somebody about this, but there's a you know, a
Speaker 5: classic argument from child ski against behaviorism.
Speaker 2: Right.
Speaker 5: Behaviorism is the view that we learn everything through listen response.
Speaker 4: Roughly, that's not exactly.
Speaker 5: It, but I'm not a philosopher of mine, so I
Speaker 5: can get away with with that.
Speaker 4: So Chomsky says, look, we don't get enough.
Speaker 5: Stimulus to learn language as quickly as we do just
Speaker 5: through watching.
Speaker 4: Other people's behavior and copying it.
Speaker 5: We have to have some in built grammatical structures that
Speaker 5: language is latching onto. And there's been some papers arguing
Speaker 5: that chatchipt shows Chomsky was wrong because it doesn't have
Speaker 5: the inbuilt grammatical structure.
Speaker 4: But one interesting thing.
Speaker 5: Is it requires ten to one hundred times more data
Speaker 5: than a human child does when learning language. Right, So
Speaker 5: Chomsky's argument was we don't get enough stimulus, and chat
Speaker 5: GPT can kind of do it without the structure, but
Speaker 5: it's not quite doing it as well, and it gets
Speaker 5: like orders of magnitude more more input than a human
Speaker 5: does before a human learns language, which is kind of interesting.
Speaker 4: You're going to do something as basically as putting the word.
Speaker 6: Yeah, yeah.
Speaker 2: It doesn't even see and it doesn't have the knowledge
Speaker 2: to say, request more context because it doesn't perceive context.
Speaker 2: And that's kind of the interesting thing. So there was
Speaker 2: another paper out of Oxver I think that's talking about
Speaker 2: cognition and chat GPT and all this thing, and it's
Speaker 2: just it doesn't feat chat GPT features in no way
Speaker 2: any of the things that the human mind is really
Speaker 2: involved in. It seems it's mostly just not even memorization,
Speaker 2: because it doesn't memorize. It's just guessing based on a
Speaker 2: very blood pile of stuff. But this actually does lead
Speaker 2: me to marvel question, which is, you don't like the
Speaker 2: term hallucination. Why is that hallucination?
Speaker 3: It makes it sound a bit like I'm usually doing
Speaker 3: something right and I'm looking around seeing the world that's
Speaker 3: something like what it really is. And then one little
Speaker 3: bit of the feature for a visual hallucination, one feature
Speaker 3: of my visual field actually isn't represented in the real world,
Speaker 3: it's not actually there.
Speaker 4: Everything else might well might well be right.
Speaker 3: Imagine I hallucinate, there's a red balloon in front of me.
Speaker 3: I still see Mike, I still see James, I still
Speaker 3: see the laptop.
Speaker 4: One bit is.
Speaker 3: Wrong, everything else is right, and I'm doing the same
Speaker 3: thing that I'm usually doing, Like my eyes are still
Speaker 3: working pretty much normally, I think, I and this.
Speaker 4: Is the way I usually get knowledge about the world.
Speaker 3: This is a pretty reliable process for me learning from
Speaker 3: it right and representing the world in this way. So
Speaker 3: when talking about hallucinations, this suggests that Chatgypt and other
Speaker 3: similar things they're going through this process that is usually
Speaker 3: quite good at representing the world. And then oh, it's
Speaker 3: made a mistake this one time, but actually no, it's
Speaker 3: bullshitting the whole time, and like sometimes it gets things
Speaker 3: right right, bullshitting just like a politician.
Speaker 4: Imagine a politician that bullshits all the time, if you.
Speaker 3: Could possibly imagine it, Sometimes they might just get some
Speaker 3: things true, and we should still call them bullshitting because
Speaker 3: that's what they're doing. And this is what at GPT
Speaker 3: is doing every time it produces an output. So this
Speaker 3: is why we think bullshit is a better way of
Speaker 3: thinking about this. One of the reasons why we think
Speaker 3: bullshit's better way think about it.
Speaker 5: I also kind of think that some of the ways
Speaker 5: we talk about chat gpt LIN, even when it makes mistakes,
Speaker 5: lind themselves to overhyping its ability or overestimating its abilities,
Speaker 5: and talking about it is hallucinating. Is one of these
Speaker 5: because when you say that it's hallucinating, as you're pointed out,
Speaker 5: you're giving the idea that it's representing the world in
Speaker 5: some way and then telling you what the.
Speaker 2: Content and it has perception.
Speaker 1: Yeah, exactly, like it has perceived something and like, oh no,
Speaker 1: it's taken some computer acid and now it's hallucinating like
Speaker 1: mastery things and yeah, just as you say, and.
Speaker 4: That's not what it's doing.
Speaker 5: And so when the kind of people who are trying
Speaker 5: to promote these these things as products talk about the
Speaker 5: AI hallucination problem, they're kind of selling a product that
Speaker 5: is a product that's representing the world usually checking things
Speaker 5: and occasionally makes mistakes. And if the mistakes were, like
Speaker 5: Joe said, we're a misfiring of a normally a reliable
Speaker 5: process or you know, something that normally represents going wrong
Speaker 5: in some way that would lend itself to certain solutions
Speaker 5: to them, and it would make you think there's an
Speaker 5: underlying reliable product here, right, which is exactly what somebody
Speaker 5: who's making a product to go on the market will.
Speaker 4: Want you to think.
Speaker 3: Right.
Speaker 5: But if that's not what it's doing in a certain sense,
Speaker 5: they're misrepresenting what it's doing even when it gets things right.
Speaker 4: And that's that's bad for all of us who are
Speaker 4: going to be using these.
Speaker 5: Systems, especially since people, you know, most people don't know
Speaker 5: how this works. They're just understanding the product as it's
Speaker 5: described and using these kind of metaphors. So the way
Speaker 5: the metaphor describes it's going to really influence how they
Speaker 5: think about it and how they use it.
Speaker 6: Yeah, just to sort of cat fire if I can.
Speaker 1: Is one of the responses to some corners has been
Speaker 1: to sort of say, of us, look, you winch about
Speaker 1: people anthropomorphosizing chat GPT, but look, if you've got a bullshity,
Speaker 1: you're doing exactly the same thing. And I mean there
Speaker 1: might be some center which it's just really hard not
Speaker 1: to anthropomorphise it. I don't know why I picked the
Speaker 1: word that I can barely say, like we've been doing
Speaker 1: it constantly throughout this discussion. Right when we were talking
Speaker 1: through the kind of through the paper, we kept talking
Speaker 1: about chat GPT as if it had intention, right, as
Speaker 1: if it was thinking about anything. That might be another
Speaker 1: reason to call it bullshit. We go, look, if we
Speaker 1: have to treat it as if it's doing something like
Speaker 1: what we do, it's not hallucinating, it's not lying, it's
Speaker 1: not confabulating, it's bullshitting, right, And if we have to
Speaker 1: treat it as if it's behaving in some kind of
Speaker 1: human like way, here's the appropriate human like behavior to
Speaker 1: describe it.
Speaker 2: I also think the language in this case, and one
Speaker 2: of the reasons they probably really like the large language
Speaker 2: model concept is language gives life too things when we
Speaker 2: describe the processes through which we interact with the world
Speaker 2: and interact with living beings, even cats, dogs, even we
Speaker 2: anthropomiz living things. But also when we communicate with something,
Speaker 2: languages life, and so it probably works out really fucking
Speaker 2: well for them. Samultman was saying a couple of weeks back,
Speaker 2: maybe a month or two. He was saying, oh, yeah,
Speaker 2: AI is not a creature with something he said, and
Speaker 2: it was just so obvious what he wanted people to
Speaker 2: do was say, but what if it was or are
Speaker 2: people saying this is a creature and it almost feels
Speaker 2: like just part of the con Yeah, yeah, yeah.
Speaker 5: I hadn't thought about that as a reason for them
Speaker 5: to go for large language models as a way of
Speaker 5: kind of, I don't know, being the gateway into more investment.
Speaker 2: Into fight consciousness.
Speaker 6: Yeah.
Speaker 5: Yeah, but I had thought about, like how this might
Speaker 5: have been caused by just like deep misunderstandings of returning test.
Speaker 2: Right, go ahead, hear this one.
Speaker 5: Yeah, yeah, so that like the Turing test, I think
Speaker 5: this is closer to what Turing was thinking. But the
Speaker 5: turning test is a way of getting evidence that something
Speaker 5: is conscious. Right, So you know, I'm not in your head,
Speaker 5: so I can't feel your feelings or think your thoughts directly. Right,
Speaker 5: I have to judge whether you're conscious based on how
Speaker 5: you interact with me, and the way I do it
Speaker 5: is by listening to what you say, right, and talking
Speaker 5: to you and turning sort.
Speaker 4: Of was asked, you know, how would you know if
Speaker 4: a computer was conscious?
Speaker 5: So, you know, we think that our brains are doing
Speaker 5: something similar to what computers do. That's a reason to
Speaker 5: think that maybe computers eventually could have thoughts.
Speaker 4: Like ours, right, and some of us I think that
Speaker 4: I think it's possible. It's great.
Speaker 5: Yeah, I didn't know if they thought it was possible,
Speaker 5: because not everybody thinks it's possible.
Speaker 2: It's possible. I just don't know how.
Speaker 4: Yeah, yeah, exactly. So Turing was kind of, you know,
Speaker 4: thinking like, how would we know? And one way we
Speaker 4: would know.
Speaker 5: The obvious way is to do the same thing you
Speaker 5: do to humans, talk to it and see how it responds.
Speaker 5: And that's actually pretty good evidence if you don't have
Speaker 5: the ability to look deeper. But it's not constitutive of
Speaker 5: being conscious. It's not what makes something conscious or determines
Speaker 5: whether they're conscious or in any way like grounds their consciousness. Right,
Speaker 5: Their ability to talk to you is just evidence. It's
Speaker 5: just one signal you can get. And that's the way
Speaker 5: to think of the Turing test. So as a result
Speaker 5: of people thinking in a kind of behaviorist way, thinking, ah,
Speaker 5: passing the Turning test is just all it is to
Speaker 5: be a thinking thing. There have been, at least since
Speaker 5: the nineties, attempts to design chatbucks that can be the
Speaker 5: Turning test, right, and popularizations of these attempts and run
Speaker 5: throughs of the Turing test that talk as if, oh,
Speaker 5: if a computer finally beats the.
Speaker 4: Turing test, I should say what the Turing test is?
Speaker 5: Right? The way turns suggests that the test works as
Speaker 5: you have a computer and a person both chatting in
Speaker 5: some way with a judge, and the judge is also
Speaker 5: a person, right, And if the judge can't tell which
Speaker 5: of the people he's chatting with is a human, then
Speaker 5: the computer's one the right test because it's distinguishable from
Speaker 5: a human.
Speaker 4: Right.
Speaker 5: So people have taken this and it's been popularized as
Speaker 5: a way of determining sort of full stop, whether something's conscious.
Speaker 4: But it's just a piece of evidence, and we have
Speaker 4: a lot.
Speaker 5: More evidence, Like we know a lot more than Turning
Speaker 5: did about how the internal functioning of a mind works functionally,
Speaker 5: what it's doing, how representation works, how experience and belief works,
Speaker 5: how they are connected to action, and how they're connected
Speaker 5: to speech and thought. And once you know all that stuff,
Speaker 5: you have a lot of other avenues to get more
Speaker 5: evidence about whether the thing is conscious and whether it passes.
Speaker 5: The Turning test is just like a drop in the
Speaker 5: bucket compared to these, especially if you know how it's
Speaker 5: internal functioning.
Speaker 1: Is the other the story is problem with the showing test,
Speaker 1: and I think to be fair, Cheuring did mention this.
Speaker 1: If not in the original then kind of later on. One
Speaker 1: problem with the chewing test is that it's like the
Speaker 1: de void camp in de androids gene of electric cheap right.
Speaker 1: Plenty of humans would fail the cheering test. Yeah, it
Speaker 1: is a piece of evidence, but it was never as
Speaker 1: Mike said, it was supposed to be constitutive. It wasn't like,
Speaker 1: if you can do this thing, your conscious kind of
Speaker 1: full stops. It was supposed to be here is a
Speaker 1: thing that might indicate that being you're talking to is conscious.
Speaker 4: Happily, as my said, we've got the loads and loads
Speaker 4: of other evidence.
Speaker 5: So these guys have made a machine that's just designed
Speaker 5: to do one thing, and that's past the Turing test.
Speaker 2: I can give you one more annoying example. Are you
Speaker 2: familiar with Francoise Chole the abstraction and reasoning corpus, So
Speaker 2: this is going to make you laugh. So he's a
Speaker 2: Google engineer and he created this thing of the arc
Speaker 2: the abstraction reasoning corpus, the test whether a machine was intelligent,
Speaker 2: and someone created a model that could be it, and
Speaker 2: then he immediately went, Okay, you can't just train the
Speaker 2: model on the answers to the test.
Speaker 1: This is why people, well I see people a fairly
Speaker 1: small subset of weird nerds, But this is why a
Speaker 1: small subset of weird nerds have been for the last
Speaker 1: twenty years emphasizing artificial general intelligence, right, and kind of
Speaker 1: like what we'll call it. When something really is a
Speaker 1: thinking being is when it's not specialized to do one
Speaker 1: and only one task, but rather when it's capable of
Speaker 1: applying reasoning to multiple kinds of different and this analogous cases.
Speaker 1: On the one hand, it does seem a little bit
Speaker 1: like the guy flipping the table and go, oh, for
Speaker 1: fox sake, now you've one. I'm changing the rules. But
Speaker 1: on the other I think he's got a point, right, Yeah,
Speaker 1: if you're training a thing to do very specific you know,
Speaker 1: like activate certain shiboleths, then unless you're some kind of
Speaker 1: mad hard behaviorist, then yeah, like it's not that doesn't
Speaker 1: demonstrate intelligence. It is one thing that might indicate intelligence.
Speaker 2: It's the same problem with the chat GPT. It's built
Speaker 2: to resemble intelligence and resemble conscious This will resemble these things,
Speaker 2: but it isn't. It's almost like it's meaningless. On a
Speaker 2: very high end philosophical I find the whole generative a
Speaker 2: I think deeply nihilistic.
Speaker 5: I mean, one thing that connects to this is how
Speaker 5: bad it is at reasoning. And this is kind of
Speaker 5: good for us, especially in philosophy, because our students when
Speaker 5: they use it to write papers, the papers have to
Speaker 5: have arguments, and chat GPT is very bad at doing reasoning.
Speaker 5: If it has to be you know, sort of extended
Speaker 5: argument or a proof or something like that, it's very
Speaker 5: bad at it. I think also that if there's one
Speaker 5: thing kind of we learned from chat GPT, it's that
Speaker 5: this is not the way to get to artificial general intelligence.
Speaker 2: I was going to ask, do you think that this
Speaker 2: is getting to that?
Speaker 5: No, partially because it's so subject specific, right, It's one
Speaker 5: it's trained to do one task, and it takes quite
Speaker 5: a lot of training to get it to do that task. Well,
Speaker 5: it's bad at many of the other tasks that we
Speaker 5: think are connected with intelligence. It's bad at logical and
Speaker 5: mathematical reasoning. I understand that open AI is trying to
Speaker 5: fix that. Some sometimes it sounds like they want to
Speaker 5: fix it by just connecting it to a database or
Speaker 5: a program that can do that for it. But either way,
Speaker 5: when you have with these kind of big base nets models,
Speaker 5: it's something that is really good at whatever you train
Speaker 5: it to do, but not going to be good at
Speaker 5: anything else. You know, it's got a lot of data
Speaker 5: on one thing. It finds patterns in that, it finds
Speaker 5: regularities in that, it represents those. The more data you
Speaker 5: feed it, the better it will be at that. But
Speaker 5: it's not going to have this kind of general ability,
Speaker 5: and it's not going to grow it out of learning
Speaker 5: how to speak English.
Speaker 4: Have you heard the Terry Pratchett quote about It's quite
Speaker 4: early on.
Speaker 1: He's talking about Hex, the kind of steampunk computer a
Speaker 1: make and unseen university, and it just has this off
Speaker 1: hand line. A computer program is exactly like a philosophy professor.
Speaker 1: Unless you ask it the question in exactly the right way,
Speaker 1: it will delight in giving you a perfectly accurate, completely
Speaker 1: unhelped answer.
Speaker 6: Right. So if you abstract that justified you got a
Speaker 6: philosophy lecture.
Speaker 4: Is there?
Speaker 1: Basically what intelligent things do is go You can't have
Speaker 1: meant that, you must have meant this right, chat GPTs goes,
Speaker 1: I will take a question as read. I mean, of
Speaker 1: course it doesn't have an eye. There is nothing in
Speaker 1: the rest. Like I've just answered, formal play it again.
Speaker 1: But it's the same thing. And if trained to do
Speaker 1: attractively specific things, and you get the same problem as
Speaker 1: any program like garbage in Garbage album.
Speaker 2: So have you found a lot of students using chat GBT,
Speaker 2: because this is a hard problem to quantify? Is it
Speaker 2: all the time?
Speaker 6: I mean it's it's a lot.
Speaker 4: I wouldn't say all the time.
Speaker 6: I changed that you.
Speaker 3: Thought that there were more seas indeed, yeah, so I
Speaker 3: think there are quite a few. And sometimes it is
Speaker 3: difficult for us to prove and if we can't prove
Speaker 3: it then at our university, then.
Speaker 4: Well shucks, they kind of get away with it.
Speaker 3: We can interview them, but unless we're like, unless we
Speaker 3: have the proof that you could take before like a court,
Speaker 3: then we are we're not able to really nail them
Speaker 3: for it, which is a bit of a shame. So suspicion, Yeah,
Speaker 3: we suspect that a lot of them are very I'm
Speaker 3: one hundred percent on some of them.
Speaker 2: I just know what are the clues I want to
Speaker 2: hear from all of you on this one, like, what
Speaker 2: are the side?
Speaker 6: It's the uncanny value.
Speaker 1: I will not shut up about this, right you know
Speaker 1: that like you of course will be imagine a lot
Speaker 1: of your kind of the reader will be as well.
Speaker 4: But the uncanny Valley.
Speaker 1: Thing in respective humans is that there's a kind of
Speaker 1: a point up to which humans seem to trust artificial
Speaker 1: things more the more they look like a human. So like,
Speaker 1: we'll trust a robot if it's kind of bipedal or
Speaker 1: if it looks like a dog, But after a point,
Speaker 1: we really rapidly start to trust me. So like if
Speaker 1: it basically when it starts looking like data, some deep
Speaker 1: buried lizard brain goes danger.
Speaker 6: Danger. This thing's trying to fuck with you somehow.
Speaker 4: Yeah, right, and.
Speaker 6: Chatter GPT writes exactly like that.
Speaker 1: It writes almost, but not entirely, like a thing that
Speaker 1: is trying to convince you that it's human.
Speaker 4: I mean, the dead giveway for me is that it never.
Speaker 1: Makes any spelling mistakes, but it can't form out a
Speaker 1: paragraph to save its life. Normally, you would expect someone
Speaker 1: who didn't know how to kind of order their sentences
Speaker 1: to misspell the occasional word chat GPT spells everything correctly
Speaker 1: and doesn't know what like subject object agreement is.
Speaker 6: It's like, it's.
Speaker 2: So, can you just define that, please? Subject object the.
Speaker 6: Beer that I drink, right, not the drink that I bear.
Speaker 2: Right, Because it's interesting. It almost feels like there is
Speaker 2: just an entire way of processing and delivering information as
Speaker 2: a human being that we do not understand. There is
Speaker 2: something missing.
Speaker 5: I mean, for me, like it's very good at summarizing,
Speaker 5: but when it responds, it responds in them in really
Speaker 5: trite and repetitive ways, or like you'll get a paper
Speaker 5: that summarizes a bunch of literature at length very effectively,
Speaker 5: and then respond by saying, well, you know, this person
Speaker 5: said this, that is doubtful. They should say more to
Speaker 5: support that, you know, which is basically saying nothing right,
Speaker 5: And that's pretty common.
Speaker 4: It also does lists in formats things and kind of
Speaker 4: like a list.
Speaker 5: And even if this like make it look less like
Speaker 5: a list, the paper still reads like a list.
Speaker 2: Oh, because someone has asked give me a few thoughts
Speaker 2: on X. Yeah, yeah, that's very depressing.
Speaker 3: Things like the introductions you get you'll get this is
Speaker 3: an interesting and complex question that parst of us have
Speaker 3: asked you the ages, and this is one of the
Speaker 3: things we shouted students not to write from first year.
Speaker 4: And then you'll get this garbage right back at.
Speaker 3: You, and then at the end it will be oh overall,
Speaker 3: this is a complicated and difficult question with many nuancewers.
Speaker 2: It's a world of contrasts.
Speaker 3: One of the things we tell them, don't ever fucking
Speaker 3: do this. This is terrible and right back at you
Speaker 3: in perfect English. That's one of English you'd expects a
Speaker 3: really good student might have written. But clearly they can't
Speaker 3: be a good student because otherwise they've listened to a
Speaker 3: fucking instruction.
Speaker 4: I also, I had some papers that I suspected were
Speaker 4: chatept this year, but they were already failing, so I
Speaker 4: didn't okay, yeah, I didn't think it was worth it
Speaker 4: to pursue them, you know, as a plagiarism or Chatchepte case.
Speaker 2: So it's never good papers. Then it's never like an eight,
Speaker 2: like a first.
Speaker 6: No, absolutely not.
Speaker 4: It's so I think that part of what goes on
Speaker 4: is you can get a passing grade with the Chatcheapte paper.
Speaker 4: Sometimes in the first couple of years, when the papers
Speaker 4: are shorter and we're not expecting as much. But then
Speaker 4: when you move into the what we call honors level here,
Speaker 4: which is like upper level classes in the US, like
Speaker 4: third and fourth.
Speaker 2: Year, I've got first ever wrist with I know.
Speaker 4: Yeah, yeah, exactly, great, well done.
Speaker 7: Well you would not have gotten it with chat Gipt
Speaker 7: because you get dropped in these classes where we expect
Speaker 7: you to have gained writing skills minimal ones in your
Speaker 7: first two years and then we're going to build on
Speaker 7: that and have you do more complicated stuff, and chat
Speaker 7: Gipt doesn't build on that, right, it just stays where
Speaker 7: it was. So you go from writing kind of C
Speaker 7: grade passing papers, yeah, your F grade paper.
Speaker 5: And it's also more obvious because the papers are longer,
Speaker 5: and chat gypt can write long text. But it gets
Speaker 5: very repetitive and noticeably repetitive, right, and so you're kind
Speaker 5: of lost, like you haven't done the work of figuring
Speaker 5: out how to write on your own and the tool
Speaker 5: that you've been using is not up to the task
Speaker 5: that you're now presented with. And so I think I
Speaker 5: have seen a few papers that I was suspicious of,
Speaker 5: but the papers that I was certain of were ones
Speaker 5: that were like senior thesis, the very clear person just
Speaker 5: had no way of writing culture.
Speaker 2: That's insane at that stage.
Speaker 1: Yeah, I mean it's fun because I mean one of
Speaker 1: the things that we get told about is like, oh,
Speaker 1: students have got to learn how to use you know,
Speaker 1: AI and large language model plagiarism machines responsibly and in
Speaker 1: a kind of positive way. Well, if they're using them
Speaker 1: in a way that means they don't learn how to write,
Speaker 1: then it's not positive. Visit like, yeah, it's fucking hard
Speaker 1: to write a good essay. Yes, it is hard to write.
Speaker 1: That's why we practice, That's why we have editors, that's
Speaker 1: why we do this collaboratively. And if you're using this
Speaker 1: as a sort of oh I don't know how to write, well,
Speaker 1: tough shit, you're never going to know how to write that.
Speaker 1: That doesn't seem a positive use of any of this.
Speaker 2: Well that's the thing. Writing is also reading the consumption
Speaker 2: of information and then bouncing the ideas off of your
Speaker 2: brain allegedly.
Speaker 5: Yeah, I worry about like, because CHATCHBT is good at summarizing,
Speaker 5: So I worry that one of the uses people will think, ah,
Speaker 5: this is a pretty good use for it is summarizing
Speaker 5: the paper that they're supposed to read, and it will
Speaker 5: do that effectively enough for them to discuss it in
Speaker 5: class if they're.
Speaker 6: Willing to be here.
Speaker 4: Yeah, right, but they're.
Speaker 5: Not going to pick up a lot of the nuances
Speaker 5: in a lot of the kind of like stylistic ways
Speaker 5: of presenting ideas that you get when you actually do
Speaker 5: the reading.
Speaker 2: And it's it's so frustrating as well, because like for this,
Speaker 2: for example, I've just got the printing off things that
Speaker 2: don't read PDFs anymore because I feel like you do
Speaker 2: need to focus.
Speaker 1: Yeah, there's some evidence that reading physical copies makes you
Speaker 1: engage more.
Speaker 2: Sorry I'm very old fashioned, I guess, but no, it's
Speaker 2: true though, But also reading that I wouldn't have really
Speaker 2: on a PDF. I wouldn't have given it as much attention.
Speaker 2: But also, going through this paper, you could see what
Speaker 2: you were doing, like you could see that you were
Speaker 2: lining up. Here are the qualities that we use to
Speaker 2: judge bullshit. But also summarizing a paper does not actually
Speaker 2: give you the argument, it gives you an answer. So
Speaker 2: what do you actually want students to do instead? Because
Speaker 2: I don't think there's any reason to use chat GPT
Speaker 2: for these reasons that it doesn't seem to do anything
Speaker 2: that's useful for them.
Speaker 5: I don't have, no, I don't actually have any use
Speaker 5: for chat shabt that I can put to my students
Speaker 5: and say, here's.
Speaker 4: What I think you should do with it.
Speaker 5: We are like kind of developing strategies for keeping them
Speaker 5: from using it, So like building directly on what you're saying.
Speaker 4: Like, in my class.
Speaker 5: Next year, I'm going to have the students do regular assignments,
Speaker 5: which are argument summaries and not paper summaries. So the
Speaker 5: idea is they have to read the paper, find an argument,
Speaker 5: and tell me what are the premises, what's the conclusion?
Speaker 5: And that's something that chatchept is not good at, right,
Speaker 5: But it's also something that will give them critical reading skills,
Speaker 5: which is what I want to do.
Speaker 4: Right. So Yeah, I think that I've.
Speaker 5: Mostly been thinking about ways to keep them from relying
Speaker 5: on it because I think that often if they rely
Speaker 5: on it, they'll they'll they'll put.
Speaker 4: Themselves in the worst position.
Speaker 5: Yeah, when it comes to future work, they won't develop
Speaker 5: the skills that they're going to need, and the skills
Speaker 5: that we tell them and their parents they're gonna get
Speaker 5: with their college degree.
Speaker 2: Right, it almost feels like we need more deliberacy in
Speaker 2: university education because I was not taught to write. I
Speaker 2: just did a lot of it until I got good
Speaker 2: enough grades and Daniel CHOYNDL look great mental, but I've
Speaker 2: had tons of them, and it almost feels like we
Speaker 2: need classes where it's like, Okay, no computer for this one.
Speaker 2: I'm gonna print this paper out and you're going to
Speaker 2: underline the things that are important and talk to me
Speaker 2: about Almost feels like we need to rebuild this because yes,
Speaker 2: we shouldn't be using chet GPT to half us our essays.
Speaker 2: But at the same time, human beings are lazy.
Speaker 5: Yeah, I mean for me, I also prefer to read
Speaker 5: off the computer, but I often read PDFs because I'm
Speaker 5: terrible at keeping files, right, physical Like, you know, I'm
Speaker 5: not going to keep a giant filed for with all
Speaker 5: the papers that I've read and then written my liner
Speaker 5: notes in.
Speaker 4: You guys can see in my office they're just piled around,
Speaker 4: like you can't see this end.
Speaker 2: That's academia.
Speaker 5: Yeah, but I just have piles of paper with empty
Speaker 5: coffee bugs everywhere that the camera is not facing.
Speaker 4: But it's the terrible system so at least.
Speaker 5: On my computer, if I'm like, oh, I read that
Speaker 5: paper like a year ago, what did I think?
Speaker 4: I can click on it and see my own notes, and.
Speaker 5: I do think that there's something to keeping those records
Speaker 5: and kind of actively.
Speaker 4: Reading in that way. I don't know how I ended
Speaker 4: this without telling you how to make students do that. No,
Speaker 4: you started with the correct answer, which is don't use
Speaker 4: chat GPT. Yeah.
Speaker 6: I actually I've got a certain amount of sympathy with like,
Speaker 6: just keep writing it till you get good at it.
Speaker 1: But I realized as a lecturer of that camp in
Speaker 1: my official possession, and I certainly think that it's the
Speaker 1: case that certainly Glasgow has got better over the last
Speaker 1: few years about going, oh, actually you do like, we
Speaker 1: do need to give you some kind of structuring and
Speaker 1: some buttressing on here's how to write academically, here's how.
Speaker 6: To do research.
Speaker 1: And I think that's all to the good. It's worth
Speaker 1: saying this started happening well before chat GPT's started pissing
Speaker 1: a lover our doorsteps, so they don't get to claim
Speaker 1: that as being a benefit.
Speaker 2: There was the whole Wikipedia panic when I was in school.
Speaker 6: Yeah, they think about Wikipedia the right.
Speaker 4: It's like I should to say this to my students.
Speaker 2: WIKIPII one of the best resources.
Speaker 1: Absolutely fine as a starting point for research, absolutely with
Speaker 1: it whatsoever. But if you're turning in and on his level, essay,
Speaker 1: I want you to go and read the fucking things
Speaker 1: it's referencing.
Speaker 4: Yeah, that's right.
Speaker 5: Yeah, I think these things are often great as sources.
Speaker 5: I'm worry about chat GPT is that it's not great
Speaker 5: as a source.
Speaker 2: It's just yeah, we've.
Speaker 5: Been saying it often gets things wrong, and it often
Speaker 5: it'll make that sources, whereas Wikipedia will never do that.
Speaker 6: There are some famous hoaxes, but they get cool.
Speaker 1: Yeah, Joe, have you got any positive things to say
Speaker 1: about chat gipt.
Speaker 4: Fan?
Speaker 3: So I know some people who have used these kinds
Speaker 3: of things productively, not in ways that our students would,
Speaker 3: but I know some mathematicians who have been using it
Speaker 3: to do sort of informal proofs and things like that.
Speaker 3: And it does still bullshit, and it bullshits very convincingly,
Speaker 3: which makes it very difficult to use for this kind
Speaker 3: of purpose.
Speaker 4: But it can do some interesting and cool things.
Speaker 3: Yeah, I think some people in that sort of field
Speaker 3: have found useful and also We've mentioned this before, like,
Speaker 3: if you want CHAT to be teach and write, you're
Speaker 3: a bibliography. You've got a bibliography in one style. Tell
Speaker 3: it to put something into a different one. Then it's that,
Speaker 3: And it's good for like coding the process doing certain things.
Speaker 4: I also think, I don't know, I'm not sure how
Speaker 4: I feel about what I'm about.
Speaker 2: To say, but perfect, Yeah.
Speaker 4: I'm going for it.
Speaker 5: It is a somewhat positive thing maybe for a CHAT GPT,
Speaker 5: which is that we often have students who have like
Speaker 5: really interesting ideas and well thought out arguments, but for
Speaker 5: whom English isn't their first language, and the.
Speaker 4: Actual writing is kind of rough and you have.
Speaker 5: To like push through reading it to get the good idea,
Speaker 5: which is often really there and quite creative and insightful. Yeah,
Speaker 5: and so I do wonder if there's a way to
Speaker 5: use it so it just smooths off the edges of
Speaker 5: this kind of thing. But I worry that if you
Speaker 5: tell students to do that, they'll just first if they
Speaker 5: can develop the language skills.
Speaker 4: They offering really good by the year. Yeah, what are
Speaker 4: you going to say? Do well?
Speaker 1: So I want to say, yeah, you can see me
Speaker 1: getting agitated. Yeah, I think much correct about it, like
Speaker 1: that this is a kind of possible use. But I
Speaker 1: think this and this is why I'm getting visibly agitated here.
Speaker 1: That's students either need to or feel they need to use.
Speaker 1: This speaks to a deeper issue, right to a social issue,
Speaker 1: to political issue, to an issue about how universities work.
Speaker 1: If a student is having problems with English, then there's
Speaker 1: a number of like explanations or a number of kind
Speaker 1: of responses.
Speaker 3: Right.
Speaker 1: The one response is that, like Glasgow is an English
Speaker 1: teaching university, if someone's English isn't good enough to be
Speaker 1: taking a degree, then possibly it shouldn't have been let in,
Speaker 1: and why have they been learned well?
Speaker 6: Because of money?
Speaker 1: Or alternatively, if someone's having problems with English for like
Speaker 1: whatever reason at all, there should be supported. There should
Speaker 1: be kind of tutors. They should be people who can
Speaker 1: with English. But again that would cost university money. So
Speaker 1: of course that doesn't happen.
Speaker 6: Right, and it doesn't happen anywhere that it doesn't extent.
Speaker 1: It would have to happen in order for this to
Speaker 1: be a general policy.
Speaker 5: Right, Yeah, I think it could be better, But I
Speaker 5: do think that universities often have like a writing center
Speaker 5: or a tutoring center that you can.
Speaker 6: Send, but they don't.
Speaker 1: They don't have the sort of spread that would be
Speaker 1: needed or the staff that would be needed for this
Speaker 1: to be Instead of using chat GPT to sound the edges.
Speaker 4: Off, yeah, I think for example twenty three with your supervisor.
Speaker 5: My worry especially would be that this is my first
Speaker 5: year here at Glasgow.
Speaker 4: But I probably have a good Yeah.
Speaker 5: I think they probably have a good writing center. Universities
Speaker 5: have been in the past. I felt very confident sending
Speaker 5: students to the writing center when they have these problems.
Speaker 5: But I think James is completely right that we don't
Speaker 5: want the universities to see this as a way to
Speaker 5: get rid of the writing center. And that's one hundred
Speaker 5: percent a risk given the financial problems.
Speaker 4: That universities are facing, and maybe they're already not. Yeah,
Speaker 4: Like given the quality of papers, they're often good.
Speaker 5: I think another thing, as far as this is like
Speaker 5: a social problem, is that when grading, I myself tried
Speaker 5: to grade in terms of like the ideas and argument,
Speaker 5: because this is a philosophy and not the quality of
Speaker 5: the right. But not everybody does that. So I kind
Speaker 5: of think that another part of this is figuring out
Speaker 5: how we want to evaluate the students and what we
Speaker 5: want to privilege in that evaluation.
Speaker 6: Yeah.
Speaker 1: Sure, So then again the kind of that that becomes
Speaker 1: a problem about what people are checking for. Not let's
Speaker 1: take this arched backwards approach to marketing, which is like
Speaker 1: how fancy is your English?
Speaker 6: Are fancy English? Is good English? Have an a? But
Speaker 6: rather we should be kind.
Speaker 4: Of checking for different things.
Speaker 1: Right, So again the blame blows differently in that case,
Speaker 1: but it still becomes a question.
Speaker 4: It's not solved technological.
Speaker 2: Yeah, almost feels like large language models are taking advantage
Speaker 2: of a certain kind of organizational failure.
Speaker 4: What an idea?
Speaker 2: Crazy that the tech industry manipulating parts of society.
Speaker 6: That wait, I have a kind.
Speaker 5: Of related tangent here, which is like what are the
Speaker 5: use cases that open ai was expecting but didn't want
Speaker 5: to emphasize Because for everybody in universities, as soon as
Speaker 5: this came out, the first thought was students are going
Speaker 5: to use this to cheat And certainly like the people
Speaker 5: in open ai went to college, right, and that's what
Speaker 5: I hear about.
Speaker 2: Them, so they must well, sam Oman drop down, Oh yeah, I'm.
Speaker 5: Sure he really understands the value of a secondary He's like, I'm.
Speaker 2: Gonna write at least fucking essays.
Speaker 5: Anyway, Maybe he was thinking I would have loved to
Speaker 5: have a computer write my essays.
Speaker 4: I'll devote my life.
Speaker 5: But I mean, I'm sure that they like recognize these
Speaker 5: bad use cases, right, but they're doing nothing to mitigate
Speaker 5: them as far as I can see. And like, another
Speaker 5: one that's very related is like, you know, I'm sure
Speaker 5: you've heard of this ed phishing, right, a lot of
Speaker 5: you know, corporations get attacked and get hacked not by
Speaker 5: someone cleverly figuring out a backdoor to their system, but
Speaker 5: by somebody sending.
Speaker 2: A social engineering.
Speaker 5: Yeah, asking for the password to somebody else. And one
Speaker 5: of the biggest barriers to that is that a lot
Speaker 5: of the people who are engaging in fishing aren't from
Speaker 5: the same country as the company they're targeting, right, so
Speaker 5: they're not able to write a convincing email or make
Speaker 5: a phone call that sounds like that person's supervisor. But
Speaker 5: with a tool like this, you could one percent write
Speaker 5: that email. Right. It's going to make it a lot
Speaker 5: easier for these kinds of illicit schemes to work.
Speaker 2: That has been a market increase CNBC, which would just
Speaker 2: brow up, is that on two hundred and sixty five
Speaker 2: percent increase in malicious fishing emails since the launch of
Speaker 2: chat JPTA. Great style. I mean if I could have
Speaker 2: thought of that, imagine what a criminal could do.
Speaker 6: Right.
Speaker 4: But also, weren't the people at open AI thinking about.
Speaker 2: That because they don't care?
Speaker 4: Yeah?
Speaker 6: Yeah, we will see in duastic fuck right.
Speaker 3: Yeah.
Speaker 1: Yeah, they were so busy thinking about what they could
Speaker 1: do they never thought about whether they should.
Speaker 5: Yeah, this is the kind of problem with the move
Speaker 5: fast and break things mentality, like obvious.
Speaker 4: I mean, I think it might be the only person
Speaker 4: who has raised in the US.
Speaker 8: But we had future problems solvers, where you you know,
Speaker 8: you think about a future problem and what bad consequences
Speaker 8: there could be of some technology and how to solve them,
Speaker 8: usually through social cues. If I could do that in
Speaker 8: fifth grade.
Speaker 5: I would expect these people to have thought through some
Speaker 5: of the bad consequences of the technology they're putting out.
Speaker 5: And you know, some of those are cheating on tests
Speaker 5: and they don't seem to have worried about that.
Speaker 4: Yeah, And another one is fishing. They don't seem to
Speaker 4: have a worried about that.
Speaker 1: You know, biases and algorithms, right, So yeah, yeah, comes
Speaker 1: no surprise to you. It turns out so with a
Speaker 1: lot of the facial recognition systems they were incredibly racist.
Speaker 2: They were going back to Microsoft's connect they could not
Speaker 2: see back.
Speaker 1: Yeah yeah, and CCTV stuff that basically just sort of nless.
Speaker 1: It was presented with a blindingly white Caucasian.
Speaker 6: Where I don't know, right, but like.
Speaker 1: The sort of stuff where these like language knowledge are
Speaker 1: trained on certain sets of data, and they trained on
Speaker 1: certain assumptions and like shitting shit out right, and particularly
Speaker 1: if people think that it's actually doing any kind of thinking,
Speaker 1: and if they kind of cargo cult it, we again
Speaker 1: get a kind of social problem multiplied by technology feeding
Speaker 1: back into a social problem, right. And it's the So
Speaker 1: these guys have heard me when you're about it so much,
Speaker 1: I love it or so, but I'm profoundly skeptical of
Speaker 1: technology's ability to solve anything unless we know exactly the
Speaker 1: respect in which we want to solve it and how
Speaker 1: that technology is going to be applied. You know, like sure,
Speaker 1: experiment with bringing back dinosaurs, but but like, don't tell
Speaker 1: me that it's going to save the healthcare system unless
Speaker 1: you can demonstrate it to be step by step. How
Speaker 1: that big old rex run around on Eiland Nublade is
Speaker 1: going to save anything.
Speaker 6: They just tried to blind.
Speaker 3: People actually bringing back dinosaurs.
Speaker 4: That would be great.
Speaker 1: All right, this isn't the best example, but I already had.
Speaker 1: I had Jeff Gobwoman in my head.
Speaker 2: I had to go to the fellas. This has been
Speaker 2: such a pleasure. Don't put that in the recording.
Speaker 4: That's right.
Speaker 2: We won't edit it in post, and you will give
Speaker 2: us your names.
Speaker 5: I'm Mike Hicks, but my papers are written by Michael
Speaker 5: Townsend Hicks and I'm a lecturer at the University of Glasgow.
Speaker 4: My website is Hicks.
Speaker 2: It will be in the podcast profile.
Speaker 6: Don't you worry. We'll get that just plugging my luggy stuff.
Speaker 4: My name is Joe Slater.
Speaker 3: I'm a university lecturer in moral and political policy at Glasgow.
Speaker 6: I'm James Joffrees.
Speaker 1: I'm a lecturer in political theory at the University of Glasgow.
Speaker 4: And even if I.
Speaker 6: Wanted to or couldn't give you what websites, I don't have.
Speaker 2: Everyone you've been listening to Better Offline, thank you so
Speaker 2: much for listening. Everyone guys, thank you for joining me.
Speaker 4: Thanks having me here.
Speaker 6: This is.
Speaker 2: Thank you for listening to Better Offline. The editor and
Speaker 2: composer of the Better Offline theme song is Matasowski. You
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