Speaker 1: Cauzone Media.
Speaker 2: Hey everyone, it's me ed Ze Tron and we're doing
Speaker 2: a rerun episode this week. Sadly, the in studio recording
Speaker 2: we did with Ashmund Rodriguez and Victoria's song we had
Speaker 2: a technical fall. Nothing we can do. It sucks, but
Speaker 2: we're doing a rerun this week. We're doing the Academics
Speaker 2: that think Chat GPT is BS and this is one
Speaker 2: of my favorite episodes I ever recorded. It changed how
Speaker 2: I do interviews for at Large. It's with these three academics,
Speaker 2: Michael Townsend Higgs, James Humphries and Joe Slater, who wrote
Speaker 2: a paper using the actual Frank thirty in definition of
Speaker 2: bullshit to say that chat GPT bullshits. It's so much fun.
Speaker 2: It's one of my favorite episodes I've recorded. I will
Speaker 2: have you a monologue this week as well. I do
Speaker 2: apologize for not having something new for you this week.
Speaker 2: You'll still get the monologue on Friday, though. Thank you
Speaker 2: for your time, your patience and of course for listening
Speaker 2: to Better Offline. Hello and welcome to Better Offline. I'm
Speaker 2: your host ed Ze Trunk. In early June, three researchers
Speaker 2: from the University of Glasgow published a paper in the
Speaker 2: Ethics of Information and Technology Journal called chat GBT is bullshit,
Speaker 2: And I just want to be clear. This is a
Speaker 2: great and thoroughly researched, from well argued paper. This is
Speaker 2: not silly at all. It's actually great academia. And today
Speaker 2: I'm joined by the men who wrote it, academics Michael
Speaker 2: Townsend Hicks, James Humphries, and Joe Slater, to talk about
Speaker 2: chat gbt's mediocrity and how it's not really built to
Speaker 2: represent the world at all. So, for the sake of argument,
Speaker 2: could 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 3: or not right.
Speaker 2: And you define between like soft and hard bullshit? Can
Speaker 2: you also get into that as well? Can you also
Speaker 2: identify yourselves as well too?
Speaker 4: Yeah?
Speaker 5: Yeah, and Joe.
Speaker 3: So the soft bullshit hard bullshit distinction is a very
Speaker 3: serious and technical distinction, right, 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 3: and he talks about the about bullshit that there is
Speaker 3: in a popular culture and just in general discourse these days.
Speaker 3: Some of the ways he talks about bullshit seem to
Speaker 3: suggest that it needs to be accompanied by a sort
Speaker 3: of malign intention.
Speaker 4: I'm doing something kind of bad.
Speaker 3: I'm intending to mislead you about the enterprise of of
Speaker 3: what I'm.
Speaker 4: Doing, maybe about who you are or what you know.
Speaker 1: So you might be trying to portray yourself as someone
Speaker 1: who is knowledgeable about a particular subject. Maybe you're a
Speaker 1: student who showed up to class without doing the work.
Speaker 1: Maybe you're trying to portray yourself as someone who's virtuous
Speaker 1: in ways you're not. Maybe you're a politician who wants
Speaker 1: to seem like you care about your constituents, but actually
Speaker 1: you don't. So you're not trying to mislead somebody about
Speaker 1: what you're saying the content of your utterance.
Speaker 4: You're trying to.
Speaker 1: Mislead them instead about like why you're saying it's that's
Speaker 1: what we call hard bullshit.
Speaker 4: Yeah, it's one of the things Frankfort talked about.
Speaker 3: Yeah, so Frankfurt doesn't make this hard bullshit soft bullshit
Speaker 3: in the station, but we do because sometimes it seems
Speaker 3: like Frankfurt 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 6: Well, I'm James, by the way, I suppose we strictly
Speaker 6: don't want to say that they aren't hard bullshitters. And
Speaker 6: we just think if you don't think that large language
Speaker 6: models are sapient, if you don't think they're kind of
Speaker 6: minds in any important way, then they're not hard bullshitters.
Speaker 6: So I think in the paper we don't take a
Speaker 6: position on whether or not they are. We just say,
Speaker 6: if they are, this is the way in which they are.
Speaker 6: But minimally they're soft bullshitters, So they're kind of soft bullshites,
Speaker 6: as Joe says, doesn't require that speakers attempting to deceive
Speaker 6: the audience about the nature of the enterprise.
Speaker 5: Hard bullshit does.
Speaker 6: So if it turns out that large language models are sapient,
Speaker 6: which they're definitely not, like that's just.
Speaker 2: Technicio property, Yeah, that's nonsense.
Speaker 6: Yeah, But if they are, then they're hard bullshitters and
Speaker 6: minimally 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 6: And yeah, this again, I think it's something I don't
Speaker 6: know what the kind of hive mind consensus on this is.
Speaker 6: I'm sort of sympathetic to the idea that if you
Speaker 6: take this kind of purposive or teleological attitude towards right,
Speaker 6: what an intention is an effort to do something, then
Speaker 6: maybe they do have intentions. But again, I think in
Speaker 6: the paper we just sort of wanted I mean, it's
Speaker 6: a standard philosophical move, right, just sort of go, look,
Speaker 6: here's all this uncontroversial stuff as we can make it.
Speaker 6: Now we can hit you with the really controversial shit
Speaker 6: that we wanted to get to. So in the paper
Speaker 6: we sort of deliberately when maybe you might think it
Speaker 6: has intentions for it this reason, we kind of have
Speaker 6: no judgment on this efficiently, I'm sympathetic to the sort
Speaker 6: of view that you're putting.
Speaker 5: I think you're kind of sympathetic this as well.
Speaker 1: Right, Yeah, so, I Mike, there are a few ways
Speaker 1: that you can think of CHATCHYBT as having intentions.
Speaker 4: I think, and we talk about a few of them.
Speaker 1: One is by thinking of the designers who created it
Speaker 1: as kind of imbuing it with intentions. So they created
Speaker 1: it for a purpose and to do a particular task,
Speaker 1: and that task is to make people think that it's
Speaker 1: a normal sounding person.
Speaker 4: Right.
Speaker 1: It's to make people, when they have a conversation with it,
Speaker 1: not be able to distinguish between what it's outputting and
Speaker 1: what a normal human would say.
Speaker 4: Right.
Speaker 1: Right, And that kind of goal, if it amounts to
Speaker 1: an intention, is the kind of intention we think a
Speaker 1: bullshitter has.
Speaker 4: Right.
Speaker 1: It's not trying to deceive you about anything it's saying.
Speaker 1: It doesn't care whether what it's saying is true. That's
Speaker 1: not part of the goal. But the goal is to
Speaker 1: do is to make it seem as if it's something
Speaker 1: that it's not, like specifically a human interlocator. And one
Speaker 1: source for that goal is the programmers who designed it.
Speaker 1: Another is the training method. So it was trained by
Speaker 1: being given sort of positive and negative feedback in order
Speaker 1: to achieve a specific thing, right, And that specific thing
Speaker 1: is just sounding normal. And that's so similar to what
Speaker 1: our students are doing when they try to pretend that
Speaker 1: they read something they haven't read.
Speaker 2: There is something very collegiate about the way it bullshits.
Speaker 4: Though.
Speaker 2: It reminds me of when I was in college, as
Speaker 2: when Penn State and Aporistwyth two very different institutis.
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 really is
Speaker 2: like the intro body can clu usion. There was a
Speaker 2: certain formula behind it, and it's just it feels exactly
Speaker 2: like it. But that kind of brings me to my
Speaker 2: next question, which is, how did you decide to write this?
Speaker 2: What inspired this?
Speaker 1: Right?
Speaker 6: I would feel this because whenever Mike tells the story,
Speaker 6: you get a sanitized version.
Speaker 5: We were in the pub winsing about student essays. Perfect, yeah,
Speaker 5: like you were not long here, right, you're not long yeah,
Speaker 5: long post of.
Speaker 6: A bunch of us sort of went to the pub
Speaker 6: on a notional let's let's welcome our new members of
Speaker 6: staff sort of event, and inevitably we had about two
Speaker 6: points we were all passing and moaning and I think
Speaker 6: it might have been Neil that kind of prompted this.
Speaker 4: No, I don't think he was about it.
Speaker 5: We talked about it after, right, Yeah, in a case
Speaker 5: like sort of came out.
Speaker 6: We were talking about this sort of prevalence of chat
Speaker 6: GPT generated stuff and kind of what it said about
Speaker 6: how at least some students were kind of approaching assessments
Speaker 6: and you know, I forget who. Someone sort of just
Speaker 6: went off hand, Yeah, but it's all just Frank thirty
Speaker 6: and bullshit though, wasn't it. And we sort of collectively
Speaker 6: went hello, because obviously we all having a background plosophy,
Speaker 6: we've all read on bullshit. You know, we all went
Speaker 6: we get to say bullshit and allegedly serious academic work.
Speaker 6: So the start of it was we've had this experience
Speaker 6: of having to read all of this kind of uncanny
Speaker 6: Valley stuff, and when prompted, we all went, oh, it
Speaker 6: really is like Frank thirteen bullshit in a way that
Speaker 6: we can probably get paper ouse off.
Speaker 1: Yeah, And at that point I think we were in
Speaker 1: the department meetings talking about how to deal with chat
Speaker 1: GPT written papers. And there were discussions going on all
Speaker 1: over the university, including kind of in high levels and
Speaker 1: the administration to come up with a policy. And we
Speaker 1: specifically wanted a stronger policy because our university is very
Speaker 1: interested in cutting into technology, right, and so they wanted
Speaker 1: the students to have an experience using and figuring out
Speaker 1: how to use whatever the freshest technology.
Speaker 2: Is, right, as they should as they should, but not
Speaker 2: for essays.
Speaker 4: Right, that's the worry we had. We thought, you know,
Speaker 4: if we're not very clear.
Speaker 1: About this, the students will be using it in a
Speaker 1: way that will detract from their educational experience.
Speaker 4: Right. And at the same time, it was becoming more.
Speaker 1: Widely known how these machines work, like specifically how they're
Speaker 1: doing next token or next word prediction in order to
Speaker 1: come up with a digestible string of text. And when
Speaker 1: you know that that's how they're doing it, I mean,
Speaker 1: it seems so similar to what humans do when they
Speaker 1: have no idea what they're talking about. And so when
Speaker 1: we were talking about it just seemed like an obvious
Speaker 1: paper that someone was going to write, and we thought
Speaker 1: that had better bs. You know, eventually people are going
Speaker 1: to see this connection and it's a great papa.
Speaker 4: I think it worked very well.
Speaker 1: I mean, I think that it's the kind of thing
Speaker 1: where when I pitch this to other philosophers, it doesn't
Speaker 1: take them very long to just agree to be like, ah, yes.
Speaker 5: 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 other ways what people are giving
Speaker 2: it consciousness. I just sort of tweet just now if
Speaker 2: someone talk 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: GIPT in large language buddels, 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 1: I mean kind of as background, I do philosophy of science,
Speaker 1: but my thoughts about something like chatchipt are largely inspired
Speaker 1: by the fact that I also teach a class called
Speaker 1: Understanding Philosophy through Science Fiction.
Speaker 4: Oh and and now we like talk.
Speaker 1: About whether computers could be conscious, and I don't know
Speaker 1: what you guys think. Actually I think they could, right,
Speaker 1: I just don't think this one is. And part of
Speaker 1: the reason I think they could, but this one isn't
Speaker 1: is that I think that in order to sort of
Speaker 1: represent the world or have the kinds of things we
Speaker 1: have their like beliefs, desires, thoughts that are about external things,
Speaker 1: you have to have internal states that are connected in
Speaker 1: some way to the external world. Usually causation, we're perceiving things,
Speaker 1: information is coming in. Then we've got some kind of
Speaker 1: state in our brain that's designed just to track these
Speaker 1: things in the external world.
Speaker 3: Right.
Speaker 1: That's a huge part of our cognitive lives is just
Speaker 1: tracking external world things, and it's a very important part
Speaker 1: of childhood development when you figure out how to act.
Speaker 2: It's semiotics, right, Daniel Chanler Aperistwyth told me semiotics it's
Speaker 2: like anception of the world.
Speaker 1: And this is like theory of meaning stuff. So yeah,
Speaker 1: semiotics is like theory of science. How is it that
Speaker 1: assign It can be both the representation of the thing
Speaker 1: and the thing itself. That can happen, yeah, but not always.
Speaker 1: Sometimes it's just the representation of the thing. And there's
Speaker 1: a lot of philosophy is about figuring out how brain
Speaker 1: states or words on a page can be about external
Speaker 1: world things. And a big part of it, at least
Speaker 1: from my perspective, has to do with tracking those things,
Speaker 1: keeping tabs on them, changing as a result of seeing
Speaker 1: differences in the external world. And chatch ept is not
Speaker 1: doing any of that, right, that's not what it's designed
Speaker 1: to do. It's taking in a lot of data once
Speaker 1: and then using that to respond to text. But it's
Speaker 1: not remembering individuals, tracking things in the world in any
Speaker 1: way perceiving things in the world. It's just forming a
Speaker 1: sort of statistical model of what people say. And that's
Speaker 1: 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 5: Yeah.
Speaker 6: And that's one of the things that this, I think
Speaker 6: of in one way, comes down to, is that if
Speaker 6: you sort of post this sufficiently far, someone is going
Speaker 6: to go, ah, isn't this just bioschauvinism, right, Like, aren't
Speaker 6: you just assuming that unless something runs on like meat,
Speaker 6: it can't be sentient? And this isn't something we get
Speaker 6: into into the paper, partly because we didn't really think
Speaker 6: it was worth addressing. But the sorts of things that
Speaker 6: seem like they're like never mind consciousness, right, But it
Speaker 6: seemed to be necessary in order for something to be
Speaker 6: trying to track the world, or in order to be
Speaker 6: corresponding the world, or to form beliefs about the world.
Speaker 6: Chat GPTs just doesn'tthing to meet any of them. Kind Of,
Speaker 6: it's if it does turn out that it's sapient, and
Speaker 6: then chat gpt has got some profoundly serious executive function disorders, right, sapient, Right,
Speaker 6: so we don't have to worry about it. But kind
Speaker 6: of it's not the case that we've got some blundering
Speaker 6: proto general intelligence that's trying to figure out how to
Speaker 6: represent the world. It's not trying to represent the world
Speaker 6: at all. This utterances are designed to look as if
Speaker 6: it's trying to represent the world, and then we just
Speaker 6: go that that's just bullshit. This is a classic case
Speaker 6: 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 accurately but not accurately enough.
Speaker 6: Yeah, it's got various guidelines that allow it to throw
Speaker 6: shit at the wall where a sort of reasonably high
Speaker 6: degree of Actually, no, you're right, I mean one of
Speaker 6: the things that a human bullshitter could at least be
Speaker 6: characterized as doing is. They'd have to try and kind
Speaker 6: of judge their audience, right, they'd have to try and
Speaker 6: make the bullshit plausible to the audience that they're speaking to,
Speaker 6: and chat GPT can do that, right. All it can
Speaker 6: do is go on a statistically large enough model and
Speaker 6: it looks like Z follows, why follows X. It's not
Speaker 6: kind of got any or doesn't have any consciousness at all,
Speaker 6: of course, but it doesn't have any sensitivity to the
Speaker 6: sorts of things that people are in fact likely to
Speaker 6: find plausible. It just does a kind of brute number crunch.
Speaker 6: So it's more complicated than that, but I think it
Speaker 6: boils down it effectively to kind of number crunching, right,
Speaker 6: kind of data and context.
Speaker 2: In which probablistic 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 1: I think one thing between when we came up with
Speaker 1: the idea and when we finish writing the paper, we
Speaker 1: spend some time reading about how it works and how
Speaker 1: it represents language and what the statistical.
Speaker 4: Model is like.
Speaker 1: And I was maybe more impressed than James about that,
Speaker 1: because it is like doing things that are similar to
Speaker 1: what we do when we understand language. It does seem
Speaker 1: to kind of locate words in a meaning space right right,
Speaker 1: and connect them to other words, and you know, show
Speaker 1: similarity and meaning, and it also does seem to be
Speaker 1: able to in some way understand context.
Speaker 4: But we don't know how.
Speaker 1: Similar that is for a variety of reasons, but mostly
Speaker 1: because it's too big of a system and we can
Speaker 1: only kind of probe it. And it's trained indirectly, right,
Speaker 1: so it's not programmed by individuals. And even though that's
Speaker 1: kind of a very impressive model of language and meaning
Speaker 1: and may in some ways be similar to or what
Speaker 1: we do when we understand language, we're doing a lot
Speaker 1: more things like planning, things like tracking things in the world.
Speaker 1: Just having desires and representing the way you want the
Speaker 1: world to be and thereby generating goals doesn't seem to
Speaker 1: be something that it has anything any room for in its.
Speaker 6: Architect So this is something of the time of it's
Speaker 6: just goods to me, and you were talking about the
Speaker 6: kind of in some ways it learns language the same
Speaker 6: way that we do. I mean, it's got no grasp
Speaker 6: of expleted in fixation, right, this is one of chum's here.
Speaker 2: What does that mean? Just for not for me? I
Speaker 2: definitely know.
Speaker 6: Yeah, yeah, of course, if I give you the sentence
Speaker 6: that's completely crazy, man, and tell you to put the
Speaker 6: word fucking into that sentence, there's a number of ways
Speaker 6: in which any language speaker is going to do it
Speaker 6: that they'll just go yeah, like of course.
Speaker 5: That where it goes, right.
Speaker 6: But we it seems that we've got a grasp on
Speaker 6: this incredibly early on in a way that doesn't look
Speaker 6: like it's the ways at least most of us that
Speaker 6: taught language, right, we get quite highly told off when
Speaker 6: we try and do.
Speaker 5: Expleted in fixation.
Speaker 6: Yes, So this I think would be one of those
Speaker 6: cases where you could do a sort of dis analogy
Speaker 6: by cases. Right, you present chat GPT a sentence and
Speaker 6: say insert the word fucking correctly in this sentence, and
Speaker 6: I doesn't think it would be very good at it.
Speaker 4: I think it would be.
Speaker 5: I think you reckon. I mean we probably couldn't.
Speaker 4: We could, but we shouldn't. Yeah.
Speaker 1: One of the things that I thought was like kind
Speaker 1: of interesting about how it works is that it does
Speaker 1: learn language, probably differently from the way we do, but
Speaker 1: it does it all by examples, you know, So it's
Speaker 1: looking at all these pieces of text and thinking, ah,
Speaker 1: this is okay, that's okay. And one kind of interesting
Speaker 1: thing about how humans understand language is that we're able
Speaker 1: to kind of understand meaningless but grammatical sentences. It's not
Speaker 1: clear to me that chat GPT would understand those. That's
Speaker 1: another Chopski example. So you know, Chowski has this example that's.
Speaker 5: Like, what is it colorless? Us sleep furiously color?
Speaker 1: It's colorless green ideas, right, And that's a meaningless sentence,
Speaker 1: but it's grammatically well formed, and we can understand that
Speaker 1: it's grammatically well formed, but also that it's meaningless. Because
Speaker 1: chat Gibt kind of combines different aspects of what philosophers
Speaker 1: of language, logicians, linguistics people see y as like different
Speaker 1: components of meaning. It sees these as all kind of
Speaker 1: wrapped up in the same thing. It puts them in
Speaker 1: the same big model. I'm not sure it could differentiate
Speaker 1: between ungrammaticality and meaninglessness.
Speaker 2: So we're doing real time science right now. I just yeah,
Speaker 2: we should check the word shoe into the following sentence
Speaker 2: in the correct way. Man, that's crazy. And I did
Speaker 2: it six times, and I would say fifty percent of
Speaker 2: the time it got it right. And it did. Man,
Speaker 2: that's fucking crazy. Man, that's crazy. Fucking Man, that's fucking crazy. Man,
Speaker 2: that's crazy. Fucking My favorite is man, comma, that's crazy,
Speaker 2: Comma fucking.
Speaker 5: To be fair.
Speaker 6: Reliable, you take the commas out of that last one,
Speaker 6: and you've got a grammatical sentence.
Speaker 2: Yeah.
Speaker 6: Of course in Glasgow you can also start with fucking man,
Speaker 6: 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 4: 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 5: Yeah, yeah, exactly.
Speaker 1: There's a I don't know if you have already talked
Speaker 1: to somebody about this, but there's a you know, a
Speaker 1: classic argument from child Skyt against behaviorism.
Speaker 2: Right.
Speaker 1: Behaviorism is the view that we learn everything through stimulus
Speaker 1: and response.
Speaker 4: Roughly, that's not exactly.
Speaker 1: It, but I'm not a philosopher of mine, so I
Speaker 1: can get away with with that.
Speaker 4: So Chomsky says, look, we don't get enough.
Speaker 1: Stimulus to learn language as quickly as we do just
Speaker 1: through watching other people's behavior and copying it. We have
Speaker 1: to have some in built grammatical structures that language is
Speaker 1: latching onto. And there's been some papers arguing that chatchipt
Speaker 1: shows Chomsky was wrong because it doesn't.
Speaker 4: Have the inbuilt grammatical structure.
Speaker 1: But one interesting thing is it requires ten to one
Speaker 1: hundred times more data than a human child does when
Speaker 1: learning language. Right, So Chomsky's argument was, we don't get
Speaker 1: enough stimulus, and chat GPT can kind of do it
Speaker 1: without the structure, but it's not quite doing it as well,
Speaker 1: and it gets like orders of magnitude more input than
Speaker 1: a human does.
Speaker 4: Before a human learns language, which is kind of interesting.
Speaker 6: And it's still going to do something as basically as
Speaker 6: putting the word 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 Oxford, 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 blooge 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.
Speaker 4: That's something like what it really is.
Speaker 3: And then one little bit of the feature for a
Speaker 3: visual hallucination, one feature of my visual field actually isn't
Speaker 3: represented in the real world. It's not actually there. Everything
Speaker 3: else might well might well be right. Imagine I hallucinate
Speaker 3: there's a red balloon in front of me. I still
Speaker 3: see Mike, I still see James, I still see the laptop.
Speaker 3: One bit is wrong, everything else is right. And I'm
Speaker 3: doing the same thing that I'm usually doing, Like my
Speaker 3: eyes are still working pretty much normally, I think, I
Speaker 3: and this is the way that I usually get knowledge
Speaker 3: about the world. This is a pretty reliable process for
Speaker 3: me learning from it right and representing the world in
Speaker 3: this way. So when talking about hallucinations, this suggests that
Speaker 3: chat Gypt and other similar things, they're going through this
Speaker 3: process that is usually quite good at representing the world
Speaker 3: and then oh, it's made a mistake this one time,
Speaker 3: but actually no, it's bullshitting the whole time, and like
Speaker 3: sometimes it gets things right right bullshitting just like a politician.
Speaker 3: 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 a 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. A one of the reasons why we
Speaker 3: think bullshit's a better way think about it.
Speaker 1: I also kind of think that some of the ways
Speaker 1: we talk about chat gpt lin, even when it makes mistakes,
Speaker 1: lind themselves to overhyping its.
Speaker 4: Ability or overestimating its.
Speaker 1: Abilities, and talking about it is hallucinating is one of
Speaker 1: these because when you say that it's hallucinating, as you're
Speaker 1: pointed out, you're giving the idea that it's representing the
Speaker 1: world in some way and then telling you what the
Speaker 1: content and.
Speaker 6: It has perception, Yeah, exactly like it has perceived something
Speaker 6: and like, oh no, it's taken some computer acid and
Speaker 6: now it's hallucinating like mattery things.
Speaker 5: Yeah, just as you say, And.
Speaker 4: That's not what it's doing.
Speaker 1: And so when the kind of people who are trying
Speaker 1: to promote these these things as products talk about the
Speaker 1: AI hallucination problem, they're kind of selling a product that
Speaker 1: is a product that's representing the world usually checking things
Speaker 1: and occasionally makes mistakes. And if the mistakes were, like
Speaker 1: Joe said, we're a misfiring of a normally a reliable
Speaker 1: process or you know, something that normally represents going wrong
Speaker 1: in some way, that would lend itself to certain solutions
Speaker 1: to them, and it would make you think there's an
Speaker 1: underlying reliable product here, right, which is exactly what somebody
Speaker 1: who's making a product to go to the market.
Speaker 4: Will want you to think. Right.
Speaker 1: But if that's not what it's doing in a certain sense,
Speaker 1: they're misrepresenting what it's doing even when it gets things right.
Speaker 4: And that's that's for all of us who.
Speaker 1: Are going to be using these systems, especially since people,
Speaker 1: you know, most people don't know how this works. They're
Speaker 1: just understanding the product as it's described to them using
Speaker 1: these kind of metaphors. So the way the metaphor describes
Speaker 1: it is going to really influence how they think about
Speaker 1: it and.
Speaker 4: How they use it.
Speaker 5: Yeah, just to sort of catfit off if I can.
Speaker 6: Is one of the responses to some corners has been
Speaker 6: to sort of say, of us, look, you wings about
Speaker 6: people at anthropomorphosizing chat GPT, but look, if you've got
Speaker 6: a bullshitty, you're doing exactly the same thing. And I
Speaker 6: mean there might be some center which it's just really
Speaker 6: hard not to antropomorphise it. I don't know why I
Speaker 6: picked the word that I can barely say, like we've
Speaker 6: been doing it constantly throughout this discussion, right when we're
Speaker 6: talking through the kind of through the paper we kept
Speaker 6: talking about chat GPT as if it had intention, right,
Speaker 6: as if it was thinking about anything. That might be
Speaker 6: another reason to call it bullshet We go, Look, if
Speaker 6: we have to treat it as if it's doing something
Speaker 6: like what we do, it's not hallucinating, it's not lying,
Speaker 6: it's not confabulating, it's bullshitting, right.
Speaker 5: And if we have to treat it as if.
Speaker 6: It's behaving in some kind of human like way, here's
Speaker 6: the appropriate human like behavior to describe it.
Speaker 2: I also think the language in this case, and one
Speaker 2: of the reasons they probably like the large language model
Speaker 2: concept is language gives life too things when we describe
Speaker 2: the processes through which we interact with the world and
Speaker 2: interact with living beings, even cats, dogs, even we anthropomoise
Speaker 2: living things. But also when we communicate with something, languages life,
Speaker 2: and so it probably works out really fucking well for them.
Speaker 2: Sam Oltman was saying a couple of weeks back, maybe
Speaker 2: a month or two. He was saying, oh, yeah, AI
Speaker 2: is not a creature with something he said, and it
Speaker 2: was just so obvious. What he wanted people to do
Speaker 2: was say, but what if it was? Or are people
Speaker 2: saying this is a creature and it almost feels like
Speaker 2: just part of the con Yeah.
Speaker 1: Yeah, yeah, I hadn't thought about that as a reason
Speaker 1: for them to go for large language models as a
Speaker 1: way of kind of, I don't know, being the gateway
Speaker 1: into war investment in fight consciousness. Yeah yeah, but I
Speaker 1: had thought about, like how this might have been caused
Speaker 1: by just like deep misunderstandings of the turning test.
Speaker 2: Right, go ahead, I want to hear this one.
Speaker 4: Yeah.
Speaker 1: Yeah, so that like the turning test, I think this
Speaker 1: is closer to what Turing was thinking. But the turning
Speaker 1: test is a way of getting evidence that something is conscious. Right,
Speaker 1: So you know, I'm not in your head, so I
Speaker 1: can't feel your feelings or think your thoughts directly, right,
Speaker 1: I have to judge whether you're conscious based on how
Speaker 1: you interact with me, And the way I do it
Speaker 1: is by listening to what you say, right, and talking
Speaker 1: to you and turning sort.
Speaker 4: Of was asked, you know, how would you know if
Speaker 4: a computer was conscious?
Speaker 1: So, you know, we think that our brains are doing
Speaker 1: something similar to what computers do. That's a reason to
Speaker 1: 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.
Speaker 1: Great, Yeah, I didn't know if they thought it was possible,
Speaker 1: because not everybody thinks it's possible.
Speaker 2: It's possible, I just don't know how.
Speaker 4: Yeah. Yeah. The Turing was kind of you know, thinking
Speaker 4: like how would we know?
Speaker 1: And one way we would know, the obvious way is
Speaker 1: to do the same thing you do to humans, talk
Speaker 1: to it and see how it responds. And that's actually
Speaker 1: pretty good evidence if you don't have the ability to
Speaker 1: look deeper. But it's not constitutive of being conscious. It's
Speaker 1: not what makes something conscious or determines whether they're conscious
Speaker 1: or in any way like grounds their consciousness. Right, Their
Speaker 1: ability to talk to you is just evidence. It's just
Speaker 1: one signal you can get. And that's the way to
Speaker 1: think of the Turing test. So as a result of
Speaker 1: people thinking in a kind of behaviorist way, thinking, ah,
Speaker 1: passing the Turning test is just all it is to
Speaker 1: be a thinking thing. There have been at least since
Speaker 1: the nineties, attempts to design chatbots that can beat the
Speaker 1: Turning test right, and popularizations of these attempts and run
Speaker 1: throughs of the Turing test that talk as if, oh,
Speaker 1: if a computer finally beats the Turing test.
Speaker 4: I should say what the Turing test is? Right?
Speaker 1: The way turn suggests that the test works is you
Speaker 1: have a computer and a person both chatting in some
Speaker 1: way with a judge, and the judge is also a person, right,
Speaker 1: And if the judge can't tell which of the people
Speaker 1: he's chatting with is a human, then the computer's one
Speaker 1: the right test because it's ditinguishable from a human.
Speaker 4: Right.
Speaker 1: So people have taken this and it's been popularized as
Speaker 1: a way of determining sort of full stop, whether something's conscious.
Speaker 4: But it's just a piece of evidence, and.
Speaker 1: We have a lot more evidence, Like we know a
Speaker 1: lot more than Turning did, about how the internal functioning
Speaker 1: of a mind works functionally, what it's doing, how representation works,
Speaker 1: how experience and belief works, how they are connected to action,
Speaker 1: and how they're connected to speech and thought. And once
Speaker 1: you know all that stuff, you have a lot of
Speaker 1: other avenues to get more evidence about whether the thing
Speaker 1: is conscious and.
Speaker 4: Whether it passes.
Speaker 1: The Turning test is just like a drop in the
Speaker 1: bucket compared to these, especially if you know how internal functioning.
Speaker 6: The other the story is problem with the showing test,
Speaker 6: and I think to be fair cheering did mention this,
Speaker 6: If not in the original then kind of later on.
Speaker 6: One problem with the Chewing test is that it's like
Speaker 6: the de void camp in de androids gream of electric
Speaker 6: cheap right. Plenty of humans would fail the Cheuring test. Yeah,
Speaker 6: it is a piece of evidence, but it was never
Speaker 6: as Mike said, it was supposed to be constitutive. It
Speaker 6: wasn't like, if you can do this thing, your conscious
Speaker 6: kind of full stopped. It was supposed to be here
Speaker 6: is a thing that might indicate that being you're talking
Speaker 6: to is conscious.
Speaker 4: Happily, as Ma said, we've got loads and loads of
Speaker 4: other evidence.
Speaker 1: So these guys have made a machine that's just designed
Speaker 1: 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 Francois Cholet 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 6: This is why people, well I see people a fairly
Speaker 6: small subset of weird nerds. But this is why a
Speaker 6: small subset of weird nerds have been for the last
Speaker 6: twenty years emphasizing artificial general intelligence, right, and kind of
Speaker 6: like what we'll call it. When something really is a
Speaker 6: thinking being is when it's not specialized to do one
Speaker 6: and only one task, but rather when it's capable of
Speaker 6: applying reasoning to multiple kinds of different and this analogous cases.
Speaker 6: On the one hand, it does seem a little bit
Speaker 6: like the guy flipping the table and go, oh, for
Speaker 6: Fox's sake, now you've won.
Speaker 5: I'm changing the rules.
Speaker 6: But on the other I think he's got a point, right, Yeah,
Speaker 6: if you're training a thing to do very specific you know,
Speaker 6: like activate certain shiboleths, then unless you're some kind of
Speaker 6: mad hard behaviorist, then yeah, like it's not that doesn't
Speaker 6: demonstrate intelligence.
Speaker 5: It is one thing that might indicate intelligence.
Speaker 2: It's the same problem with chat GPT. It's built to
Speaker 2: resemble intelligence, and resemble conscious this, and resemble these things,
Speaker 2: but it isn't. It's almost like it's meaningless. On a
Speaker 2: very high end philosophical levels. I find the whole generative
Speaker 2: a I think deeply nihilistic.
Speaker 1: I mean, one thing that connects to this is how
Speaker 1: bad it is at reasoning. And this is kind of
Speaker 1: good for us, especially in philosophy.
Speaker 4: Because our students when they use.
Speaker 1: It to write papers, the papers have to have arguments,
Speaker 1: and chat GPT is very bad at doing reasoning. If
Speaker 1: it has to be you know, sort of an extended
Speaker 1: argument or a proof or something like that, it's very
Speaker 1: bad at it. I think also that if there's one
Speaker 1: thing kind of we learned from chat GPT, it's that
Speaker 1: 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 1: No, partially because it's so subject specific, right, It's one
Speaker 1: it's trained to do one task, and it takes quite
Speaker 1: a lot of training to get it to do that task. Well,
Speaker 1: it's bad at many of the other tasks that we
Speaker 1: think are connected with intelligence. It's bad at logical, mathematical reasoning.
Speaker 1: I understand that open AI is trying to fix that.
Speaker 1: Sometimes it sounds like they want to fix it by
Speaker 1: just connecting it to a.
Speaker 4: Database or a program that can do that for it.
Speaker 4: But either way when you have with.
Speaker 1: These kind of big base nets models, it's something that
Speaker 1: is really good at whatever you train it to do,
Speaker 1: but not going to be good at anything else. You know,
Speaker 1: it's been a lot of data on one thing. It
Speaker 1: finds patterns in that, it finds regularities in that, it
Speaker 1: represents those.
Speaker 4: The more data you feed it, the better it will
Speaker 4: be at that.
Speaker 1: But it's not going to have this kind of general
Speaker 1: ability and it's not going to grow it out of
Speaker 1: learning how to speak English.
Speaker 4: Have you heard the Terry Pratchet quote about It's quite
Speaker 4: early on.
Speaker 6: He's talking about Hex, the kind of steampunk computer they
Speaker 6: make and Unteen University, and it just has this off
Speaker 6: hand line. A computer program is exactly like a philosophy professor,
Speaker 6: unless you ask it the question in exactly the right way,
Speaker 6: delight in giving you a perfectly accurate, completely unhelpful answer.
Speaker 7: Right.
Speaker 6: So, if you abstract the justified you got a philosophy
Speaker 6: lecture is there. Basically what intelligent things do is go
Speaker 6: You can't have meant that, you must have meant this
Speaker 6: right chat GPTs goes. I will take a question as
Speaker 6: read and of course it doesn't have an eye. There
Speaker 6: is nothing in the rest like I've just answered formal
Speaker 6: pite it again. But it's the same thing, and it's
Speaker 6: trained to do contractively specific things, and you get the
Speaker 6: same problem with any program like garbage in Garbage album.
Speaker 2: So have you found a lot of students using chat GPT,
Speaker 2: because this is a hard problem to quantify? Is it
Speaker 2: all the time?
Speaker 5: I mean it's it's a lot. I wouldn't say it
Speaker 5: all the time.
Speaker 4: I'm changed that you thought that there were more season
Speaker 4: DEDs this semester.
Speaker 3: Yeah, so I think there are quite a few, and
Speaker 3: sometimes it is difficult for us to prove and if
Speaker 3: we can't prove it.
Speaker 4: Then at our university, then well shucks. They kind of
Speaker 4: 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.
Speaker 4: Which is a bit of a shame.
Speaker 2: So's suspicion.
Speaker 3: Yeah, we suspect that a lot of them are very
Speaker 3: I'm 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 like, I will not shut up
Speaker 6: about this, right as you know, the like you of
Speaker 6: course will imagine a lot of here kind of readerbole
Speaker 6: be as well. But the uncanny Valley thing in respective
Speaker 6: humans is that there's a kind of a point up
Speaker 6: to which humans seem to trust artificial things more the
Speaker 6: more they look like a human. So like, we'll trust
Speaker 6: a robot if it's kind of bipedal or if it
Speaker 6: looks like a dog, But after a point, we really
Speaker 6: rapidly start to trust me, So like if it basically
Speaker 6: when it starts looking like data, some deep buried lizard
Speaker 6: brain goes danger.
Speaker 5: Danger. This thing's trying to fuck with you somehow.
Speaker 2: Yeah, right, And chat gpt.
Speaker 5: Writes exactly like that.
Speaker 6: It writes almost but not entirely, like a thing that
Speaker 6: is trying to convince you that it's human. I mean,
Speaker 6: the dead give way for me is that it never
Speaker 6: makes any spelling mistakes, but it can't form out a
Speaker 6: paragraph to save its life. Normally, you would expect someone
Speaker 6: who didn't know how to kind of order their sentences
Speaker 6: to misspell the occasional word. Chat GPT spells everything correctly
Speaker 6: and doesn't know what like subject object agreement is it's.
Speaker 2: Like it's so, can you just define that please? Subject
Speaker 2: object the beer that I drink, right, not the drink
Speaker 2: that I beer right. Because it's interesting. It almost feels
Speaker 2: like there is just an entire way of processing and
Speaker 2: delivering information as a human being that we do not understand.
Speaker 2: There is something missing.
Speaker 1: I mean, for me, like it's very good, it's summarizing,
Speaker 1: but when it responds, it responds in them in really
Speaker 1: trite and repetitive ways, or like you'll get a paper
Speaker 1: that summarizes a bunch of literature at length very effectively,
Speaker 1: and then respond by saying, well, you know, this person
Speaker 1: said this, that is doubtful. They should say more to
Speaker 1: support that, you know, which is basically saying nothing right,
Speaker 1: And that's pretty common.
Speaker 4: It also does lists in formats things and kind of
Speaker 4: like a list.
Speaker 1: And even if this like make it look less like
Speaker 1: 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 the postomers have asked
Speaker 3: you the ages and this is one of the things
Speaker 3: we shout at students not to write from first year,
Speaker 3: and then you'll get this garbage right back at you,
Speaker 3: and then at the end it will be oh, overall,
Speaker 3: this is a complicated and difficult question, but.
Speaker 2: Many nuances 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 sort of english you'd expect 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 1: I also, I had some papers that I suspected were
Speaker 1: chatcheapt this year, but they were already failing, so I
Speaker 1: didn't Okay, yeah, I didn't think it was worth it
Speaker 1: to pursue them, you know, as a plagiarism or Chatcheapte case.
Speaker 2: So it's never good papers. Then it's never like an
Speaker 2: eight like a first.
Speaker 5: No, absolutely not.
Speaker 1: It's so I think that part of what goes on
Speaker 1: is you can get a passing grade with the Chatcheapte paper,
Speaker 1: sometimes in the first couple of years when the papers
Speaker 1: are shorter and we're not expecting as much. But then
Speaker 1: when you move into the what we call honors level here,
Speaker 1: which is like upper level classes in the US like
Speaker 1: third and fourth year.
Speaker 2: I've got first ever wrist with I know.
Speaker 1: Yeah, yeah, exactly, great, well done, well Doe. You would
Speaker 1: not have gotten it with chatchipt because you get dropped
Speaker 1: in these classes where we expect you to have gained
Speaker 1: writing skills minimal ones in your first two years and
Speaker 1: then we're going to build on that and have you
Speaker 1: do more complicated stuff. And Chatchept doesn't build on that, right,
Speaker 1: it just stays where it was. So you go from
Speaker 1: writing kind of C grade passing papers, yeah, that your
Speaker 1: F grade paper. And it's also more obvious because the
Speaker 1: papers are longer, and chat gipt can write long text,
Speaker 1: but it gets very repetitive and noticeably repetitive, right, and
Speaker 1: so you're kind of lost, like you haven't done the
Speaker 1: work of figuring out how to write on your own
Speaker 1: and the tool that you've been using is not up
Speaker 1: to the task that you're now presented with. And so
Speaker 1: I think I have seen a few papers that I
Speaker 1: was suspicious of, but the papers that I was certain
Speaker 1: of were ones that were like senior thesis, very clear
Speaker 1: a person just had no way of writing culture.
Speaker 2: That's insane at that stage.
Speaker 6: Yeah, I mean it's fun because I mean one of
Speaker 6: the things that we get told about is like, oh,
Speaker 6: the students have got to learn how to use you know,
Speaker 6: AI and large language model, plagiarism machines and responsibly and
Speaker 6: in a kind of positive way. Well, if they're using
Speaker 6: them in a way that means they don't learn how
Speaker 6: to write, then it's not positive. Visit like, yeah, it's
Speaker 6: fucking hard to write a good ess. Say, yes, it
Speaker 6: is hard to write. That's why we practice, That's why
Speaker 6: we have editors, that's why we do this collaboratively. And
Speaker 6: if you're using this as a sort of oh I
Speaker 6: don't know how to write, well, tough shit, you're never
Speaker 6: going to know how to write that. That doesn't seem
Speaker 6: 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 of your brain allegedly.
Speaker 1: Yeah, I worry about like because CHATCHYBT is good at summarizing,
Speaker 1: So I worry that one of the uses people will think, ah,
Speaker 1: this is a pretty good use for it is summarizing
Speaker 1: the paper that they're supposed to read, and it will
Speaker 1: do that effectively enough.
Speaker 4: For them to discuss it in class if they're willing
Speaker 4: to be here.
Speaker 1: Yeah, right, but they're not going to pick up a
Speaker 1: lot of the nuances in a lot of the kind
Speaker 1: of like stylistic ways of presenting ideas that you get
Speaker 1: when you actually do 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 6: Yeah, there's some evidence that reading physical copies makes you
Speaker 6: engage more.
Speaker 5: Sorry I'm very old fashioned, I guess.
Speaker 2: But no, it's true though, But also reading that I
Speaker 2: wouldn't have really on a PDF. I wouldn't have given
Speaker 2: it as much attention. But also, going through this paper,
Speaker 2: you could see what you were doing, like you could
Speaker 2: see that you were lining up here are the qualities
Speaker 2: that we use to judge bullshit. But also summarizing a
Speaker 2: paper does not actually give you the argument, it gives
Speaker 2: you an answer. So what do you actually want students
Speaker 2: to do instead? Because I don't think there's any reason
Speaker 2: to use chat GPT for these reasons like that it
Speaker 2: doesn't seem to do anything that's useful for them.
Speaker 1: I don't have No, I don't actually have any use
Speaker 1: for chat shapt that I can put to my students
Speaker 1: and say here's what I think you should do with it.
Speaker 1: We are like kind of developing strategies for keeping them
Speaker 1: from using it, So like building directly on what you're saying. Like,
Speaker 1: in my class next year, I'm going to have the
Speaker 1: students do regular assignments, which are argument summaries and not
Speaker 1: paper summaries. So the idea is they have to read
Speaker 1: the paper, find an argument, and tell me what are
Speaker 1: the premises, what's the conclusion. And that's something that chatchipt
Speaker 1: is not good at, right, But it's also something that
Speaker 1: will give them critical.
Speaker 4: Reading skills, which is what I want to do, right.
Speaker 1: So yeah, I think that I've mostly been thinking about
Speaker 1: ways to keep them from relying on it, because I
Speaker 1: think that often if they rely on it, they'll they'll
Speaker 1: they'll put.
Speaker 4: Themselves it's the worst position.
Speaker 1: Yeah, when it comes to future work, they won't develop
Speaker 1: the skills that they're going to need and the skills
Speaker 1: that we tell them and their parents they're gonna get
Speaker 1: 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 Chohnd 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 going to print this paper out and you're going
Speaker 2: to underline the things that are important and talk to
Speaker 2: me about almost feels like we need to rebuild this
Speaker 2: because yes, we shouldn't be using chet GPT to half
Speaker 2: us our essays. But at the same time, human beings
Speaker 2: are lazy.
Speaker 1: Yeah, I mean for me, I also prefer to read
Speaker 1: off the computer, but I often read PDFs because I'm
Speaker 1: terrible at keeping files right physical Like, you know, I'm
Speaker 1: not going to keep a giant filed with all the
Speaker 1: papers that I've read and written my liner notes in.
Speaker 1: You guys can see in my office they're just piled around,
Speaker 1: like you can't.
Speaker 2: See this in that's academia.
Speaker 4: Yeah, but I just.
Speaker 1: Have piles of paper with empty coffee bugs everywhere that
Speaker 1: the camera is not facing.
Speaker 4: But it's the terrible system.
Speaker 1: So at least on my computer, if I'm like, oh,
Speaker 1: I read that paper like a year ago, what did
Speaker 1: I think I can click on it and see my
Speaker 1: own notes, and I do think that there's something to
Speaker 1: keeping those records 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.
Speaker 6: Well, you started with the correct answer, which is don't
Speaker 6: use chat GPT. Yeah, yeah, I actually I've got a
Speaker 6: certain amount of sympathy with like, just keep writing till
Speaker 6: you get good at it. But I realized as a
Speaker 6: lecturer that can't be my official possession. And I certainly
Speaker 6: think that it's the case that certainly Glasgow has got
Speaker 6: better over the last few years about going, Oh, actually
Speaker 6: you do like, we do need to give you some
Speaker 6: kind of structuring and some buttressing on his is how
Speaker 6: to write academically.
Speaker 5: Here's how to get do research.
Speaker 6: And I think that's all to the It's worth saying
Speaker 6: this started happening well before chat GPT started pissing all
Speaker 6: over our doorsteps, so they don't get to claim that
Speaker 6: as being a benefit.
Speaker 2: There was a whole Wikipedia panic when I was in school.
Speaker 5: Yeah, think about Wikipedia the right. It's like I used
Speaker 5: to say this to my students.
Speaker 2: Wikichili one of the best resources.
Speaker 6: Absolutely fine as a starting point for research absolutely whatsoever.
Speaker 6: But if you're turning in and on his level, essay,
Speaker 6: I want you to go and read the fucking things
Speaker 6: it's referencing.
Speaker 4: Yeah, that's right.
Speaker 1: Yeah, I think these things are often great as sources.
Speaker 1: I'm worry about chat GPT is that it's not great
Speaker 1: as a source.
Speaker 2: It's just yeah, we've been.
Speaker 1: Saying it often gets things wrong, and it often it'll
Speaker 1: make up sources, whereas Wikipedia.
Speaker 4: Will never do that.
Speaker 6: There are some famous hoaxes, but they get cool. Yeah, Joe,
Speaker 6: have you got any positive things to say 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. But it can do some interesting and cool things. Yeah,
Speaker 3: I think some people in that sort of field have
Speaker 3: found useful. And also we've mentioned this before, like if
Speaker 3: you want chat to be teach to write you a bibliography,
Speaker 3: you've got a bibliography in one style tell it to
Speaker 3: put something into a different one. Then it's and it's
Speaker 3: good for like coding process doing certain things.
Speaker 1: I also think I don't know, I'm not sure how
Speaker 1: I feel about what I'm about to say, but perfect, Yeah.
Speaker 4: I'm going for it.
Speaker 1: It is a somewhat positive thing maybe for a chat ept,
Speaker 1: which is that we often have students who have like
Speaker 1: really interesting ideas and well thought out arguments, but for
Speaker 1: whom English isn't their first language, and the actual writing
Speaker 1: is kind of rough and you have to like push
Speaker 1: through reading it to get the good idea, which is often.
Speaker 4: Really there and quite you know, creative and insightful.
Speaker 1: Yeah, And so I do wonder if there's a way
Speaker 1: to use it so it just smooths off the edges
Speaker 1: of this kind of thing. But I worry that if
Speaker 1: you tell students to do that, they'll just first it
Speaker 1: if they can develop the language skills they offered it
Speaker 1: really good by the yea, yeah.
Speaker 4: What are you going to say to you?
Speaker 6: Well, so I want to say, it's yeah, you can
Speaker 6: see me getting agitated. Yeah, I think much correct about
Speaker 6: it like that this is a kind of possible use.
Speaker 6: But I think this and this is why I'm getting
Speaker 6: visibly agitated here. That's students either need to or feel
Speaker 6: they need to use.
Speaker 5: This speaks to a deeper.
Speaker 6: Issue, right to a social issue, to political issue, to
Speaker 6: an issue about how universities work. If a student is
Speaker 6: having problems with English, then there's a number of like
Speaker 6: explanations or a number of kind of responses. Right that
Speaker 6: one response is that, like Glasgow is in English teaching university,
Speaker 6: if someone's English isn't good enough to be taking a degree,
Speaker 6: then plausibly they shouldn't have been let in, and why
Speaker 6: have they been learned well?
Speaker 4: Because of money?
Speaker 6: Or alternatively, if someone's having problems with English for like
Speaker 6: whatever reason at all, that should be supported. There should
Speaker 6: be kind of tutors. There should be people who can
Speaker 6: help with English. But again that would cost university money.
Speaker 6: So of course that doesn't happen, right, It doesn't happen
Speaker 6: anywhere that it doesn't in the extent it would have
Speaker 6: to happen in order for this to be a general policy.
Speaker 1: Yeah, I think it could be better, But I do
Speaker 1: think that universities often have like a writing center or
Speaker 1: a tutoring center that you can.
Speaker 5: Send, but they don't.
Speaker 6: They don't have the sort of spread that would be
Speaker 6: needed or the staff that would be needed for this
Speaker 6: to be instead of using chat GVD to sound the
Speaker 6: edges off.
Speaker 4: Yeah, I think for example twenty two with your supervisor.
Speaker 4: My worry especially would be that this is my first
Speaker 4: year here.
Speaker 1: At Glasgow, but I have a good Yeah, I think
Speaker 1: they probably have a good writing center universities have been
Speaker 1: in the past. I felt very confident sending students to
Speaker 1: the writing center when they have these problems. But I
Speaker 1: think James is completely right that we don't want the
Speaker 1: universities to see this as a way to get rid
Speaker 1: of the writing center. And that's one hundred percent a
Speaker 1: risk given the financial problems that.
Speaker 4: Universities are facing, and maybe they're already not ye like,
Speaker 4: given the quality of papers, they're often good.
Speaker 1: I think another thing, as far as this is like
Speaker 1: a social problem, is that when grading, I myself tried
Speaker 1: to grade in terms of like the ideas and argument
Speaker 1: this is philosophy.
Speaker 4: And not the quality of being right. But not everybody
Speaker 4: does that.
Speaker 1: So I kind of think that another part of this
Speaker 1: is figuring out how we want to evaluate the students
Speaker 1: and what we want to privilege in that evaluation.
Speaker 4: Yeah.
Speaker 6: Sure, so then again the kind of that that becomes
Speaker 6: a problem about what people are checking for. Not Let's
Speaker 6: take this arch backwards approach to marking, which is like,
Speaker 6: how fancy is your English?
Speaker 5: Are fancy? English? Is good English? Have an A? But
Speaker 5: rather we should be kind of checking for everythings.
Speaker 6: Right, So again the blame blies differently in that case,
Speaker 6: but it still becomes a question.
Speaker 5: 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 1: I have a kind of related tangent here, which is like,
Speaker 1: what are the use cases that open ai was expecting
Speaker 1: but didn't want to emphasize, Because for everybody in universities,
Speaker 1: as soon as this came out, the first thought was
Speaker 1: students are going to use this to cheat And certainly
Speaker 1: like the people in open AI went to college, right,
Speaker 1: and that's what I hear about.
Speaker 2: Them, so they must well, sam Oman drop down, Yeah.
Speaker 1: I'm sure he really understands the value of a secondary He's.
Speaker 2: Like, I'm gonna write at least fucking essays.
Speaker 1: Anyway, maybe he was thinking I would have loved to
Speaker 1: have a computer write my essays, I'll devote my life.
Speaker 1: But I mean, I'm sure that they like recognize these
Speaker 1: bad use cases, right, but they're doing nothing to mitigate
Speaker 1: them as far as I can see. And like, another
Speaker 1: one that's very related is like, you know, I'm sure
Speaker 1: you've heard of this ED phishing, right, A lot of
Speaker 1: you know, corporations get attacked and get hacked not by
Speaker 1: someone cleverly figuring out a backdoor to their system, but
Speaker 1: by somebody sending in social engineering, yeah, asking for the
Speaker 1: password to somebody else.
Speaker 4: And one of the biggest.
Speaker 1: Barriers to that is that a lot of the people
Speaker 1: who are engaging in fishing aren't from the same country
Speaker 1: as the company they're targeting, right, so they're not able
Speaker 1: to write a convincing email or make a phone call
Speaker 1: that sounds like that person's supervisor.
Speaker 4: But with a tool like this, you could one hundred
Speaker 4: percent write that email. Right.
Speaker 1: It's going to make it a lot easier for these
Speaker 1: kinds of illicit schemes to work.
Speaker 2: That has been a market increase CNBC, which is just
Speaker 2: brought 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 4: Right, But also, weren't the people that open a eye
Speaker 4: thinking about.
Speaker 2: That because like they don't care.
Speaker 6: Yeah, yeah, we've all seen Durassic Park, right, Yeah, Yeah,
Speaker 6: they were so visically thinking about what they could do,
Speaker 6: they never thought about whether they should.
Speaker 4: Yeah.
Speaker 1: This is the kind of problem with the move fast
Speaker 1: and break things mentality, like obvious. I mean, I think
Speaker 1: it might be the only person who has raised in
Speaker 1: the US, but we had future.
Speaker 4: Problems solvers, where you you know, you think.
Speaker 1: About a future problem and what bad consequences there could
Speaker 1: be of some technology and how to solve them, usually
Speaker 1: through social cues.
Speaker 4: If I could do that in fifth grade, Yeah.
Speaker 1: I would expect these people to have thought through some
Speaker 1: of the bad consequences of the technology they're putting out.
Speaker 1: And you know, some of those are cheating on tests
Speaker 1: and they don't seem to have worried about that. Yeah,
Speaker 1: And another one is fishing. They don't seem to have
Speaker 1: worried about that.
Speaker 6: You know, biases and algorithms, right, so yeah, yeah, comes,
Speaker 6: and I suppose to you it turns out, so with
Speaker 6: a lot of the facial recognition systems, they were incredibly racist.
Speaker 2: They were going back to Microsoft's connect could not see.
Speaker 6: Bad Yeah yeah, and kind of CCTV stuff that basically
Speaker 6: just sort of unless it was presented with a blindingly
Speaker 6: white Caucasian.
Speaker 5: Where I don't know, right, but like.
Speaker 6: The sort of stuff where these like langage knowledge are
Speaker 6: trained on certain sets of data, and they trained on
Speaker 6: certain assumptions and like shifting shit out right, and particularly
Speaker 6: if people think that it's actually doing any kind of thinking,
Speaker 6: and if they kind of cargo cult it, we again
Speaker 6: get a kind of social problem multiplied by technology feeding
Speaker 6: back into a social problem, right.
Speaker 5: And it's the so these guys have heard me when
Speaker 5: you're about it so much, I love it or so, but.
Speaker 6: I'm profoundly skeptical of technology's ability to solve anything unless
Speaker 6: we know exactly the respect in which we want to
Speaker 6: solve it and how that technology is going to be applied.
Speaker 6: You know, like sure, experiment with bringing back dinosaurs, but
Speaker 6: but like, don't tell me that it's going to save
Speaker 6: the healthcare system unless you can demonstrated to be step
Speaker 6: by step how that big OLDI rect run around on
Speaker 6: Eiland new Blayer is going to save anything, and they
Speaker 6: just tried to blind people.
Speaker 4: Actually bringing back dinosaurs with good that would be great.
Speaker 6: All right, this isn't the best example, but I already had.
Speaker 6: I had Jeff Gobwum in my head and I had
Speaker 6: to go.
Speaker 2: To the drastic fellas. This has been such a pleasure.
Speaker 2: Don't put that in the recording. That's right, we won't
Speaker 2: edit it in post. You will give us your names.
Speaker 4: I'm Mike Hicks, but my papers are written by Michael.
Speaker 1: 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 part. Don't you worry.
Speaker 4: We'll get that right.
Speaker 5: Just plugging my luggy staff.
Speaker 4: My name is Joe Slater.
Speaker 3: I'm a university lecturer in moral and political philosophy at LASKOW.
Speaker 6: I'm James Joffrees. I'm a lecturer in political theory at
Speaker 6: the University of Glasgow. And even if I wanted to
Speaker 6: or couldn't give you more 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 5: 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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