CZM Rewind: The Academics That Think ChatGPT Is BS

In a paper released earlier this year, three academics from the University of Glasgow classified ChatGPT's outputs not as "lies," but as "BS" - as defined by philosopher Harry G. Frankfurt in "on BS" (and yes I'm censoring that) - and created one of the most enjoyable and prescient papers ever written. In this episode, Ed Zitron is joined by academics Michael Townsen Hicks, James Humphries and Joe Slater in a free-wheeling conversation about ChatGPT's mediocrity - and how it's not built to represent the world at all. Paper: https://link.springer.com/article/10.1007/s10676-024-09775-5 Michael Townsen Hicks: https://www.townsenhicks.com/ Joe Slater: https://www.gla.ac.uk/schools/humanities/staff/joeslater/ Original Air Date: 7.18.24 YOU CAN NOW BUY BETTER OFFLINE MERCH! Go to https://cottonbureau.com/people/better-offline and use code FREE99 for free shipping on orders of $99 or more. BUY A LIMITED EDITION BETTER OFFLINE CHALLENGE COIN! https://cottonbureau.com/p/XSH74N/challenge-coin/better-offline-challenge-coin#/29269226/gold-metal-1.75in --- LINKS: https://www.tinyurl.com/betterofflinelinks Newsletter: https://www.wheresyoured.at/ Reddit: https://www.reddit.com/r/BetterOffline/ Discord: chat.wheresyoured.at https://discord.com/invite/QUUQUP9szv Ed's Socials: https://twitter.com/edzitron https://www.instagram.com/edzitron See omnystudio.com/listener https://omnystudio.com/listener for privacy information.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

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• 1:46Speaker 02

  Call Zone Media.

• 1:48Ed Zitron?

  Hey everyone, it's me Ed Zitron and we're doing a rerun episode this week. Sadly, the in-studio recording we did with Ashwin Rodriguez and Victoria Song, we had a technical fault, nothing we can do. It sucks, but we're doing a rerun this week. We're doing the academics that think ChatGPT is BS. And this is one of my favorite episodes ever recorded. It changed how I do interviews writ large. It's with these three academics, Michael Townsend Hicks, James Humphries and Joe Slater, who wrote a paper using the the actual Frank Furtian definition of bullshit to say that ChatGPT bullshits. It's so much fun. It's one of my favourite episodes I've recorded. I will have you a monologue this week as well. I do apologise for not having something new for you this week.

• 2:30Ed Zitron?

  You'll still get the monologue on Friday though. Thank you for your time, your patience and of course for listening to Better Offline.

• 2:46Ed Zitron?

  Hello and welcome to Better Offline. I'm your host Ed Zitron.

• 3:02Ed Zitron?

  In early June, three researchers from the University of Glasgow published a paper in the Ethics of Information and Technology Journal called ChatGPT is Bullshit. And I just want to be clear, this is a great and thoroughly researched and well argued paper. This is not silly at all. It's actually great academia. And today I'm joined by the men who wrote it, academics Michael Townsend Hicks, James Humphries and Joe Slater, to talk about ChatGPT's mediocrity and how it's not really built to represent the world at all. So for the sake of argument, could you define bullshit for me?

• 3:36Speaker 04

  So you are bullshitting if you are speaking without caring about the truth of what you say. So normally if I'm telling you stuff about the world, in a good case, I'll be telling you something that's true and like trying to tell you something that's true. If I'm lying to you, I'll be knowingly telling you something that's false or something I think is false. If I'm bullshitting, I just don't care. I'm trying to get you to believe me. I don't really care about whether what I say is true. I might not have any particular view on whether it's true or not.

• 4:08Ed Zitron?

  Right. And you define between like soft and hard bullshit. Can you also get into that as well? Can you also identify yourselves as well?

• 4:16Speaker 04

  Sorry, yeah, I'm Joe. So the soft bullshit, hard bullshit distinction is a very serious and technical distinction.

• 4:22Ed Zitron?

  Right.

• 4:23Speaker 04

  Um, so we came up with this because bullshitters in the technical philosophical sense comes from Harry Frankfurt, recently deceased, but a really great philosopher. And he talks about the amount of bullshit that there is in like popular culture and just in general discourse these days. Some of the ways he talks about bullshit seem to suggest that it needs to be accompanied by a sort of malign intention.

• 4:50Ed Zitron?

  Right.

• 4:50Speaker 04

  I'm doing something kind of bad. I'm intending to mislead you about the enterprise of what I'm doing.

• 4:57Speaker 05

  Yeah, maybe about who you are or what you know. So you might be trying to portray yourself as someone who is knowledgeable about a particular subject. Maybe you're a student who showed up to class without doing the work. Maybe you're trying to portray yourself as someone who's virtuous in ways you're not. Maybe you're a politician who wants to seem like you care about your constituents, but actually you don't. So you're not trying to mislead somebody about what you're saying, the content of your utterance. You're trying to mislead them instead about like why you're saying it. That's that's what we call hard bullshit. Yeah, it's one of the things Frankfurt talked about.

• 5:36Speaker 04

  Yeah. So Frankfurt doesn't make this hard bullshit, soft bullshit distinction, but we do. Uh, because sometimes it seems like Frankfurt has this particular kind of intention in mind. But sometimes he's just a bit looser with it. And we want to say that ChatGPT and other large language models, like they don't really have this intention to deceive because they're not people that have these intentions. They're not trying to mess with us in that kind of way. But they do lack this kind of caring about truth.

• 6:04Speaker 06

  Well, I'm James, by the way. I suppose we strictly don't want to say that they aren't hard bullshitters. And we just think if you don't think that large language models are sapient, if you don't think they're kind of minds in any important way, then they're not hard bullshitters. So I think in the paper, we don't, we don't take a position on whether or not they are. We just say if they are, this is the way in which they are, but minimally they're soft bullshitters. So the kind of soft bullshitters, as Joe says, doesn't require that speakers attempting to deceive the audience about the, the nature of the enterprise. Hard bullshit does. So if it turns out that large language models are sapient, which they're definitely not, like that's just tech pro nec.

• 6:39Ed Zitron?

  Yes, that's nonsense.

• 6:40Speaker 06

  Yeah. But if they are, then they're hard bullshitters and minimally they're soft bullshitters or they're bullshit machines.

• 6:47Ed Zitron?

  So you also make the distinction in there, the intention, so the very fabric of hard bullshit, that you intentionally are bullshitting someone. You kind of make this distinction that the intention of the designer and the involved prompting could make this hard bullshit. Because with a lot of these models, and someone recently jailbroke ChatGPT and it listed all of the things it's prompted to do. Could prompting be considered intentional? That Sam Altman, CEO of OpenAI, could be intentionally bullshitting? I think he is.

• 7:19Speaker 06

  And yeah, this again, I think it's something, I don't know what the kind of hive mind consensus on this is. I'm sort of sympathetic to the idea that if you take this kind of perpressive or teleological attitude towards, right, kind of the what an intention is, is an effort to do something, then maybe they do have intentions. But again, I think in the paper, we just sort of wanted, I mean, it's a standard philosophical move, right? Just sort of go, look, here's all this uncontroversial stuff as we can make it. Now we can hit you with the really controversial shit that we wanted to get to. So in the paper, we sort of deliberately went, maybe you might think it has intentions for this reason. We kind of have no judgment on this officially. I'm sympathetic to the sort of view that you're putting. I think you're kind of sympathetic to this as well, right?

• 7:58Speaker 05

  Yeah. So I'm Mike. There are a few ways that you can think of ChatGPT as having intentions, I think. And we talked about a few of them. One is by thinking of the designers who created it as kind of imbuing it with intentions. So they created it for a purpose and to do a particular task. And that task is to make people think that it's a normal sounding person, right? It's to make people, when they have a conversation with it, not be able to distinguish between what it's outputting and what a normal human would say, right?

• 8:30Ed Zitron?

  Right.

• 8:31Speaker 05

  And. That kind of goal, if it amounts to an intention, is the kind of intention we think a bullshitter has, right? It's not trying to deceive you about anything it's saying. It doesn't care whether what it's saying is true. That's not part of the goal. What the goal is to do is to make it seem as if it's something that it's not, like specifically a human interlocutor. And one source for that goal is the programmers who designed it. Another is the training method. So it was trained by being given sort of positive and negative feedback in order to achieve a specific thing, right? And that specific thing is just sounding normal. And that's so similar to what

• 9:13Speaker 05

  our students are doing when they try to pretend that they read something they haven't read.

• 9:18Ed Zitron?

  There is something very collegiate about the way it bullshits, though. It reminds me of when I was in college, I went to Penn State and Aberystwyth, two very different institutions.

• 9:27Speaker 05

  Right, right. Both kind of in the middle of nowhere.

• 9:31Ed Zitron?

  Yeah, both very sad. But the one thing you saw with like students who were doing like B+ homework is they were using words they didn't really understand. They were putting things together in a way that really was like the intro body conclusion. There was a certain formula behind it and it's just, it feels exactly like it. But that kind of brings me to my next question, which is, how did you decide to write this? What inspired this?

• 9:54Speaker 06

  I will tell you this because whenever Mike tells the story, you get sanitized version. We were in the pub whinging about student essays.

• 10:00Ed Zitron?

  Perfect.

• 10:01Speaker 06

  Yeah, like Mike, you were not long here, right? You were not long.

• 10:03Speaker 05

  I just started.

• 10:04Speaker 06

  Yeah, not long in post. A whole bunch of us went to the pub and on a notional, let's, let's welcome our new members of staff sort of event. And inevitably, we had about two pints, we were all pissing and moaning. And I think it might have been Neil that kind of prompted this.

• 10:17Speaker 05

  No, I don't think he was there.

• 10:18Speaker 06

  You're not? Okay, fair enough.

• 10:19Speaker 05

  We talked to him about it.

• 10:21Speaker 06

  We talked to him about it after, right? In any case, like sort of came up, we were talking about this sort of prevalence of chat GPT-generated stuff and what it said about. How at least some students were kind of approaching the assessments. And you know, I forget who someone sort of just went off handedly. Yeah, but it's all just Frank Furtian bullshit though, isn't it? And we sort of collectively went, oh, hello. Because obviously all having a background in philosophy, we've all read on bullshit. You know, we all went, hee hee hee, we get to say bullshit in allegedly serious academic work. So the the start of it was, we'd had this experience of having to read all of this kind of uncanny valley stuff. And when prompted, we all went, oh, it really is like Frank Furtian bullshit in a way that we can probably get a paper out of.

• 10:58Speaker 05

  Yeah. And at that point, I think we were in the department meetings talking about how to deal with chat G P T written papers. And there were discussions going on all over the university, including kind of in high levels in the administration to come up with a policy. And we specifically wanted a stronger policy. Because our university is very interested in cutting edge technology, right? And so they wanted the students to have an experience using and figuring out how to use whatever the freshest technology is.

• 11:30Ed Zitron?

  Right, as they should.

• 11:31Speaker 05

  As they should, right.

• 11:33Ed Zitron?

  But not for essays.

• 11:34Speaker 05

  Right, that's the worry we had. We thought, you know, if we're not very clear about this, the students will be using it in a way that will detract from their educational experience. Right? And at the same time, it was becoming more widely known how these machines work, like specifically how they're doing next token or next word prediction in order to come up with a digestible string of text. And when you know that that's how you're they're doing it, I mean, it seems so similar to what humans do when they have no idea what they're talking about. And so when we were talking about it, it just seemed like an obvious paper that someone was going to write. And we thought that had better be us. You know, eventually people are going to see this connection and.

• 12:19Ed Zitron?

  It's a great paper.

• 12:21Speaker 05

  I think it worked very well. I mean, I think that it's the kind of thing where when I pitch this to other philosophers, it doesn't take them very long to just agree, to be like, ah, yes, that's.

• 12:31Ed Zitron?

  Right. It's an interesting philosophical concept as well, because the way that people look at chat G P T and large language models is very much machine do this. But when you think about it, there are other ways where people are giving it consciousness. I just saw a tweet just now where someone talked about saying please with every request. And it's like, no, I will abuse the computer in whatever manner I see fit. But it's. It's curious because I think more people need to be having conversations like this. And one particular thing I like that you said that I actually would love you to go into more detail is you said that ChatGPT and large language models, they're not designed to represent the world at all. They're not lying or misrepresenting, they're not designed to do that. What do you mean by that?

• 13:12Speaker 05

  I mean, kind of as background, I do philosophy of science, but my thoughts about something like ChatGPT are largely inspired by the fact that I also teach a class called Understanding Philosophy Through Science Fiction.

• 13:25Ed Zitron?

  Oh.

• 13:25Speaker 05

  And now we like talk about whether computers could be conscious. And I don't know what you guys think, actually, I think they could, right? I just don't think this one is. And part of the reason I think they could, but this one isn't, is that I think that in order to sort of represent the world or have the kinds of things we have that are like beliefs, desires, thoughts that are about external things, you have to have internal states that are connected in some way to the external world, usually causation. We're perceiving things, information is coming in. Then we've got some kind of stain in our brain that's designed just to track these things in the external world, right?

• 14:08Speaker 05

  That's a huge part of our cognitive lives is just tracking external world things. And it's a very important part of childhood development when you figure out how to track them.

• 14:19Ed Zitron?

  It's semiotics, right?

• 14:21Speaker 05

  Yeah.

• 14:21Ed Zitron?

  Daniel Chandler Aberystwyth taught me semiotics. It's like the perception of the world.

• 14:25Speaker 05

  This is like theory of meaning stuff, so yeah, semiotics is like theory of signs. How is it that a sign?

• 14:32Speaker 06

  A sign can be both the representation of the thing and the thing itself.

• 14:35Speaker 05

  That can happen, yeah, but not always. Sometimes it's just the representation of the thing. And there's a lot of philosophy is about figuring out how brain states or words on a page can be about external world things. And a big part of it, at least from my perspective, has to do with tracking those things, keeping tabs on them, changing as a result of seeing differences in the external world.

• 15:02Speaker 05

  And ChatGPT is not doing any of that, right? That's not what it's designed to do. It's taking in a lot of data once and then using that to respond to text. But it's not remembering individuals, tracking things in the world, in any way perceiving things in the world. It's just forming a sort of statistical model of what people say. And that's kind of so divorced from what most thinking beings do.

• 15:33Ed Zitron?

  It's divorced from experience.

• 15:35Speaker 05

  Yeah, I mean, as far as I can tell, it doesn't have anything like.

• 15:39Speaker 06

  Yeah, and that's one of the things that this, I think, sort of in one way comes down to is that if you sort of post this sufficiently far, someone is going to go, ah, isn't this just biochauvinism, right? Like, aren't you just assuming that unless something runs on, like, meat, it can't be sent here? And this isn't something we get into in the paper, partly because we didn't really think it was worth addressing. But the sorts of things that seem like they're like, never mind consciousness, right? But that seem to be necessary in order for something to be trying to track the world or in order to be corresponding the world or to form beliefs about the world. ChatGPT just doesn't seem to meet any of them. I kind of it's, if it, if it does turn out that it's sapient, then ChatGPT has got some profoundly serious executive function disorders, right? And but of course, it's not sapient, right? So we don't have to worry about it.

• 16:22Speaker 06

  But kind of it's not the case that we've got some blundering proto-general intelligence that's trying to figure out how to represent the world. It's not trying to represent the world at all. Their utterances are designed to look as if it's trying to represent the world and then we just go, that's just bullshit. This is a classic case of bullshit.

• 16:37Ed Zitron?

  Yeah, it seems to be making stuff up, but making stuff up doesn't even seem to describe it. It's just throwing shit at a wall very accurately, but not accurately enough.

• 16:45Speaker 06

  Yeah, it's got various guidelines that allow it to throw shit at the wall with a sort of reasonably high degree of action. No, you're right. I mean, one of the things that a human bullshitter could at least be characterized as doing is they'd have to try and kind of judge their audience, right? They'd have to try and make the bullshit plausible to the audience that they're speaking to. And ChatGPT can't do that, right? All it can do is go on a statistically large enough model and. It looks like Z follows Y follows X.

• 17:09Ed Zitron?

  Right.

• 17:10Speaker 06

  It's not kind of got any, well, it doesn't have any consciousness at all, of course, but it doesn't have any sensitivity to the sorts of things that people are in fact likely to find plausible. It just does a kind of brute number crunch. So it's more complicated than that, but I think it boils down effectively to kind of number crunching, right? Kind of data and context in which.

• 17:27Ed Zitron?

  Yeah, probabilistic planning of, well, planning, it doesn't plan. That's the thing. It's interesting. There are these words you use to describe things that when you think about it are not accurate. You can't say it plans or thinks.

• 17:40Speaker 05

  I think one thing between when we came up with the idea and when we finished writing the paper, we spent some time reading about how it works and how it represents language and what the statistical model is like. And I was maybe more impressed than James about that because it is like doing things that are similar to what we do when we understand language. It does seem to kind of locate words in a meaning space, right?

• 18:10Ed Zitron?

  Right.

• 18:10Speaker 05

  And connect them to other words and, you know, show similarity and meaning. And it also does seem to be able to, in some way, understand context. But we don't know how similar that is for a variety of reasons, but mostly because it's too big of a system and we can only kind of probe it and it's trained indirectly, right? So it's not programmed by individuals. And even though that's kind of a very impressive model of language and meaning and may in some ways be similar to what we do when we understand language, we're doing a lot more, things like planning, things like tracking things in the world,

• 18:52Speaker 05

  just having desires and representing the way you want the world to be and thereby generating goals doesn't seem to be something that it has anything, any room for in its architecture.

• 19:02Speaker 06

  This is something I've been telling you, it's just good to me. You were talking about the kind of, in some ways, it learns language the same way that we do. I mean, it's got no grasp of expletive and fixation, right? This is one of Chalk's.

• 19:10Ed Zitron?

  What does that mean just for, not for me, I definitely know.

• 19:14Speaker 06

  Yeah, of course. If I give you the sentence, that's completely crazy, man, and tell you to put the word fucking into that sentence. There's a number of ways in which any language speaker is going to do it, that they'll just go, yeah, of course, that's where it goes, right?

• 19:28Speaker 05

  Right.

• 19:28Speaker 06

  But we, it seems that we've got a grasp on this incredibly early on in a way that doesn't look like it's the ways, at least most of us, that talk language, right? We get quite harshly told off when we try and do explicit infixation.

• 19:40Speaker 05

  Yes.

• 19:40Speaker 06

  So this, I think, would be one of those cases where you could do a sort of disanalogy by cases, right? You present ChatGPT a sentence and say, insert the word fucking correctly in this sentence. And I don't think it would be very good at it.

• 19:52Speaker 05

  I think it would be. I think.

• 19:53Speaker 06

  You reckon? Well, I mean, we probably could test this.

• 19:55Speaker 05

  We could.

• 19:56Speaker 06

  But we shouldn't.

• 19:57Speaker 05

  Not right now. Yeah. One of the things that I thought was like, kind of interesting about how it works, is that it does learn language probably differently from the way we do, but it does it all by examples. You know, so it's looking at all these pieces of text and thinking, ah, this is okay, that's okay. And one kind of interesting thing about how humans understand language is that we're able to kind of understand meaningless but grammatical sentences. It's not clear to me that ChatGPT would understand those. That's another Chomsky example. So, you know, Chomsky has this example that's like, what is it?

• 20:34Speaker 06

  Is it the green, green, green weasels sleep furiously?

• 20:37Speaker 05

  Color, it's colorless green ideas, I think, sleep furiously, right? And that's a meaningless sentence, but it's grammatically well-formed. And we can understand that it's grammatically well-formed, but also that it's meaningless. Because ChatGPT kind of combines different aspects of what philosophers of language, logicians, linguistics people see as like different components of meaning. It sees these as all kind of wrapped up in the same thing. It puts them in the same big model. I'm not sure it can differentiate between grammaticality and meaninglessness.

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• 23:22Ed Zitron?

  So we're doing real time science right now. I just put the word fucking into the following sentence in the correct way. Man, that's crazy. And I did it six times and I would say 50% of the time it got it right. And it did. Man, that's fucking crazy. Man, that's crazy fucking. Man, that's fucking crazy. Man, that's crazy fucking. My favorite is man, comma, that's crazy, comma, fucking.

• 23:46Speaker 06

  To be fair.

• 23:48Speaker 05

  I think you're right. Very unreliable.

• 23:50Speaker 06

  You take the commas out of that last one and you've got a grammatical sentence.

• 23:53Ed Zitron?

  Yeah.

• 23:54Speaker 06

  Of course, in Glasgow, you can also start with fucking, man, that's crazy.

• 23:57Ed Zitron?

  West London as well. But the thing is though, it doesn't know what correct means there.

• 24:01Speaker 05

  Yeah, no.

• 24:02Ed Zitron?

  When it's trained on this language, when it's trained on thousands of internet posts it stole, it's not like it reads them and says, oh, I get this. Like, I see what they're going for. It just learns structures by looking, which is kind of how we learn language. But it kind of reminds me of like when I was a kid and I'd hear someone say something funny and I'd repeat it. And my dad, who's wonderful, would just say, that doesn't make any sense. And he'd have to explain. Because if you're learning everything through copying, you're not learning, you're just memorizing.

• 24:34Speaker 05

  Yeah.

• 24:34Speaker 06

  Yeah, exactly.

• 24:35Speaker 05

  There's a, I don't know if you have already talked to somebody about this, but there's a, you know, a classic argument from Chomsky against behaviorism.

• 24:44Ed Zitron?

  Right.

• 24:44Speaker 05

  Behaviorism is the view that we learn everything through stimulus and response. That's not exactly it. But I'm not a philosopher of mine, so I can get away with with that. So Chomsky says, look, we don't get enough stimulus to learn language as quickly as we do just through watching other people's behavior and copying it. We have to have some inbuilt grammatical structures that language is latching on to. And there's been some papers arguing that ChatGPT shows Chomsky was wrong because it doesn't have the inbuilt grammatical structure. But one interesting thing is, it requires 10 to 100 times more data than a human child does when learning

• 25:26Speaker 05

  language, right? So Chomsky's argument was, we don't get enough stimulus. And ChatGPT can kind of do it without the structure, but it's not quite doing it as well. And it gets like orders of magnitude more, more input than a human does before a human learns.

• 25:46Speaker 05

  Which is kind of interesting.

• 25:47Speaker 06

  And it still can't do something as basic as putting the word fuck in.

• 25:51Speaker 05

  Right, it still can't do that.

• 25:53Ed Zitron?

  Yeah, it doesn't even see and it doesn't have the knowledge to say, request more context. Because it doesn't perceive context and that's kind of the interesting thing. So there was another paper out of Oxford, I think, that's talking about cognition. And chat GPT and all this thing. And it's just, it doesn't feed chat GPT features in no way, any of the things that the human mind is really involved in. It seems it's mostly just not even memorization because it doesn't memorize. It's just guessing based on a very large pile of stuff. But this actually does lead me to my question, which is you don't like the term hallucination. Why is that?

• 26:29Speaker 04

  Hallucination, it makes it sound a bit like I'm usually doing something right. And I'm looking around, seeing the world as something like what it really is. And then like one little bit of the feature for a visual hallucination. One feature of my visual field actually isn't represented in the real world. It's not actually there. Everything else might well be, right? Imagine I hallucinate, there's a red balloon in front of me. I still see Mike, I still see James, I still see the laptop. One bit is wrong, everything else is right. And I'm doing the same thing that I'm usually doing, like my eyes are still working pretty much normally. I think, and this is the way I usually get knowledge about the world.

• 27:12Speaker 04

  This is a pretty reliable process for me, seeing stuff and learning from it, right? And representing the world in this way. So we talk about hallucinations. This suggests that chat GPT and other similar things. They're going through this process that is usually quite good at representing the world. And then, oh, it's made a mistake this one time. But actually, no, it's bullshitting the whole time. And like, sometimes it gets things right for bullshitting. Just like a politician, imagine a politician that bullshits all the time. If you could possibly imagine it, sometimes they might just get some things true. And we should still call them bullshitting.

• 27:55Ed Zitron?

  Yeah.

• 27:55Speaker 04

  Because that's what they're doing. And this is what chat GPT is doing every time it produces an output. So this is why we think bullshit is a better way of thinking about this. Or one of the reasons why we think bullshit is a better way of thinking about it.

• 28:08Speaker 05

  I also kind of think that some of the ways we talk about chat GPT. Even when it makes mistakes, lend themselves to overhyping its ability or overestimating its abilities. And talking about it as hallucinating is one of these because when you say that it's hallucinating, as Joe pointed out, you're giving the idea that it's representing the world in some way and then telling you what the content.

• 28:34Ed Zitron?

  And it has perception.

• 28:35Speaker 06

  Yeah, exactly. Like it has perceived something and like, oh no, it's taken some computer acid and now it's hallucinating. Like imaginary things. And yeah, just, just as you say.

• 28:44Speaker 05

  And that's not what it's doing. And so when the kind of people who are trying to promote these, these things as products, talk about the AI hallucination problem. They're kind of selling a product that is a product that's representing the world, usually checking things and occasionally makes mistakes. And if the mistakes were, like Joe said, were a misfiring of a normally reliable process. Or, you know, something that normally represents going wrong in some way. That would limit itself to certain solutions to them. And it would make you think there's an underlying reliable product here, right? Which is exactly what somebody who's

• 29:27Speaker 05

  making a product to go on the market will want you to think, right? But if that's not what it's doing, in a certain sense, they're misrepresenting what it's doing, even when it gets things right. And that's, that's bad for all of us who are going to be using these systems, especially since people, you know, most people don't know how this works. They're just understanding the product as it's described to them, using these kind of metaphors. So the way the metaphor describes it is going to really influence how they think about it and how they use it.

• 29:57Speaker 06

  Yeah, just to sort of cap that off if I can. This, one of the responses to some corners has been to sort of say, and of us, look, you whinge about people anthropomorphizing ChatGPT, but look, if you call it a bullshit, you're doing exactly the same thing. And I mean, there might be some extent to which it's just really hard not to anthropomorphize it. I don't know why I picked a word that I can barely say.

• 30:17Speaker 06

  Like we've been doing it constantly throughout this discussion, right? When we were talking through the kind of through the paper, we kept talking about ChatGPT as if it had intention, right? As if it was thinking about anything. That might be another reason to call it bullshit. We go, look, if we have to treat it as if it's doing something like what we do, it's not hallucinating. It's not lying, it's not confabulating, it's bullshitting. And if we have to treat it as if it's behaving in some kind of human-like way, here's the appropriate human-like behavior to describe it.

• 30:42Ed Zitron?

  I also think the language in this case, and one of the reasons they probably really like the large language model concept is language gives life to things. When we describe the processes through which we interact with the world and interact with living beings, even cats, dogs, even. We anthropomorphize living things, but also when we communicate with something, language is life. And so it probably works out really fucking well for them. Sam Altman was saying a couple of weeks back, maybe a month or two, he was saying, oh yeah, AI is not a creature. It was something he said, and it was just so obvious what he wanted people to do was say, but what if it was? Or are people saying this is a creature? And it almost feels like just part of the con.

• 31:23Speaker 06

  Yeah.

• 31:23Speaker 05

  Yeah, yeah.

• 31:24Speaker 06

  It fully is.

• 31:25Speaker 05

  I hadn't thought about that as a reason for them to go for large language models as a way of kind of, I don't know, being the gateway into more investment in AI.

• 31:37Ed Zitron?

  Fate consciousness.

• 31:38Speaker 05

  Yeah, yeah. But I had thought about like how this might have been caused by just like deep misunderstandings of the Turing test.

• 31:47Ed Zitron?

  Right, go ahead. No, I want to hear this one.

• 31:49Speaker 05

  Yeah, yeah. So that like the Turing test, I think this is closer to what Turing was thinking, but the Turing test is a way of getting evidence that something is conscious, right? So you, you know, I'm not in your head, so I can't feel your feelings or think your thoughts directly, right? I have to judge whether you're conscious based on how you interact with me. And the way I do it is by listening to what you say, right? And talking to you and turning sort of was asked, you know.

• 32:22Speaker 05

  How would you know if a computer was conscious? So, you know, we think that our brains are doing something similar to what computers do. That's a reason to think that maybe computers eventually could have thoughts like ours.

• 32:33Ed Zitron?

  Right.

• 32:33Speaker 05

  And all of us think that. I think it's possible.

• 32:36Ed Zitron?

  I think it's possible.

• 32:37Speaker 05

  Great, yeah. Well, I didn't know if they thought it was possible. Because not everybody thinks it's possible.

• 32:41Ed Zitron?

  It's possible, I just don't know how.

• 32:43Speaker 05

  Yeah. Yeah, exactly. So Turing was kind of, you know, thinking like, how would we know? And one way, we would know, the obvious way, is to do the same thing you do to humans. Talk to it and see how it responds. And that's actually pretty good evidence if you don't have the ability to look deeper. But it's not constitutive of being conscious. It's not what makes something conscious or determines whether they're conscious. Or in any way like grounds their consciousness, right? Their ability to talk to you is just evidence. It's just one signal you can get. And that's the way to think of the Turing test.

• 33:21Speaker 05

  So as a result of people thinking in a kind of behaviorist way, thinking, ah, passing the Turing test is just all it is to be a thinking thing. There have been, at least since the 90s, attempts to design chatbots that can beat the Turing test, right? And popularizations of these attempts and run-throughs of the Turing test that talk as if, oh, if a computer finally beats the Turing test, I should say what the Turing test is, right?

• 33:45Ed Zitron?

  Yeah.

• 33:46Speaker 05

  The way Turing suggested the test works is you have a computer and a person, both chatting in some way with a judge, and the judge is also a person.

• 33:56Ed Zitron?

  Right.

• 33:56Speaker 05

  And if the judge can't tell which of the people he's chatting with is a human, then the computer's won the Turing test. Because it's distinguishable from a human, right? So people have taken this, and it's been popularized as a way of determining sort of full stop, whether something's conscious. But it's just a piece of evidence and we have a lot more evidence. Like, we know a lot more than Turing did about how the internal functioning of a mind works. Functionally, what it's doing, how representation works, how experience and belief works, how they are connected to action, and how they're connected to speech and thought. And once you know all that stuff, you have a lot of other avenues to get more evidence about whether the thing is conscious.

• 34:39Speaker 05

  And whether it passes the Turing test is just like a drop in the bucket compared to these, especially if you know how its internal functioning.

• 34:46Speaker 06

  The other notorious problem with the Turing test, and I think, to be fair, Turing did mention this, if not in the original, then kind of later on. One problem with the Turing test is that it's like the Voicamp in Do Androids Do You Read Electric Sheep? Right? And plenty of humans would fail the Turing test.

• 35:02Speaker 05

  Yeah.

• 35:03Speaker 06

  Yeah, it is a piece of evidence, but it was never, as Mike says, it wasn't supposed to be constitutive. It wasn't like, if you can do this thing, you're conscious, kind of full stop. It was supposed to be, here is a thing that might indicate that being you're talking to is conscious. Happily, as Mike said, we've got loads and loads of other evidence.

• 35:19Speaker 05

  So these guys have made a machine that's just designed to do one thing, and that's pass the Turing test.

• 35:26Ed Zitron?

  I can give you one more annoying example. Are you familiar with Francois Chollet? The Abstraction and Reasoning Corpus, so this is gonna make you laugh. So he's a Google engineer and he created this thing called the ARC, the Abstraction Reasoning Corpus, to test whether a machine was intelligent. And someone created a model that could beat it. And then he immediately went, okay, you can't just train the model on the answers to the test.

• 35:50Speaker 06

  This is why people, well, I say people, a fairly small subset of weird nerds. And but this is why a small subset of weird nerds have been for the last 20 odd years emphasizing artificial general intelligence, right? And the kind of what we'll call it when something really is a thinking being is when it's not specialized to do one and only one task, and but rather when it's, you know, capable of applying reasoning to multiple kinds of different and disanalogous cases. On the one hand... It does seem a little bit like the guy flipping the table and go, oh, for fuck's sake. Now you've won, I'm changing the rules. But on the other, I think he's got a point, right? Yeah, if you're if you're training a thing to do very specific, like, you know, like, activate certain chivalry. Then unless you're some kind of mad hard behaviorist, then yeah, like, it's not that doesn't demonstrate intelligence.

• 36:34Speaker 06

  It is one thing that might indicate intelligence.

• 36:36Ed Zitron?

  It's the same problem with chat GPT. It's built to resemble intelligence and resemble consciousness and resemble these things, but it isn't. It's almost like it's meaningless on a very high end philosophical level. It's I find the whole generative AI thing deeply nihilistic.

• 36:52Speaker 05

  I do too. I mean, one thing that connects to this is how bad it is at reasoning. And this is kind of good for us, especially in philosophy, because our students, when they use it to write papers, they're the papers have to have arguments and chat GPT is very bad at doing reasoning. If it has to be, you know, sort of an extended argument or a proof or something like that, it's it's very bad at it. I think also that if there's one thing kind of we learned from chat GPT, it's that this is not the way to get to artificial general intelligence.

• 37:27Ed Zitron?

  I was gonna ask, do you think that this is getting to that?

• 37:29Speaker 05

  No, partially because it's so subject specific, right? It's one it's trained to do one task and it takes quite a lot of training to get it to do that task well. It's bad at many of the other tasks that we think are connected with intelligence. It's bad at logical and mathematical reasoning. I understand that OpenAI is trying to fix that. Some sometimes it sounds like they want to fix it by just connecting it to a database or a program that can do that for it. But either way, what you have with these kind of big base networks models is something that is really good at whatever you train it to do, but not going to be good at anything else. You know, it's fed a lot of data on one thing.

• 38:14Speaker 05

  It finds patterns in that. It finds regularities in that. It represents those. The more data you feed it, the better it'll be at that. But it's not going to have this kind of general ability. And it's not going to grow it out of learning how to speak English.

• 38:30Speaker 06

  Have you heard the Terry Pratchett quote about, it's quite early on. He's talking about Hex, the kind of steampunk computer they make in UNC University. And he just has this offhand line, a computer program is exactly like a philosophy professor. Unless you ask it the question in exactly the right way, it will delight in giving you a perfectly accurate, completely unhelpful answer, right? So if you abstract the justified, you've got a philosophy lectures there. Basically, what intelligent things do is go, you can't have meant that. You must have meant this, right? Chat G P T goes, uh, I will take a question as read. And of course, it doesn't have an I, there is nothing like I've just answered. We'll pass it again, but it's the same thing. And it's trained to do incredibly specific things. And you get the same problem as any program like garbage in, garbage out.

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• 40:26Ed Zitron?

  So have you found a lot of students using ChatGPT? Because this is a hard problem to quantify. Is it all the time?

• 40:34Speaker 06

  I mean, it's a lot. I wouldn't say all the time.

• 40:37Speaker 05

  Jake, you thought that there were more seasons?

• 40:41Speaker 04

  Yeah, so I think there are quite a few. And sometimes it is difficult for us to prove. And if we can't prove it, then at our university, then, well, shucks. They kind of get away with it. Uh, we can interview them, but unless we're like, unless we have the proof that you could take before like a court, then we are, we're not able to really nail them for it, which is a bit of a shame.

• 41:05Ed Zitron?

  So it's suspicion?

• 41:06Speaker 04

  Yeah, we suspect that a lot of them are very, I'm a hundred percent on some of them. I just know.

• 41:12Ed Zitron?

  What are the clues? I want to hear from all of you on this one. Like, what are the signs?

• 41:14Speaker 06

  It's the uncanny valley. Like, I will not shut up about this, right? You know, the, um, like you, of course, will be, I imagine a lot of you kind of, um, this really will be as well. But the uncanny valley thing in respect of humans is that there's a kind of a point up to which humans seem to trust artificial things more, the more they look like a human. So like, we'll trust a robot if it's kind of bipedal or if it looks like a dog. But after a point, we really rapidly start to trusting it. So like, if it, basically, when it starts looking like data, some deep buried lizard brain goes, danger, danger. This thing's trying to fuck with you somehow.

• 41:48Ed Zitron?

  And yeah, right.

• 41:49Speaker 06

  And ChatGPT writes exactly like that. It writes almost, but not entirely, like a thing that is trying to convince you that it's human. I mean, the dead giveaway for me is that it never makes any spelling mistakes, but it can format a paragraph to save its life. Normally, you would expect someone who didn't know how to kind of order their sentences to misspell the occasional word. ChatGPT spells everything correctly and doesn't know what like subject-object agreement is. It's like, it's bad.

• 42:17Ed Zitron?

  So can you just define that, please? Subject-object.

• 42:21Speaker 06

  The beer that I drink, right? Not the drink that I bear.

• 42:24Ed Zitron?

  Right, because it's interesting. It almost feels like there is just an entire way of processing and delivering information as a human being that we do not understand. There is something missing.

• 42:36Speaker 05

  I mean, for me, like, it's very good at summarizing. But when it responds, it responds in the, in really trite and repetitive ways. Sort of like, you'll get a paper that summarizes a bunch of literature at length very effectively. And then responds by saying, "Well, you know, this person said this. That is doubtful. They should say more to support that," you know, which is basically saying nothing, right?

• 43:02Ed Zitron?

  Yeah.

• 43:02Speaker 05

  And that's pretty common. It also does lists. It formats things in kind of like a list. And even if the students like, make it look less like a list, the paper still reads like a list.

• 43:15Ed Zitron?

  Oh, because someone has asked, "Give me a few thoughts on X."

• 43:18Speaker 05

  Yeah.

• 43:19Speaker 06

  Yeah.

• 43:19Speaker 05

  Yeah.

• 43:20Ed Zitron?

  That's very depressing.

• 43:22Speaker 06

  What about you?

• 43:23Speaker 04

  Also, things like the introductions you get. Um, you'll get, um, this is an interesting and complex question that philosophers have asked you the ages. And this is one of the things we shout at students not to write from first year. And then you'll get this garbage right back at you. And then at the end, it'll be, "Oh, overall, this is a complicated and difficult question, um, with many nuances."

• 43:47Ed Zitron?

  It's a world of contrasts.

• 43:49Speaker 04

  One of the things we tell them, "Don't ever fucking do this. This is terrible." And write back at you in perfect English, uh, the sort of English you'd expect. A really good student might have written, but clearly they can't be a good student because otherwise they've listened to our fucking instructions.

• 44:06Speaker 05

  I also, I had some papers that I suspected were ChatGPT this year, but they were already failing. So I didn't think- Yeah, I didn't think it was worth it to pursue them, you know, as a plagiarism or ChatGPT case.

• 44:22Ed Zitron?

  So it's never good papers then? It's never like an A, like a first?

• 44:25Speaker 06

  Oh, no, absolutely not.

• 44:27Speaker 05

  So I think that part of what goes on is you can get a passing grade with a ChatGPT paper sometimes in the first couple of years when the papers are shorter and we're not expecting as much. But then when you move into the, what we call honors level here, which is like upper level classes in the US, like third and fourth year.

• 44:47Ed Zitron?

  I got a first to Aberystwyth, oh no.

• 44:50Speaker 05

  Oh, yeah. Yeah, exactly. Great. Well done. Well done. You would not have gotten it with ChatGPT because you get dropped in these classes where we expect you to have gained writing skills, minimal ones in your first two years, and then we're going to build on that and have you do more complicated stuff. And ChatGPT doesn't build on that, right? It just stays where it was. So you go from writing a kind of C-grade passing paper to your F-grade paper. And it's also more obvious because the papers are longer and ChatGPT can write long texts, but it gets very repetitive and noticeably repetitive, right? And so you're kind of lost. Like you haven't done the

• 45:34Speaker 05

  work of figuring out how to write on your own and the tool that you've been using is not up to the task that you're now presented with. And so I think I have seen a few papers that I was suspicious of, but the papers that I was certain of were ones that were like senior theses. But very clearly, the person just had no way of writing.

• 45:55Ed Zitron?

  That's insane at that stage.

• 45:57Speaker 05

  Yeah.

• 45:57Speaker 06

  I mean, it's bonkers. I mean, one of the things that we get told about is like, oh, students have got to learn how to use, you know, AI and large language model plagiarism machines responsibly and in a kind of positive way. Well, if they're using them in a way that means they don't learn how to write, then it's not positive, is it? Like, yeah, it's fucking hard to write a good essay. Yes, it is fucking hard to write. That's why we practice. That's why we have editors. That's why we do this collaboratively. And if you're using this as a sort of, oh, I don't know how to write, well, tough shit. You're never gonna know how to write that. That doesn't seem to need to be a positive use of any of this.

• 46:30Ed Zitron?

  Well, that's the thing. Writing is also reading. The consumption of information and then bouncing the ideas off of your brain allegedly.

• 46:38Speaker 05

  Yeah, I worry about like, because ChatGPT is good at summarizing. So I worry that one of the uses people will think, ah, this is a pretty good use for it, is summarizing the paper that they're supposed to read. And it will do that effectively enough for them to discuss it in class if they're willing to be.

• 46:57Ed Zitron?

  Yeah.

• 46:58Speaker 05

  Right? But they're not going to pick up a lot of the nuances and a lot of the kind of like stylistic ways of presenting ideas that you get when you actually do the reading.

• 47:08Ed Zitron?

  And it's, it's so frustrating as well, because like, for this, for example, I've just got to printing off things that don't read PDFs anymore, because I feel like you do need to focus.

• 47:19Speaker 05

  Yeah.

• 47:19Speaker 06

  There's some evidence that reading physical copies makes you engage more. Sorry, I'm very old fashioned, I guess.

• 47:23Ed Zitron?

  But no, it's true though, but also, reading that, I wouldn't have really, on a PDF, I wouldn't have given it as much attention, but also, going through this paper, you could see what you were doing. Like, you could see that you were lining up, here are the qualities that we use to judge bullshit, but also, summarizing a paper does not actually give you the argument, it gives you an answer. So what do you actually want students to do instead? Because I don't think there's any reason to use ChatGPT for these reasons. Like, it doesn't seem to do anything that's useful for them.

• 47:55Speaker 05

  I don't have, no, I don't actually have any use for ChatGPT that I can put to my students and say, here's what I think you should do. We are like, kind of developing strategies for keeping them from using it. So like, building directly on what you're saying, like in my class next year, I'm going to have the students do regular assignments, which are argument summaries and not paper summaries. The idea is they have to read the paper, find an argument, and tell me, what are the premises, what's the conclusion? And that's something that ChatGPT is not good at, right? But it's also something that will give them critical reading skills, which is what I want to do, right?

• 48:34Speaker 05

  So, yeah, I think that I've mostly been thinking about ways to keep them from relying on it, because I think that often if they rely on it, they'll, they'll, they'll, uh... Put themselves in a worse position, yeah, when it comes to future work. They won't develop the skills that they're gonna need. And the skills that we tell them and their parents, they're gonna get with their college degree, right?

• 48:58Ed Zitron?

  It almost feels like we need more deliberacy in university education, because I was not taught to write. I just did a lot of it until I got good enough grades. And Daniel Chandler, great mentor, but I've had tons of them. And it almost feels like we need classes where it's like, okay, no computer for this one. I'm gonna print this paper out and you're gonna underline the things that are important and talk to me about. Almost feels like we need to rebuild this because, yes, we shouldn't be using ChatGPT to half-ass our essays, but at the same time, human beings are lazy.

• 49:31Speaker 05

  Yeah, I mean, for me, I also prefer to read off the computer. But I often read PDFs because I'm terrible at keeping files, right? Physical, like, you know, I'm not gonna keep a giant file drawer with all the papers that I've read and then written my liner notes in. You guys can see in my office, they're just piled around. Like, you can't see this end.

• 49:54Ed Zitron?

  That's academia.

• 49:55Speaker 05

  Yeah, but I just have piles of paper with empty coffee bugs everywhere that the camera is not facing. But it's the terrible system. So at least on my computer, if I'm like, oh, I read that paper like a year ago, what did I think? I can click on it and see my own notes. And I do think that there's something to keeping those records and kind of actively reading in that way. I don't know how, I ended this without telling you how to make students do that.

• 50:19Speaker 06

  No, you started with the correct answer, which is don't use ChatGPT.

• 50:23Speaker 05

  Yeah.

• 50:25Speaker 06

  I actually, I've got a certain amount of sympathy with like, just keep writing until you get good at it. But I realize as a lecturer, that can't be my official position. And I certainly think that it's the case that certainly Glasgow has got better over the last few years about going, oh, actually, you do like, we do need to give you some kind of structuring and some buttressing on here's how to write academically. Here's how to do research. And I think that's all to the good. It's worth saying this started happening well before ChatGPT started. Pissing all over our doorstep, so they don't get to claim that as being a benefit.

• 50:55Ed Zitron?

  There was the whole Wikipedia panic when I was in school.

• 50:58Speaker 06

  Yeah. Yeah, the thing about Wikipedia, right, it's like I say this to my students.

• 51:02Ed Zitron?

  It's really one of the best resources.

• 51:04Speaker 06

  It's absolutely fine as a starting point for research. Yeah. Absolutely no problem with it whatsoever. But if you're turning in an honors level essay, I want you to go and read the fucking things it's referencing. Yeah, that's right. Um, yeah.

• 51:16Speaker 05

  I, I think these things are often great as sources. My worry about chat G P T is that it's not great as a source. It's just.

• 51:22Ed Zitron?

  Yeah.

• 51:23Speaker 05

  We've been saying it often gets things wrong and it often it'll make up sources, whereas Wikipedia will never do that.

• 51:30Speaker 06

  There are some some famous hoaxes, but. Yeah, it gets edited out fast. They get caught, yeah. Um, Joe, have you got any positive things to say about chat G P T?

• 51:38Speaker 04

  Positive things to say about.

• 51:40Ed Zitron?

  Big fan.

• 51:42Speaker 04

  Um, so I, I know some people who have used these kinds of things productively, not in ways that our students would, but I know some mathematicians who have been using it and to do sort of informal proofs and things like that. And it does still bullshit. And it bullshits very convincingly, which makes it very difficult to use for this kind of purpose. Uh, but it can do some interesting and cool things. You know, I think some people in that sort of field have found useful. And also, we've mentioned this before, like if you want chat G P T to write you a bibliography, you've got a bibliography in one file. Tell it to put something into a different one, then it's good for that and it's good for like coding.

• 52:21Ed Zitron?

  Data processing.

• 52:22Speaker 04

  Doing some things.

• 52:23Speaker 05

  And I also think, I don't know, I'm not sure how I feel about what I'm about to say, but.

• 52:30Ed Zitron?

  Perfect.

• 52:31Speaker 05

  Yeah, I'm going for it. It is a somewhat positive thing, maybe for chat G P T, which is that we often have students who have like really interesting ideas and well thought out arguments, but for whom English isn't their first language and the actual writing is kind of rough and you have to like.

• 52:48Speaker 05

  Push through reading it to get the good idea, which is often really there and quite, you know, creative and insightful. And so I do wonder if there's a way to use it so it just smoothes off the edges of this kind of thing. But I worry that if you tell students to do that, they'll just, first, if they can develop the language skills, they often get really good by the end. Yeah, what are you gonna say, James?

• 53:10Speaker 06

  Well, I want to say, like, yeah, you can see me getting agitated.

• 53:13Speaker 05

  Yeah.

• 53:13Speaker 06

  I think much correct about like that this is a kind of possible use, but I think this, and this is why I'm getting visibly agitated here. That students either need to or feel they need to use this speaks to a deeper issue, right? To a social issue, to a political issue, to an issue about how universities work. If a student is having problems with English, then there's a number of like explanations or a number of kind of responses, right? One response is that like Glasgow isn't English teaching university. If someone's English isn't good enough to be taking a degree, then plausibly shouldn't have been let in. And why have they been let in? Well, because of money. Or alternatively, if someone's having problems with their English for like whatever reason at all, they should be supported. There should be kind of tutors, there should be people who can help with English. But again, that will cost the university money. So of course that doesn't happen, right?

• 53:57Speaker 06

  And like, it doesn't happen anywhere, it doesn't happen anywhere in the extent it would have to happen in order for this to be a general policy.

• 54:06Speaker 05

  Yeah, I think it could be better, but I do think that universities often have like a writing center or a tutoring center that you can send students to.

• 54:15Speaker 06

  But they don't, they don't have the sort of spread that would be needed or the staff that would be needed for this to be, instead of using chat G P T to sound the edges off.

• 54:24Speaker 05

  Yeah.

• 54:24Speaker 06

  To, for example, to work through with your supervisor.

• 54:26Speaker 05

  My worry especially would be that, this is my first year here at Glasgow, but I, I think they probably have a good. I, I think they probably have a good writing center. Universities I've been at in the past, I felt very confident sending students to the writing center when they have these problems. But I think James is completely right, that we don't want the universities to see this as a way to get rid of the writing center. And that's one hundred percent a risk, given the financial problems that universities are facing. And maybe we're already not.

• 54:56Speaker 06

  Yeah.

• 54:58Speaker 05

  Like, given the quality of papers. But they're often good. I think another thing, as far as this is like a social problem, is that when grading, I myself try to grade in terms of like the ideas and arguments, because this is philosophy, and not the quality of the image, right? But not everybody does that. So I kind of think that another part of this is figuring out how we want to evaluate the students and what we want to privilege in that evaluation.

• 55:26Speaker 06

  Yeah, sure. So then again, that becomes a problem about what people are checking for, not let's take this arse backwards approach to marking, which is like, how fancy is your English? Ah, fancy English is good English, have an A, but rather we should be kind of checking for different things, right? So again, the blame lies differently in that case, but it still becomes a question that's not solved technologically.

• 55:45Speaker 05

  Yeah.

• 55:46Ed Zitron?

  Almost feels like large language models are taking advantage of a certain kind of organizational failure.

• 55:52Speaker 05

  Oh, yes. What an idea.

• 55:55Ed Zitron?

  Crazy that the tech industry manipulating a part of society that was weak.

• 56:00Speaker 05

  I have a kind of related tangent here, which is like, what are the use cases that OpenAI was expecting but didn't want to emphasize? Because for everybody in university, as soon as this came out, the first thought was students are going to use this to cheat. And certainly, like, the people at OpenAI went to college, right? And that's what I hear about them. So they must know.

• 56:23Ed Zitron?

  Well, Sam Altman dropped out.

• 56:26Speaker 05

  Yeah, I'm sure he really understands the value of a secondary.

• 56:29Ed Zitron?

  He's like, I've got to write all these fucking essays.

• 56:31Speaker 05

  Anyway, maybe he was thinking, I would have loved to have a computer write my essays. I'll devote my life. But I mean, I'm sure that they like recognize these bad use cases, right? But they're doing nothing to mitigate them as far as I can see. And like another one that's very related is like, you know, I'm sure you've heard of this, Ed, phishing, right? A lot of, you know, corporations get attacked and get hacked, not by someone cleverly figuring out a backdoor to their system, but by somebody sending an email.

• 57:02Ed Zitron?

  Social engineering.

• 57:02Speaker 05

  Yeah, asking for the password of somebody else. And one of the biggest barriers to that is that a lot of the people who are engaging in phishing aren't from the same country as the company they're targeting, right? So they're not able to write a convincing email or make a phone call that sounds like that person's supervisor. But with a tool like this, you could 100% write that email, right? It's gonna make it a lot easier for these kinds of illicit schemes to work.

• 57:31Ed Zitron?

  There has been a marked increase according CNBC, which you just brought up. So 1,265% increase in malicious phishing emails since the launch of ChatGPT. Great start.

• 57:40Speaker 05

  I mean, if I could have thought of that.

• 57:43Ed Zitron?

  Imagine what a criminal could do.

• 57:45Speaker 05

  Right, but also weren't the people at OpenAI thinking about that? Like.

• 57:50Ed Zitron?

  Ah, they don't care.

• 57:51Speaker 06

  Yeah. We've all seen Jurassic Park, right?

• 57:53Speaker 05

  Yeah, yeah.

• 57:53Speaker 06

  They were so busy thinking about what they could do, they never thought about whether they should.

• 57:57Speaker 05

  Yeah, this is the kind of problem with the move fast and break things mentality. Like, there are obvious, I mean, I think I might be the only person who was raised in the US, but we had future problem solvers where you, you know, you think about a future problem and what bad consequences there could be of some technology and how to solve them, usually through social cues. If I could do that in fifth grade, I would expect these people to have thought through some of the bad consequences of the technology they're putting out. And, you know, some of those are cheating on tests and they don't seem to have worried about that. And another one is phishing. They don't seem to have worried about that, you know?

• 58:35Speaker 06

  And by using algorithms, right?

• 58:37Speaker 05

  Yeah, yeah.

• 58:40Speaker 06

  Comes no surprise to you, it turns out. So with a lot of the facial recognition systems, they were incredibly racist.

• 58:45Ed Zitron?

  They were going back to Microsoft's Kinect. They could not see black people.

• 58:49Speaker 06

  Yeah, yeah, yeah. And kind of CCTV stuff that basically just sort of, unless it was presented with a blindingly white Caucasian went, I don't know, right? And but like.

• 59:01Speaker 06

  The sort of stuff where these large language models are trained on certain sets of data and they're trained on certain assumptions and, you know, like shit in, shit out, right? And particularly if people think that it's actually doing any kind of thinking and if they kind of cargo cult it, we again get a kind of social problem multiplied by technology feeding back into a social problem, right? And it's the, sorry, these guys have heard me whinge about it so much.

• 59:26Ed Zitron?

  Oh, I love it.

• 59:26Speaker 06

  Year or so, but I'm profoundly skeptical of technology's ability to solve anything unless we know exactly. The respect in which we want to solve it and how that technology is going to be applied. And, you know, like, sure, experiment with bringing back dinosaurs, but, but like, don't tell me that it's going to save the healthcare system unless you can demonstrate it to me step by step, how that big old T-Rex ran around on Island New Play is going to save anything. And they just tried blind people.

• 59:54Speaker 04

  Actually, bringing back dinosaurs would be just good in itself.

• 59:57Speaker 05

  That would be great.

• 59:58Speaker 06

  Yeah, all right. This isn't the best example, but I already had, uh, Jeff, I had Jeff Goldblum in my head. I had to go to the drop.

• 1:00:06Ed Zitron?

  Fellas, this has been such a pleasure.

• 1:00:08Speaker 05

  Oh, great. Are we out of time?

• 1:00:10Ed Zitron?

  Don't put that in the recording.

• 1:00:13Ed Zitron?

  Nah, it's fine. We won't edit it in post. Can you all give us your names?

• 1:00:18Speaker 05

  I'm Mike Hicks, but my papers are written by Michael Townsend Hicks, and I'm a lecturer at the University of Glasgow. My website is Townsend Hicks.

• 1:00:27Ed Zitron?

  It'll be in the podcast profile. Don't you worry.

• 1:00:30Speaker 05

  You'll get that. All right. Just plug in my.

• 1:00:32Ed Zitron?

  Plug your stuff. Plug it.

• 1:00:34Speaker 04

  My name is Joe Slater. I'm a university lecturer in moral and political philosophy at Glasgow.

• 1:00:39Speaker 06

  Uh, I'm James Humphries. I'm a lecturer in political theory at the University of Glasgow. And even if I wanted to, I couldn't give you my website because I don't have it. Fuck you.

• 1:00:49Ed Zitron?

  Everyone, you've been listening to Better Offline. Thank you so much for listening, everyone. Guys, thank you for joining me.

• 1:00:54Speaker 06

  Thanks, Adam.

• 1:00:55Speaker 05

  See you.

• 1:00:55Ed Zitron?

  Cheers.

• 1:01:05Ed Zitron?

  Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Osowski. You can check out more of his music and audio projects at mattosowski.com. M A T T O S O W S K I dot com. You can email me at [email protected] or visit betteroffline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat dot where's your ed dot at to visit the Discord and go to r slash better offline to check out our Reddit. Thank you so much for listening.

• 1:01:38Speaker 02

  Better Offline is a production of Cool Zone Media. For more from Cool Zone Media, visit our website, coolzonemedia.com, or check us out on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

• 1:02:11Speaker 00

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