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Speaker 1: dot com. Hey you welcome back to dot net rocks.
Speaker 1: Carl Franklin and I'm Richard Campbell. Yeah, so I said
Speaker 1: it for me because I just like massing the pronouns.
Speaker 2: I don't know why.
Speaker 1: Yeah. So we're talking about AI. Mark Semen is here.
Speaker 1: We'll get to him in a minute, but first I
Speaker 1: have a related better know a framework.
Speaker 2: Awesome, It's all right, what do you got?
Speaker 1: Ezra Kline is a New York Times columnist. He does
Speaker 1: a great podcast called Ezra Kleinhow Yeah, I highly recommend it.
Speaker 2: I don't know nobody's naming strategy, but I'll tell you
Speaker 2: what his theme song on point.
Speaker 1: Yeah. Well, anyway, the the article or the podcast that
Speaker 1: I listened to this week was how the attention economy
Speaker 1: is devouring gen Z and the rest of us? Right,
Speaker 1: and uh, we're going to get in into the weeds
Speaker 1: here with with Mark in a bit, But I just
Speaker 1: wanted to point this out as an absolute necessary, absolutely
Speaker 1: necessary required listening slash reading. Even if you don't have
Speaker 1: gen Z sons and daughters, or you know someone or
Speaker 1: you are gen Z, this is a really good perspective
Speaker 1: piece about you know, well in general, he's saying that
Speaker 1: gen Z came of age during COVID, Right, Yeah, I
Speaker 1: have a gen Z daughter. She Yeah, she was graduating
Speaker 1: high school during COVID. She was robbed of her high
Speaker 1: school senior year. She did not have any social activity
Speaker 1: that whole year. Then she went off to college. Everything
Speaker 1: is on zoom. All the information that she learned school
Speaker 1: wise is on zoom, like anything of importance. And so
Speaker 1: that kind of shapes the way the gen z Ers
Speaker 1: think about things, and in particular the nihilism attitude about
Speaker 1: why should I, why should I try? Why should I
Speaker 1: go to college? Why should I better myself? Because the
Speaker 1: AI is going to take my lunch. There's no entry
Speaker 1: level jobs anymore. Like it's a very kind of a
Speaker 1: dark place that the gen Zers are incause how of
Speaker 1: their experience and because of what has happened in the
Speaker 1: last few years, and I think, yeah, Mark is nodding
Speaker 1: his head, we're going to get into this. This is
Speaker 1: a good point for me to mention that I have
Speaker 1: a TikTok carl at atfenex dot com, and one of
Speaker 1: the first things that I've done is a video that
Speaker 1: basically says, AI is no excuse. It's no excuse to
Speaker 1: give up, to stop learning, to stop trying to be
Speaker 1: the person that you wanted to be when you were
Speaker 1: a kid, whatever it is. You know your hopes and dreams.
Speaker 1: Don't give up. You know, we don't know what's going
Speaker 1: to happen in the AI future. We don't know if
Speaker 1: what the jobs are they are going to be available,
Speaker 1: but we do know this, if you just try to
Speaker 1: be the best whatever that you can be, you have
Speaker 1: a better chance of surviving no matter what the AI
Speaker 1: landscape is. Here's an example. You want to go into
Speaker 1: a trade, let's say carpentry because that's an AI proof,
Speaker 1: So you think profession. But then you think, oh, well,
Speaker 1: you know the robots are going to just start building
Speaker 1: houses and all that stuff, so why bother. Well, here's
Speaker 1: why you should be the best carpenter that you can
Speaker 1: possibly be. So that when the robots do come and
Speaker 1: they're affordable, and construction people general contractors are hiring them.
Speaker 1: You're the boss because you're awesome, and that's how you
Speaker 1: got to be the boss, and then you can hire
Speaker 1: the robots to work for you. Do you know what
Speaker 1: I'm saying. I mean, it's a weird example and it
Speaker 1: probably isn't going to be true anytime soon, but it's
Speaker 1: no excuse to stop trying and to stop learning and
Speaker 1: to just put the brakes on your life and resign to,
Speaker 1: you know, flipping burgers for the rest of your life,
Speaker 1: you know, unless that's what you want to do.
Speaker 2: So anyway, although let's face it, flipping burgers is far
Speaker 2: more automatable.
Speaker 1: Yes, yes, yes, so so anyway, I highly recommend this,
Speaker 1: and by the way, check out my TikTok because I
Speaker 1: have a bit to say about this that will continue
Speaker 1: this conversation, I'm sure. So that's when I got Richard
Speaker 1: who's talking to us today?
Speaker 2: Well, knowing we were going to get philosophical today. When
Speaker 2: I was taking around for a comment, I grabbed one
Speaker 2: off of the artificial intelligence geek out that we did
Speaker 2: back in twenty fifteen, whoa ten years ago well, now,
Speaker 2: you know, the other thing is to realize, why did
Speaker 2: we do that? Geek out? Then, Yeah, that was the
Speaker 2: time when Bill Gates and Elon Musk and Stephen Hawking
Speaker 2: were all going on about AI emerging and then like,
Speaker 2: we have to be careful now. Then the subtext of
Speaker 2: this is that Google had successfully hired many of the
Speaker 2: best minds, including guys like Jeff Hinton and so forth,
Speaker 2: and they were doing some extraordinary things in a group
Speaker 2: called Google Brain even then, and so really this was
Speaker 2: a push for we've got to get those scientists out
Speaker 2: of Google. What you were seeing was the setup that
Speaker 2: would become open AI, but we didn't know that at
Speaker 2: the time. It would emerge another year or two later,
Speaker 2: and with all of the problems that that had attacked
Speaker 2: and continues to have attached to it. Again, nobody expected
Speaker 2: any of the things that have happened here.
Speaker 1: Well, and Hinton famously started warning the public against general
Speaker 1: AI and the things that I'm sure Mark is going
Speaker 1: to be talking about too.
Speaker 2: But yeah, once his shares in Google were fully vested, right, Yeah,
Speaker 2: so you know it should be clear.
Speaker 1: Not that you're cynical about that or anything.
Speaker 2: Just watching for people's motivations here. That's right, follow the money.
Speaker 2: So there was a lot of comments on that show
Speaker 2: because we were pretty far ahead of our time when
Speaker 2: we were at that particular point. You know, we're reading
Speaker 2: in a lot of the tea leaves much more science
Speaker 2: fiction based, so makes it ten years all the more extraordinary, right,
Speaker 2: And I grabbed this comment one of literally dozens, and
Speaker 2: one of them was Mark Semens comment too. And Mark
Speaker 2: probably doesn't remember either because he writes lots of comments
Speaker 2: on lots of shows. But you referenced a book called
Speaker 2: blind Site, Sir, which is a very interesting study and consciousness,
Speaker 2: because we did go down that path twenty fifteen about
Speaker 2: what's consciousness, what is sentiency, and what is intelligence that
Speaker 2: kind of thing. So Tom Kirkhoff's comment, another past guest
Speaker 2: of the show, he says, as you mentioned, it depends,
Speaker 2: So what is artificial intelligence? People such as Bill Gates
Speaker 2: are cautious with AI and tells us we should not
Speaker 2: do it, But we have and we entered the era
Speaker 2: where AI is in the past, Apple has Siri and
Speaker 2: Microsoft has Cortana and as personal assistants who are more
Speaker 2: and more integrated in all our toys. So where do
Speaker 2: we draw the line. Isn't it cool to think that?
Speaker 2: In twenty fifteen we Tartana. Yeah, and when I Robot
Speaker 2: came out, and that's the Will Smith version of Isaac
Speaker 2: Asimov's movie. We saw robots helping us as humans in
Speaker 2: our day to day work, which you know, the funny
Speaker 2: part is here we are with some interesting software, but
Speaker 2: still build a robot that humans can be around. Is
Speaker 2: that artificial intelligence? Because we can get this today in
Speaker 2: some sort I don't think of remote qualifies. We have
Speaker 2: robots that are capable of walking like animals. We have
Speaker 2: sensors such as connect connect I remember connect that can
Speaker 2: detect walls, open doors, and well, plus we have Cortana
Speaker 2: and with our knowing our schedule and helping us to
Speaker 2: remind stuff and look stuff up the combine this together.
Speaker 2: Are we getting close to those robots? Also? Where are
Speaker 2: we with laws supporting lists like getting self driving cars
Speaker 2: and personal assistance and stuff? How will we protect ourselves
Speaker 2: from human hackers or AI going wrong? These are all
Speaker 2: interesting talking.
Speaker 1: Well, you'd be happy to know nothing has happened in
Speaker 1: law government to protect us from anything because they don't
Speaker 1: even know what the heck is going on.
Speaker 2: Well, that's not true. The EU has passed an interesting self.
Speaker 1: I was talking about our government, Richard, Well, my government
Speaker 1: they have no clue about AI or what to do
Speaker 1: about it. So the other civilized civilizations we have a
Speaker 1: little bit more to do.
Speaker 2: It is I mean, we were thinking about these same
Speaker 2: problems ten years ago, but with obviously some gaps, right, Like, Yeah,
Speaker 2: the voice assistance of ten years ago actually worked better
Speaker 2: than they did in just the past couple of years
Speaker 2: before the LM showed up, because they never made money. Yeah,
Speaker 2: And as they didn't make money, their budgets got squeezed
Speaker 2: tighter and tighter, and less compute resources were used on them,
Speaker 2: and they degraded. And eventually, just before the LLM breakout,
Speaker 2: before chatchpt, both Google and Amazon came out said hey,
Speaker 2: we're cutting these groups back because they're just not doing
Speaker 2: what they were intended doing with attending not being helping people,
Speaker 2: but making the company money. And then of course it
Speaker 2: did chatchpt lands and the whole thing's up in the
Speaker 2: air and they're all scrambling. So Tom, thank you so
Speaker 2: much for your comment. Great to hear from your friend
Speaker 2: nine years ago, and a coffee of music. Cobe is
Speaker 2: on its way to you, and if you'd like a
Speaker 2: coffee of musicobe, I write a comment on the website
Speaker 2: and done at rocks dot com or on the facebooks.
Speaker 2: You publish every show there, and if you comment there
Speaker 2: and I read in the show, we'll send you copy
Speaker 2: go by Music.
Speaker 1: To code by Still Going Strong. Thank you Mark Semen
Speaker 1: for that idea that you gave me long those many
Speaker 1: years ago. Oh that turned into music to code by
Speaker 1: twenty two tracks now and you can get them an
Speaker 1: MP three wave or flack of twenty five minute compositions
Speaker 1: at music too coode by dot net.
Speaker 3: So that's the end of analysis of music.
Speaker 1: Yeah pretty much? Wow? Yeah Yeah. And it's designed to
Speaker 1: be in that beat per minute range that was cited
Speaker 1: in the study with the baroque music the children that
Speaker 1: were doing math problems when it was six between sixty
Speaker 1: five and seventy two piece per minute. I think it is.
Speaker 1: And it's neither too distracting nor is it too boring,
Speaker 1: Like you're not going to lose your mind listening to it.
Speaker 1: There is some variation in there, but nothing's going to
Speaker 1: jump out and scream at you. So it works, and
Speaker 1: it works for a ton of happy customers, including me.
Speaker 1: All right, well, let's formally introduce Mark Mark Seamen.
Speaker 2: Hmmm, we got to do nineteen sixty.
Speaker 1: Oh yeah, we do. Why don't I always forget that? Richard?
Speaker 1: I don't know.
Speaker 2: Maybe we should let this go at some point, but
Speaker 2: I kind of want to run until we get to
Speaker 2: two thousand and two and we have inception.
Speaker 1: I do too, Yeah, yeah, yeah. So significant events in
Speaker 1: nineteen sixty included the independence of seventeen African nations, the
Speaker 1: Greensboro sit ins for civil rights in the US, and
Speaker 1: the first televised presidential debate between John F. Kennedy and
Speaker 1: Richard Nixon.
Speaker 2: MM, which went well.
Speaker 1: Kennedy was very telegenic, he was you wore a dark suit,
Speaker 1: and Richard Nixon blended in with the background. I remember that.
Speaker 2: All these things you didn't need to think about until
Speaker 2: television came along.
Speaker 1: It was also marked by the U two incident, where
Speaker 1: an American spy plane was shot down over Soviet airspace,
Speaker 1: escalating Gary Powers old war tensions.
Speaker 2: They believe that plane flew too high to be shot down,
Speaker 2: and they were wrong. So what's on your list, Richard?
Speaker 2: The first laser is rendered operational. A guy named Theodore
Speaker 2: Maymon where can at a huge research used a synthetic
Speaker 2: ruby with flash lamps based on a bunch of science
Speaker 2: a group of other smart folks over the past few years.
Speaker 2: But he's the guy who actually implemented coherent light. Wow
Speaker 2: your DBD, thanks you Wow.
Speaker 1: And shortly after that, Star Trek came online and something
Speaker 1: they're using phasers because they couldn't say lasers.
Speaker 2: And the very first weather satellite ever TIROS one, the
Speaker 2: US satellite TIRO short for Television Infrared Observation satellite, launched
Speaker 2: by aor able rocket. It had solar panels on it
Speaker 2: in nineteen sixty at solar panels Wow, a wide narrow
Speaker 2: angle infrared cameras and it took about twenty three thousand
Speaker 2: pictures before an electrical failure after ten weeks knocked it out,
Speaker 2: beginning this idea of being able to look at weather
Speaker 2: at a macroscale from orbit, which is incredibly important. It's
Speaker 2: amazing to think that that's only it's only been sixty
Speaker 2: years of being able to do that. Yeah, it's still
Speaker 2: actually in orbit. There was this electrical failure in the
Speaker 2: battery system knocked it out early. Could have lasted longer,
Speaker 2: but it was followed up by many more, but that
Speaker 2: that began in nineteen sixty.
Speaker 1: I shall also mention the pill was ratified in nineteen sixty,
Speaker 1: so that ushered in a whole era of women's reproductive
Speaker 1: rights and all of that.
Speaker 2: Next, we're going to talk about the age of Aquarius.
Speaker 1: No, no, no, come on, man, come on.
Speaker 2: All works together.
Speaker 1: Where would we be without the pill?
Speaker 3: There?
Speaker 1: You seriously, I wouldn't have gotten laid in high school.
Speaker 1: I don't know about you guys. But all right, So
Speaker 1: is there any other computer oriented events or computers that
Speaker 1: were breakthroughs in nineteen sixty that you can.
Speaker 2: No, but nineteen sixty one is a big one. So
Speaker 2: hang in there, will all right, we'll talk a lot
Speaker 2: about the integrated circuit, all right next week.
Speaker 3: Did they start with four trend back then? Or is
Speaker 3: that that's around that time maybe a little bit earlier?
Speaker 2: Yeah, yeah, yeah, no, four trends already around by then.
Speaker 2: Oh yeah, okay, you know, not on a you know,
Speaker 2: we're talking kind of pre This is before we actually
Speaker 2: have digital computers per se. Right, they're largely electrical mechanical
Speaker 2: like the the uh. We have transistors, but we haven't
Speaker 2: really got an integrated circuit, so the compute power is
Speaker 2: not the same at all.
Speaker 1: All right, So the bio that I'm going to read
Speaker 1: was not written by me. It was written by Mark himself.
Speaker 1: Mark Semen is a bad economist who's found a second
Speaker 1: career as a programmer. He has worked as a web
Speaker 1: and enterprise developer since the late nineteen nineties, and he
Speaker 1: blogs regularly at blog dot plo dot dk. That's p
Speaker 1: l o e h did I pronounce that writer? Is
Speaker 1: it more like pl that's plur pl That's right.
Speaker 3: Okay, you got that the second time around. Yeah, that's
Speaker 3: pretty good. That's pretty good.
Speaker 1: Well, welcome back, and uh, thank you. I just had
Speaker 1: to formally introduce you there. That's even though we've been
Speaker 1: talking to you for ten fourteen minutes.
Speaker 3: Yes we have.
Speaker 1: Yeah, all right, So what are your thoughts? Are you
Speaker 1: fan of Ezra Klein?
Speaker 3: First of all, I've I I usually used used to
Speaker 3: listen to a podcast by Sam Harris and these two
Speaker 3: sort of enemies, if you will, also, so I haven't
Speaker 3: really listened to to Ezra Klein. But but on the
Speaker 3: other hand, I think it was if Scott Fitzgerial who
Speaker 3: said something like and you know, the sign of intelligence
Speaker 3: is being able to hold two opposing thoughts in your
Speaker 3: mind at the same time and not go insane. So
Speaker 3: maybe I should. I mean, it's it also sounds like
Speaker 3: he's been doing he's been on sort of Isra Klein
Speaker 3: has been on some sort of journey where he's starting
Speaker 3: to realize that, you know, some of the problems that
Speaker 3: you just talked about here are actually really important. So yeah,
Speaker 3: so maybe I should. So I haven't really been a
Speaker 3: fan there, but you know, I have no beef with
Speaker 3: him personally, so maybe maybe I should give it a listen.
Speaker 1: Well, what about the idea of gen Z being sort
Speaker 1: of caught in this vortex of impossibility?
Speaker 3: That that that absolutely rings true. I have two gen
Speaker 3: Z kids, and the well, the old one is old
Speaker 3: enough so she's almost not a gen Z, so she's
Speaker 3: she's sort of got you know, through most of this
Speaker 3: stuff without too many, you know, too much impact that.
Speaker 3: But the other one, he's eighteen now, and he's really
Speaker 3: he's really you know, grabbed by TikTok and phones and
Speaker 3: so on. So that's yeah, that's that's a bit of
Speaker 3: a problem.
Speaker 2: Yeah, yeah, I mean my kids are just that bit
Speaker 2: much older that maybe they slipped past us to some degree,
Speaker 2: but the the bait here, and you brought it up
Speaker 2: right at the top there, Carl, is how much of
Speaker 2: this is just the attention economy in general? And how
Speaker 2: much of it is the impacts of the pandemic of
Speaker 2: that two years of psychosis just this crazy time.
Speaker 1: It was really psychosis, absolutely crazy time.
Speaker 3: I think we were seeing signs of this already before.
Speaker 3: I mean, was it the Senta shoop Off who wrote
Speaker 3: this book about the attention economy? I think that predates
Speaker 3: the the pandemics. I remember, So there were definitely people
Speaker 3: talking about this, you know, even in the in the
Speaker 3: twenty tens. But that's not really what we're here to
Speaker 3: talk about.
Speaker 1: No, no, no, is it. We're just getting started here?
Speaker 3: Yeah, I know, but but maybe I should start with
Speaker 3: an other experience I had with a young person. So
Speaker 3: I was following a university course on something computer science
Speaker 3: I don't exactly remember. And because I was doing that,
Speaker 3: they you know, they have us do some group exercises
Speaker 3: as well. So I was doing a little paper with
Speaker 3: some young people and we were having a discussion about
Speaker 3: how to interpret a certain algorithm, and you know, whether
Speaker 3: we were in one regime or another and we couldn't
Speaker 3: really agree. And then the other one he was just
Speaker 3: writing on on what some DM what's it called? I
Speaker 3: can't remember anyway, So we were dming back and forth and
Speaker 3: he writes to me, well, but I just asked chat
Speaker 3: GBT and it says blah blah blah. So I'm right ah,
Speaker 3: and I'm sort of like, I don't care. I don't
Speaker 3: care what Chad GBT says. I wrote back and he
Speaker 3: was like, oh my god, you don't care what chat
Speaker 3: GGBT says. How can you? I mean, it was that
Speaker 3: was very much a generational divide there, and every time
Speaker 3: we came back to that, he's sort of like, oh, yeah, Mark,
Speaker 3: is this weird person who doesn't believe in everything that
Speaker 3: jaduary it.
Speaker 1: But you're lude you don't know anything about technology?
Speaker 3: Yeah, And I should probably preface I'm going to say
Speaker 3: a lot of critical things about AI, but it's not
Speaker 3: that I'm a complete lot eyed. I actually do see
Speaker 3: that there's some you know, benefits to be gained as well,
Speaker 3: but that's not what we here to talk about. So
Speaker 3: if the listener gets the impression that I'm just in
Speaker 3: grumby old man shouting at the cloud. It's not the
Speaker 3: entire picture, but let's just pretend.
Speaker 1: Well, that's besides the point.
Speaker 3: Let's just pretend that that's the case.
Speaker 2: Anyway, Yeah, that cloud didn't need shouting.
Speaker 3: Indeed, indeed. But I keep running into this thing where
Speaker 3: people are backing up their claims by saying, well, I
Speaker 3: just asked you know, some chat TBT or some other
Speaker 3: EI online EI system, Last Language model, whatever you want
Speaker 3: to call it, and then they're using that as their
Speaker 3: appeal to authority and saying, well, it's true because it's
Speaker 3: it says so, And it's really hard to argue against that,
Speaker 3: because if people are actually in that mindset where they
Speaker 3: think that's as an authority that they can trusts, it's
Speaker 3: hard to get them out of that mindset.
Speaker 2: But it is actually new. This is not new to LMS,
Speaker 2: no chat GBT. People have been saying the computer says
Speaker 2: uh huh, yeah, since we put computers in front of people.
Speaker 3: Right, Well, that's a fair argument, but I think we
Speaker 3: are We're we sort of reached a new level there
Speaker 3: because usually, you know, in the old days, when the
Speaker 3: computer said something, it was usually correct under the count
Speaker 3: take that you know, in which it would say something.
Speaker 1: Right, data came out of a database somewhere.
Speaker 3: Yeah, you would ask it about something in the database,
Speaker 3: And of course you can you could have wrong data
Speaker 3: inside that database, or you could have a bug in
Speaker 3: the program and so on. But in general, if you
Speaker 3: understood the context in which the you know, the computer
Speaker 3: and the program and the software would actually be giving
Speaker 3: you answers, there would be some sort of knowledge to
Speaker 3: be gained. And that's not really where we are with
Speaker 3: those you know, new systems, and that's it's not you know,
Speaker 3: one thing is the system itself, but it's how people
Speaker 3: are interacting with these sites concerned me a bit. Yeah, yeah.
Speaker 3: But also the thing is that that they tend to
Speaker 3: see them as oracles, is that you can go and
Speaker 3: ask them about anything and then you a lot of
Speaker 3: people seem to just blindly trust them. Which that's that's
Speaker 3: really what concerns me here, because.
Speaker 1: Yeah, I did an experiment Mark, I asked for a
Speaker 1: recommendation of a product on Amazon based on my parameters,
Speaker 1: and it recommended something, and then I went it was
Speaker 1: an electronic piece for electronic Here, I went on Amazon,
Speaker 1: I looked at all the reviews and there was very
Speaker 1: many one star reviews saying this thing overheats and then
Speaker 1: goes to crap, don't buy it. So then I brought
Speaker 1: that up to Chatchip. He said, you're right, let me
Speaker 1: look for another one. Sound familiar. Let me look for
Speaker 1: another one that doesn't overheat. Here's here's the one.
Speaker 2: You want.
Speaker 1: This is because it's going to satisfy this condition, that condition,
Speaker 1: And I said, okay, and you know it's fairly well reviewed.
Speaker 1: So I bought it and it didn't work. It didn't
Speaker 1: do some of the things that I asked for with
Speaker 1: chat GPT, and it was vague because when I went
Speaker 1: back then looked at the description didn't explicitly say this
Speaker 1: thing that I needed. I just assumed that it would
Speaker 1: do it because most things like this did it. So
Speaker 1: I ended up returning it and something else. But it's
Speaker 1: conscinary town. So yeah, it's an experiment. I wanted to
Speaker 1: see if I could rather than going through the tedious
Speaker 1: task of searching on Amazon and then starting by most
Speaker 1: favorite reviews and reading them. Rather than doing that, I
Speaker 1: just asked chapter GPT to do my bidding. And it
Speaker 1: didn't work.
Speaker 3: It lies, but indeed, in this in this case, you
Speaker 3: were still in a scenario where you were able to
Speaker 3: you're still able to verify or in this case, in
Speaker 3: this case as actually falsify the claim that was made
Speaker 3: by the LMS. So of course, because you were ordering
Speaker 3: I assume a physical product, it took some time to
Speaker 3: actually get that verification of falsification in place, but you
Speaker 3: could still do that, and that's that's not even you know,
Speaker 3: I'm not too concerned about people using lms in that
Speaker 3: way because I actually use them like that as well.
Speaker 3: You know, if I if I have a problem where
Speaker 3: I can you know, I know, I don't know exactly
Speaker 3: what the answer is going to be, but if I
Speaker 3: get the answer, I can do a verification check and
Speaker 3: then I can see if that solves my problem or not.
Speaker 3: I had I've had very nice, you know, experiences with
Speaker 3: the limbs that do that for me and save me
Speaker 3: a ton of time. So I don't really have a
Speaker 3: problem with that because you know, if you can get
Speaker 3: an answer and then you can verify whether or not
Speaker 3: it works, you're still on solid ground in terms of epistemology.
Speaker 3: So okay, so now we said the big word here,
Speaker 3: but it basically means the theory of knowledge. So how
Speaker 3: do we know that we know things, why do we
Speaker 3: think that we know some things?
Speaker 1: I know that in the term of epistemological studies that
Speaker 1: are basically just tabulating answers from people, but you don't
Speaker 1: know whether or not they lied, right, the entomologic entomological studies,
Speaker 1: there's another All right, I'm mixing up my words.
Speaker 3: Here, go ahead, Yeah, so where we so? Yeah?
Speaker 2: So?
Speaker 3: But that's the on thing. So if you can ask
Speaker 3: you know, a system and then get get it to
Speaker 3: give you an answer that you can then later verify,
Speaker 3: I think that's that sound. I don't really have a
Speaker 3: problem with that. My problem is really with you are
Speaker 3: asking a system to do something and you have no
Speaker 3: way of verifying whether or not it actually, you know,
Speaker 3: is what it is that you wanted to do. Then
Speaker 3: I think now I'm getting concerned. And since we are
Speaker 3: on a podcast where we usually talk about software development,
Speaker 3: you know, one of the things that really concerned me
Speaker 3: is when people ask you know, these systems to write
Speaker 3: code for them, because but then again, you know, if
Speaker 3: you if you do that, well, if you can actually
Speaker 3: read through on the code and then you have an
Speaker 3: idea at what you're looking at, well that might actually work,
Speaker 3: but often you hear people so yeah, I know you
Speaker 3: talked about vibe coding already, and and for me it
Speaker 3: is a pejorative. I think it's it sounds like a
Speaker 3: really really bad idea because if you don't know, if
Speaker 3: you if you don't know how to code, or if
Speaker 3: you ask this system to write code in a language
Speaker 3: that you don't really understand, then how you know it works? Right?
Speaker 4: Well, the compiler has to say if you are writing
Speaker 4: in a language that actually does compile, and people are
Speaker 4: using a lot of often they get it to write
Speaker 4: JavaScript or Python or something like that for them, and
Speaker 4: they those languages don't even compile.
Speaker 1: The point is something that they don't know. Yeah, right,
Speaker 1: Why would Why would I ask an l M or whatever,
Speaker 1: an agent to write me an assembly program because I
Speaker 1: think it's going to be faster When I don't read
Speaker 1: assembly and I can't verify it, and I can't step
Speaker 1: through the code, and I don't know what the heck
Speaker 1: that thing does. It might look like it works, but
Speaker 1: I ain't going to run that thing, right, I'm gonna
Speaker 1: If you ask an agent to write code in a
Speaker 1: language that you don't know how to verify, you get
Speaker 1: you know, you get what you get, right, you get
Speaker 1: what you pay for basically deserve it.
Speaker 2: That being said, I'm now having experiences with very experienced
Speaker 2: software helpers where we spend an entire day working through
Speaker 2: a sprint of code that we estimated it would have
Speaker 2: been six weeks worth of work, and then knocked it
Speaker 2: out in a weekend using these tools. Yep, yeah, right,
Speaker 2: Like but in the hands of skilled people who understand
Speaker 2: what they're doing and are working hard with these tools,
Speaker 2: you can get extraordinary results. Not you cancre mental results,
Speaker 2: but literally weeks of working days.
Speaker 1: We just heard and I don't remember if this was
Speaker 1: talking to you, Richard or somebody else, might have been
Speaker 1: Brian McKay that he had a guy that was in
Speaker 1: a meeting, a two hour meeting about a spec and
Speaker 1: about building a prototype, and by the end of the
Speaker 1: meeting he had it done.
Speaker 2: Right. That seems to becoming more common again known problem space.
Speaker 2: You know, these these were forms over data problems, so
Speaker 2: they were pretty automatical anyway, and with someone who knew
Speaker 2: the tools and the language well, and they have a
Speaker 2: put together assembly and their productivity is astonishing.
Speaker 3: Yeah, but again, how do you measure productivity? In software development,
Speaker 3: because it seems to me that we are forgetting that
Speaker 3: lines of code is not a measurement of productivity, you.
Speaker 2: Know, Yeah, this is delivering features to customers.
Speaker 3: Yeah, and that makes a lot of sense, of course,
Speaker 3: if you can measure that. But that's a whole different discussion.
Speaker 3: Whether that's because one feature is not necessarily equivalent to
Speaker 3: another feature. You know, some features are big and someone small.
Speaker 3: But that's probably a different discussion.
Speaker 2: No, but I think it's a really valid one that
Speaker 2: there's a bar here that these tools seem to be
Speaker 2: able to handle at a bar, and above that bar
Speaker 2: they cannot.
Speaker 1: Yeah, above that bar. You have to sort of break
Speaker 1: it down into you know, bite sized pieces for them. So,
Speaker 1: but that's how I like to work anyway, you know, Yeah, yeah, yeah.
Speaker 3: Of course. But I'm still I'm still wondering whether we
Speaker 3: can trust these things even if we look at them.
Speaker 3: Because so we reached the story that you told, I
Speaker 3: don't know exactly the details of it and so on.
Speaker 3: But but one of the questions I would like to
Speaker 3: ask when when people do something like that, is to
Speaker 3: how do you actually know that the software works? How?
Speaker 3: How did you how did you decide that that software
Speaker 3: worked in that particular case. What was the decision criteria there?
Speaker 2: Oh? I mean again, they'd also built a set of
Speaker 2: test suites. Yeah, you know, I saw that these features
Speaker 2: need to be tested this way and measure you know,
Speaker 2: he did the complete coding solution, including the security evaluation,
Speaker 2: like all of the different pieces. Like, you didn't just
Speaker 2: spat it. This was not vibe coding. No, this was
Speaker 2: a thoroughly thought out architectural solution.
Speaker 3: But who wrote who wrote the tests?
Speaker 2: Though? With the tools?
Speaker 3: How do you why do you trust those? Then?
Speaker 2: Well, because you could see the code, right, there's no
Speaker 2: secrets here. Tests are pretty straightforward to understand.
Speaker 1: Yeah, I guess the thing that we can agree on
Speaker 1: is if you let it get away from you, right,
Speaker 1: and you don't follow up on every change your AI
Speaker 1: is making for you and test it and on it
Speaker 1: and you know, observe it, and you just let it
Speaker 1: go wild, you're you're going to lose control. And so
Speaker 1: staying in control, I think this is the key.
Speaker 2: The question you keep asking is why do you trust it?
Speaker 2: It's like, don't don't trust it? Yeah, don't know exactly. Yeah, Look,
Speaker 2: I already do distributed development. I have people contributing to
Speaker 2: my projects that I never meet, that I only interact with,
Speaker 2: you know, through issues on GitHub. You don't trust them either, No,
Speaker 2: but you evaluate the code.
Speaker 3: You review the code.
Speaker 2: Yeah, yeah, that's the job. But the reality is it's
Speaker 2: still a force multiplier to have multiple people contributing to
Speaker 2: a project. It takes less time to review code than
Speaker 2: it takes to write it.
Speaker 3: And I do not disagree with that. That's reasonable enough,
Speaker 3: but it's still my concern is still that we you know,
Speaker 3: if we have an output of code that is multiplied,
Speaker 3: you know, tenfold, one hundredfold in comparison to what we
Speaker 3: had a couple of years ago, then we should also
Speaker 3: to have that that we should also spend that much
Speaker 3: more energy on actually reviewing the things that are being produced.
Speaker 3: And I'm not really getting the impression that that's the case.
Speaker 1: So it's the case at my house, I can tell
Speaker 1: you that.
Speaker 3: Yeah.
Speaker 2: Well, but again, you know, well, and this will be
Speaker 2: this is also some with self fulfilling. Those who trust
Speaker 2: these tools, right, will get burned. Absolutely.
Speaker 3: Yeah, that's that's also what I'm what I'm concerned about.
Speaker 3: And we can just hope that it's just some simple
Speaker 3: forms over the data and and they're they're probably only
Speaker 3: hurting the company that actually owns that software. But what if,
Speaker 3: what if the actually we're beginning to see people, you know,
Speaker 3: writing fly you know, h operating systems or systems for
Speaker 3: controlling hardware or elevators and medical systems and so on.
Speaker 3: And then I'm getting a little bit concerned here that's
Speaker 3: probably not going to happen this year. But well, in
Speaker 3: a couple of is we'll see, we'll see those people
Speaker 3: who do use those systems at the moment, they some
Speaker 3: of them will graduate to writing those kinds of systems,
Speaker 3: and I'm just concerned that they're probably going to take
Speaker 3: some of their bad habits with them.
Speaker 2: Without a doubt, I think, yeah, let's do the break
Speaker 2: and then I want to dig into the next tier
Speaker 2: of this problem, which I think is the junior developer.
Speaker 1: Yeah, okay, and we'll be right back after these very
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Speaker 1: we're back. It's dot at Rox. I'm Carl Franklin and
Speaker 1: I'm Richard Campbell and that is Mark Seaman, and we're
Speaker 1: talking about AI concerns. And just as a reminder, if
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Speaker 1: Take it away, Richard.
Speaker 2: The folks that I'm seeing that will be successful these
Speaker 2: tools are very experienced developers. Yeah, you know, really they've
Speaker 2: spent most these days. They don't even write a lot
Speaker 2: of code, and maybe they do some spikes and things,
Speaker 2: but they're mostly supervising a group of developers. They are
Speaker 2: the architects, you know, they run at a high level
Speaker 2: of responsibility, and they're starting to see these tools act
Speaker 2: as inexperienced developers under fairly strict guidance with constant code reviews,
Speaker 2: but ultimately productive. And it begs a question like where
Speaker 2: does the junior developer go now?
Speaker 1: Right? Are we the last generation of people who came
Speaker 1: up as junior developers?
Speaker 2: Yeah?
Speaker 3: Yeah, that's my concern too, because well, I think you
Speaker 3: said it pretty well, Richard. I'm not sure that I
Speaker 3: have a lot of stuff to add to that.
Speaker 2: Actually, I mean, I am meeting young developers right now
Speaker 2: that are pretty freaked out, and I wonder if it's
Speaker 2: because we trained them poorly at this point, Like here
Speaker 2: we are, this inflection point where things are changing. And
Speaker 2: the funny part is when I have a conversation with
Speaker 2: them about solutions, and I'm thinking back to the show
Speaker 2: that we did together Carl with the Imagining Cup folks.
Speaker 1: Wow, what an inspirational group.
Speaker 2: Phenomenal, But you know what, they didn't care about tool stacks? Yeah,
Speaker 2: do you remember there were one of the ladies asked us,
Speaker 2: like you make a podcast about dot net? Like why
Speaker 2: why would you do that?
Speaker 1: Right?
Speaker 2: Right? And I realized, like we've got old thinking. You know,
Speaker 2: when it was a nine to twelve month commit to
Speaker 2: get to an MVP of a piece of software, you
Speaker 2: could spend a couple of weeks arguing over what stack
Speaker 2: to use. Right, but with the productivity level that we're
Speaker 2: talking about right now, who cares? Just take the tool
Speaker 2: out for a spin. You know, there's so many different
Speaker 2: there's Even before the LM showed up, it was so
Speaker 2: much easier to learn a new programming environment. It was
Speaker 2: so much easier to experiment that those times are getting
Speaker 2: shorter and shorter, and the stacks are just not that
Speaker 2: different from each other. You know, fundamentally, they still draw
Speaker 2: on screens and they still communicate over the Internet. Like
Speaker 2: a lot of this stuff is the same, and if
Speaker 2: you focus on the solution, you're fine.
Speaker 1: Like I don't.
Speaker 2: I wonder if we're not actually growing the right generation,
Speaker 2: next generation of software developers, because they are not hung
Speaker 2: up on the stuff that we're hung up on. Well,
Speaker 2: at the same time, maybe they should be. I mean,
Speaker 2: so here's here's a scenario, and this came from a
Speaker 2: real story that I heard from somebody. Somebody is a
Speaker 2: back end dot net developer, and a full stack dot
Speaker 2: net developer does the front end, does Blazer and all
Speaker 2: that stuff. Somebody comes to them and says, hey, we
Speaker 2: want to use React for the front end, but still
Speaker 2: keep the as peanut core back end.
Speaker 1: Can you do that? And they think, hey, I've got
Speaker 1: chat GPT or I've got the agent and they say, yes,
Speaker 1: yes I can. They know nothing about React, right, but
Speaker 1: they generate all this code and it work. Would you
Speaker 1: say yes? Would you say yes, I can do that,
Speaker 1: or would I say no? I think you better get
Speaker 1: a React programmer to run the tool.
Speaker 3: Yeah, I wouldn't.
Speaker 2: What would you do it? Yeah? Yeah?
Speaker 1: And if I did it that way, would you accept
Speaker 1: the code if I didn't know anything about React?
Speaker 3: Yeah? That's that's the other problem. So this reminds me
Speaker 3: of of an experience I had many years ago. So
Speaker 3: obviously it is because long before the ill a limbs.
Speaker 3: But I was working with a with a customer of mine,
Speaker 3: trying to teach them to move in small increments and
Speaker 3: do test driven development and all these things that I
Speaker 3: usually do, and it's actually working pretty well, you know,
Speaker 3: trying to also give them an idea about how to
Speaker 3: do pull requests and work in this sort of like
Speaker 3: quasi open source way of working with the small, small
Speaker 3: itserations and all of that. And they hadn't told me
Speaker 3: that they actually had an offside group sitting in another country.
Speaker 3: And you know, three weeks into my engagement with this customer,
Speaker 3: I get this pull request from hell from this you know,
Speaker 3: team sitting in another country because no one had told
Speaker 3: them that I was actually now trying to you know,
Speaker 3: change the things around, and they hadn't told me about
Speaker 3: that system as well. So I get this pull request
Speaker 3: and it's just like a you know, fifty thousand lines
Speaker 3: of code or something like that, and I'm trying to
Speaker 3: get the people that I was working with, you know,
Speaker 3: going through and saying well, okay, if you write the code,
Speaker 3: we need someone else to review it, and well you
Speaker 3: can do it with path programming, or we can do
Speaker 3: it with pull requests.
Speaker 2: I don't really care.
Speaker 3: I just want to have more than one person actually
Speaker 3: looking at this code. And then I get this thing
Speaker 3: in from the from the outside, and I'm sort of like, okay,
Speaker 3: what do I do with this now, because you know,
Speaker 3: the usual reaction to something like that is to say, well,
Speaker 3: looks good to me, because that's what you already always
Speaker 3: do with those big, big requests.
Speaker 2: The classic sign of I have not read.
Speaker 3: This exactly exactly. And now, fortunately I was actually, you know,
Speaker 3: engaged by the CEO of the company, so I know
Speaker 3: that I had I had pretty free, you know, range
Speaker 3: of deciding what to do. So I wrote back and say, well, okay,
Speaker 3: so I'm really sorry that you weren't in on what
Speaker 3: is what it is that we're doing at the moment,
Speaker 3: but I'm actually going to politely decline this pull request
Speaker 3: because it's just too much. And I don't know whether
Speaker 3: it works. And it's not that I don't trust you
Speaker 3: in the sense that I think you have you know,
Speaker 3: ill intent, but I don't even trust myself to write
Speaker 3: you know, flawless code. So that's why we need someone
Speaker 3: else to actually review with it, because it's part of
Speaker 3: this whole you know, process of figuring out does the code,
Speaker 3: does the software actually work as intended? Does the code
Speaker 3: do what it is that we wanted to do, And
Speaker 3: we can't do that if you just give me, you know,
Speaker 3: all of that in one go. So I said, well,
Speaker 3: I'm not going to take this one. But on the
Speaker 3: other hand, you still have all the codes, so I'll
Speaker 3: work with you and try to break it down into
Speaker 3: smaller pieces and we can get.
Speaker 2: It in that way.
Speaker 3: So we sort of made that work. But my mind
Speaker 3: here is that we're sort of in that situation now
Speaker 3: where we do get you know, something that reminds us
Speaker 3: of you know, the pull request from Hell. But it's
Speaker 3: just like it's not written by a person anymore. It's
Speaker 3: just now it's written by some statistical system. And and
Speaker 3: then if you just get all of that code in
Speaker 3: one big, you know chunk, you don't really you can't
Speaker 3: really fit it in your head, and then you.
Speaker 2: Just supplies submit a series of smaller requests exactly.
Speaker 3: And if you can get these systems to do to
Speaker 3: work in that way, which I suppose you could, then yeah,
Speaker 3: that would that would probably help. So it's not that
Speaker 3: I so that's that's actually a pretty that's actually that
Speaker 3: maybe showing us a way out and trying to work
Speaker 3: with LMS as though they were you know, contributors on
Speaker 3: an open source project and then try to tease them
Speaker 3: to do small increments that you can review.
Speaker 2: Well, that's certainly the way I look at it, because
Speaker 2: it's yeah, you know, more and more we're in a
Speaker 2: situation where all you can do is see the code.
Speaker 2: You don't really see the person, and you certainly have
Speaker 2: no way to measure the qualifications. Let's face it, we've
Speaker 2: almost never had a way to measure qualifications in software
Speaker 2: that was meaningful in any way. In the end, the
Speaker 2: code had to speak, and so if they could, you
Speaker 2: have to engage with the person. Yeah, you know, there's
Speaker 2: an argument here that looks just like, oh, you don't
Speaker 2: have a PhD in COMMSI you can't contribute to our.
Speaker 1: Project, right, which is silly.
Speaker 2: In the end, let the code speak, and if you
Speaker 2: can insist that the tool delivers the code in a
Speaker 2: form that is viable for you to validate, because in
Speaker 2: the end, it's your butt on the line, right, you
Speaker 2: are the professional engineer. You're going to sign off on this,
Speaker 2: then you have a chance of being able to use
Speaker 2: these tools. And I you know, this seems like the
Speaker 2: most solvable problem in the LLM space compared to what
Speaker 2: people are talking about are playing with doing it outside
Speaker 2: of software, Like, at least software has pretty good tools
Speaker 2: and governments. We already have a method doing distributed programming.
Speaker 2: Oh yeah, oh yeah, oh yeah, name other industries that
Speaker 2: are even close to this ability.
Speaker 1: Yeah, before we leave software, just there's another gotcha for
Speaker 1: junior programmers that more experienced programmers won't necessarily have. And
Speaker 1: I've talked about this on dot Ner Rocks before, which
Speaker 1: is a junior developer will ask a question that they
Speaker 1: think is the right question to ask when it might
Speaker 1: not be. So they'll ask a question, you know, they'll well,
Speaker 1: they'll say something like, please make me a you know,
Speaker 1: a thread safe list component or list control. Let a
Speaker 1: thread safe list class, right that I can use that
Speaker 1: that's completely thread safe and locking and all that stuff.
Speaker 1: And that's the wrong question asked, because in dot net anyway,
Speaker 1: there is one in the framework. So the first question
Speaker 1: that should be asked is, hey, is there a way
Speaker 1: that I can use a list in a thread safe manner?
Speaker 1: And then you know, if the thing is worth anything,
Speaker 1: it will say yeah, well there's the spread safe collection, right,
Speaker 1: But instead a junior programmer might go down the very
Speaker 1: difficult path of doing one themselves because they don't know
Speaker 1: what else is available. So whereas an experienced developer would
Speaker 1: would know and not ask that question, and a junior
Speaker 1: developer would not. Most likely this question is isn't this
Speaker 1: very teachable?
Speaker 3: Yeah?
Speaker 1: Sure it is, but but how many hours is the
Speaker 1: junior developer going to waste working on something that when
Speaker 1: they you know, check it in you say, hey, you
Speaker 1: know that there is something like this?
Speaker 2: Yeah, again, there's a teachable moment around. Make sure you
Speaker 2: ask the question what already exists and you know, yeah, we.
Speaker 3: Still imagine a future version of large language models will
Speaker 3: probably be able to you know, loop in that kind
Speaker 3: of questioning saying, oh you're asking about this, have you
Speaker 3: do you really want? You know, a ground of implementation
Speaker 3: here or can you use the one that already exists?
Speaker 3: Have you looked into the framework and see whether they
Speaker 3: as a reusable component. I mean, I could probably imagine
Speaker 3: that even if they don't do that right now, they
Speaker 3: could they actually do probably be yeah, yeah, yeah, yeah,
Speaker 3: because it's it's a fairly common question to ask ask
Speaker 3: if you're a senior developer anyway, so anybody, Yeah.
Speaker 1: You may need your own implementation because it may need
Speaker 1: features that the base class doesn't have or isn't extendable to.
Speaker 3: So but usually not.
Speaker 2: But maybe maybe now you're starting, you're already seeing in
Speaker 2: these tools that you can put in and pre prompts
Speaker 2: like this should be included with every prompt, right so
Speaker 2: that you could see the idea of an enterprise group
Speaker 2: setting a set of rules that any prompt code generation
Speaker 2: has to follow these rules. But they don't always follow
Speaker 2: the rules. That's the problem.
Speaker 1: I don't know if you've noticed this, but in my
Speaker 1: system prompts and in my user prompt if I say
Speaker 1: don't do this, sometimes it will anyway. And that's just
Speaker 1: that's just the way it goes.
Speaker 2: Yeah, and again I'll often skip things as well if
Speaker 2: the gets too complex, so you still see some development
Speaker 2: need to be done to do more work on validating
Speaker 2: the outBut.
Speaker 1: Right, yeah, all right, well I'm ready for Harry carry
Speaker 1: are you guys?
Speaker 2: I'm actually really excited about all this because it does
Speaker 2: seem to empower more people to build software. As these
Speaker 2: tools mature and they get more reliable. They said they're
Speaker 2: as bad as they're going to be right now. I
Speaker 2: don't see an exponential growth here. You know, we've basically
Speaker 2: indexed all the Internet into these models. As it is,
Speaker 2: there is no more data to consume, and so far,
Speaker 2: training against stuff generated by these tools is degenerative. It
Speaker 2: makes it worse, not better.
Speaker 1: Yeah, and most of the time that's not going to happen.
Speaker 1: Like I've got confirmation that if I have a private
Speaker 1: repo and I use the GitHub Copilot agent to generate code,
Speaker 1: it's not going to train their models. They're not going
Speaker 1: to train their models on the code that it generates.
Speaker 1: In other words, my code isn't going to leak out
Speaker 1: into the ether and you know somebody else is using it.
Speaker 1: That's just a promise by gethub. I don't know it's true,
Speaker 1: but that's a promise.
Speaker 3: But even if it's true. Now you know what's going
Speaker 3: to happen in the future.
Speaker 2: Yeah, well but.
Speaker 1: You mean when Oracle buys get ub uh huh.
Speaker 3: Pretty sure it's not for sale, you know, a completely
Speaker 3: unrelated you know. Note, you know, there were people who
Speaker 3: were submitting that DNA sample suport to form you know,
Speaker 3: tes be and me, And I'm so happy I never
Speaker 3: did that because even back, you know, ten years ago,
Speaker 3: I thought, that's not data that I want, you know,
Speaker 3: sitting in someone else, you know, repository that I can't control.
Speaker 1: And yeah, too late, I've already cloned you.
Speaker 2: Indeed.
Speaker 3: Yeah, so I think we should be, you know, a
Speaker 3: little bit careful with trusting these things, you know, because
Speaker 3: things change, you know, so even if you try to
Speaker 3: trust an entity like Microsoft, get up and you might
Speaker 3: not want to trust it forever.
Speaker 1: But then again, Mark, you know, how long will it
Speaker 1: be before your iPhone twenty four will be able to
Speaker 1: sequence your genome just by taking a picture of a
Speaker 1: hair follicle? Yeah?
Speaker 3: Yeah, maybe you should go back to probably have a
Speaker 3: knock here somewhere lying around that still works. You should
Speaker 3: go back to those. That's why we all kind of end.
Speaker 2: Yeah, I saw a modern flip phone the other day.
Speaker 2: That wasn't like it was still an LCD, right, Like,
Speaker 2: it wasn't the I was very tempted.
Speaker 1: Yeah, Samsung flip z Well that there's an Android phone
Speaker 1: that flips up and down and I have one of those.
Speaker 2: Yeah yeah, but those are all those are smartphones. I'm
Speaker 2: talking about a full retro flip phone. Oh wow, yeah, wow,
Speaker 2: it was exciting. You do have those retro urges without
Speaker 2: a doubt. Sure, there isn't the Again, I feel like
Speaker 2: the programming situation is the best case scenario. I think
Speaker 2: we're more cautious or more familiar with these models of
Speaker 2: needing to valid and so forth. The concern space is
Speaker 2: pretty much everything else happening in LLMS. Yeah, like you
Speaker 2: get back to the computer says stuff. Yeah yeah, But
Speaker 2: I also think we've gone through that. We used to
Speaker 2: believe everything Google said too, Like this is just yet
Speaker 2: another learning pattern that you have to go through the
Speaker 2: experience of realizing these tools are based on knowledge we
Speaker 2: have and a lot of that knowledge is inaccurate and
Speaker 2: so when you you know, quote it verbatim, you are
Speaker 2: often wrong.
Speaker 1: So maybe Mark your critique is more of the population
Speaker 1: than of the AI tools.
Speaker 3: Right, Oh, absolutely, you know.
Speaker 1: Can we trust people to do the right thing with these?
Speaker 1: And yeah, my answer is people are driven by incentives,
Speaker 1: and if the economic incentives outweigh the moral incentives or
Speaker 1: the ethical incentives, guess which one wins. It's just that simple.
Speaker 3: Yeah, yeah, that's that's a bleak. But I'm I think
Speaker 3: I'm probably agreeing with you there.
Speaker 1: Unfortunately, Well, it comes down to the developer who says
Speaker 1: yes to developing something with an AI in a language
Speaker 1: they don't understand.
Speaker 3: Oh yeah, And it's it's not that it's an moral
Speaker 3: excuse or anything. But usually what actually does happen is
Speaker 3: if you say no, someone else will say yes. So
Speaker 3: it's going to happen anyway. And again it's not and
Speaker 3: it's not an excuse for doing something that's unethical. But
Speaker 3: but still from a you know, a high level, you know,
Speaker 3: just looking at society overall, that's still the mechanism that's
Speaker 3: going to happen. You know, someone won't do it.
Speaker 1: Yeah, I wouldn't say yes, just because I'd code myself
Speaker 1: into a corner, you know.
Speaker 3: Yeah, but that's but you also that you senior, just
Speaker 3: like you know Richard is and and what Richard talked
Speaker 3: about you know. You can actually have success with these
Speaker 3: things if you you know, if you know a lot
Speaker 3: of enough about programming, and even if you've never seen
Speaker 3: a language before, you've seen other languages that are similar
Speaker 3: enough that you can probably still get a good sense
Speaker 3: of if you ask it to write you know, if
Speaker 3: you don't know, go, I don't know go. If I
Speaker 3: ask you know, an LM to write me something and go,
Speaker 3: I would probably have a fairly good idea about what
Speaker 3: it is that it produced, and I'd have to look
Speaker 3: up a few things and so on. But again the
Speaker 3: problem is if you're not even a programmer from the beginning,
Speaker 3: or if you're a junior, as we talked about, then
Speaker 3: that gets a lot harder.
Speaker 1: But still, if it was you know, a language like reactor,
Speaker 1: you know JavaScript, but you know, if Reactor is going
Speaker 1: to do something, how do I know that it did
Speaker 1: it the right way or the most efficient way, or
Speaker 1: you know that there isn't a better way?
Speaker 2: I don't Yeah.
Speaker 1: Yeah, So what I would do is I would hire
Speaker 1: a subcontractor that does React, and I would encourage them
Speaker 1: to use the LM because of the agents, because they'll
Speaker 1: be more productive, sure, especially if they're charging me by
Speaker 1: the hour. That's another question. How ethical is it to
Speaker 1: charge by the project versus by the hour?
Speaker 3: Now fair enough, but still it comes down to accountability
Speaker 3: because if you hire a sub contractor, you need to
Speaker 3: trust that some subcontractor to actually do the right thing,
Speaker 3: and and and and absolutely and and that's that's another
Speaker 3: problem that we tend to have with ais in general
Speaker 3: is they're not really accountable right now. We don't have
Speaker 3: any laws that govern how you know, who has responsibility
Speaker 3: for the output of them. And as Richard said, well,
Speaker 3: we even know how to deal with with software development,
Speaker 3: but if we're looking at the broader picture of just
Speaker 3: you know, asking it to do all sorts of other
Speaker 3: tasks for us in the in the rest of the world,
Speaker 3: you know, outside of software, you know who's accountable then,
Speaker 3: and we we don't know they're they're not, so we're
Speaker 3: sort of stuck with, you know whatever.
Speaker 1: We saw companies using their their AI bots as an excuse,
Speaker 1: uh when bot gave them bad advice. Remember that one, Richard,
Speaker 1: I think it was an line thing, whether refund or something.
Speaker 2: Yeah, that was the Air Canada incident. Yeah, where where
Speaker 2: a bot told a customer or you'll be able to
Speaker 2: get a refund on that. So they went ahead and
Speaker 2: did the thing. When they went to get the refund,
Speaker 2: they were refused and ultimately ended up in front of
Speaker 2: a judge and the judge said, use the bot as
Speaker 2: if it was an employee. If an employee said that,
Speaker 2: you'd have to make it true. So the bot qualifies. Yeah,
Speaker 2: and they had to issue the refund and it just
Speaker 2: was a you know, the interesting part was considering that
Speaker 2: Air Canada had made a publicly accessible tool that early on. Yeah,
Speaker 2: and at least in Canada now has set a piece
Speaker 2: of case law in place. Not a bad thing. I'm
Speaker 2: not unhappy with that outcome. No, that's I don't want them. Yeah,
Speaker 2: cautionary tale to other companies, right, test and be prepared
Speaker 2: to pay for the consequences exactly.
Speaker 3: So that also means that if you can sort of
Speaker 3: sense as a customer that you are that you have
Speaker 3: an ill limit the other end, you just keep asking it,
Speaker 3: you know, variations of the same questions until you get
Speaker 3: something you like and then you pounce on that. Oh yeah,
Speaker 3: I'll take that deal, thank you.
Speaker 1: Yeah. I remember asking about are you a bot? And
Speaker 1: it said no, my name is whatever from blah blah blah.
Speaker 2: Yeah, sure, yeah, I.
Speaker 3: Think we should. I think that should should be a
Speaker 3: law that says, well, they are not allowed to impersonate humans, but.
Speaker 1: You should know.
Speaker 2: I have to wonder if we had done a better
Speaker 2: job on privacy in the first place, if we wouldn't
Speaker 2: be dealing with quite as many issues as we've got. Yeah,
Speaker 2: that doesn't mean we shouldn't continue to try. Like I'm
Speaker 2: we talk about the challenges of the gen Z and
Speaker 2: younger generations. It's this using despairs and excuse not to try. Yeah, hey,
Speaker 2: it's not an excuse. You have to continue to be
Speaker 2: the best person you can be. Be the best programmer, dancer, electrician,
Speaker 2: whatever it is you are. Be the best you can
Speaker 2: possibly be, and use the tools to your advantage. Don't
Speaker 2: become a tool yourself. All right, at all? In all,
Speaker 2: I'm pretty optimistic.
Speaker 1: Yeah, I actually am too.
Speaker 3: Well, I'm not, but that's just my disposition.
Speaker 2: To see some patterns happening again. It's like, oh boy,
Speaker 2: we get to learn this problem again. Oh you've been
Speaker 2: trusting software. Huh, All right, here we go.
Speaker 1: We're gonna have to trust but you know what, the
Speaker 1: chickens will come home to rust, like we've said, Richard,
Speaker 1: and you know the rest of the people will wake
Speaker 1: up and say, oh we need to do this, we
Speaker 1: need to do that. We can't just rely on these things.
Speaker 1: So yeah, there'll there will be some pain for sure. Yeah,
Speaker 1: but ultimately come down to people and the decisions they make. Yeah, yeah, okay,
Speaker 1: is that a show?
Speaker 2: I think it's a show?
Speaker 1: All right, Mark, thank you. It's always always awesome talking
Speaker 1: to you.
Speaker 3: It's a pleasure, all right. Thank you for having me.
Speaker 1: You bet, and we'll see you next time on dot
Speaker 1: net rocks. Dot net Rocks is brought to you by
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Speaker 5: back to show number one, recorded in September two thousand
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Speaker 3: You got jam Vans
Speaker 2: And for the pass