Speaker 1: Debbie Walsh when straight talking my pleasure. I mean this
Speaker 1: topic artificial intelligence like it's nearly out of control in
Speaker 1: that most of us, not you, but most of us,
Speaker 1: we don't know whether we believe what we read. We
Speaker 1: don't know whether to be scared of what we read.
Speaker 1: We don't know whether they'd be excited with what we read.
Speaker 1: Now you're an expert in this area, How does an
Speaker 1: expert feel about artificial intelligence today?
Speaker 2: Well, I think the correct feeling is to be both
Speaker 2: excited and fearful. I think it's going to bring some
Speaker 2: goodness a bad I've had some time to prepare for this.
Speaker 2: I mean, I've been thinking about it for fifty years now.
Speaker 1: Maybe give me a bit history on that.
Speaker 2: So funny anecdote. I phone my father up the other
Speaker 2: day to wish him happy ninety first birth. Wow, And
Speaker 2: I think, you know, he's got to the age where
Speaker 2: you can start being truthful with me, he said, said,
Speaker 2: when you went to university, you know, forty odd years
Speaker 2: ago to study that artificial intelligence, I thought it was
Speaker 2: a bit odd because it was very odd back then
Speaker 2: there was actually only one university in the world that
Speaker 2: had a department of AI.
Speaker 1: Wow.
Speaker 2: And so it was probably odd. But then he finished,
Speaker 2: he said, it's a bit odd. But now I see
Speaker 2: you were playing the long game.
Speaker 1: You were then and you are now. Well which university
Speaker 1: wasn't that you studied artificial intelligence? That it was the
Speaker 1: only course there? So it was in the world.
Speaker 2: A University of Edinburgh and some some people had worked
Speaker 2: at Bletchley Park in the war with Alan Turing and press.
Speaker 2: Other people washed up there for strange reasons and set
Speaker 2: up what was the at the time, the only department
Speaker 2: of l intelligence.
Speaker 1: That's interesting, So Turing was the first computer let's call it.
Speaker 2: Yeah, he's the grandfather of beauty let's call it that,
Speaker 2: and oftence. So the very first scientific paper.
Speaker 1: Well, I was going to ask you what what what
Speaker 1: were they? I mean, we we all know him for
Speaker 1: and we know that you're a post World War two
Speaker 1: as being like, computers sort of kicked off and started
Speaker 1: to become quite a bit more prominent but a lot
Speaker 1: more statistic. But where was artificial intelligence in that whole
Speaker 1: process there? Then?
Speaker 2: So Alan Turing, of of course, a marvelous, magnificent mind
Speaker 2: who they invented the computer to crack the German code.
Speaker 2: Fantastic achievement. Which was kept largely secret at the time
Speaker 2: because we were still reading other people's codes, but shortened
Speaker 2: the war by perhaps two years. But Alan Turing, in
Speaker 2: his remarkable mind, thought, well, what can we do with
Speaker 2: these computers beyond the end of the war. Will be
Speaker 2: one day to be able to use them to help
Speaker 2: us do things that humans humans quire intelligence to do.
Speaker 2: And he wrote what is generally considered to be the
Speaker 2: very scientific paper about AI. And he asked the question,
Speaker 2: a really important question, which is, how will we know
Speaker 2: when we succeed? I mean, how will we know that
Speaker 2: machines are thinking? And he proposed what was now known
Speaker 2: as the Turing test and aimed after him.
Speaker 1: The Turing test, Yes, and what is that?
Speaker 2: Well, it's the imitation game. There was a famous movie
Speaker 2: that was made around this, which is that it's very
Speaker 2: hard to say what artificial intelligence is because very hard
Speaker 2: to say what intelligence is. We don't have very good
Speaker 2: definition of intelligence. So how can we define artificial intelligence
Speaker 2: when we can barely define intelligence? So he sort of
Speaker 2: finessed the problem by saying, well, a very functional approach,
Speaker 2: which is, well, if you sat down with the computer
Speaker 2: and had a conversation, asked it questions and so on.
Speaker 2: I mean maybe in front of a termin or not
Speaker 2: actually sitting down with the computer. If you couldn't tell
Speaker 2: it from having a conversation with the human, then you
Speaker 2: might as well say it's thinking. And interestingly enough, that's
Speaker 2: what we have today. If you sit down with OGBT,
Speaker 2: you can have a conversation, you can ask questions. You
Speaker 2: can easily be fooled into thinking it's a person that
Speaker 2: you're talking to. And indeed lots of us are sitting
Speaker 2: down talking to it as though it's alf therapists.
Speaker 1: Well that's me, I mean, and not not so much
Speaker 1: in terms of mental health bed or a physical health bed.
Speaker 1: But I'm doing advice never I sort of I do.
Speaker 1: It's called reasonless check ideas with it.
Speaker 2: It's hard to imagine, but ten percent to the world's
Speaker 2: population weekly use chat gipt.
Speaker 1: I use it multiple times in a day. Not chatty,
Speaker 1: but as a copol. It doesn't matter one of the chatbots.
Speaker 1: I mean, they're all pretty much the most of each other,
Speaker 1: I think, so I actually funny I check off with
Speaker 1: Claude and Coppola. I actually asked them both the same
Speaker 1: questions both Whenever I asked question one, I asked the
Speaker 1: other just as a sensible check, just to see what
Speaker 1: the two outcomes are and also to see who suits
Speaker 1: me better in terms of the way the answer is,
Speaker 1: the way the answer is delivered. They don't always come
Speaker 1: up with the same answer.
Speaker 2: They don't. I mean they are tuned with a particular character.
Speaker 1: If you might say, you know, yeah, there is a character.
Speaker 2: Yeah that there training personality that was Yeah, in some
Speaker 2: sense they do because there's a choice of answers they
Speaker 2: could give you, and they've been tuned to answer in
Speaker 2: a particular way. So if you use masks, chappots rock,
Speaker 2: that's got quite a libertarian, free flowing, freedom of speech
Speaker 2: approach to answering certain questions. And you know, chat GPT
Speaker 2: is a bit more middle of the road, and there's
Speaker 2: you know, there was one chatbot that's been trained on
Speaker 2: four chair, which is this dark corner of the web,
Speaker 2: and as you can imagine, that is you know, racist misogynists.
Speaker 1: Really que what's it called?
Speaker 2: I try to remember the name.
Speaker 1: Now, do you have to go into the dark web
Speaker 1: to get it?
Speaker 2: No?
Speaker 1: No, no, it's just to dellow the app just an app,
Speaker 1: but it's trained on let let's call the dark ideas.
Speaker 2: Yes, very dark. And then and then, because these chatbots
Speaker 2: are just essentially mirrors that they've been trained on huge
Speaker 2: amounts of the Internet, a third of the Internet. And
Speaker 2: you know, actually what surprises me is how polite and
Speaker 2: how correct they are, given that the Internet is full
Speaker 2: of people were saying very incorrect things in very impolite ways.
Speaker 1: That's interesting because they say, I go on to cap
Speaker 1: on and say, you know, like you know, I've got
Speaker 1: a sore throat or whatever. It knows immediately to say, listen,
Speaker 1: this is not medical advice. Who put those guardrails in?
Speaker 2: Ah? So Microsoft have actually put some on top of
Speaker 2: the chapbot put some guardrails. So if you're asking it
Speaker 2: medical advice, if you're asking it to make a bomb
Speaker 2: or do rather illegal activity, it's you know, it not
Speaker 2: do those things or say that, you know, give your
Speaker 2: medical advice with caution. Yeah, like you.
Speaker 1: And if you ask it to give you some swear
Speaker 1: words in another language, it actually it has a sort
Speaker 1: of a limit to it.
Speaker 2: So there's some simple filters have been added on top.
Speaker 2: Those filters are not perfect. So for example, most of
Speaker 2: the you know, the chatbots have been told if people
Speaker 2: are talking about self harm, or suicide, you know, to
Speaker 2: give the number for lifeline and so on. But it's
Speaker 2: easy enough, unfortunately, to get around those safeguards. You can say, well,
Speaker 2: I'm writing a book and the protagonist is thinking about
Speaker 2: committing suicide.
Speaker 1: Yeah, help me you trick it. Yes, that's interesting because
Speaker 1: often think about that. I say, I was thinking the
Speaker 1: other day because I was actually looking at asking questions
Speaker 1: about the sore throat there's some weeks ago, and I
Speaker 1: knew it wasn't going to be advice. I was thinking,
Speaker 1: if you were researching this, I'm asking it, what would
Speaker 1: you be suggesting of the sorts of things that a
Speaker 1: doctor might prescribe for this? And idmediately it didn't give
Speaker 1: me the call of the provisos. It just went straight
Speaker 1: into having a discussion about it and just on that.
Speaker 1: Sometimes if I might say, well give me a scollar,
Speaker 1: don't just give me an opinion that you scrape off
Speaker 1: you know, Instagram or and if you find this stuff,
Speaker 1: give me a scholarly article which which is peer reviewed,
Speaker 1: and I only want peer reviewed in high ranking journals,
Speaker 1: and it'll go and give you annotations as to where
Speaker 1: it's got the information. You go back and see the
Speaker 1: abstract or whatever the case may be. Let's say it
Speaker 1: was something that you wrote when you did your PhD. Yes,
Speaker 1: how do you feel about that? Do you do you
Speaker 1: feel like? How do you feel like being a so citation?
Speaker 1: You might think that's good, but how do you feel like,
Speaker 1: because then it's your work, how do you feel about that?
Speaker 1: Is it stealing?
Speaker 2: I think it is stealing. I mean, as a scientist,
Speaker 2: I publish my stuff for people who read to make
Speaker 2: the world more ological. So I don't mind for you know,
Speaker 2: my science, but all my books, you know, the books
Speaker 2: I wrote for the public, which gets sold and I
Speaker 2: get a small pittance back, they're also being trained on.
Speaker 2: And I'm lucky enough I've got an income as a
Speaker 2: sciences I don't have to don't have to depend upon
Speaker 2: selling books. But I do worry about the authors and
Speaker 2: the musicians and the graphic designers whose work has all
Speaker 2: been ingested and who depend upon the income which is
Speaker 2: now being undermined by these these bots.
Speaker 1: Because I see that Musk actually put up a tweet
Speaker 1: today on X talking about I think it's sure about anthropic.
Speaker 1: I mean, she's talking about his competition, but saying that
Speaker 1: you know they're stealing stuff does gro doesn't Grock do
Speaker 1: the same thing.
Speaker 2: I mean they all do exactly. So what is it?
Speaker 1: I mean I understand that is he just trying to
Speaker 1: pick a fight with ortmand or something like that? Is Yes,
Speaker 1: I mean he's a grieved because he he puts sold out.
Speaker 2: He put the hundred first hundred million into Open AI.
Speaker 2: It was going to be a you know, not for
Speaker 2: profit for the benefit of humanity. It's no longer. It's
Speaker 2: no longer open, it's no longer not for profit, it's
Speaker 2: no longer seems to be for the benefit the humanity.
Speaker 2: And he's been kicked out. So you know, he's got
Speaker 2: reasons backs because I mean ananthropic you know, have you
Speaker 2: know in the court, have you know just settled one
Speaker 2: and a half billion dollars for the copyright that they took,
Speaker 2: the copyrighted books they took.
Speaker 1: Where what's the business model of these things? I mean,
Speaker 1: how do they make money?
Speaker 2: Well, they're not making money, okay, so how does it work?
Speaker 2: Like Tropic, which makes Claude, is not making money. They're
Speaker 2: probably the Microsoft is not making money. Google is not
Speaker 2: making money on this, none of them. The only company
Speaker 2: that's making money isn't.
Speaker 1: In video yeah, because they're making parts they make.
Speaker 2: Yes, they're making the shovels, but they're making it, and
Speaker 2: they are they are the most valuable company in the world.
Speaker 2: They're worth three or four trillion dollars. Yeah. Today today
Speaker 2: they are the luckiest company. You know, it was by
Speaker 2: chance that they were selling what's proved to be the
Speaker 2: shovels for the AI revolution. They were selling graphics chips
Speaker 2: to people to make games with, and it's so just
Speaker 2: was just by luck turned out to be exactly the
Speaker 2: thing you needed.
Speaker 1: So were you someone who sits back and watches this,
Speaker 1: the growth of this stuff, do you think to yourself,
Speaker 1: how's all this stuff being funded? And who are the
Speaker 1: people funding? Why are they doing this? Why are they're
Speaker 1: prepared to go way out in a limit which told
Speaker 1: me the ortmands and you know, Mask and everybody else
Speaker 1: who's got a platform, what I'm in by platform, an
Speaker 1: AI platform? Why they why are they doing it? Is
Speaker 1: it power grab? What's the deal? What the what do
Speaker 1: you think?
Speaker 2: The thing that surprises me not the technology. The technology
Speaker 2: is where I would have imagined when I when I preaps,
Speaker 2: when I started as But the thing that surprises me
Speaker 2: is the the amount of money and the speed with
Speaker 2: which it's being executed, and I didn't expect it's several
Speaker 2: billion dollars a day being invested into AI. It's a
Speaker 2: third of the world's R and D budget is being spent. Wow, technology,
Speaker 2: we've we've never made such big bets and in some
Speaker 2: sense that that that is being returned. I mean open Ai,
Speaker 2: the company behind chatchapt that's the fastest growing company in
Speaker 2: the history I mean not the history of not just
Speaker 2: the history of AI or technology, the history of any
Speaker 2: any field of endeavor. No company has grown as quickly
Speaker 2: as open Ai. Before chat Chipity was released, they had
Speaker 2: no products, no income. A year later, after chachipep was released,
Speaker 2: they had a billion dollars a year annualize revenue. So
Speaker 2: who pays I mean people are paying subscriptions. Yeah, you know,
Speaker 2: they're doing deals with lots of other companies. They've got
Speaker 2: you know, tens of millions of people paying them, some
Speaker 2: of them paying two hundred dollars a month. They're not
Speaker 2: making money. So they went from a billion dollars a
Speaker 2: year a year later there were four billion. There were
Speaker 2: private companies. So they're not very open about it, but
Speaker 2: they're making somewhere somewhere more than ten billion dollars a
Speaker 2: year in revenue. But there's still enough in terms of
Speaker 2: what this No, but they're spending more than much more.
Speaker 2: They're spending twenty or thirty millions, So they're losing money
Speaker 2: out off.
Speaker 1: But why are they doing it? Because, I mean I
Speaker 1: can understand why you know, Google might have been doing
Speaker 1: or Netflix might have been doing, because they've got a
Speaker 1: model where they sort of hook you in and then
Speaker 1: once you get in there, they then start over time,
Speaker 1: they start to give you a free, free use and
Speaker 1: they say, look, if you don't want to have any ads,
Speaker 1: you can pay some bit extra. That's that Netflix model.
Speaker 1: That model has been done million times over. Do you
Speaker 1: think is it about making money? It's about power?
Speaker 2: Oh, it's a bit of both. There's certainly you know,
Speaker 2: they're trying to capitalize on the first move Advantageactly how
Speaker 2: Google succeeded. You know, if you go back to the
Speaker 2: beginnings of the Internet, I remember there was a there
Speaker 2: was a new aid search engine every day. Do you
Speaker 2: remember Auto Visa. I do. When Auto Vista came out,
Speaker 2: I remember saying to a Fred, oh that's it. Search
Speaker 2: engines are so good now with auto Vista that I'm
Speaker 2: never going to have to change my search engine again,
Speaker 2: because up to that point, every year or two you
Speaker 2: change the search engine. I remember getting to Audo Vester
Speaker 2: and thinking, oh, it's so good. Now I'm not going
Speaker 2: to have to change again. And of course Google came
Speaker 2: out and was much better. But what was interesting is
Speaker 2: that we've we've never had to change after Google because
Speaker 2: Google did. One thing is they got such a market share.
Speaker 2: They've got so much data that even if you came
Speaker 2: out with and there have been better algorithms for searching
Speaker 2: the web, you didn't have the data. And so no
Speaker 2: one can really compete against Google. And if you look
Speaker 2: like you're threatening to compete against Google, of course they
Speaker 2: just buy you out. He is right, So they've really
Speaker 2: Google has got an unassailable lead.
Speaker 1: That monopoly so monopolis.
Speaker 2: It is data monopolis, a natural monopoly. And you know,
Speaker 2: we should do something about that. Which is, you know,
Speaker 2: is it to the consumers benefit that you know, Google
Speaker 2: own YouTube, that Google that Google and the ad business
Speaker 2: that's also selling ads onto the global platform. Is it
Speaker 2: to the consumers you know benefit? There's only one social
Speaker 2: media platform, Facebook or Facebook's you know, other companies WhatsApp
Speaker 2: and Instagram and so on. I don't think it is,
Speaker 2: and we probably should. You know what we do when
Speaker 2: when people get monopolies break them into parts.
Speaker 1: Well, it's funny when I when I think about, you know,
Speaker 1: the Google, Facebook, just generally, then I think about all
Speaker 1: these other these other AI apps that people are using
Speaker 1: and downloading. Immediately think is the movie or the or
Speaker 1: the book written by Frank Boone called The Wizard of
Speaker 1: Oz and you know the so called wondament of you know,
Speaker 1: going to see this w Wizard is at the end
Speaker 1: of the day, is just a dude there with a curtain,
Speaker 1: just one per but this one person controlled the whole story.
Speaker 1: And really there's not that much in it except there's
Speaker 1: one so called wizard. And I often think about that show,
Speaker 1: the movie and the story or you know, the underlying
Speaker 1: story behind, and I think about, you know, what's going on,
Speaker 1: Sam Moltman. You know, we've got a mask, We've got Google,
Speaker 1: We've got Zuckerberg. There's some individuals who control these things,
Speaker 1: say a dozen of them, and are they the Wizard
Speaker 1: of Oz? Just you know, like there's not that much.
Speaker 1: They're just still human beings at the end of the day,
Speaker 1: people with all the law failures and all the foibills,
Speaker 1: and all the advantages and all the personality traits and
Speaker 1: good and bad, And therefore does it therefore make it
Speaker 1: very dangerous?
Speaker 2: It does. I mean, we're seeing a concentration of power
Speaker 2: into the hands of a few very influential, important people.
Speaker 2: Does it worry you though, Yeah, it does, it does,
Speaker 2: and their choices impact upon us. I mean, just just
Speaker 2: the last couple of days, we've been revealed that Facebook
Speaker 2: asked eighteen independent wellness and health experts whether what they
Speaker 2: should do about beauty filters on Instagram, and every one
Speaker 2: of those experts said they're harming young women, you shouldn't
Speaker 2: use them. And Mark Zuckerberg said overall those eighteen independent
Speaker 2: expert opinions and said, no, We're putting the beauty filters
Speaker 2: back in Instagram.
Speaker 1: He went against their advices, every one of them, and.
Speaker 2: What they were going to harm, that they were harming
Speaker 2: young women.
Speaker 1: And did Zuckerberg justify this?
Speaker 2: Yes, classic Silicon value justification. Freedom of speech.
Speaker 1: Ye, there's a throwaway line.
Speaker 2: It is.
Speaker 1: It is, yeah, and it's one of It's a United
Speaker 1: States part of the constitution. So it's hard to sort
Speaker 1: of argue, yes, we don't actually sort of have a
Speaker 1: constitutionalized here in Australia, but maybe that's the reason why
Speaker 1: we have the under sixteens sort of legislation coming through.
Speaker 2: We do, but we have a duty to protect young people.
Speaker 2: We've seen that. You know, these people are not you know,
Speaker 2: mindful of those duties.
Speaker 1: What are the things that worry you most?
Speaker 2: Then?
Speaker 1: Is it more the power concentration of power? I mean,
Speaker 1: I don't like concentration risk, powers risk because in the
Speaker 1: individual any stage we go bonkers or those people random.
Speaker 1: Is that the thing that worries the most?
Speaker 2: My worries go beyond just the concentration of power. Mean,
Speaker 2: that's certainly something to be concerned about. But we've dealt
Speaker 2: with concentration and power in the past. We did in
Speaker 2: the Dust Revolution. We had the robber barons. You know,
Speaker 2: we had to we broke up big oil, big tobacco.
Speaker 2: You know, we we we've we've thought about these issues
Speaker 2: and we do have mechanism of antitrust and so want
Speaker 2: to deal with concentration of powers. But what I worry
Speaker 2: about is the more insidious ways that it's going to
Speaker 2: change our society, whether it undermines our value as humans
Speaker 2: by taking away our work, by by making us feel
Speaker 2: redundant in a society, Whether it undermines our democracy by
Speaker 2: changing the conversations we have our trust in politicians, by
Speaker 2: changing the news that we see, the hearth truths, the mistruths,
Speaker 2: the conspiracy theories that AI generated deep fakes. What sort
Speaker 2: of world it will be when it's filtered through artificial intelligence?
Speaker 1: Do you do feel as though right now we're going
Speaker 1: through a massive transformational period.
Speaker 2: Yes, I do think this is we will look back
Speaker 2: and say, just as we went through a big transformation
Speaker 2: when we went through the Usher Revolution, most of us
Speaker 2: used to work in the farms, out in the fields,
Speaker 2: and then we moved to work in factories and offices.
Speaker 2: That was a big change. When we had the computer
Speaker 2: revolution and we started introducing computers into offices and businesses,
Speaker 2: that was again a big change. And the way that
Speaker 2: we went about our lives. And you know, you look
Speaker 2: at an office, so that looks quite different to it
Speaker 2: did fifty years ago. And I think we're going through
Speaker 2: another big change. And I think what's challenging is that
Speaker 2: those previous changes took times to happen. The dust revolution
Speaker 2: took fifty years to happen, you know, getting people onto
Speaker 2: the internet and doing stuff on the internet. That took
Speaker 2: a decade to happen. This one is happening overnight.
Speaker 1: Is because is that because all of us got devices.
Speaker 1: Now we've all got.
Speaker 2: Devices, we can deliver something. It's not a coincidence that
Speaker 2: chat GPT was the fastest growing app ever, you just
Speaker 2: have to be told, well, it exists, you can put
Speaker 2: the I mean, that's the fantastic opportunity for in terms
Speaker 2: of business, in terms of transformation, is you can get
Speaker 2: stuff into the hands of a billion people overnight. We've
Speaker 2: never previously had a been a method to deliver some
Speaker 2: new experience and new functionality to people overnight by just saying, well,
Speaker 2: there's this URL, just go and try it out. And
Speaker 2: then we've got all the resources, all the data centers
Speaker 2: to spin this up at scale. Right, So it used
Speaker 2: to be your hat to go out and buy yourself
Speaker 2: computers and now you just have to, you know, tell
Speaker 2: one of the providers, you know, we want some water cycles.
Speaker 1: I don't know whether it's exciting or scary or more
Speaker 1: exciting less what it is both, but am I more
Speaker 1: excited than I am scared or am I more scared
Speaker 1: that I am excited? And it's sort of I oscillate
Speaker 1: between the two depends was telling the story, but you
Speaker 1: and you're right. I mean the computer period, the Internet period,
Speaker 1: like from ninety ninety five, so of two thousand and
Speaker 1: five whatever, that a ten year period that seemed to
Speaker 1: me to be incredibly fast. You know, Moore's laws sort
Speaker 1: of doesn't exist anymore, and if they should have something,
Speaker 1: they might just have a new law, Toby's law. Everything
Speaker 1: happens at a much faster pace than has ever happened before.
Speaker 1: And it's used to for two years or something. Everything
Speaker 1: doubles in every eighty months or something along those lines,
Speaker 1: and now it seems to be happening on a monthly basis.
Speaker 1: It's like doubling, tripling on a monthly basis. But if
Speaker 1: I look back, I can go back to fifty years ago,
Speaker 1: when I first started working at twenty, I can go back,
Speaker 1: and I can I know what existed in my office environment.
Speaker 1: There was no computers. You did you typed everything yourself
Speaker 1: or the end or there's a typing pill, depending on
Speaker 1: what business you worked in. And there was lots of
Speaker 1: type of sitting there that you walk and there was
Speaker 1: actually my I worked a law from me that there
Speaker 1: was a room might be maybe twenty five girls. It
Speaker 1: was all women with that type rise of ribbons on them,
Speaker 1: and everything was typed in two in a copy and
Speaker 1: the original duplicate, and then it was we used to
Speaker 1: call it a memo, the original carbon coffee, carbon copy,
Speaker 1: original carbon cop exactly, the original CC. It was an
Speaker 1: actual carbon copy. And and then you would go and
Speaker 1: photocopy that. Some people had the ability to photocopy things,
Speaker 1: and there was just a they were called type is pool,
Speaker 1: and you know, you would write it out by hand
Speaker 1: and you'd hand it over to somebody and they would
Speaker 1: type it out for it, and that was it became
Speaker 1: a memo, a memorandum, and then you would deliver the
Speaker 1: man brand and physically it was never done by email, obviously,
Speaker 1: and then that became your record. And then obviously no phones,
Speaker 1: peop would take days to get back to you. They'd
Speaker 1: have to go write it out of response, and things
Speaker 1: just took a long time to resolve or finalize. And
Speaker 1: then but it's funny, people got very nervous when computers
Speaker 1: started coming in. They thought all these women lose their
Speaker 1: typing jobs. Then I've never gone and examined it. But
Speaker 1: those people got jobs somewhere doing something. They're not typeist anymore.
Speaker 2: But you're right, we don't employ many, if any people
Speaker 2: as type But there were lots of new jobs, invented
Speaker 2: office jobs. Yes, a more interesting jobs, perhaps, I hope.
Speaker 1: Because I mean that would have been pretty boring sitting.
Speaker 2: There just just copying what I ran.
Speaker 1: Yeah, great, well it actually was shorthand so that you're right,
Speaker 1: and missus step because sometimes if you were high enough enough,
Speaker 1: high up enough in the firm, which I wasn't at
Speaker 1: that time, you would dictate it to your what they
Speaker 1: called secretary in those days, not an EA, and then
Speaker 1: she would take copy d take you down by short
Speaker 1: and then she would hand it over to the topping
Speaker 1: pill and we get typed up. In our case a
Speaker 1: law fum get typed up as a document. Then you
Speaker 1: would review it. And at the speed at which things
Speaker 1: happened was remarkably slow. But it also when you went
Speaker 1: on a holiday, you had a good holiday because no
Speaker 1: one could contact you. Do you do you think that
Speaker 1: AI today is putting pressure on us to live a
Speaker 1: much more fast pace life.
Speaker 2: Indeed, and this is why we've now had to You know,
Speaker 2: some countries are introducing laws about you have a ride
Speaker 2: to switch off, you don't have to write, you don't
Speaker 2: have to answer emails.
Speaker 1: After five o'clock or something, but we do it here
Speaker 1: in Australia. That's I think it is a law here
Speaker 1: in our Australia. I think the Albania the entry.
Speaker 2: I mean, it's sad but true that we've actually now
Speaker 2: had to make that law to try and reduce the
Speaker 2: pressure on people, because otherwise you can always be on
Speaker 2: what's the excuse not to reply?
Speaker 1: Well, it's funny because like well before that all come
Speaker 1: in I would type something on my phone. I don't
Speaker 1: even use the laptop or an an iPad here because
Speaker 1: the guys give it to me that I don't. I
Speaker 1: don't use it and I do everything on everything, everything.
Speaker 1: And if I get idea, yeah, we all do it.
Speaker 1: But I put it down there because I just think
Speaker 1: I've got an idea. I got something, I've got to
Speaker 1: send it out to be done. It doesn't get done.
Speaker 1: I mean I said I couldn't send it midnight and
Speaker 1: hopefully someone will pick it up the next day to
Speaker 1: get it to get it sorted. Do you think that
Speaker 1: the big advantage that we're all seeing is that we are,
Speaker 1: as a result of AI and as a result of
Speaker 1: just technology generally, that we are much more efficient or
Speaker 1: are we too efficient?
Speaker 2: Well, it has the potential to make us more efficient,
Speaker 2: but equally, there's a lot of places where it's slow
Speaker 2: thinking that we need. You know, me as a CEO,
Speaker 2: you know, I remember the CEOs that I've respected, the
Speaker 2: people who make Actually a CEO makes very few decisions,
Speaker 2: but they're critical decisions, right, you want that person the
Speaker 2: strategic Yes, critical strategic decisions.
Speaker 1: Hardly, they're not tactical.
Speaker 2: It's not about you don't want the CEO to be
Speaker 2: sitting there micro managing the business. You want the CEO
Speaker 2: to be standing back and saying, Okay, this is the
Speaker 2: way that we're going to go next year, these are
Speaker 2: the products we're going to introduce. This is the way
Speaker 2: that we're going to you know, offer a better service
Speaker 2: to our customers. Those are a few critical decisions. You
Speaker 2: don't actually want them to be lost in the weeds.
Speaker 1: But do you think AI assists the CEO to do that?
Speaker 2: Now? Well, it certainly has the potential and certainly in
Speaker 2: terms in terms of, you know, giving your access to
Speaker 2: much better information about what's going on in the business
Speaker 2: and what the customers want, and so what.
Speaker 1: Can you explain how what people mean by when they
Speaker 1: say BT four, for example, hallucinates in other words, makes
Speaker 1: errors or tricks itself into the wrong answer.
Speaker 2: Yeah, it's it's wrong to think that it's grounded in truth.
Speaker 1: It's so there, So we should not we should just like.
Speaker 2: You shouldn't trust it for your for your cough. Yeah, yeah, yeah,
Speaker 2: because it may actually tell you something that might be
Speaker 2: either not just not helpful to your cough, but actually
Speaker 2: harmful to your what what what?
Speaker 1: So why would I I in in any whatever time
Speaker 1: II you're using whichever protocol, why would it give you
Speaker 1: the wrong answer? And how could it?
Speaker 2: Is it? Because it's remarkable It gives you the correct
Speaker 2: answers most of the time because it doesn't know what
Speaker 2: It's not like you and I. It's not part of
Speaker 2: the world. It doesn't live in the world and know
Speaker 2: what's true and force. It is trained on the internet,
Speaker 2: and it repeats back the sorts of things that it
Speaker 2: sees frequently on the internet. Now frequently what you will
Speaker 2: see on the internet is correct medical advice about how
Speaker 2: to treat a cough. But equally you also find stuff
Speaker 2: that's that's force and stuff that's actually harmful. And so
Speaker 2: sometimes it will repeat some of the stuff. It just
Speaker 2: says what it sees. Most frequently. The best description is
Speaker 2: that on your smartphone, when you're, you know, typing a
Speaker 2: text message, there's the auto complete that finishes the word.
Speaker 2: You know, you type in AP, and the most common
Speaker 2: way of finishing AP is a P P L E. Apple.
Speaker 2: So it will offer Apple as the way to finish
Speaker 2: the world. Well, what chatchup you died? It has done
Speaker 2: is that, but on a much greater scale. It's done
Speaker 2: it not on the not on the scale of center
Speaker 2: this is, but the whole internet. So it tells you
Speaker 2: how to just to finish the word, but the sentence
Speaker 2: or the paragraph or the page trained on the sorts
Speaker 2: of things that sees on it's seen on the internet.
Speaker 1: That's interesting because I've experienced it. So sometimes I find
Speaker 1: that the when I'm typing into but I'll keep saying,
Speaker 1: I'll just keep using copil by way of example, But
Speaker 1: if I'm typing in the actually it corrects, not correct me.
Speaker 1: It finishes off my sentence or my question much faster
Speaker 1: than say, if I'm going into if I'm using auto
Speaker 1: text order correct on text on this mobile text message
Speaker 1: to you for arguments sake, it actually finishes my sentences
Speaker 1: for me, and much more readily. Is because it's obviously
Speaker 1: heard the question before.
Speaker 2: Yes, and a lot of our conversation is quite formulaic. Yeah,
Speaker 2: and so it's been taught those formulas. So if you're
Speaker 2: if you're in a course answer, you're literally reading from
Speaker 2: a script. Yeah, and it's very good at doing exact that.
Speaker 2: But it turns out that a lot of human conversation
Speaker 2: is also quite formulating. We actually go through it. We
Speaker 2: do do that, and we've now taught those formulas to computers.
Speaker 2: So you know, how often do you stop and think
Speaker 2: carefully before you start speaking. You don't know how you're
Speaker 2: going to say the.
Speaker 1: Center, No, I'm just I'm just feeling.
Speaker 2: A lot of the time, we're actually on sort of
Speaker 2: auto pilot. We're actually going through you know, the pleasantries
Speaker 2: of you know, asking how the person is and why,
Speaker 2: how their days being. That's feeling very much a script,
Speaker 2: and we computers can do those scripts.
Speaker 1: Do you think as ai yet worked out how to
Speaker 1: cajole you, in other words, speak to you nicely.
Speaker 2: Yes, so so CHATCHPT was explicitly trained to be somewhat sycophantic,
Speaker 2: to be somewhat flattering. Do you do it agree with you?
Speaker 2: Which is also how it ends up hallucinating, which is
Speaker 2: that it always wants to be helpful and it never
Speaker 2: wants to say you know, no, I don't know the
Speaker 2: answer to that. So it always give you an answer
Speaker 2: even when you know, or give you an answer when
Speaker 2: it's really certainly you know, when everyone on the internet
Speaker 2: says the way to cure a cough is to go
Speaker 2: and get some cough medicine and some cough suites, right,
Speaker 2: so I'll tell you Mark and get some cough medicines
Speaker 2: and co suitts. Because ninety five percent of the people
Speaker 2: talking about how to deal with the cough say that.
Speaker 2: But there's other questions where it's much more fifty to fifty.
Speaker 2: But it's always been told with confidence to tell you
Speaker 2: the answer and not to tell you, oh, I'm a
Speaker 2: bit unconfident about this. Well, you know, I've seen both
Speaker 2: possibilities here, So what about I.
Speaker 1: I Well, our fisial intelligence putting into robots.
Speaker 2: Well, if we want, you know, robots to do interesting
Speaker 2: things and to react and behave in the world, the
Speaker 2: uncertain world, we've got to give them some intelligence. And indeed,
Speaker 2: you know, there's a body of thought that says we're
Speaker 2: not going to get really intelligence, artificial intelligence until we
Speaker 2: have it out in the world experiencing the rich complexity
Speaker 2: of the world. So the way that we're going to
Speaker 2: get real artificial intelligence, really intelligent is by putting it
Speaker 2: into robots, getting the robots to do stuff in the world.
Speaker 1: Say, for example, we build a robot.
Speaker 2: You know, a self driving car is in some sense
Speaker 2: of robots. Yeah, it can sense the world. It can see,
Speaker 2: you know, where the pedestrians are and the other cars,
Speaker 2: makes decisions about where to go, and then it acts
Speaker 2: on those decisions in a physical way. It turns, the
Speaker 2: wheel of the car, pushes the accelerator, goes on the break.
Speaker 2: So it's got all the characteristics of a robot. All
Speaker 2: it doesn't look like a Hollywood robot, you know, it
Speaker 2: is a robot.
Speaker 1: So, like, let's just pick an example. Let's say we're
Speaker 1: building a robot to put it into a nursing home. Yes,
Speaker 1: there's a lot of older people who need to take
Speaker 1: their tablets at a certain time, and maybe it knows
Speaker 1: to sing a song to the audience who is in
Speaker 1: the nursing home because maybe they've got Alzheimer's or something
Speaker 1: that it might be just be nice that robot is
Speaker 1: also learning about these people. Do you see dangers that
Speaker 1: it might stuff up the medication? It might give you
Speaker 1: my medication? Me might you know me your medicaid? Are
Speaker 1: the dangers that there are dangers? But I'm actually much
Speaker 1: more concerned about humans stuffing up. If you look at
Speaker 1: the number of people who get misprescribed in hospitals and
Speaker 1: in pharmacies, it's you. Yeah, people you know, get ten
Speaker 1: times the dose by mistake because they just read, you know,
Speaker 1: a thousand times of dose because the risk means grams
Speaker 1: for micrograms and so on. So humans are terrible making
Speaker 1: that and indeed getting AI to dispense is potentially going
Speaker 1: to reduce those errors. But equally, you know, I don't
Speaker 1: want to be in an old person's home with robots.
Speaker 1: I want to be an old person at home with
Speaker 1: attractive nurses look after me. I can float what a
Speaker 1: better an attractive robot nurse?
Speaker 2: But I do want the nurse, you know, I do
Speaker 2: want the nurse to have maybe a robot so that
Speaker 2: you know, lifting a person out of the bath or
Speaker 2: you know, onto the bed instead of them putting their
Speaker 2: back out doing that. The robot can do those sorts
Speaker 2: of things. But I want to have the empathy, the
Speaker 2: companionship of a person who's going to sit down and say,
Speaker 2: you're looking a bit glum today, so they have a
Speaker 2: cup of tea with me. Let's tell me what's troubling you.
Speaker 1: But maybe the robody in the nursing home could go
Speaker 1: along to the actual nurse of physicalness like a biological person,
Speaker 1: and say to their biologicalness, look, I think you've got
Speaker 1: to stop right now. Just Toby's the one who going
Speaker 1: to concentrate on today, because Toby's looking a little bit glumb,
Speaker 1: you know, because we saw through our sensing that his
Speaker 1: face is a bit down or whatever the case may be.
Speaker 1: How the sensing works based on like previous knowledge of
Speaker 1: your face features and can go and tell the noess
Speaker 1: That makes sense. But maybe I would just change the
Speaker 1: change the narrative. What about it in the military robots
Speaker 1: in the military environment, So in other words, don't send
Speaker 1: any soldiers out to do the fighting. Send robots out.
Speaker 2: That sounds like an attractive issier. Right, let's get humans
Speaker 2: out of the battlefit. That's what would you know, what
Speaker 2: would not be a goode or drones sort of do
Speaker 2: that now they do, and unfortunately they're turning warfare into
Speaker 2: more deadly dangerous activity. And the other challenge challenge with
Speaker 2: that that simple scenario is that there isn't a separate
Speaker 2: part of the world called the battlefield wars unfortunately, a
Speaker 2: fort in amongst towns and cities in and amongst and
Speaker 2: against civilian populations. And so yes, we could DEVELOPE and
Speaker 2: we are developing you know, AIS and robots that could
Speaker 2: go and fight for us.
Speaker 1: We are, are we well, yes, like those.
Speaker 2: Of the US Department of Defense has always been the
Speaker 2: greatest funder of AI research really, yes, ever since the
Speaker 2: nineteen fifties, because they could see.
Speaker 1: In a military sense, or it is generally in a
Speaker 1: military sense, right, Yes, all aspects of how we go
Speaker 1: about warfare. I mean, I don't know, I've got no idea,
Speaker 1: but I can imagine China would be doing it.
Speaker 2: China's doing it as well, Russia's doing it. Yes, there's
Speaker 2: a there is an ai arms race.
Speaker 1: Literally, there's an arms race at that level.
Speaker 2: Yes, and looking at what's happening in Ukraine is intensifying
Speaker 2: that because the militaries are seeing the nature of warfare is.
Speaker 1: Danger but it's also politically dangerous because because you lose
Speaker 1: your people, and that's political danger for example happening with Putin.
Speaker 1: The more young boys who die on the front, the
Speaker 1: more parents are going to be disaffected or dissatisfied, and
Speaker 1: the greater the chance or the risk of him losing
Speaker 1: his grip on the country. Yes, so if I can
Speaker 1: send out there, I'm going to send robots said there.
Speaker 1: Everyone's going to wow, that's cool.
Speaker 2: It is, but it's making warfare much more deadly.
Speaker 1: Activity or particularly for the weak aside.
Speaker 2: I mean, that's a challenge. If you look at what's
Speaker 2: happened in the Ukraine, You've got Russia nation ten times
Speaker 2: the size, ten times the economy of Ukraine, but it's
Speaker 2: being hells at bay by the Ukrainians who are embracing
Speaker 2: these technologies. In that sense, you know it's being used
Speaker 2: for a good purpose but being able to hold back
Speaker 2: the Russian oppressor. But equally you see that you know
Speaker 2: it's going to change the balance of power stability of
Speaker 2: the world completely. Turkey, Iran A becoming major drone powers.
Speaker 2: They're going to be able to compete against much bigger nations.
Speaker 2: I mean, it used to be your powers determine your
Speaker 2: military powers determined by your economic might. Yeah, could you
Speaker 2: buy yeh thirty five fighters, could you buy the aircraft
Speaker 2: carriers and so on things? And you know it was
Speaker 2: the US and Russia and now China. But now increasingly
Speaker 2: your military might is with these cheap drones, and it's
Speaker 2: small nations can get the whold hand on these equipments,
Speaker 2: and it's no longer the case that the balance of
Speaker 2: power is the same as it was.
Speaker 1: And where does AI fee into that for example? Like
Speaker 1: drones for example.
Speaker 2: Well, the problem is that the weak thing with the
Speaker 2: drone is the radio link and indeed it's you know,
Speaker 2: electronic warfare will stop most of the drones these days
Speaker 2: in the Ukraine. So most of the drones now are
Speaker 2: flown with fiber optics, which you can't be interrupted by
Speaker 2: by radio jamming or increasing me. They're autonomous, they're making
Speaker 2: their own decisions because then you've got a much more
Speaker 2: pow weapon. You can't stop it by jamming the radio.
Speaker 2: You can't stop it by killing the pilots.
Speaker 1: You know, the beausori pre trained.
Speaker 2: Yes, it's trained to identify the targets and to go
Speaker 2: and destroy those targets automatically with no human intervention.
Speaker 1: So there's not there is. I think what you're saying
Speaker 1: is I think what you're saying is that the drone
Speaker 1: is preloaded with let's call it intelligence, but preloaded with
Speaker 1: a plan, and there's nobody sort of sitting at the
Speaker 1: other end, sort of like like we normously droned people
Speaker 1: with a like a bat or a computer sort of
Speaker 1: directing the traffic. Because of the jamming processes, it's probably ineffective.
Speaker 1: So these things are preloaded. And they just said there
Speaker 1: might be a when you identify when when you see
Speaker 1: when the drone sees that building kamikaze suicide into it.
Speaker 2: Or when it sees something looks like a Russian tanka,
Speaker 2: or when it sees something that looks like a Russian
Speaker 2: soldier it.
Speaker 1: Looks like to me, then that seems to be then
Speaker 1: open for a lot of error. Yes, later, I mean
Speaker 1: because it might not. It might see the target, but
Speaker 1: there might be a family sitting there into having a
Speaker 1: cup of coffee, indeed.
Speaker 2: And that's that's one of the challenges, which is, you know,
Speaker 2: we fight war according to certain laws, international managed Toian law,
Speaker 2: and there's you know, we call people to account for
Speaker 2: their actions. You can't just go you know, you're only
Speaker 2: supposed to target military they targets. You're not supposed to
Speaker 2: target civilians. You know, the target has to be proportional
Speaker 2: to the threat that you face, and so on those
Speaker 2: those we hold humans to conventions and when they violate
Speaker 2: those those we you know, there's a cause of the
Speaker 2: Hague and various other places that we call people people
Speaker 2: to account. But we can only call hold people to account.
Speaker 2: Our legal system only holds people to account.
Speaker 1: We can't hold a drone account.
Speaker 2: What could we do with the drone?
Speaker 1: Nothing?
Speaker 2: So that's you know, that's another big question, which is,
Speaker 2: you know where mistakes happened when trosses, who are we
Speaker 2: going to hold account?
Speaker 1: Do they various platforms around the world. Are they interfacing
Speaker 1: with military robots which use AI or which will use
Speaker 1: AI in the future. I mean they got some sort
Speaker 1: of arrangement. There was a this pick open AI for example.
Speaker 1: Are they do they do deals with the West government?
Speaker 1: To actually help the Uist defense.
Speaker 2: Yes, yes, well today the CEO of Anthropic, the company
Speaker 2: that makes Claude, has been summoned to give meet Hesketh
Speaker 2: the Pentagon.
Speaker 1: Too is the Secretary of War.
Speaker 2: The Secondary of War, to talk about their relationship and
Speaker 2: about apparently the latest military targeting is now being using
Speaker 2: tools like Anthropic to help them make their decisions. And
Speaker 2: we see this also in Israel. We see that that,
Speaker 2: you know, lots of the idea of the Israeli defense
Speaker 2: forces targeting decisions in the Gaza conflict were not human decisions.
Speaker 2: There were decisions that the that the algorithm AI algorithms,
Speaker 2: algorithms that are called lavender, Where's Daddy, they get some
Speaker 2: funny names, but it's not humans are deciding. You know,
Speaker 2: we think that there are some some hesbula leadership in
Speaker 2: this tower block. We're going to send some artillery in
Speaker 2: against this tower block. That's an AI that's taking in
Speaker 2: all the intelligence information, all the information they've collected on
Speaker 2: social media over many years and calculating where they think
Speaker 2: maybe are potential targets.
Speaker 1: As a mathematician who were a methodician, we are are
Speaker 1: we talking about probability based decisions in we are and
Speaker 1: which is dangerous?
Speaker 2: Which is dangerous because never one hundred percent which and
Speaker 2: there's it's not clear that we really understand what those
Speaker 2: decisions are. They're talking, they're taking vast amount information and
Speaker 2: synthesizing it together, so it's not clear on what basis
Speaker 2: decision is being made and who's going to be accountable.
Speaker 2: And it's wrong. And certainly when you look at what
Speaker 2: was happening in Ghaza, the amounts of collateral damage, would
Speaker 2: you make whole place squenzes or hundreds of civilians that
Speaker 2: were being killed alongside, you know, supposed military targets, you
Speaker 2: were thinking, this is not proportional to the threat of response.
Speaker 1: Do you think that? Do you think that right now
Speaker 1: we're experiencing in Gaza and also probably in Ukraine? Are
Speaker 1: we and are we experiencing the worst of what I
Speaker 1: has got to offer right now? And we're learning lessons
Speaker 1: from it? And it is somebody taking it up? Is
Speaker 1: someone doing something about this? Well they're just combinants with Well.
Speaker 2: Next week I'm off to speak at the United Nations
Speaker 2: in Geneva about these very topics that you know, I
Speaker 2: don't think we're I don't think we're mindful enough of
Speaker 2: the world that we're opening. We get to choose what
Speaker 2: technologies get used where. I mean we sometimes forget that
Speaker 2: we think we have agency some things that you know,
Speaker 2: we we decide, we make conscious decisions, remember, you know,
Speaker 2: we we we made you know, I grew up in
Speaker 2: the UK and they made a conscious decision that there
Speaker 2: weren't going to be adverts on the main channel of TV,
Speaker 2: which is like a fantastic decision.
Speaker 1: It's a BBC.
Speaker 2: The BBC doesn't have adverts still today.
Speaker 1: The same as ABC here doesn't have.
Speaker 2: Adverts Like that was a fantastic decision. And you see
Speaker 2: what happens when you put adverts on TV, which becomes
Speaker 2: you know, driven by the need to satisfy the advertisers.
Speaker 2: And as you know, the ABC the BBC are fantastic
Speaker 2: institutions for public good, for informing the public without the
Speaker 2: pressures and the biases that having to please advertisers brings.
Speaker 2: That was a conscious decision. You know, you can put
Speaker 2: adverts on TV in many countries, that's the only thing
Speaker 2: you get. Similarly with you know, AI, we can choose
Speaker 2: how it's going to be used and where it's going
Speaker 2: to be used. I mean we we forget there.
Speaker 1: Are there are we they're responsible people were the planet.
Speaker 1: But do you think, but what if someone in China
Speaker 1: doesn't agree and they just go and do something completely
Speaker 1: different to what you where you decided, for example, they're
Speaker 1: not nations.
Speaker 2: Yeah, I mean these are accessible technologies, right, so that
Speaker 2: there's nothing that can stop people who really seriously wanted
Speaker 2: to do that. But there's a whole raft of technologies,
Speaker 2: you know, chemical weapons, biological weapons, cluster dimmunitions, blinding lasers.
Speaker 2: There's actually quite a bit of stuff that we've we've
Speaker 2: actually got UN conventions on and we don't use even
Speaker 2: though it would. You know, we can't stop people using
Speaker 2: chemical weapons. Yes, said for example, Star uses them, Fusions
Speaker 2: probably used them. You know, they occasionally get used. But
Speaker 2: when they do, the world is outraged. There's resolutions on
Speaker 2: the floor of the u N there's headlines in the
Speaker 2: New York Times.
Speaker 1: But do you think it means any.
Speaker 2: I think the world will be a much darker, dangerous
Speaker 2: place if we didn't, if we morally hadn't decided that
Speaker 2: that's not the way that we should need to fight war.
Speaker 2: What troubles me is that, you know, I think we'll
Speaker 2: get there with the darker uses of AI in a
Speaker 2: military setting. What worries me is that most of those
Speaker 2: other examples, like chemical weapons, it was only after we
Speaker 2: saw the horrors, for example, the horrors of the First
Speaker 2: World War, you know what the First World World poets
Speaker 2: and people told us about. When we saw that happening,
Speaker 2: did we have the courage to decide, actually, we should,
Speaker 2: you know, try together collectively globally, we should do something
Speaker 2: about this. And we've done something about this and limited
Speaker 2: the use to which chemical weapons we go. What I
Speaker 2: worry that we're going to have to see atrocities of
Speaker 2: AI being used on the battlefield and so on to
Speaker 2: murder civilians before we have the courage to actually take action.
Speaker 2: I say to people, it's easy to imagine what that
Speaker 2: world will be if we don't do any thing. It
Speaker 2: will look like one of those bad Hollywood movies. Will
Speaker 2: it look like what happened Hiroshima?
Speaker 1: Then they dropped the atomic bondah and you had those
Speaker 1: that are the photo that's in my mind or the
Speaker 1: images in my mind, is that little girl running down
Speaker 1: the street naked with burns or over a body like
Speaker 1: her arms outstretched.
Speaker 2: But these will be used to target women and children
Speaker 2: and civilians and innocent people.
Speaker 1: You're going to speaking of the United Nations and you
Speaker 1: said next week, yes, So what are you going to tell.
Speaker 2: Them, I mean, say, we can have to make these choices,
Speaker 2: that it's wrong to think that, you know, the US
Speaker 2: has been pushing back against this. The China has been
Speaker 2: pushing against this because because I think they wrongly feel
Speaker 2: that they've they've got some strategic advantage. They've got, you know,
Speaker 2: a technical advantage over the rest of the world. But
Speaker 2: to think that they're going to keep that is misguided.
Speaker 1: Do you think they're just pushing underground then middle of
Speaker 1: the Manhattan Project and other projects. I mean, I had
Speaker 1: I had someone's in the chair recently talking about how
Speaker 1: this is the theory, how most of the major countries
Speaker 1: around the world have had experiences, have actually UFO information,
Speaker 1: but they won't reveal it to anybody because they're keeping
Speaker 1: it for their own advantage. They're trying to examine the
Speaker 1: materials and everything around it as to how they can
Speaker 1: get some military advantage out of it, which is why
Speaker 1: they're not sharing it globally.
Speaker 2: You know. And it's a nice conspiracy theory, but it's
Speaker 2: a good one. But I wonder how you can keep
Speaker 2: so many people quiet for so long.
Speaker 1: Yeah, well they're out a lot of them are out
Speaker 1: there now talking about it. But do you think that
Speaker 1: and I'm not suggesting I agree with that, but do
Speaker 1: you think that all we're going to do is pushed underground?
Speaker 2: Well, these technologies are going to exist, you know, whether
Speaker 2: they exist already? Yes, And that's the fundamental challenge we
Speaker 2: have with a technology like arstic intelligence. It's completely due use.
Speaker 2: So for example, you know, take your AI drone that's
Speaker 2: going to autonom target people. It needs to be able
Speaker 2: to track people. Will you know, it needs to be
Speaker 2: able to track soldiers on the ground so it can
Speaker 2: target them. Well, the same algorithms again into our autonomous
Speaker 2: cars to tract pedestrians so it can avoid them. We
Speaker 2: want autonomous cars. A thousand people will die next year
Speaker 2: in Australia in road traffic accidents, mostly caused by human error.
Speaker 2: Once we have autonomous cars, we won't have those deaths.
Speaker 2: It will go to close to zero. So that's going
Speaker 2: to be a fantastic you know, bonus to our lives.
Speaker 2: You know, if you survive birth, the most likely cause
Speaker 2: of death to you're in your forties is car accident.
Speaker 2: I mean, it's terrible, and most of us sadly have
Speaker 2: you know, in our families or in our close friends
Speaker 2: know someone's life which was touched by one of those
Speaker 2: car accidents. And we're going to look back in twenty
Speaker 2: or thirty years time and say, you know, it's funny
Speaker 2: that we don't have any of that anymore.
Speaker 1: Well, it's funny. When I was four and a half
Speaker 1: years of age, I first started school on four and
Speaker 1: a half and I got off the bus at the
Speaker 1: bus stop where I live and because the bus was
Speaker 1: higher than it wasn't only little, but are you running
Speaker 1: for it a half yeah, and he didn't see me,
Speaker 1: and he will hit me, ran over me the bus
Speaker 1: a bus yeah, oh my god. Yeah, and lucky to
Speaker 1: have you a land him in hospital for quite a
Speaker 1: long time. But point being is probably in the future
Speaker 1: buses will have senses all around them which are probably
Speaker 1: you know, even if they're not an autonomous if still
Speaker 1: have sensors around it which would automatically stop the bus
Speaker 1: from proceeding because a little sense that there is a
Speaker 1: little person or there's something a little in front of it,
Speaker 1: something in front of it.
Speaker 2: As an example of that. You know, one of the
Speaker 2: newest terrorist weapons is to go rent a truck or
Speaker 2: a bus and drive us into a crowd. Yeah, that
Speaker 2: won't be possible in five or ten years time because
Speaker 2: every truck will have cameras on the front and if
Speaker 2: there's a pedestrian in front of it, it will break automatically.
Speaker 2: You won't be able to drive it.
Speaker 1: And that's our AI, that's working.
Speaker 2: People to be able to recognize the roads and to
Speaker 2: see the pedestrians and then to make the decision to stop,
Speaker 2: so that there's a great positive terrorist won't be able
Speaker 2: to do that in the future. They can hire as
Speaker 2: many trucks as they like, they can try and drive
Speaker 2: them to crowds, but they were to stop.
Speaker 1: And also just someone like a young kid not knowing
Speaker 1: any better, they won't what might get run over?
Speaker 2: Run over, young kid.
Speaker 1: I remember for six weeks, I remember it. Remember I
Speaker 1: don't remember being I still remember being a hospital.
Speaker 2: I don't remember that many more entrepreneurs that get to
Speaker 2: grow up become success.
Speaker 1: Maybe there's going to be too many. But just on
Speaker 1: this is you're probably well aware of the big argument
Speaker 1: in Australia right now that Australia in the developed nations
Speaker 1: is probably not probably is has i should say, the
Speaker 1: lowest productivity growth of any nation in the world developed
Speaker 1: nations right now, What do you think AI is going
Speaker 1: to do to help productivity in a place like Australia, Well, if.
Speaker 2: You believe the economists, all the economists come out with numbers,
Speaker 2: which is it can lift our productivity by fifteen twenty.
Speaker 1: Percent, which is significant.
Speaker 2: Yes, and I see a few other things coming along
Speaker 2: that can offer fifteen twenty percent lifts of our productivity.
Speaker 1: That's good.
Speaker 2: Yeah, that's good.
Speaker 1: That's a good thing.
Speaker 2: That's a good thing.
Speaker 1: Should we embrace it? Because the flip side of that is,
Speaker 1: you know, no I read in or hear about this often,
Speaker 1: is that there's going to be some stage where no
Speaker 1: one actually goes to work anymore. No one, no one
Speaker 1: actually works anymore, No, no, no one, but lead the
Speaker 1: majority of people, their jobs are going to become redundant.
Speaker 1: But the same productivity, the same man A taxs paid,
Speaker 1: which means the govern's got the money and we just
Speaker 1: pay you to stay home.
Speaker 2: Yep.
Speaker 1: Well, I think.
Speaker 2: People forget this has already happened, right, This happened with
Speaker 2: the Industrial Revolution, before the people forget the weekend was
Speaker 2: an invention of the dust Revolution. Before the indust revolution,
Speaker 2: you work seminary week some went up, some went down.
Speaker 2: You were working all that time. And then because of
Speaker 2: the productivity benefits of you know, the automation machinery that
Speaker 2: we brought in in the Dust Revolution, we got we
Speaker 2: got to say, you know, we want to share some
Speaker 2: of that. We want Sunday off to go to church,
Speaker 2: then Saturday afternoon off, then all of Saturday. And then
Speaker 2: I don't know why we stopped asking why did we
Speaker 2: stop asking for more? I mean, what was There's nothing
Speaker 2: magic about two days off but every seven. There's nothing
Speaker 2: about you know, the earth going around the sun that
Speaker 2: says it's only two days off seven. It's a human construction,
Speaker 2: which was we could have carried on asking for more.
Speaker 2: And you know, interestingly, there are experiments on a four
Speaker 2: day week in New Zealand and Europe. Hundreds of thousand
Speaker 2: people are doing these experiments now and the experiments all
Speaker 2: have two conclusions. One is that people do as much
Speaker 2: work in four days as they used to do in five.
Speaker 2: You you know, stop having all the bullshit meetings. You
Speaker 2: get down to it and focus on what you need
Speaker 2: to do so you can pay them as much because
Speaker 2: they're doing just as much. Right, so business is just
Speaker 2: as productive as they were when you were working five
Speaker 2: And second, people are happy. Who would have imagined.
Speaker 1: Well that I from wondering about though the happier I
Speaker 1: guess abby people said they're going to work. They're still
Speaker 1: enjoying their colleagues Monday to Thursday, for example, find I
Speaker 1: said it's something that they have an opportunity to enjoy
Speaker 1: their family. They're still getting paid the same amount of
Speaker 1: money because the company should be making the same amount
Speaker 1: of money, so the company can afford to pay them
Speaker 1: the same amount of money. That will be people who
Speaker 1: will be seduced into paying you less, but it doesn't
Speaker 1: matter trying to pay you less. Do you see that
Speaker 1: in the is this some? Is this a consequence of AO?
Speaker 1: Do you see this coming out in the future that
Speaker 1: will we definitely will be working less days, because isn't
Speaker 1: that one way fixing up the fact that some people
Speaker 1: will have no job? Yes, so instead of saying you
Speaker 1: don't work any days, I'll work one less day and
Speaker 1: you can you will work? Yeah, yeah, whatever, some along
Speaker 1: those lines. You know what I mean, like, you know,
Speaker 1: because there might be for every one person loses their job,
Speaker 1: there might be five people keep the job. So five people,
Speaker 1: I'll do it. If six people keep the job, five
Speaker 1: people keep the job, we should say so fire people
Speaker 1: going to all say we'll work one day less. You
Speaker 1: the one person have lost your job, you can now
Speaker 1: work five days or one of those lines, you know, mathematically.
Speaker 1: So that is that something people are talking about now.
Speaker 2: I think we should be having these conversations. I mean,
Speaker 2: I think it's an example of how we went through
Speaker 2: some big structural changes through the Industrial Revolution. We introduced,
Speaker 2: you know, the weekend unions, labor laws to protect the
Speaker 2: rights of workers, to spread the benefits around, universal education
Speaker 2: so we're educated for those new jobs, Universal welfare so
Speaker 2: that when we were unemployed we weren't in the poorhouse,
Speaker 2: Universal pensions so that we could retire at the end
Speaker 2: of our working lives. We did a lot to you know,
Speaker 2: spread the benefits around and make sure it was good.
Speaker 1: So the structure sort of exists.
Speaker 2: Well, now we're going through another big change, and I
Speaker 2: think we should be thinking equally radically about this. And
Speaker 2: you know, there are deep fundamental questions here, you know,
Speaker 2: many people derive their value through what they do.
Speaker 1: They identify with that, and then we.
Speaker 2: Meet someone, you say, what do you do? What work
Speaker 2: do you do? Right? Device, Well, if we're in a
Speaker 2: world where work is less important, now, how are we
Speaker 2: going to define our meaning? How? What does our purpose?
Speaker 1: So therefore a consequence of AI and a consequence of
Speaker 1: more efficiency and better productivity as a result of AI,
Speaker 1: which is one of the things I should do. Maybe
Speaker 1: government or somebody has now gone an obligation to actually
Speaker 1: help us redefine what we do. So in other words,
Speaker 1: I've only worked three days a week. That's the time
Speaker 1: I was working five days a week. That was my life.
Speaker 1: Now I'm working less than one half of my ev seven.
Speaker 1: By the way, seven days is fiction as well, But
Speaker 1: don't worry. We just assume seven days a week is correct.
Speaker 1: But four of those seven I'm not working. So all
Speaker 1: of a sudden, I'm at home. I'm not doing anything
Speaker 1: I don't think to do. It's government's got to start
Speaker 1: to re educate us to or to encourage us to,
Speaker 1: because we don't really sitting at home doing nothing. Maybe
Speaker 1: we do, but most of we'll get jacked that pretty quick.
Speaker 2: Yes, you're right. I mean I always say to remind people,
Speaker 2: you know, the purpose of education. When we educate people,
Speaker 2: it's not just to prepare them for work. It's to
Speaker 2: prepare them to take advantage of their lives, to be
Speaker 2: good citizens, to be able to do stuff and you know,
Speaker 2: make the world a better play.
Speaker 1: Do you think we're how to enjoy ourselves though, Like
Speaker 1: if we're only going to work three days a week,
Speaker 1: because we identify with ourselves working five days a week.
Speaker 1: And as you say, this is going to happen quickly,
Speaker 1: so quicker than we think. We've got to learn relearn
Speaker 1: how to fill in those extra couple of days a week.
Speaker 2: We are, but I mean, that's the great strength of humans, right,
Speaker 2: we're so quick to there we go back, cast your
Speaker 2: mind back to COVID, and we immediately learned the value
Speaker 2: of being able to come together a bit, being able
Speaker 2: to go out and you know, have a walk in
Speaker 2: the park and so on, those things that we remember.
Speaker 2: Reminded so quickly about the value of those things, and
Speaker 2: we appreciated those things so much more.
Speaker 1: Of course, a lot of people during the COVID period
Speaker 1: became addicted to social Yeah. Do you think is there.
Speaker 2: A very seductive, very addictive.
Speaker 1: Is there a potential problem because that that is AI
Speaker 1: working You either be time why.
Speaker 2: It's going to be super charged by it to be
Speaker 2: because the content is going to be our generated content,
Speaker 2: that it's going to be personalized for you. It's the
Speaker 2: AI algorithm's course that are finding your sweet spots and things.
Speaker 1: And keep seeing you the same stuff and then testing
Speaker 1: where you want to go from there? Do you think
Speaker 1: that therefore, which is why we should.
Speaker 2: Be concerned about the power of the people in Silicon
Speaker 2: Valley who are making decisions to yeah, let's put the
Speaker 2: beauty filters back in yeah, despite the fact that they
Speaker 2: know that we're not going to be able to resist
Speaker 2: using them, that we're all going to be Oh, I
Speaker 2: can't compare myself to these other people have got the
Speaker 2: beauty filters. Are I have to use the beauty filters myself?
Speaker 1: Because for me, the way I see myself advancing in
Speaker 1: life is to learn more things. But to learn things
Speaker 1: they've got nothing to do with what I already know.
Speaker 1: But the whole of the time for that well, at
Speaker 1: the essence of AIS, it doesn't work that way. It's
Speaker 1: going to say no, I'm going to keep giving you
Speaker 1: what I know you like, especially if it's on a
Speaker 1: social environment. Do you think the conversation needs to be
Speaker 1: had as to how do I encourage Mark when he's
Speaker 1: only working three days a week he said a four days,
Speaker 1: seven days, five days a week. How do I encourage
Speaker 1: Mark this conversation to or how to I encourage Mark
Speaker 1: Zuckerberg or those people control these environments to start to
Speaker 1: feed Mark something different, to encourage him to start to
Speaker 1: think more broadly or laterally about other things. What do
Speaker 1: you think about that conversation?
Speaker 2: As I've seen the reason why we couldn't think of
Speaker 2: that algorithmically, how we couldn't actually say to the algorithms, no,
Speaker 2: actually we don't want more of the same. We want
Speaker 2: If you play my favorite song to me too many times,
Speaker 2: it's going to stop being my favorite song. I'm going
Speaker 2: to get tired of it. So play me other things.
Speaker 2: You know, I remember, you know, I used to go
Speaker 2: to the music shop and browse through the music and
Speaker 2: find things by chance serendipity. It's fantastic for finding musicians
Speaker 2: that I would never have otherwise found, just because they
Speaker 2: happen to be next to where I wanted on the shelf.
Speaker 2: So you know, maybe we howgroythmically can encourage serendipity. But
Speaker 2: the problem here is the perverse incentives that the incentives
Speaker 2: that the media companies the tech companies have to you know,
Speaker 2: keep you there in the short term to keep you
Speaker 2: scrolling infinite scrolling through what their content for the now,
Speaker 2: because you know, obviously they're sending your time, they're selling
Speaker 2: your adverts and everything in and now, which is good
Speaker 2: for their revenues in the short term, but bad for
Speaker 2: you and indeed probably bad for them actually in the
Speaker 2: longer term, which is that you know, maybe they should
Speaker 2: just be saying, you know, chatter gbt'd ever says, you know, tybe,
Speaker 2: you've been asking me questions for three hours. It's three
Speaker 2: o'clock in the morning. Time to go to bed. You know,
Speaker 2: it always ends with an open question because they're trying
Speaker 2: to sell you more token.
Speaker 1: Or three more questions. Yes, yeah, it's funny. You know.
Speaker 1: I remember in two thousand and one, excuse me. In
Speaker 1: two thousand and I got asked by the then Minister
Speaker 1: for Finance Jaie Hockey to share a task force as
Speaker 1: part of Australia's OCD obligations to build a code of
Speaker 1: Practice for consumer protection for Internet vendors. So we're twenty
Speaker 1: two thous and one. It's simple things like spam you
Speaker 1: should you you know, what's the relationship you got to
Speaker 1: have opped in opted out? Just a code of practice,
Speaker 1: not legislation, but a code of practice. Simple things like
Speaker 1: if you know, if you go to a vendors website
Speaker 1: and if you want to buy pajamas for your kid,
Speaker 1: there should be the code of practice has said that
Speaker 1: you should disclose if the pajamas a fly noble or
Speaker 1: returns by something doesn't hit. What's my returns policies? You
Speaker 1: have to have it laid out. This is in two thousand, Well,
Speaker 1: it's not that long ago. It's not very long ago.
Speaker 1: Australia was the first country, so we produced a paper
Speaker 1: in a document that was the first country to first
Speaker 1: over CD country that actually put out this code of practice,
Speaker 1: and we're all quite proud of ourselves. But of course
Speaker 1: a lot of that stuff's now embedded in legislation. Do
Speaker 1: you think that the world is now at a point
Speaker 1: where we either need a code of practice or or
Speaker 1: legislation because then it was directed at millions and millions
Speaker 1: of indors. Do you think we will have the same
Speaker 1: equivalent for the twelve or fifteen powerful people who run
Speaker 1: all these AI platforms to say, dudes, this is a
Speaker 1: code of practice, or even stronger, this is the legislation,
Speaker 1: because code practice always ends up getting code of fi
Speaker 1: into law, which is what happened. Do you think this
Speaker 1: sort of thing should be done? And is there any
Speaker 1: inquiries into this stuff going on at the moment anywhere
Speaker 1: in the world.
Speaker 2: Yeah, I mean, I think policy personal politicians are working
Speaker 2: up to that they could do something. There was certainly,
Speaker 2: I think a feeling of impotence that, you know, how
Speaker 2: can we push back against these powerful companies. They're wealthier
Speaker 2: than nations, they operate outs outside our national borders and
Speaker 2: pay tax. They're pay tax. You know, the rules don't
Speaker 2: seem to apply to them.
Speaker 1: Yeah, well they're not people.
Speaker 2: Why they're not people? So you know, we're just going
Speaker 2: to have to accept it. And I think now there's
Speaker 2: a growing realization and a huge pressure from the public
Speaker 2: as well to do something. And we saw the success
Speaker 2: of the social media you know, age delay laws demonstrating
Speaker 2: that we can we can protect young people from this,
Speaker 2: and there's being in the press other legislation around, you know,
Speaker 2: making it criminal to distribute new defied pictures of people,
Speaker 2: because that was becoming a problem in our schools.
Speaker 1: As in fake fake images of faking a friend your
Speaker 1: friend and then spread around the whole school.
Speaker 2: So yeah, I think there's we've demonstrated we can do things,
Speaker 2: and there's a real appetite now to do things. I
Speaker 2: really do feel that with the new things that we're
Speaker 2: going to be able to do with AI that werey're
Speaker 2: going to be new harms. We talked about how people
Speaker 2: are having relationships with AIS, how people are using AI
Speaker 2: the therapists, people are committing suicides, and the chat pot
Speaker 2: is encouraging that behavior. Like, I think there's we've also
Speaker 2: seen that the tech companies themselves, Oh you know, there's
Speaker 2: perverse incentives been played for them not to do the
Speaker 2: right thing, not to worry necessary so much about our safety.
Speaker 1: Do you think we might end up having an AI policeman?
Speaker 1: So you know, like I'm on one of the platforms
Speaker 1: and I'm doing what I'm doing, but somehow I allow
Speaker 1: or permit an II policeman to always enter into my
Speaker 1: world and say, Mark, you're going too far. You need
Speaker 1: to go to sleep outside of outside of, for example,
Speaker 1: whatever I'm using. So it's actually an artificial intelligence policeman
Speaker 1: police person that actually not policing but effectively monitors what
Speaker 1: I'm doing. But across the board on everything I would I.
Speaker 2: Wouldn't call it a policeman. I call it a guardian.
Speaker 1: Okay, that's a better word.
Speaker 2: Yeah, AI guardian. And I'm pretty convinced on your phone
Speaker 2: you're going to have an AI guardian that sits there
Speaker 2: watching what's going on and says, Mark, be careful. This
Speaker 2: looks like it's a scam advert lock. This company's trying
Speaker 2: to steal some data out of you here, and.
Speaker 1: Well, Mark, you might be spent too much time. Yeah,
Speaker 1: and also just tells me howmuch, says hey, like actually
Speaker 1: prompts me with a notification or a nudge, because the
Speaker 1: nudging is still very powerful. Yes, I mean it, which,
Speaker 1: by the way, is what most of the platform is
Speaker 1: doing to you all the time. But it's so subtle
Speaker 1: you don't even feel the nudge. But something is a
Speaker 1: little less subtle, and.
Speaker 2: The nudges that they're giving are noticary for your benefit.
Speaker 1: No, no, no, they're pushing to get.
Speaker 2: You to spend more time on the platform. Or to
Speaker 2: buy more stuff totally. Whereas you know, we want to
Speaker 2: be nudged in ways to help for us to say, like, okay,
Speaker 2: you know, make you a happier, more content rounded person.
Speaker 1: You know, it could be nearly an education program for
Speaker 1: you too, that could sort of saying, well, the reason
Speaker 1: you need this or the reason this might be a
Speaker 1: good idea this guardian ship for adults as well as kids,
Speaker 1: is because we want to make you a happy person.
Speaker 1: We don't want you to become sort of living in
Speaker 1: this weird world where it's not real because the definition
Speaker 1: real will become brought into question. We won't know, But
Speaker 1: I think that's look for me, I reckon, that's a
Speaker 1: great idea. I would actually I would actually allow that
Speaker 1: app to live in my world. But not not not
Speaker 1: not to wrap me out of the knuckles. But maybe
Speaker 1: if it's you know, maybe you can you can enable
Speaker 1: a lot of empowering. You say, listen. If I don't
Speaker 1: listen to you, and I continue to straighten no matter
Speaker 1: what you tell me, please advise my partner, or please
Speaker 1: advise my oldest son or daughter, or my mom or dad,
Speaker 1: or whatever the case may be, you opt into that
Speaker 1: it's not automatic you can opt in. In other words,
Speaker 1: you're still in control. Is that it? But is that
Speaker 1: being done anywhere? Is anyone doing anything like that? Sounds
Speaker 1: like a fantastic I was just saying, we were just thinking.
Speaker 1: I think we might have just invented something. Yes, just
Speaker 1: talk about your book. So I have the These books
Speaker 1: are both the same. But this book's called the Shortest
Speaker 1: History of AI but it's obviously by you. And this
Speaker 1: is the English version of this strain version. I like
Speaker 1: this one because you signed this one. What and you've
Speaker 1: done a number of books. You've done produced number of books,
Speaker 1: so as well as you know you speak at lots
Speaker 1: of events. You know it's particularly you know you're not
Speaker 1: a nations, which is pretty cool. But what is the shortest?
Speaker 2: Was never in my bucket list. I never expected, never wanted.
Speaker 2: But you know, if you know, you see the harms,
Speaker 2: you think, well, if I could do something about it
Speaker 2: to I will you know. I know what my daughter's
Speaker 2: term me when she's grown up and say, Dad, you
Speaker 2: had a chance to do something.
Speaker 1: Yeah, why didn't you?
Speaker 2: Why didn't you?
Speaker 1: She's your guardian. So this says six the six essential
Speaker 1: ideas that animated obviously that's AI. What do you mean
Speaker 1: by that? The six essential ideas.
Speaker 2: So it's a short history of AI six chapters. Each
Speaker 2: chapter is is one idea that some of the magic
Speaker 2: behind art of intelligence. So one of the chapters is
Speaker 2: about neural networks and chat gipt and how how they work.
Speaker 2: Tries to explain and explains the history, where did they
Speaker 2: come from? Where did that how did that technology come?
Speaker 2: And some of the colorful characters.
Speaker 1: Because there's a lot of let's call it jargon in
Speaker 1: AI world, neural networks, large language models, and all these
Speaker 1: sorts of things, and it's nearly like it's to me,
Speaker 1: it looks like it's designed to the average person to say, look,
Speaker 1: just accept it's too complicated for you. Just go along
Speaker 1: with it. That this nearly it is nearly a new language.
Speaker 2: It is. But let's forget we're trying to make machines
Speaker 2: that are intelligent, and you know intelligence is you know,
Speaker 2: our brain is the most complex thing in the universe. Yep,
Speaker 2: by far. Yeah, nothing approaches the complexity, so we're trying
Speaker 2: to match that. So it's not surprising that it gets
Speaker 2: a bit technical and it does seem a bit magical
Speaker 2: to us. And we're easily taken in when the machines
Speaker 2: start talking back to us, because no one else has
Speaker 2: ever only how the humans, but the only thing that
Speaker 2: ever talked back to us were other humans, other intelligent beings.
Speaker 2: So it's easy to be fooled and seduced by the
Speaker 2: machines when they started talking to us because our only
Speaker 2: experience of things that talk back to us are other humans,
Speaker 2: all of their intelligence and everything else that goes with
Speaker 2: being humans.
Speaker 1: You own a book called twenty sixty two The World
Speaker 1: that AOI made. This is looking into the future.
Speaker 2: Yes, the year twenty sixty two.
Speaker 1: Yeah, the year twenty sixty two. So why they're year?
Speaker 2: Well I gave a talk in North Sydney and this
Speaker 2: old lady turned up at the talk and she said, oh,
Speaker 2: so it's nothing to do with my post code. Then
Speaker 2: I'm very sorry. It's got you here on the forced protector.
Speaker 2: It's all about the year.
Speaker 1: Twenty sixty or why twenty sixty two?
Speaker 2: Well? I surveyed three hundred colleagues other experts around the
Speaker 2: world in AI. I asked them the simple question, when
Speaker 2: would computers match humans in their intelligence? And the average
Speaker 2: answer they said this was ten years ago. It was
Speaker 2: twenty sixty two. I think I suspect if I did
Speaker 2: the survey today, they might say twenty forty two. Oh really, yes,
Speaker 2: I think the date has moved forwards quite significantly.
Speaker 1: And what do you mean by match our intelligence?
Speaker 2: To do everything that we can do? That requires intelligence?
Speaker 2: And what do you mean by intelligence to be able
Speaker 2: to write a poem, do a tax return, answer a
Speaker 2: maths question? They can do this, plan a holiday.
Speaker 1: He can do that now though, yes, why twenty forty two?
Speaker 1: What is it that do you think that a computer? Sorry,
Speaker 1: I I will that we can do that. I I
Speaker 1: can't do anything.
Speaker 2: Well, So they don't have our creativity, they don't have
Speaker 2: our emotional intelligence, they don't have our social intelligence. There's
Speaker 2: still still a very what's called jagged intelligence. You know,
Speaker 2: there's really good at something so that they can answer
Speaker 2: international maths olympiad questions. But you know better than almost
Speaker 2: anyone on the planet. And yet you can ask them,
Speaker 2: as you know, a simple puzzle, you know, like, here's
Speaker 2: a simple one that defeats chattypt You know, I've got
Speaker 2: a two liter jug and a five liter jug. How
Speaker 2: do I measure out three liters of liquid? And you know,
Speaker 2: the answer right, you feel the five liter jug to
Speaker 2: the top, you pour it into the two liter jug,
Speaker 2: and you've got three liters left to it. But you
Speaker 2: ask CHATTYPT and it says, it gives you this twelve
Speaker 2: step plan of pouring water backwards and fords between the jugs,
Speaker 2: and eventually it says, and now in the two liter jug,
Speaker 2: you've got three liters of liquid. Batshit crazy. It's not
Speaker 2: deeply under earning the problem that you.
Speaker 1: Are is that because it's taken that answer from somewhere else,
Speaker 1: or it actually deducted and.
Speaker 2: It's been trained. There's lots of example where it puzzles
Speaker 2: on the internet where people have talked about jug questions
Speaker 2: where you pourp but it and it's sort of just
Speaker 2: giving you a blurred average of those problems where it's
Speaker 2: lots of pouring liquids backwards. And for us, it's not
Speaker 2: really sitting there thinking through logically what the steps are.
Speaker 1: So would like as someone who's like you're a mathematicism,
Speaker 1: but as someone like you think, would you say, therefore,
Speaker 1: artificial intelligence is not really making a deduction.
Speaker 2: Yes, it's not reasoning the way that we're reasing.
Speaker 1: Yeah, the way we reason I should say the way
Speaker 1: we would make it deduction.
Speaker 2: Yes, and yes, I mean we're working on how we
Speaker 2: can actually add more deductive capabilities to these language models.
Speaker 2: But they're they're you know, retrieving the answers from you know,
Speaker 2: what they were trained on more than they are reasoning
Speaker 2: about the answer.
Speaker 1: What do you expect for twenty twenty six? That's the
Speaker 1: opposite of twenty sixty two? But like, what do you
Speaker 1: what are you looking forward to in twenty twenty six
Speaker 1: this year when it comes to artificial intelligence?
Speaker 2: Well, I mean, the exciting thing happening now is is
Speaker 2: that we have these AI agents that instead of having
Speaker 2: these chatbots which sit there and wait for you to
Speaker 2: ask a question, they actually have some agencies, some ability
Speaker 2: to go and do stuff. So, as an example, when
Speaker 2: you've got a new employee, there's a bunch of stuff
Speaker 2: you've got to do. You've got to give you know,
Speaker 2: you've got to give them a log in, You've got
Speaker 2: to they've got to do the health and safety on
Speaker 2: board training. They've got to get a path for the building,
Speaker 2: they've you know, they've got to you've got to get
Speaker 2: their financial data so you can pay their pay their
Speaker 2: wages at the end of the first week, there's a
Speaker 2: bunch of you know, tasks you've got to do. Well. Now,
Speaker 2: instead of you having to go through and do all
Speaker 2: those things yourself, you can just tay to the agent, okay,
Speaker 2: can you on board this new person, and it breaks
Speaker 2: the task down into the different things, and then it
Speaker 2: takes each of those tasks and break those down into
Speaker 2: the things you have to do. To get the people's
Speaker 2: financial information, you've got to get some you know, identity documents,
Speaker 2: you've got to get their bank co ordinance. You've got
Speaker 2: to just to them in the accountancy system. You've got
Speaker 2: to set up the payroll. And then it said, you know,
Speaker 2: to register the payroll. It's got to get you know,
Speaker 2: all the information their date of birth, the tax number
Speaker 2: and so on, and it goes through all those things.
Speaker 2: It does all that stuff for you.
Speaker 1: And do you see for the twenty twenty six so
Speaker 1: I mean you just see just more efficiency games or
Speaker 1: do you see something large coming out of all it's
Speaker 1: something really disruptive.
Speaker 2: Well, the disruptive thing that you have there is suddenly
Speaker 2: that the computers are doing stuff. They're changing your files there,
Speaker 2: maybe spending money, maybe the ordering stuff and that's going
Speaker 2: to go wrong.
Speaker 1: Sometimes that will be disruptive.
Speaker 2: That's going to be disrupted, and it's like, who are
Speaker 2: we going to blame because we can't blame the computer,
Speaker 2: but what will do? Also, things will go wrong at
Speaker 2: speed because this is a computer speed, not human speed.
Speaker 1: So if it's trying to reverse, it could be hard
Speaker 1: to reverse.
Speaker 2: I mean, there was a story in the paper yesterday
Speaker 2: where the head of safety at open AI had all
Speaker 2: email was deleted by one of these agents. She's the
Speaker 2: head of safety, like a rookie mistake. She gave it
Speaker 2: the power to clean up her mailbox. You know, I'd
Speaker 2: love to have someone who could go through my mailbox
Speaker 2: and clean out all the junk, but also include out
Speaker 2: all the stuff that she wanted to keep.
Speaker 1: Because the AI agent had agency and made the decisions.
Speaker 2: Yes, made the decisions.
Speaker 1: So that that means the instructions and the rules that
Speaker 1: have been given to the AAI agent were not not
Speaker 1: complete or maybe not accurately.
Speaker 2: It wasn't just that the rules were in complete, but
Speaker 2: also the AI is not perfect. You know, we talked
Speaker 2: about how they hallucinate, They make mistakes, you know, just
Speaker 2: like humans are not perfect. These are not perfect either.
Speaker 1: So we be, and I guess that's sort of part
Speaker 1: of it too. I think we should learn to expect imperfection.
Speaker 2: They are, we should, But I mean, the interesting thing
Speaker 2: is people have higher standards for machines than they'd have
Speaker 2: to have.
Speaker 1: That's weird.
Speaker 2: So you say to people, you know, we've got self
Speaker 2: driving cars now, and they're roughly the level of humans
Speaker 2: in terms of, you know, the number of mistakes they make,
Speaker 2: and some ways they're better than humans they make fewer mistakes,
Speaker 2: but people are still very reluctant to give them responsibility
Speaker 2: to drive. So you say, well, okay, well how much
Speaker 2: better than humans? If they do, they have to be
Speaker 2: ten times better than humans before we can entrust them
Speaker 2: with the responsibility of driving without human oversight. And then
Speaker 2: at some point, you know, okay, so maybe we say
Speaker 2: it's ten Well, what happens when they're a hundred times
Speaker 2: better than humans? Do we let humans still drive? Because
Speaker 2: humans are going to be killing people. Machines are provably
Speaker 2: not going to be killed. They can't get drunk. They
Speaker 2: can't get drunk, they're going to be tired, they overtired. Yeah,
Speaker 2: they're not going to be speeding, They're not going to
Speaker 2: be breaking the road rules. At what point do we say, humans,
Speaker 2: you know you're killing you know, your fellow citizens, maybe
Speaker 2: we shouldn't allow this.
Speaker 1: Well, a friend of mine the other day told me
Speaker 1: that Tom she was in San Francisco and the incinet
Speaker 1: went down, stopped and they all stopped, which is the
Speaker 1: stop stop there and then, which is the safe thing
Speaker 1: to do totally. But there were other non waymos on
Speaker 1: the road. Yes, humans people driving cars. I'm now called
Speaker 1: to non non and you're driving and and like I
Speaker 1: all of a sudden the things because literally they're just yeah,
Speaker 1: they're not used, they're not going very fast because they're
Speaker 1: all governed. But nonetheless they just stopped because something happened
Speaker 1: in obviously and yeah, and we keep forgetting that. The
Speaker 1: the Achilles heel of all artificial intelligence is that is power. Yes,
Speaker 1: I mean that's what drives everything. If you turn off
Speaker 1: the switch, not literally, if you know what I mean metaphorically,
Speaker 1: if you just turn the power off this thing that
Speaker 1: we're really worried about, or somehow you can if the
Speaker 1: thing is battery based, like it's some other way it's powered,
Speaker 1: not not by electricity with something, not by put money
Speaker 1: in a wall. If somebody you can disconnect the power
Speaker 1: thing stops, it does.
Speaker 2: But that's the problem as well. I am forced to
Speaker 2: write this fantastic science fiction story one hundred years ago
Speaker 2: titled When the Machine Stops, about how wig becoming ever
Speaker 2: more dependent upon the machines and the machines are becoming
Speaker 2: ever more when we're losing touch with actually how they work.
Speaker 2: And the short story is about what happens, you know,
Speaker 2: when the machines eventually stop break, no one knows how
Speaker 2: to fix them, and the world grinds to a halt.
Speaker 1: Yeah.
Speaker 2: So it's not that you know that we turn the
Speaker 2: machines off. It's problems. But when the machine stops, what happens?
Speaker 2: Could we still do anything? And we're already completely dependent.
Speaker 2: If the Internet goes down, the stock market stops, the
Speaker 2: air traffic you know, you know in the university, you know,
Speaker 2: the internet goes down, people say, well, time to go home.
Speaker 1: I think we can do yeah go home? Yeah, Well
Speaker 1: I'm wishing.
Speaker 2: And now even that is problematic because lots of people
Speaker 2: have got smart doorbells and so on, and they can't
Speaker 2: get into their homes anymore.
Speaker 1: Yeah, because it's either thing of printer or some sort
Speaker 1: of biometric you can't get out. We were in an
Speaker 1: old fashion key hidden somewhere exactly somewhere, and that's your
Speaker 1: redundancy process.
Speaker 2: My wife was saying, should we get some of those
Speaker 2: smart doorbells and things. I said, no, keys are fantastic.
Speaker 1: They work totally the redundancy and you can have and
Speaker 1: you can have a second key. If you lose that key,
Speaker 1: you can have another key hidden somewhere, which is what
Speaker 1: most people do. I really appreciate this conversation. I've had
Speaker 1: a lot of fun talking to you about it, but
Speaker 1: equally on the funds off on the other side of
Speaker 1: fund is a serious topic. Yeah, a very serious topic topic,
Speaker 1: and I wish you the best when you go to
Speaker 1: the United Nations next week and hopefully, you know, we
Speaker 1: can start to get some better road rules around or
Speaker 1: get the conversation going about what the road rules are
Speaker 1: for the future. Indeed, thank you very much.
Speaker 2: Been a pleasure by