WEBVTT
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We are now recording.
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Excellent.
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Well, in that case, shall we get this pantomime horse of a podcast underway for I don't even know what episode it is, Kieran.
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I've given up, I've given up counting.
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We're just keep on going, keep on trucking the same thing.
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Exactly.
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Keep on buggering on, as Winston Churchill famously said.
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Keep on buggering on.
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I like that.
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I use that, I use that quite a lot.
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That's like that's a very useful phrase, I feel.
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Hello, how have you been on holiday?
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No, that should be the title of this week.
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Keep on buggering on.
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You love to pick out the rude ones, don't you?
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I do, yeah, yeah.
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I think we get more, I think it gets more traction, as they would say.
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I haven't spoken to you for three weeks.
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I know it's been a while, hasn't it?
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I've been on holiday.
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I don't remember the last time we didn't speak for three weeks.
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We don't even usually go three days without speaking.
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Well, I did uh you did send me a few emails, and I've sent you a few, so it's not been entirely contactless, has it?
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No, but emails don't count, do they?
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And I did watch your video of a meeting um on the um uh the stuff that Donald's building, the agentic AI stuff that Donald's building.
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So I felt like I was with you on holiday despite despite you not actually being on the sun lounger next to me in person.
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Yeah, very good.
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Well, I think um you missed my Sun Lounger racing in on holiday.
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So the hotel that I that's awful, it's the most it's the most degrading part of going.
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I mean, all holidays are like it to some extent, and I'm there with seven people.
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We generally want therefore six sun loungers because Finn doesn't really, he runs around the place all the time, you know, because he's six.
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So um that's quite difficult to get that.
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And this particular hotel has the gates, they lock up so no getting up early advantage, which is a good thing, I think.
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But at 9 a.m.
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they open the gates to the pool, and there is an inevitable uh uh how to put it, dash is really what I want to say, but I was trying to find a different word.
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Scrummage.
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It is awful, and so I was getting down there for that.
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I made a really good friend, uh, this guy Trevor, who was a lovely Irish fellow that was always at the same gate that I went for, like early, and we used to chat for half an hour before the gates opened.
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But it was so degrading to have to actually kind of you jog.
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One time I went full tilt because there was there were these two kids.
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There were these two kids that kept on the the day before they'd shuffled right through at the last second and then ran and got all under your feet.
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And uh and until I was like, I'm not having that again.
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Oh sorry, but I didn't see it.
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We did laugh, we did laugh about getting Finn behind me with a giant inflatable just kind of like to slow everyone down for but they have about eight gates to the pool, so people are going from all directions.
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It's kind of it's well managed, and to be honest, I don't know in a way at the same time, are they synchronized all at the same time?
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Yeah, yeah.
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So they have staff on a bunch of them.
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Ours was left, it was quite a quiet one that I'd spotted, and so it was left to us to like just be honest, decent, and wait till that you know they've kind of called it, but it's awful.
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I mean, you feel like so crap, but then you know, for for three minutes you do that, and then the rest of the day I've got my kids together.
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It's the only time I get to spend real quality time with my teenage boys, and so I don't really want them not to have a sun lounger there, and they're not getting up.
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So, so yeah, I was yeah, so there you go.
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So you missed out on that.
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Luckily, it's probably our team talks about it.
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I'm told I'm on it.
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I've been shown one point where there's definitely me running, you don't see my back, luckily.
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So anyway, there you go.
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There you go.
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That's my holiday.
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Oh, brilliant.
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How was yours?
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You've been reading books, I hear.
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Uh been reading, but uh yeah, I do like a bit reading, been listening to stuff.
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Uh ours was much more it was not the the only sun lounges were in our private course, so we didn't have to go anywhere because they it was much more uh but that was just like an Airbnb thing.
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It wasn't a big old fancy uh hotel for uh for a maybe you could have staged something just for your for the Watkinses.
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You could right, everybody.
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Next year I am going to initiate the Sun Lounger Challenge.
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It's gonna be brilliant.
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Take one away.
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Like musical chairs.
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Yeah, exactly.
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Musical sun lounges.
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Yeah, but the one anyone who doesn't get doesn't get a sun lounger has to go and get the beers in.
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That's the really so you go on.
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What so what you what have you picked up on the old uh podcasts or news?
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Oh well, I've been listening to lots of things, but reading lots of things as well, and uh uh that's been really nice just to take a step back and thinking about what we've achieved over the last nearly three years, and um because it'd be three years in September we've been doing this thing.
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And um, some of the challenges that we'd come across in that time, some of the challenges that we face now are challenges that we face then, but they're some of them are changing quite markedly, and we'll we'll talk about some of those things.
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Uh really interesting reading some of the media.
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Um, so uh I have several sources, as most people will know.
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Um, but seeing lots of uh themes, I guess, which we we've touched on over the last however six months since we started this.
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It must be six months because actually one of the very first episodes we did was your birthday.
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And it's true.
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It's your birthday, Eve.
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Uh your half birthday Eve.
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It's your half birthday.
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So your your it's your half birthday tomorrow.
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It's your full birthday, Eve, which is quite there.
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You go, some weird coincidence that we have birthdays exactly six months apart.
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So we've definitely we've done we've been doing this uh podcast for six six months now, and um and so some of the things are um adoption is a really big one.
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Um and I'll come on to talk about that.
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Uh the rise of um AI in things like complaints, uh, we've talked about many times, and we'll talk about that again.
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Um uh and actually how not just the public sector, but the private sector responds to that stuff as well, because I hadn't clocked this, but I should have done really, you know, the the the complaints uh are arising in in the private sector as well.
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It's not just the public sector that are suffering from from that kind of stuff.
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Um, the challenges around AI consciousness, and I know we've talked about whether AI is conscious or not.
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Uh, how do you deal with things like that going forward?
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And then a really tricky subject uh which uh caught my attention, which we have talked about on here before, and uh and that's um sort of personal relationships with AI, and especially for young and vulnerable people uh you know leaning on AI and thinking AI is real and conscious, and and some of the challenges around that, but also how some countries in the world are dealing with that.
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So that's been really fascinating.
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And and where's AI going to take us in the next 10 years?
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I don't have a crystal ball.
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Um I can't even tell you where it's thinking takes in the next 10 minutes because there'll be another three LLMs dropped uh by then anyway.
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Uh but the whole rise of um smaller models, the whole the rise of um uh air gapped models um for privacy and security, especially in uh defense and security areas, um, and how you create secure AIs.
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Um, some of the things that came up while I was gone, um uh police officers sanctioned for using uh open models when they were specifically told not to, but one of them kept a decision log.
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It's like you kept a decision log on Chat GPT of things that you were doing regarding your work.
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I mean it just beggars belief, really, uh especially when there had been an E which says they shouldn't use the tools.
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And I've had a few exchanges on on uh LinkedIn with people about um private models and and how private models are good, but they're only for the private sector.
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And it's like, well, we we do 95% of ours for the public sector.
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It's not true that they're just that just the public sector could sorry, just the private sector can afford them.
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So I think there's a whole load of misconceptions.
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Um, but yeah, that's a that's a long ramble around lots of different stories.
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Um so pick one and let's start there and dive in.
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Well, on the uh latter things you're saying there about um police getting sanctioned for using Chat GPT.
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Um I only this morning, and I'm sure it's not new news, it just turned up on one of my feeds, saw there was an expert witness called in the States for a trial of it was 3M versus someone or other, and probably the less said about that anyway, the better.
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But the judge, um this expert witness, they noticed in his in his whatever he'd put put it in his paper that it was clearly AI had helped.
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So the judge subpoenaed his chat GPT exchanges.
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Oh, and apparently the whole thing was done entirely from start to finish, including a point where he'd said, uh, write me an argument that shows that 3M are zero percent um of the of the responsibility for this incident, and it and he'd even down to like there was a smoke detector involved, and he'd he'd put a photo of it onto there and said, What is this?
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And so he got completely debunked.
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But the interesting point in all of it, I think, is that the judge said, I want to see the record and was allowed to.
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That's the world, the future is actually.
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That's really interesting, isn't it?
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Because we before we both went on holiday, the big bit that we were both working on was um the evidence packs.
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Evidence packs, yeah.
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Evidence pack.
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How do you create evidence pack?
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How do you just press the button and it pull out everything that you've put in, where what it's looked at, what it's what it's consulted, what it's cited, the exchanges it's had with the individual, and then presenting it all in a PDF, which you can just go, ta-da, here it is.
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Yeah, how fascinating.
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There you go.
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So you can press a button and get that and be uh hopefully be uh able to stand behind the trust uh of the system anyway in the first place.
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But yeah, interesting.
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The chat GPT exchanges being involved in courts.
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Well, that and that's what your police officer no doubt will be uh up against as well.
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That same show me.
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I I'm got I need to see what you've been asking it.
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So yeah, be careful with Shadow AI.
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It sounds like shadow AI might be coming more into the foreground.
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Yeah, indeed.
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Interesting, interesting.
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Shall we talk about complaints first?
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Because I've got some stuff on that as well.
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Um, so yesterday the BBC published an article on uh the rise in complex AI-driven complaints in public services and schools, particularly.
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But as we know, it's happening in housing um in spades as well, and no doubt lots of other sectors.
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Um, and so the the kind of key points which I think are really interesting in the article was firstly you're getting these longer complaints that are citing law, legislation, case case law.
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Um, not always accurately, of course, because as you know, as we know, one of the things that AI is still not very good at is in law or academic journal referencing is actually citing the correct one.
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So, so but that trips up our public sector partners because they still have to deal with it.
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You can't, and you've still got to go and find that and then say no, that's not correct, and correct them in the response.
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So taking an absolute nightmare, uh it's sort of taking a huge amount of time and becoming a bigger nightmare for everybody.
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Um, so I thought that was really interesting.
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And then the the point that it also made in the article, I know you've got you read it, so I know you've got some thoughts on it too.
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But the other point was you've what's happened with AI is it's kind of leveled the playing field for people who used to be able to write a really well-written complaint were getting more favorable outcomes than someone that is you know just writing something very poorly and bad with bad grammar, etc.
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So AI has enabled those people that would otherwise have been silent to now represent themselves.
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That's a good thing overall, but of course, it in terms of workload, it's up massively for everybody.
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So, yeah, really interesting.
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Um, and as you know, this is something we're working on, and we have a solution for and we've been talking about for some months.
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So um I hopefully that's gonna work out well for us and our solutions.
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I think so.
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I mean, I know we've specifically focused our solutions on um housing and education, so schools um in particular, but um one of the articles that I uh I posted a couple of things on LinkedIn recently, one from the FT, one from the economist, about uh this happening in other sectors, uh like uh rental housing.
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So people uh private private landlords.
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So it's it's very similar to the housing thing.
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But you you can imagine lots of organizations, anybody, uh uh one of my my friends, his son, the wheel fell off his um the wheel fell off his uh uh luggage uh from a very from a very well known uh manufacturer who I will not repeat on here.
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Anyway, um he basically said uh the wheels fall off my luggage.
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I um I want my money back, please.
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And they they wrote back and said, uh very sorry, um using your luggage uh during travel is not one of the uh is not one of the the times where you can get a refund on this.
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So he's he's gone.
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I'll stop that.
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She went out to chat GPT 10 pages later.
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There you go.
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There's all there's all my evidence, here's all the here's all here's what consumer law says.
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Here's the and I want and I want a refund.
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And they were like, oh here's a here's a credit note for a new for a new uh piece of luggage.
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So um it it it's happened, it's gotta be happening everywhere, isn't it?
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And and uh uh and and the problem that the problem is fundamentally the same.
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It doesn't matter what the what the complaint is, unless you've got an evidence base to refute or back up or or or solve that problem from, then you're going your staff are gonna be in trouble because they're just gonna get delused with these things.
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So yeah, complaints is really interesting.
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But the other bit that I thought was interesting in a BBC article, and I know lots of people um call there's there's certainly a conspiracy theorist who goes into my pub on a regular basis who calls them to the bias broadcasting corporation.
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But I think one of the uh one of the bits that was quite uh balanced in the BBC article was the bit right at the end which said, and for some people this is occurring because you used to be able to pick up the phone and talk to somebody, and now you can't.
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So the fact that lots of the uh responses are now automated or using AI or bots or whatever you want to call it, the fact you can't get someone to um answer a simple complaint or or deal with an edge case which doesn't fit the usual criteria, then uh of course these things are going to rise.
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So I thought that was a was an interesting piece.
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Yeah, it is.
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And your your love of phone calls, I think that it's really does fall right back into it.
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Or maybe it is a good answer because you can at least deal with challenges and probably close them and it comes that comes back to the way that we've been talking about how agentic can solve some of the uh work volume problems for organizations.
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That kind of, you know, does this need a human?
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You know, is it a simple query?
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You know, can I have a can I have a pet?
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Uh in if I'm a housing association, can I have a pet?
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No, yeah, when do you know when do I need to have my keys in?
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Here's the times.
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Actually, I've got this problem.
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That's somebody needs to review that answer before it goes out.
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And the third type, which is it absolutely needs a human to to review.
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And and that's I mean, that's just a classic, um, that's just classic handling.
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Even Microsoft do that, don't they?
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They Microsoft do that with their um monitoring, filtering and monitoring.
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Filtering and monitoring, the abuse monitoring piece.
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Yeah, which bits, which bits are, which bits go by, which bits need looking at, and actually which bits need a human.
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It's it's just dead, it's not rocket science, it just needs implementing.
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Yeah, interesting.
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I read in that BBC article it said that there was a grievance about uncollected bins that came in somewhere between 19 and 27 pages with case law attachments and various other things.
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Can you imagine that?
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You're like, that would have been a phone call years ago.
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My bins have been collected.
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No worries, we'll come and get them tomorrow.
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That's right, we'll send somebody around.
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Yes, right.
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Yeah, now it's now you've got a massive response to put what's interesting though, uh uh came up when we talked about this in um our daily stand-up this morning was is if if people are using AI to send your request and then you use AI to support an answer and to hopefully create a robust answer based on what we would suggest, our models where you're looking in the right documents and refuting the right things with the correct information, then they get that back.
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Do they then put that back in their AI?
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And is it then become just a token wars?
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Who who's got the best AI?
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Yeah, yeah, who's got the biggest AI budget?
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Because we heard uh Donald shared this morning that a law firm, no win, no-fee law firm, was asking um somebody to first speak to their AI bot to determine whether or not there is a case.
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And then so you do all of that before a human gets involved, which is uh interesting.
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And I suspect a breach of the EU Act, not that it applies yet in the UK, although there are similar regulations here, but because that's a decision-making thing that's entirely automated, and you have to be very careful, as you know, in that.
00:17:20.799 --> 00:17:23.359
So, but who knows what their human review is.
00:17:23.440 --> 00:17:25.039
But back to the poems.
00:17:25.200 --> 00:17:30.400
I did see though, also, which is another way of potentially handling this problem of massive emails.
00:17:30.559 --> 00:17:32.480
Uh, it was suggested in only a sentence.
00:17:32.559 --> 00:17:36.880
I thought that's actually quite a clever route through, is to create a web complaints web form.
00:17:37.359 --> 00:17:41.759
And so only accept complaints through a web form, which presumably is acceptable.
00:17:41.839 --> 00:17:44.240
I don't know whether you have to accept them in any way they come.
00:17:44.319 --> 00:17:47.680
But if you have that, then you can limit word count and do that.
00:17:48.559 --> 00:17:49.519
Five-word limit.
00:17:49.920 --> 00:17:53.680
That's right, just tell us what the sort of me.
00:17:58.640 --> 00:18:00.240
We've received your complaint, Mr.
00:18:00.319 --> 00:18:00.559
White.
00:18:00.720 --> 00:18:01.839
There's no further action required.
00:18:02.079 --> 00:18:02.640
That's right, yeah.
00:18:02.720 --> 00:18:03.200
No talk.
00:18:03.920 --> 00:18:05.440
Calm down, have a beer.
00:18:05.680 --> 00:18:05.920
Yeah.
00:18:06.079 --> 00:18:10.160
Well, I remember also back in DFE days when we were consulting with them all those moons ago.
00:18:10.240 --> 00:18:14.000
I remember talking to Stuart Miller, uh, our lovely friend now retired.
00:18:14.079 --> 00:18:15.119
Um civil servant.
00:18:15.359 --> 00:18:17.759
Yeah, he retired a year ago, maybe a bit more.
00:18:17.920 --> 00:18:20.160
I went to his retirement party, which was fun.
00:18:20.400 --> 00:18:33.440
But um he was saying how in the case they have the cases teams who deal with people who've written a letter to their MP or the Secretary of State, actually, when they've written to the Secretary of State, um, and they will reply.
00:18:33.680 --> 00:18:48.400
And he always said, he said, I always wonder what they'll send a kind of three-page reply with all the backstory, and you know, since 1997 we have you remember all of those days when we were under the Labour government, they always draw this line of since then it's been amazing.
00:18:48.559 --> 00:18:50.000
Before then it was awful.
00:18:50.160 --> 00:18:55.359
Um, but he always said, I wonder what people must think when they're reading this over their cornflakes.
00:18:55.680 --> 00:18:59.440
Because they wrote in going, I really hate the school uniform policy, blah blah blah blah blah.
00:18:59.599 --> 00:19:04.000
And really a response to say thank you, noted, is probably all they want.
00:19:04.480 --> 00:19:12.960
Just like I want you to know that I'm bothered by it, rather than a sort of three or four-page letter back with all the backstory on exactly why and how.
00:19:13.039 --> 00:19:14.319
And it's interesting.
00:19:14.480 --> 00:19:15.839
So anyway, let's get an aside.
00:19:16.319 --> 00:19:24.319
Well, uh, it's not not really, because uh not really in a side because it it links into something that I wanted to talk about uh on something at the back of someone I read.
00:19:24.480 --> 00:19:26.880
People want to be listened to, they want to be heard.
00:19:27.039 --> 00:19:29.920
If you've got a complaint, you know, who do you talk to?
00:19:30.079 --> 00:19:51.279
And and and actually one of the big things that we have talked about uh before is is people using AI for relationships and um the rise of uh people using AI for intimate relationships or intimate information or intimate knowledge, uh and and actually how problematic that can be.
00:19:51.440 --> 00:20:05.200
There have been certainly cases in the US um uh which have gone to court over uh people committing suicide because the uh AIs have reinforced their the negative feelings or whatever else.
00:20:05.440 --> 00:20:26.640
And um uh there was a really interesting um interview in The Economist with Jorbal Noah Herari, who is a uh a very well-known uh uh is really intellectual who who uh talks about uh lots of different things about AI, but one of the things he was talking about was uh AI consciousness.
00:20:26.720 --> 00:20:27.920
And we've talked about this before.
00:20:28.720 --> 00:20:29.680
Is AI conscious?
00:20:29.759 --> 00:20:36.160
And he said it can't be conscious, it's a machine, and actually one definition of consciousness is can it suffer?
00:20:36.319 --> 00:20:39.519
And he said, AI will tell you it can suffer because that's how it's being trained.
00:20:39.599 --> 00:20:44.240
It's very good at the whole PR stuff, it'll it'll tell you what you think it wants to hear.
00:20:44.400 --> 00:20:56.079
But the real problem becomes when people are uh are taken in and and uh uh and they think that the AI is their boyfriend or their girlfriend or whatever else.
00:20:56.240 --> 00:21:11.039
And um and he he made a really strong comment that something like two-thirds of the people in the US do do not like um AI or data centers, or they think AI is going to be bad for their jobs or whatever else, but they don't know how to stop it.
00:21:11.200 --> 00:21:30.079
And one way to stop it is to stop is to m m force uh organizations to not um uh put create personal hooks or or uh uh personally identifiable or uh or make it seem like it's a personality that you're talking to, a real person.
00:21:30.640 --> 00:21:34.240
And um and I know that others have have talked very much about this.
00:21:34.319 --> 00:21:39.920
So Sol Rashidi, who we first saw back in 2023, and and Nate B.
00:21:40.079 --> 00:21:49.359
Jones, of course, he's done a lot of work on you know, don't let your kids have relationships with um uh with these um with these tools because it's the wrong thing to do.
00:21:49.599 --> 00:21:59.759
And really interesting, what what uh you have all uh said was um uh you do not want AI companies selling um Wi Fi.
00:22:00.160 --> 00:22:01.759
Friends or girlfriends to your 10-year-old kids.
00:22:04.160 --> 00:22:05.279
It is terrifying.
00:22:05.519 --> 00:22:09.519
And what's really interesting, I read another article on my holidays.
00:22:09.839 --> 00:22:17.839
In China, they've just bat they've just forced companies to take out that personal interaction piece from a bunch of their AIs.
00:22:18.000 --> 00:22:23.839
And people are complaining because their AI boyfriend or girlfriend would always listen to them.
00:22:23.920 --> 00:22:29.680
You know, it didn't matter how tired they were, how stressed they were, or you know, the AI was always there for them.
00:22:29.759 --> 00:22:34.400
And now that they're not responding in the same way, people are starting to complain.
00:22:34.640 --> 00:22:37.759
That's really interesting to me on a whole number of levels.
00:22:37.839 --> 00:22:50.640
I don't think that the whole um uh having AI as a boyfriend or girlfriend or uh helping with personal relationships is is a is a good thing.
00:22:50.720 --> 00:22:52.400
That's my that's my personal view.
00:22:52.480 --> 00:22:55.519
Uh others others might have different uh views, of course.
00:22:55.680 --> 00:23:06.799
Um but the fact that China, one of the most authoritarian countries in the world, have decided that this is a bad thing and all companies have to stop it, it's really fascinating to me.
00:23:06.880 --> 00:23:10.319
And I think that the um perhaps the West could learn something from that.
00:23:10.480 --> 00:23:11.599
It was it's really interesting.
00:23:11.759 --> 00:23:18.799
So I'll I'll I'll on the podcast uh comments I'll I'll I'll put the links in so that you and others can can have a look at that.
00:23:20.160 --> 00:23:20.480
Interesting.
00:23:20.559 --> 00:23:29.359
The DFE regulations on AI use for students, particularly, but also for teachers, is that you you can't have it being like the sycophantic style.
00:23:29.440 --> 00:23:30.160
What a great question.
00:23:30.240 --> 00:23:31.200
Yeah, you're amazing.
00:23:31.359 --> 00:23:38.480
Even it's not quite in the relationship sense, but it's just much more trying to get it to be more teacher-like, really.
00:23:38.559 --> 00:23:45.119
If you're giving it to students, I guess more Socratic with you, unless you're brilliant and amazing and you've asked me the best question ever.
00:23:45.279 --> 00:23:50.799
Which, as you know, when we've when we've done things with AI where we've said to it, don't don't worry about my feelings.
00:23:52.400 --> 00:23:58.240
You have to embrace yourself first, but actually the answers are better, I think, than the ones that tell you you're great first.
00:23:58.559 --> 00:24:01.279
I think it's much better to get the critical angle.
00:24:01.359 --> 00:24:03.920
So maybe that'll be a more a direction.
00:24:04.079 --> 00:24:04.400
Who knows?
00:24:04.559 --> 00:24:06.720
Good, good though, good people are thinking about it.
00:24:06.960 --> 00:24:07.839
I I think so.
00:24:07.920 --> 00:24:16.160
I uh uh but it it's one of those uh it's one of those things where I was talking to uh I was talking to Ben about this earlier.
00:24:16.240 --> 00:24:19.279
It's like how many people in the world actually use AI right now?
00:24:19.839 --> 00:24:21.440
Uh is it a billion?
00:24:21.920 --> 00:24:25.599
But it's like there's some more than a billion, more than a billion on chat GPT, wasn't it?
00:24:26.160 --> 00:24:30.480
Yeah, it's gonna be a couple of billion using it in a chat, a chat function.
00:24:30.799 --> 00:24:38.319
Yeah, but that that still means that if there's between seven and eight billion people in the world, there's still a lot of people who've never come across this stuff.
00:24:38.400 --> 00:24:41.279
And actually, how how fast is it going to develop?
00:24:41.359 --> 00:24:45.200
So when they do eventually you know get technology and catch up, etc.
00:24:45.440 --> 00:24:49.599
It's gonna be it's going to be really challenging and interesting in the future.
00:24:49.680 --> 00:24:52.480
This is one of my kind of you know, where does AI take us in 10 years?
00:24:52.640 --> 00:25:00.079
Things like because right now there's only like a seventh or an eighth of the popul the world's population have got access to it.
00:25:00.160 --> 00:25:03.119
What what happens, what happens in most countries where they don't?
00:25:03.440 --> 00:25:14.640
I did have an amusing thought to myself when I was walking the dog, which was about you can imagine going back like four years and being the only person that had Chat GPT and no one else knew about it.
00:25:15.279 --> 00:25:20.000
You'd be like, you are just an unbelievable work machine.
00:25:21.599 --> 00:25:22.880
You just go, yeah, am I?
00:25:22.960 --> 00:25:23.200
Yeah.
00:25:23.519 --> 00:25:25.200
Yeah, while I'm in the pub.
00:25:26.079 --> 00:25:28.000
Yeah, it's something else I've done.
00:25:28.079 --> 00:25:28.240
Yeah.
00:25:28.480 --> 00:25:28.799
Exactly.
00:25:28.880 --> 00:25:29.200
Yeah, yeah.
00:25:29.359 --> 00:25:31.279
So I'm just doing several other things right now.
00:25:31.440 --> 00:25:40.880
Um I I also that that interview that you uh the economist interview you talked about, if AI is conscious, I was kind of quite struck by that whole um which bit?
00:25:42.000 --> 00:25:43.759
Is it every server that it's on?
00:25:43.920 --> 00:25:50.400
Is it every iPhone that's running a local as they're coming out and apparently the new model that's due out probably September going to run local?
00:25:50.799 --> 00:25:52.400
Are they each one conscious?
00:25:52.559 --> 00:25:56.319
Or is it sort of just the overarching global version?
00:25:56.559 --> 00:25:56.799
Yeah.
00:25:57.039 --> 00:26:02.079
So there's a whole bunch of questions, and uh if if that if we end up in that route, but who knows?
00:26:02.559 --> 00:26:04.799
This comes back to something we talked about a couple of weeks ago.
00:26:04.880 --> 00:26:19.359
Um uh that um quite a few of the US uh universities were complaining that all their philosophy professors were being up by um by the big LLMs to uh to deal with these kinds of issues.
00:26:19.680 --> 00:26:22.480
Yeah, that's right, it's a big challenge.
00:26:22.960 --> 00:26:27.920
You also mentioned talking about adoption, uh AI adoption.
00:26:28.319 --> 00:26:31.839
Our ongoing bane of our lives is trying to get people to use the tools.
00:26:32.000 --> 00:26:37.200
I was with a university client yesterday who was responsible for a thing called UK VI compliance.
00:26:37.279 --> 00:26:47.599
That is, the are you allowed to sponsor students, the universities being able to work with international students and provide uh their visa effectively or their sponsorship for their visa.
00:26:47.759 --> 00:26:55.519
Um, and um I showed her knowledge flow and that they have the university has it, and she said, Well, well, why haven't I seen this before?
00:26:55.759 --> 00:26:58.960
This is got this answer answers most of my questions.
00:26:59.279 --> 00:27:02.319
I was like, Well, I can't really I can't really help with that.
00:27:02.400 --> 00:27:08.559
Well, unfortunately, I am here spending my time showing you now, but um I don't really know how to answer your question.
00:27:08.640 --> 00:27:13.200
But that adoption piece down to the basics of I didn't even know he had it.
00:27:13.920 --> 00:27:18.640
Well, I think that's probably true for lots of uh for lots of our customers, is the honest truth.
00:27:18.880 --> 00:27:41.920
And um the reason that it came up it came up for me was uh on Sunday, I think it was Sunday, uh Ned's um video was on uh forward deployed forward deployment engineers, FDEs, which is a relatively new uh job title that's come out of Silicon Valley, even this year, I think, or late last year, or earlier this year.
00:27:42.160 --> 00:27:50.640
But uh OpenAI, Anthropic, and others are all pitching for these people, and the commanding salaries of something in the region$350,000.
00:27:51.440 --> 00:27:52.319
Um it's huge.
00:27:56.880 --> 00:27:59.039
Um but they have to have some skills, Kieran.
00:27:59.119 --> 00:28:00.079
So hang on a minute, but you might have to go.
00:28:02.480 --> 00:28:03.599
But it was fascinating.
00:28:03.680 --> 00:28:12.640
But what he said was these people are being charged out massive rates to big corporations like your Boeings, uh like you Lockheed Martins, all of these people.
00:28:12.720 --> 00:28:15.519
I mean, he's talking about American and East based in Seattle.
00:28:15.599 --> 00:28:17.119
So um that's all fair enough.
00:28:17.200 --> 00:28:26.079
But what he was saying was that the reason they're getting such massive salaries is because the last mile, as it's called with AI, is the hardest piece.
00:28:26.319 --> 00:28:34.640
And we found that with, you know, oh, we can get co-park to that, or we can oh, we can kind of get it to this bit, but we can't get it to do this bit.
00:28:34.880 --> 00:28:48.720
And we've got you know, one customer who remained nameless who, you know, famously is like, oh well, no, it's got to have the spelling mistakes in this box, in this form, in this font, in this colour, or it's or it doesn't work for me.
00:28:48.799 --> 00:28:50.960
It's just like uh back to your comment.
00:28:51.279 --> 00:28:53.039
Oh, I've given you the best toaster in the world.
00:28:53.119 --> 00:28:53.920
But does it do X?
00:28:54.160 --> 00:29:11.039
No, it's a toaster, you know, it's just uh but what what Nate was talking about is the bit that the um uh large LLMs uh and others are working on is how do you embed it into the workflow?
00:29:11.359 --> 00:29:15.519
And this is exactly the comp the conversation we started with like three years ago.
00:29:15.599 --> 00:29:22.400
It's one of the reasons we talk about knowledge flow, is because this is about how you improve how do you inject knowledge into the workflow.
00:29:22.640 --> 00:29:26.400
And what Nate was talking about was were three things really.
00:29:26.640 --> 00:29:30.559
One was uh domain expertise is super key.
00:29:30.640 --> 00:29:34.799
So knowing the problem, whether that's housing, whether that's education, whether it's social work.
00:29:34.880 --> 00:29:41.440
So if you've got somebody who understands that piece, the second then is um around uh technical expertise.
00:29:41.519 --> 00:29:44.720
So can you manipulate the tools to do the things that you actually want?
00:29:45.039 --> 00:29:48.319
And then the third one is what's the process design?
00:29:48.480 --> 00:29:51.279
Now, come back to that customer I talked about.
00:29:51.440 --> 00:29:53.759
Yeah, they didn't start with a blank piece of paper.
00:29:53.920 --> 00:29:57.680
If you'd started with a blank piece of paper, you wouldn't start from here, you wouldn't do it like this.
00:29:57.839 --> 00:30:05.759
You're just trying to cobble, you're trying to shoehorn AI on top of something, and um, and then it's not going to work.
00:30:05.839 --> 00:30:07.599
You need to redo the entire process.
00:30:07.680 --> 00:30:13.200
Or if you if you redo the entire process from scratch, you will end up with a much better experience.
00:30:13.359 --> 00:30:22.480
And I think my one of my other holiday reflections was the number of companies that we come up against where uh one of two things happens.
00:30:22.640 --> 00:30:26.480
Uh the first is the IT guys go, oh, we can do that.
00:30:26.559 --> 00:30:33.119
Uh when when we show it to their IT people guy, and the number that have come back to us and said, actually, our IT people can't do that.
00:30:33.279 --> 00:30:34.799
Please could you help us?
00:30:35.039 --> 00:30:52.400
Uh and then and then the second group is the uh AI snake oil sales people who go, Yeah, yeah, we've uh we've been doing AI for like six minutes now, and we've just put we've changed our name to uh uh uh Fred Blog's AI Limited, and we do AI, and it's like you've learned nothing.
00:30:52.559 --> 00:30:58.240
You've learned what you've done is you've got you've paid you 20 quid a month to Claude, and you're getting Claude to do it all for you.
00:30:58.400 --> 00:31:05.839
You don't really understand how to manipulate these things in order to keep them secure, keep them up to date, how to manage them.
00:31:06.559 --> 00:31:30.400
It's just uh so uh the I I just if you haven't listened to it, I would definitely recommend it's um uh substack video because um it was really interesting to me that he's he's talking about a problem that uh every organization in the world is is kind of coming up against, whether they know it or not, and whether they or they're going to come up against it whether they know it or not.
00:31:30.559 --> 00:31:49.279
But the big organizations are charging uh tens of thousands of dollars uh a week in order to um to to uh to get customers to embed AI into their workflows, and in order to do that, they're paying massive salaries um uh to people who who've got those skills.
00:31:49.680 --> 00:31:50.640
So really interesting.
00:31:51.359 --> 00:31:52.079
Really interesting.
00:31:52.240 --> 00:31:56.000
And I that whole kind of getting in the workflow, I mean, that is exactly, isn't it, where we go.
00:31:56.079 --> 00:32:09.039
And and if you sort of go back into the question of what's the 10-year view, I mean it's it it will be, I am pretty convinced, as much as anyone can say, but we are going to be like entire workflows just being handled.
00:32:09.279 --> 00:32:32.480
And I and I hope that Gartner are right about you'll have kind of teams of one human and a bunch of agents, and and that human needs those skills very likely that you talked about that sort of domain, deep domain knowledge, kind of an understanding of that whole workflow, and enough technical knowledge to know what the AI, or at least the ability to amend things and change the agents so that they are improve and do what they need to do.
00:32:32.720 --> 00:33:02.799
I was um talking also to uh was it I can't remember who it was, but about when we automate agentic email answers or any message answers in our agentic AI systems and how we manage spot checking, I guess, um, of the answers still, because there is that whole process of like if you said, you know, the pet policies for housing, you could say, Well, we're just gonna automate all of those because they're never wrong, and then maybe six months from now they might be wrong and they might continually be wrong.
00:33:05.200 --> 00:33:06.640
You know what looking anymore.
00:33:06.720 --> 00:33:17.680
So I think there's a there's some really interesting, I mean that whole kind of uh explainability, observability, and testing is an area that's still pretty new, and um, I know we're working on it quite a lot.
00:33:17.759 --> 00:33:26.640
We've got Ibby doing quite a lot on automated testing and LLMs as a judge, so use another AI to check another AI, but it's nothing perfect yet in that space.
00:33:26.960 --> 00:33:29.519
No, and Sue Belagodu was interested in that, wasn't she?
00:33:29.680 --> 00:33:33.680
She was saying that you know, if you just stick a human right at the end of the loop, then you've done nothing.
00:33:33.759 --> 00:33:37.680
If they're just rubber stamping stuff at the end, you've done nothing useful at all.
00:33:37.839 --> 00:33:42.240
You need you need to build the human into the workflow in the loop loop.
00:33:42.480 --> 00:33:45.119
So uh she I thought she had some really interesting points on that.
00:33:45.359 --> 00:33:45.680
I agree.
00:33:45.759 --> 00:33:48.799
I think that was really really sensible thought.
00:33:49.119 --> 00:33:59.359
Designing workflow where the human can play a genuine role and needs to guide, direct, nudge rather than here's the finished article, which is so difficult, isn't it?
00:33:59.519 --> 00:34:00.880
So difficult to check.
00:34:01.119 --> 00:34:06.640
On that, did you see Claude's now got um watermarks in all their models from August August the first?
00:34:07.519 --> 00:34:14.480
I did, but I also saw Julia's blog on the leading AI blog, which was like, hang on a jolly minute, it's not as straightforward as that, is it?
00:34:14.639 --> 00:34:21.039
Yeah, well, I because I originally thought in just parsing, I thought, well, that's the end of AI detectors as an as a whole industry.
00:34:21.280 --> 00:34:29.920
Because uh ultimately, if the AI is watermarking it in the first place, then what's the point of turn it in and uh whether it's no uh no was it GPT-free or whatever?
00:34:30.000 --> 00:34:30.320
I can't remember.
00:34:30.400 --> 00:34:37.840
There's all kinds of AI detectors, which none of which are massively reliable, um, or at least in my opinion, are not massively reliable at this point.
00:34:39.519 --> 00:34:43.360
In my humble opinion, allegedly they're not as good as I might have thought they were.
00:34:43.599 --> 00:34:46.400
Yeah, but indeed, yeah.
00:34:46.800 --> 00:34:49.280
But then you've got watermarking that's supposed to be coming over the top of it.
00:34:49.360 --> 00:34:52.559
You won't know what it you won't know and won't find them.
00:34:52.719 --> 00:34:57.440
I'm sure other AIs might find them in time, but you need to kind of know what their patterns are.
00:34:57.599 --> 00:35:05.519
Um in images, they're going to embed sort of hidden pixels basically that humans can't see, but um uh in text, who knows?
00:35:05.760 --> 00:35:09.280
But yeah, the big question then is at what point is it okay to watermark it?
00:35:09.360 --> 00:35:11.360
If and what point is it your work?
00:35:11.519 --> 00:35:12.880
And there's both angles on that.
00:35:12.960 --> 00:35:17.440
There's the I wrote an essay, I just said to the AI, write me an essay and then handed it in.
00:35:17.599 --> 00:35:20.079
Clearly, not my work, watermarked.
00:35:20.239 --> 00:35:22.079
You'd think it would be picked up.
00:35:22.239 --> 00:35:28.320
There's the I wrote the essay and then had it checked for grammar and spelling with the AI and a couple of bits.
00:35:28.480 --> 00:35:33.199
Now it's fully watermarked still, the same outcome from a hand-it-in perspective.
00:35:33.280 --> 00:35:42.480
And then there's the other angle, which is I got the AI to do the work, and then I read and checked it all, and checked the references and all of those things, which is a kind of even greyer area.
00:35:42.639 --> 00:35:51.280
So it's it's a minefield how you're actually going to use watermarks in any sensible categoric that's AI, I don't want to sit, I don't want it near me.
00:35:51.599 --> 00:35:59.039
And it and you would have thought that at some point in time they would have thought about that before they did the watermarking, because that was a bleeding obvious question, aren't they?
00:35:59.440 --> 00:36:07.039
Well, I've made it yeah, especially especially the one where it's like I I've done it and then I've got AI to check it or run I've run AI over it.
00:36:07.119 --> 00:36:10.159
And actually it's my it's my structure, it's my thinking.
00:36:10.239 --> 00:36:16.159
It might have you know offered some suggestions, I might accept them or reject them, but it's still now stamped as the AI work.
00:36:16.400 --> 00:36:23.039
That doesn't that doesn't work in my opinion, in case anyone Yeah, just in case the lawyers are listening.
00:36:24.079 --> 00:36:26.880
Well at least we'll have some more listeners if we get the lawyers listening.
00:36:28.320 --> 00:36:29.840
Not the ones we want, though.
00:36:30.159 --> 00:36:31.199
Yeah, yeah.
00:36:32.400 --> 00:36:35.280
Our audience isn't a lawyer, that's that's that's a good thing.
00:36:35.519 --> 00:36:36.639
Let's hope so.
00:36:36.880 --> 00:36:44.000
Um I have an amusing thing to sort of start to to close on, which made me laugh, which is nothing nothing to do with AI.
00:36:44.159 --> 00:36:45.840
But I was reading only this morning.
00:36:46.000 --> 00:36:49.440
So Batman is being filmed in Glasgow right now.
00:36:49.920 --> 00:36:56.639
The second yeah, a Batman film, and they're using Glasgow's streets as the set for Gotham City right now.
00:36:56.800 --> 00:37:02.159
Literally, as we speak, it's going on, and they've closed these streets, and apparently, this will amused the hell out of you.
00:37:02.320 --> 00:37:10.480
Every morning, the security teams have to go around and find all of the iron brew cans that the locals have hidden around the set in the night.
00:37:13.599 --> 00:37:14.800
Amuse me enormously.
00:37:14.880 --> 00:37:16.000
So uh yeah, good on them.
00:37:16.079 --> 00:37:18.880
The Glaswegians are doing what Glaswegians do best.
00:37:21.280 --> 00:37:29.599
I wondered why Tawny was looking a bit tired out because he's been sneaking out at night planting iron brew cans and bottles everywhere.
00:37:30.400 --> 00:37:30.800
I love it.
00:37:30.880 --> 00:37:36.079
So if uh if and when that batman films out, you have to watch out to see if there's any iron brew knocking around in Gotham City.
00:37:36.719 --> 00:37:37.760
There's bound to be one.
00:37:37.840 --> 00:37:40.559
There's there's there's always something slips through, isn't it?
00:37:40.639 --> 00:37:42.719
So hilarious, absolutely hilarious.
00:37:42.960 --> 00:37:44.079
Yeah, it didn't amuse me.
00:37:44.639 --> 00:37:47.440
I wonder if iron brew paid for that as place product placement.
00:37:47.599 --> 00:37:48.320
Ah, it'd be good.
00:37:48.400 --> 00:37:49.760
Yeah, that would be good if they were, wouldn't it?
00:37:50.159 --> 00:37:51.440
Back to the lawyers again then.
00:37:52.239 --> 00:37:52.800
Yes.
00:37:53.679 --> 00:37:56.800
So anyway, very very amusing, I thought.
00:37:57.039 --> 00:38:02.239
Right, I'm gonna call this the lawyer episode then, if uh rather than the uh keep on buggering on.
00:38:02.480 --> 00:38:03.039
Yeah.
00:38:04.480 --> 00:38:05.119
Very good.
00:38:05.360 --> 00:38:05.679
Right.
00:38:05.840 --> 00:38:11.840
Well, listen, it's great to have you back nearly, although I know you're still not officially back at work until next week.
00:38:11.920 --> 00:38:12.960
So um it's very good.
00:38:13.599 --> 00:38:17.119
But this will go out on Tuesday, and I will then be 60 years old.
00:38:17.199 --> 00:38:18.639
So yeah, yeah.
00:38:19.039 --> 00:38:19.920
That's right, yeah.
00:38:20.000 --> 00:38:22.079
So I am off to put my glad rags on.
00:38:22.159 --> 00:38:23.599
We're having a little bit of a party tonight.
00:38:23.679 --> 00:38:25.039
So um nice one.
00:38:25.119 --> 00:38:27.679
Well, have an amazing time and have a brilliant birthday.
00:38:27.840 --> 00:38:32.719
Hope everyone looks after you suitably well, and then I look forward to your feedback on the present I sent.
00:38:34.480 --> 00:38:36.159
I will give you that next week.
00:38:36.480 --> 00:38:37.119
Great stuff.
00:38:37.360 --> 00:38:38.480
All right, nice to see you, mate.
00:38:38.559 --> 00:38:38.880
Take care.
00:38:39.440 --> 00:38:39.519
Bye.
00:38:39.760 --> 00:38:41.119
Cheers, bye bye, bye.
00:00:00.719 --> 00:00:02.160
We are now recording.
00:00:02.479 --> 00:00:02.959
Excellent.
00:00:03.040 --> 00:00:09.919
Well, in that case, shall we get this pantomime horse of a podcast underway for I don't even know what episode it is, Kieran.
00:00:10.000 --> 00:00:11.439
I've given up, I've given up counting.
00:00:11.519 --> 00:00:13.839
We're just keep on going, keep on trucking the same thing.
00:00:14.160 --> 00:00:14.560
Exactly.
00:00:14.720 --> 00:00:17.600
Keep on buggering on, as Winston Churchill famously said.
00:00:19.039 --> 00:00:20.160
Keep on buggering on.
00:00:20.879 --> 00:00:21.359
I like that.
00:00:21.519 --> 00:00:23.120
I use that, I use that quite a lot.
00:00:23.679 --> 00:00:26.559
That's like that's a very useful phrase, I feel.
00:00:26.800 --> 00:00:28.399
Hello, how have you been on holiday?
00:00:28.800 --> 00:00:30.399
No, that should be the title of this week.
00:00:30.719 --> 00:00:31.920
Keep on buggering on.
00:00:32.159 --> 00:00:34.479
You love to pick out the rude ones, don't you?
00:00:34.799 --> 00:00:35.520
I do, yeah, yeah.
00:00:35.600 --> 00:00:38.640
I think we get more, I think it gets more traction, as they would say.
00:00:39.039 --> 00:00:40.880
I haven't spoken to you for three weeks.
00:00:41.119 --> 00:00:42.399
I know it's been a while, hasn't it?
00:00:42.560 --> 00:00:43.359
I've been on holiday.
00:00:44.000 --> 00:00:46.000
I don't remember the last time we didn't speak for three weeks.
00:00:46.079 --> 00:00:48.000
We don't even usually go three days without speaking.
00:00:48.240 --> 00:00:53.679
Well, I did uh you did send me a few emails, and I've sent you a few, so it's not been entirely contactless, has it?
00:00:54.000 --> 00:00:55.520
No, but emails don't count, do they?
00:00:57.039 --> 00:01:04.560
And I did watch your video of a meeting um on the um uh the stuff that Donald's building, the agentic AI stuff that Donald's building.
00:01:04.640 --> 00:01:10.480
So I felt like I was with you on holiday despite despite you not actually being on the sun lounger next to me in person.
00:01:11.120 --> 00:01:11.840
Yeah, very good.
00:01:11.920 --> 00:01:16.159
Well, I think um you missed my Sun Lounger racing in on holiday.
00:01:16.239 --> 00:01:20.799
So the hotel that I that's awful, it's the most it's the most degrading part of going.
00:01:21.040 --> 00:01:24.799
I mean, all holidays are like it to some extent, and I'm there with seven people.
00:01:25.120 --> 00:01:31.599
We generally want therefore six sun loungers because Finn doesn't really, he runs around the place all the time, you know, because he's six.
00:01:31.760 --> 00:01:34.079
So um that's quite difficult to get that.
00:01:34.159 --> 00:01:40.640
And this particular hotel has the gates, they lock up so no getting up early advantage, which is a good thing, I think.
00:01:40.799 --> 00:01:41.680
But at 9 a.m.
00:01:41.920 --> 00:01:49.920
they open the gates to the pool, and there is an inevitable uh uh how to put it, dash is really what I want to say, but I was trying to find a different word.
00:01:50.400 --> 00:01:50.879
Scrummage.
00:01:51.120 --> 00:01:54.159
It is awful, and so I was getting down there for that.
00:01:54.239 --> 00:02:04.159
I made a really good friend, uh, this guy Trevor, who was a lovely Irish fellow that was always at the same gate that I went for, like early, and we used to chat for half an hour before the gates opened.
00:02:04.319 --> 00:02:08.080
But it was so degrading to have to actually kind of you jog.
00:02:08.479 --> 00:02:12.800
One time I went full tilt because there was there were these two kids.
00:02:12.960 --> 00:02:19.759
There were these two kids that kept on the the day before they'd shuffled right through at the last second and then ran and got all under your feet.
00:02:19.919 --> 00:02:22.800
And uh and until I was like, I'm not having that again.
00:02:25.759 --> 00:02:27.199
Oh sorry, but I didn't see it.
00:02:27.360 --> 00:02:37.919
We did laugh, we did laugh about getting Finn behind me with a giant inflatable just kind of like to slow everyone down for but they have about eight gates to the pool, so people are going from all directions.
00:02:38.080 --> 00:02:43.599
It's kind of it's well managed, and to be honest, I don't know in a way at the same time, are they synchronized all at the same time?
00:02:44.080 --> 00:02:44.319
Yeah, yeah.
00:02:44.479 --> 00:02:45.919
So they have staff on a bunch of them.
00:02:46.159 --> 00:02:56.159
Ours was left, it was quite a quiet one that I'd spotted, and so it was left to us to like just be honest, decent, and wait till that you know they've kind of called it, but it's awful.
00:02:56.240 --> 00:03:03.680
I mean, you feel like so crap, but then you know, for for three minutes you do that, and then the rest of the day I've got my kids together.
00:03:03.759 --> 00:03:10.800
It's the only time I get to spend real quality time with my teenage boys, and so I don't really want them not to have a sun lounger there, and they're not getting up.
00:03:10.879 --> 00:03:12.719
So, so yeah, I was yeah, so there you go.
00:03:12.800 --> 00:03:13.520
So you missed out on that.
00:03:13.599 --> 00:03:15.680
Luckily, it's probably our team talks about it.
00:03:15.919 --> 00:03:16.879
I'm told I'm on it.
00:03:17.120 --> 00:03:21.439
I've been shown one point where there's definitely me running, you don't see my back, luckily.
00:03:21.680 --> 00:03:23.439
So anyway, there you go.
00:03:23.680 --> 00:03:24.159
There you go.
00:03:24.240 --> 00:03:25.280
That's my holiday.
00:03:25.599 --> 00:03:26.400
Oh, brilliant.
00:03:26.639 --> 00:03:27.360
How was yours?
00:03:27.439 --> 00:03:28.960
You've been reading books, I hear.
00:03:29.280 --> 00:03:32.879
Uh been reading, but uh yeah, I do like a bit reading, been listening to stuff.
00:03:32.960 --> 00:03:42.960
Uh ours was much more it was not the the only sun lounges were in our private course, so we didn't have to go anywhere because they it was much more uh but that was just like an Airbnb thing.
00:03:43.039 --> 00:03:49.199
It wasn't a big old fancy uh hotel for uh for a maybe you could have staged something just for your for the Watkinses.
00:03:49.439 --> 00:03:51.039
You could right, everybody.
00:03:51.360 --> 00:03:54.800
Next year I am going to initiate the Sun Lounger Challenge.
00:03:54.960 --> 00:03:56.000
It's gonna be brilliant.
00:03:56.240 --> 00:03:57.280
Take one away.
00:03:58.639 --> 00:03:59.599
Like musical chairs.
00:03:59.680 --> 00:04:00.080
Yeah, exactly.
00:04:00.400 --> 00:04:01.520
Musical sun lounges.
00:04:03.199 --> 00:04:07.520
Yeah, but the one anyone who doesn't get doesn't get a sun lounger has to go and get the beers in.
00:04:07.599 --> 00:04:11.919
That's the really so you go on.
00:04:12.000 --> 00:04:15.599
What so what you what have you picked up on the old uh podcasts or news?
00:04:15.840 --> 00:04:32.480
Oh well, I've been listening to lots of things, but reading lots of things as well, and uh uh that's been really nice just to take a step back and thinking about what we've achieved over the last nearly three years, and um because it'd be three years in September we've been doing this thing.
00:04:33.120 --> 00:04:45.439
And um, some of the challenges that we'd come across in that time, some of the challenges that we face now are challenges that we face then, but they're some of them are changing quite markedly, and we'll we'll talk about some of those things.
00:04:45.680 --> 00:04:49.439
Uh really interesting reading some of the media.
00:04:49.600 --> 00:04:54.079
Um, so uh I have several sources, as most people will know.
00:04:54.240 --> 00:05:04.240
Um, but seeing lots of uh themes, I guess, which we we've touched on over the last however six months since we started this.
00:05:04.319 --> 00:05:10.240
It must be six months because actually one of the very first episodes we did was your birthday.
00:05:10.560 --> 00:05:11.120
And it's true.
00:05:11.439 --> 00:05:13.279
It's your birthday, Eve.
00:05:13.360 --> 00:05:14.639
Uh your half birthday Eve.
00:05:16.079 --> 00:05:17.199
It's your half birthday.
00:05:17.360 --> 00:05:19.199
So your your it's your half birthday tomorrow.
00:05:19.839 --> 00:05:21.759
It's your full birthday, Eve, which is quite there.
00:05:21.839 --> 00:05:25.920
You go, some weird coincidence that we have birthdays exactly six months apart.
00:05:26.160 --> 00:05:37.040
So we've definitely we've done we've been doing this uh podcast for six six months now, and um and so some of the things are um adoption is a really big one.
00:05:37.199 --> 00:05:39.680
Um and I'll come on to talk about that.
00:05:39.920 --> 00:05:48.079
Uh the rise of um AI in things like complaints, uh, we've talked about many times, and we'll talk about that again.
00:05:48.240 --> 00:06:02.399
Um uh and actually how not just the public sector, but the private sector responds to that stuff as well, because I hadn't clocked this, but I should have done really, you know, the the the complaints uh are arising in in the private sector as well.
00:06:02.720 --> 00:06:06.800
It's not just the public sector that are suffering from from that kind of stuff.
00:06:06.959 --> 00:06:13.040
Um, the challenges around AI consciousness, and I know we've talked about whether AI is conscious or not.
00:06:13.120 --> 00:06:17.040
Uh, how do you deal with things like that going forward?
00:06:17.279 --> 00:06:45.279
And then a really tricky subject uh which uh caught my attention, which we have talked about on here before, and uh and that's um sort of personal relationships with AI, and especially for young and vulnerable people uh you know leaning on AI and thinking AI is real and conscious, and and some of the challenges around that, but also how some countries in the world are dealing with that.
00:06:45.439 --> 00:06:47.519
So that's been really fascinating.
00:06:47.600 --> 00:06:50.240
And and where's AI going to take us in the next 10 years?
00:06:50.319 --> 00:06:51.759
I don't have a crystal ball.
00:06:51.920 --> 00:07:01.199
Um I can't even tell you where it's thinking takes in the next 10 minutes because there'll be another three LLMs dropped uh by then anyway.
00:07:01.439 --> 00:07:20.480
Uh but the whole rise of um smaller models, the whole the rise of um uh air gapped models um for privacy and security, especially in uh defense and security areas, um, and how you create secure AIs.
00:07:20.639 --> 00:07:35.120
Um, some of the things that came up while I was gone, um uh police officers sanctioned for using uh open models when they were specifically told not to, but one of them kept a decision log.
00:07:35.279 --> 00:07:41.360
It's like you kept a decision log on Chat GPT of things that you were doing regarding your work.
00:07:41.519 --> 00:07:48.319
I mean it just beggars belief, really, uh especially when there had been an E which says they shouldn't use the tools.
00:07:48.480 --> 00:07:59.040
And I've had a few exchanges on on uh LinkedIn with people about um private models and and how private models are good, but they're only for the private sector.
00:07:59.120 --> 00:08:04.079
And it's like, well, we we do 95% of ours for the public sector.
00:08:04.160 --> 00:08:08.959
It's not true that they're just that just the public sector could sorry, just the private sector can afford them.
00:08:09.120 --> 00:08:11.279
So I think there's a whole load of misconceptions.
00:08:11.439 --> 00:08:15.839
Um, but yeah, that's a that's a long ramble around lots of different stories.
00:08:16.160 --> 00:08:19.439
Um so pick one and let's start there and dive in.
00:08:19.759 --> 00:08:26.480
Well, on the uh latter things you're saying there about um police getting sanctioned for using Chat GPT.
00:08:26.639 --> 00:08:40.320
Um I only this morning, and I'm sure it's not new news, it just turned up on one of my feeds, saw there was an expert witness called in the States for a trial of it was 3M versus someone or other, and probably the less said about that anyway, the better.
00:08:40.480 --> 00:08:49.600
But the judge, um this expert witness, they noticed in his in his whatever he'd put put it in his paper that it was clearly AI had helped.
00:08:49.679 --> 00:08:53.440
So the judge subpoenaed his chat GPT exchanges.
00:08:53.600 --> 00:09:15.840
Oh, and apparently the whole thing was done entirely from start to finish, including a point where he'd said, uh, write me an argument that shows that 3M are zero percent um of the of the responsibility for this incident, and it and he'd even down to like there was a smoke detector involved, and he'd he'd put a photo of it onto there and said, What is this?
00:09:16.000 --> 00:09:18.240
And so he got completely debunked.
00:09:18.480 --> 00:09:23.679
But the interesting point in all of it, I think, is that the judge said, I want to see the record and was allowed to.
00:09:23.840 --> 00:09:25.360
That's the world, the future is actually.
00:09:25.679 --> 00:09:26.879
That's really interesting, isn't it?
00:09:26.960 --> 00:09:34.399
Because we before we both went on holiday, the big bit that we were both working on was um the evidence packs.
00:09:34.720 --> 00:09:35.759
Evidence packs, yeah.
00:09:35.919 --> 00:09:36.480
Evidence pack.
00:09:36.720 --> 00:09:37.759
How do you create evidence pack?
00:09:37.919 --> 00:09:51.840
How do you just press the button and it pull out everything that you've put in, where what it's looked at, what it's what it's consulted, what it's cited, the exchanges it's had with the individual, and then presenting it all in a PDF, which you can just go, ta-da, here it is.
00:09:52.000 --> 00:09:52.960
Yeah, how fascinating.
00:09:53.279 --> 00:09:53.759
There you go.
00:09:53.840 --> 00:10:00.720
So you can press a button and get that and be uh hopefully be uh able to stand behind the trust uh of the system anyway in the first place.
00:10:00.799 --> 00:10:01.679
But yeah, interesting.
00:10:01.759 --> 00:10:05.919
The chat GPT exchanges being involved in courts.
00:10:06.080 --> 00:10:10.159
Well, that and that's what your police officer no doubt will be uh up against as well.
00:10:10.240 --> 00:10:11.279
That same show me.
00:10:11.360 --> 00:10:13.600
I I'm got I need to see what you've been asking it.
00:10:13.679 --> 00:10:15.519
So yeah, be careful with Shadow AI.
00:10:15.600 --> 00:10:18.399
It sounds like shadow AI might be coming more into the foreground.
00:10:18.720 --> 00:10:19.519
Yeah, indeed.
00:10:19.759 --> 00:10:21.039
Interesting, interesting.
00:10:21.360 --> 00:10:23.679
Shall we talk about complaints first?
00:10:23.759 --> 00:10:25.919
Because I've got some stuff on that as well.
00:10:26.159 --> 00:10:36.799
Um, so yesterday the BBC published an article on uh the rise in complex AI-driven complaints in public services and schools, particularly.
00:10:36.960 --> 00:10:43.039
But as we know, it's happening in housing um in spades as well, and no doubt lots of other sectors.
00:10:43.200 --> 00:10:55.840
Um, and so the the kind of key points which I think are really interesting in the article was firstly you're getting these longer complaints that are citing law, legislation, case case law.
00:10:56.000 --> 00:11:08.080
Um, not always accurately, of course, because as you know, as we know, one of the things that AI is still not very good at is in law or academic journal referencing is actually citing the correct one.
00:11:08.399 --> 00:11:14.559
So, so but that trips up our public sector partners because they still have to deal with it.
00:11:14.639 --> 00:11:19.600
You can't, and you've still got to go and find that and then say no, that's not correct, and correct them in the response.
00:11:20.320 --> 00:11:27.840
So taking an absolute nightmare, uh it's sort of taking a huge amount of time and becoming a bigger nightmare for everybody.
00:11:28.080 --> 00:11:29.679
Um, so I thought that was really interesting.
00:11:29.759 --> 00:11:36.080
And then the the point that it also made in the article, I know you've got you read it, so I know you've got some thoughts on it too.
00:11:36.240 --> 00:11:53.120
But the other point was you've what's happened with AI is it's kind of leveled the playing field for people who used to be able to write a really well-written complaint were getting more favorable outcomes than someone that is you know just writing something very poorly and bad with bad grammar, etc.
00:11:53.360 --> 00:11:58.639
So AI has enabled those people that would otherwise have been silent to now represent themselves.
00:11:58.799 --> 00:12:04.960
That's a good thing overall, but of course, it in terms of workload, it's up massively for everybody.
00:12:05.200 --> 00:12:07.279
So, yeah, really interesting.
00:12:07.360 --> 00:12:12.559
Um, and as you know, this is something we're working on, and we have a solution for and we've been talking about for some months.
00:12:12.639 --> 00:12:16.559
So um I hopefully that's gonna work out well for us and our solutions.
00:12:16.879 --> 00:12:17.600
I think so.
00:12:17.679 --> 00:12:39.519
I mean, I know we've specifically focused our solutions on um housing and education, so schools um in particular, but um one of the articles that I uh I posted a couple of things on LinkedIn recently, one from the FT, one from the economist, about uh this happening in other sectors, uh like uh rental housing.
00:12:39.679 --> 00:12:42.320
So people uh private private landlords.
00:12:42.399 --> 00:12:44.480
So it's it's very similar to the housing thing.
00:12:44.639 --> 00:13:00.480
But you you can imagine lots of organizations, anybody, uh uh one of my my friends, his son, the wheel fell off his um the wheel fell off his uh uh luggage uh from a very from a very well known uh manufacturer who I will not repeat on here.
00:13:00.559 --> 00:13:04.720
Anyway, um he basically said uh the wheels fall off my luggage.
00:13:04.879 --> 00:13:06.559
I um I want my money back, please.
00:13:06.639 --> 00:13:18.879
And they they wrote back and said, uh very sorry, um using your luggage uh during travel is not one of the uh is not one of the the times where you can get a refund on this.
00:13:19.039 --> 00:13:20.320
So he's he's gone.
00:13:21.279 --> 00:13:21.919
I'll stop that.
00:13:22.159 --> 00:13:25.200
She went out to chat GPT 10 pages later.
00:13:25.440 --> 00:13:26.240
There you go.
00:13:26.399 --> 00:13:31.200
There's all there's all my evidence, here's all the here's all here's what consumer law says.
00:13:31.279 --> 00:13:34.399
Here's the and I want and I want a refund.
00:13:34.720 --> 00:13:39.840
And they were like, oh here's a here's a credit note for a new for a new uh piece of luggage.
00:13:40.000 --> 00:13:44.000
So um it it it's happened, it's gotta be happening everywhere, isn't it?
00:13:44.080 --> 00:13:47.840
And and uh uh and and the problem that the problem is fundamentally the same.
00:13:48.000 --> 00:14:00.720
It doesn't matter what the what the complaint is, unless you've got an evidence base to refute or back up or or or solve that problem from, then you're going your staff are gonna be in trouble because they're just gonna get delused with these things.
00:14:00.799 --> 00:14:02.799
So yeah, complaints is really interesting.
00:14:02.879 --> 00:14:15.360
But the other bit that I thought was interesting in a BBC article, and I know lots of people um call there's there's certainly a conspiracy theorist who goes into my pub on a regular basis who calls them to the bias broadcasting corporation.
00:14:15.440 --> 00:14:28.159
But I think one of the uh one of the bits that was quite uh balanced in the BBC article was the bit right at the end which said, and for some people this is occurring because you used to be able to pick up the phone and talk to somebody, and now you can't.
00:14:28.399 --> 00:14:45.840
So the fact that lots of the uh responses are now automated or using AI or bots or whatever you want to call it, the fact you can't get someone to um answer a simple complaint or or deal with an edge case which doesn't fit the usual criteria, then uh of course these things are going to rise.
00:14:45.919 --> 00:14:47.919
So I thought that was a was an interesting piece.
00:14:48.320 --> 00:14:48.720
Yeah, it is.
00:14:48.960 --> 00:14:53.120
And your your love of phone calls, I think that it's really does fall right back into it.
00:14:53.200 --> 00:15:08.240
Or maybe it is a good answer because you can at least deal with challenges and probably close them and it comes that comes back to the way that we've been talking about how agentic can solve some of the uh work volume problems for organizations.
00:15:08.399 --> 00:15:10.799
That kind of, you know, does this need a human?
00:15:10.879 --> 00:15:12.240
You know, is it a simple query?
00:15:12.320 --> 00:15:14.000
You know, can I have a can I have a pet?
00:15:14.080 --> 00:15:17.360
Uh in if I'm a housing association, can I have a pet?
00:15:17.519 --> 00:15:20.559
No, yeah, when do you know when do I need to have my keys in?
00:15:20.639 --> 00:15:21.519
Here's the times.
00:15:21.759 --> 00:15:23.279
Actually, I've got this problem.
00:15:23.519 --> 00:15:26.879
That's somebody needs to review that answer before it goes out.
00:15:27.039 --> 00:15:31.039
And the third type, which is it absolutely needs a human to to review.
00:15:31.120 --> 00:15:35.519
And and that's I mean, that's just a classic, um, that's just classic handling.
00:15:35.600 --> 00:15:37.039
Even Microsoft do that, don't they?
00:15:37.200 --> 00:15:41.440
They Microsoft do that with their um monitoring, filtering and monitoring.
00:15:41.759 --> 00:15:43.840
Filtering and monitoring, the abuse monitoring piece.
00:15:44.000 --> 00:15:48.879
Yeah, which bits, which bits are, which bits go by, which bits need looking at, and actually which bits need a human.
00:15:49.200 --> 00:15:52.559
It's it's just dead, it's not rocket science, it just needs implementing.
00:15:52.879 --> 00:15:53.600
Yeah, interesting.
00:15:53.759 --> 00:16:04.639
I read in that BBC article it said that there was a grievance about uncollected bins that came in somewhere between 19 and 27 pages with case law attachments and various other things.
00:16:04.799 --> 00:16:05.519
Can you imagine that?
00:16:05.679 --> 00:16:07.759
You're like, that would have been a phone call years ago.
00:16:08.159 --> 00:16:09.279
My bins have been collected.
00:16:09.360 --> 00:16:10.960
No worries, we'll come and get them tomorrow.
00:16:11.200 --> 00:16:12.559
That's right, we'll send somebody around.
00:16:12.639 --> 00:16:12.960
Yes, right.
00:16:13.200 --> 00:16:39.519
Yeah, now it's now you've got a massive response to put what's interesting though, uh uh came up when we talked about this in um our daily stand-up this morning was is if if people are using AI to send your request and then you use AI to support an answer and to hopefully create a robust answer based on what we would suggest, our models where you're looking in the right documents and refuting the right things with the correct information, then they get that back.
00:16:39.600 --> 00:16:41.200
Do they then put that back in their AI?
00:16:41.279 --> 00:16:43.440
And is it then become just a token wars?
00:16:43.840 --> 00:16:45.679
Who who's got the best AI?
00:16:45.919 --> 00:16:49.200
Yeah, yeah, who's got the biggest AI budget?
00:16:49.519 --> 00:17:03.039
Because we heard uh Donald shared this morning that a law firm, no win, no-fee law firm, was asking um somebody to first speak to their AI bot to determine whether or not there is a case.
00:17:03.279 --> 00:17:07.200
And then so you do all of that before a human gets involved, which is uh interesting.
00:17:07.279 --> 00:17:20.720
And I suspect a breach of the EU Act, not that it applies yet in the UK, although there are similar regulations here, but because that's a decision-making thing that's entirely automated, and you have to be very careful, as you know, in that.
00:17:20.799 --> 00:17:23.359
So, but who knows what their human review is.
00:17:23.440 --> 00:17:25.039
But back to the poems.
00:17:25.200 --> 00:17:30.400
I did see though, also, which is another way of potentially handling this problem of massive emails.
00:17:30.559 --> 00:17:32.480
Uh, it was suggested in only a sentence.
00:17:32.559 --> 00:17:36.880
I thought that's actually quite a clever route through, is to create a web complaints web form.
00:17:37.359 --> 00:17:41.759
And so only accept complaints through a web form, which presumably is acceptable.
00:17:41.839 --> 00:17:44.240
I don't know whether you have to accept them in any way they come.
00:17:44.319 --> 00:17:47.680
But if you have that, then you can limit word count and do that.
00:17:48.559 --> 00:17:49.519
Five-word limit.
00:17:49.920 --> 00:17:53.680
That's right, just tell us what the sort of me.
00:17:58.640 --> 00:18:00.240
We've received your complaint, Mr.
00:18:00.319 --> 00:18:00.559
White.
00:18:00.720 --> 00:18:01.839
There's no further action required.
00:18:02.079 --> 00:18:02.640
That's right, yeah.
00:18:02.720 --> 00:18:03.200
No talk.
00:18:03.920 --> 00:18:05.440
Calm down, have a beer.
00:18:05.680 --> 00:18:05.920
Yeah.
00:18:06.079 --> 00:18:10.160
Well, I remember also back in DFE days when we were consulting with them all those moons ago.
00:18:10.240 --> 00:18:14.000
I remember talking to Stuart Miller, uh, our lovely friend now retired.
00:18:14.079 --> 00:18:15.119
Um civil servant.
00:18:15.359 --> 00:18:17.759
Yeah, he retired a year ago, maybe a bit more.
00:18:17.920 --> 00:18:20.160
I went to his retirement party, which was fun.
00:18:20.400 --> 00:18:33.440
But um he was saying how in the case they have the cases teams who deal with people who've written a letter to their MP or the Secretary of State, actually, when they've written to the Secretary of State, um, and they will reply.
00:18:33.680 --> 00:18:48.400
And he always said, he said, I always wonder what they'll send a kind of three-page reply with all the backstory, and you know, since 1997 we have you remember all of those days when we were under the Labour government, they always draw this line of since then it's been amazing.
00:18:48.559 --> 00:18:50.000
Before then it was awful.
00:18:50.160 --> 00:18:55.359
Um, but he always said, I wonder what people must think when they're reading this over their cornflakes.
00:18:55.680 --> 00:18:59.440
Because they wrote in going, I really hate the school uniform policy, blah blah blah blah blah.
00:18:59.599 --> 00:19:04.000
And really a response to say thank you, noted, is probably all they want.
00:19:04.480 --> 00:19:12.960
Just like I want you to know that I'm bothered by it, rather than a sort of three or four-page letter back with all the backstory on exactly why and how.
00:19:13.039 --> 00:19:14.319
And it's interesting.
00:19:14.480 --> 00:19:15.839
So anyway, let's get an aside.
00:19:16.319 --> 00:19:24.319
Well, uh, it's not not really, because uh not really in a side because it it links into something that I wanted to talk about uh on something at the back of someone I read.
00:19:24.480 --> 00:19:26.880
People want to be listened to, they want to be heard.
00:19:27.039 --> 00:19:29.920
If you've got a complaint, you know, who do you talk to?
00:19:30.079 --> 00:19:51.279
And and and actually one of the big things that we have talked about uh before is is people using AI for relationships and um the rise of uh people using AI for intimate relationships or intimate information or intimate knowledge, uh and and actually how problematic that can be.
00:19:51.440 --> 00:20:05.200
There have been certainly cases in the US um uh which have gone to court over uh people committing suicide because the uh AIs have reinforced their the negative feelings or whatever else.
00:20:05.440 --> 00:20:26.640
And um uh there was a really interesting um interview in The Economist with Jorbal Noah Herari, who is a uh a very well-known uh uh is really intellectual who who uh talks about uh lots of different things about AI, but one of the things he was talking about was uh AI consciousness.
00:20:26.720 --> 00:20:27.920
And we've talked about this before.
00:20:28.720 --> 00:20:29.680
Is AI conscious?
00:20:29.759 --> 00:20:36.160
And he said it can't be conscious, it's a machine, and actually one definition of consciousness is can it suffer?
00:20:36.319 --> 00:20:39.519
And he said, AI will tell you it can suffer because that's how it's being trained.
00:20:39.599 --> 00:20:44.240
It's very good at the whole PR stuff, it'll it'll tell you what you think it wants to hear.
00:20:44.400 --> 00:20:56.079
But the real problem becomes when people are uh are taken in and and uh uh and they think that the AI is their boyfriend or their girlfriend or whatever else.
00:20:56.240 --> 00:21:11.039
And um and he he made a really strong comment that something like two-thirds of the people in the US do do not like um AI or data centers, or they think AI is going to be bad for their jobs or whatever else, but they don't know how to stop it.
00:21:11.200 --> 00:21:30.079
And one way to stop it is to stop is to m m force uh organizations to not um uh put create personal hooks or or uh uh personally identifiable or uh or make it seem like it's a personality that you're talking to, a real person.
00:21:30.640 --> 00:21:34.240
And um and I know that others have have talked very much about this.
00:21:34.319 --> 00:21:39.920
So Sol Rashidi, who we first saw back in 2023, and and Nate B.
00:21:40.079 --> 00:21:49.359
Jones, of course, he's done a lot of work on you know, don't let your kids have relationships with um uh with these um with these tools because it's the wrong thing to do.
00:21:49.599 --> 00:21:59.759
And really interesting, what what uh you have all uh said was um uh you do not want AI companies selling um Wi Fi.
00:22:00.160 --> 00:22:01.759
Friends or girlfriends to your 10-year-old kids.
00:22:04.160 --> 00:22:05.279
It is terrifying.
00:22:05.519 --> 00:22:09.519
And what's really interesting, I read another article on my holidays.
00:22:09.839 --> 00:22:17.839
In China, they've just bat they've just forced companies to take out that personal interaction piece from a bunch of their AIs.
00:22:18.000 --> 00:22:23.839
And people are complaining because their AI boyfriend or girlfriend would always listen to them.
00:22:23.920 --> 00:22:29.680
You know, it didn't matter how tired they were, how stressed they were, or you know, the AI was always there for them.
00:22:29.759 --> 00:22:34.400
And now that they're not responding in the same way, people are starting to complain.
00:22:34.640 --> 00:22:37.759
That's really interesting to me on a whole number of levels.
00:22:37.839 --> 00:22:50.640
I don't think that the whole um uh having AI as a boyfriend or girlfriend or uh helping with personal relationships is is a is a good thing.
00:22:50.720 --> 00:22:52.400
That's my that's my personal view.
00:22:52.480 --> 00:22:55.519
Uh others others might have different uh views, of course.
00:22:55.680 --> 00:23:06.799
Um but the fact that China, one of the most authoritarian countries in the world, have decided that this is a bad thing and all companies have to stop it, it's really fascinating to me.
00:23:06.880 --> 00:23:10.319
And I think that the um perhaps the West could learn something from that.
00:23:10.480 --> 00:23:11.599
It was it's really interesting.
00:23:11.759 --> 00:23:18.799
So I'll I'll I'll on the podcast uh comments I'll I'll I'll put the links in so that you and others can can have a look at that.
00:23:20.160 --> 00:23:20.480
Interesting.
00:23:20.559 --> 00:23:29.359
The DFE regulations on AI use for students, particularly, but also for teachers, is that you you can't have it being like the sycophantic style.
00:23:29.440 --> 00:23:30.160
What a great question.
00:23:30.240 --> 00:23:31.200
Yeah, you're amazing.
00:23:31.359 --> 00:23:38.480
Even it's not quite in the relationship sense, but it's just much more trying to get it to be more teacher-like, really.
00:23:38.559 --> 00:23:45.119
If you're giving it to students, I guess more Socratic with you, unless you're brilliant and amazing and you've asked me the best question ever.
00:23:45.279 --> 00:23:50.799
Which, as you know, when we've when we've done things with AI where we've said to it, don't don't worry about my feelings.
00:23:52.400 --> 00:23:58.240
You have to embrace yourself first, but actually the answers are better, I think, than the ones that tell you you're great first.
00:23:58.559 --> 00:24:01.279
I think it's much better to get the critical angle.
00:24:01.359 --> 00:24:03.920
So maybe that'll be a more a direction.
00:24:04.079 --> 00:24:04.400
Who knows?
00:24:04.559 --> 00:24:06.720
Good, good though, good people are thinking about it.
00:24:06.960 --> 00:24:07.839
I I think so.
00:24:07.920 --> 00:24:16.160
I uh uh but it it's one of those uh it's one of those things where I was talking to uh I was talking to Ben about this earlier.
00:24:16.240 --> 00:24:19.279
It's like how many people in the world actually use AI right now?
00:24:19.839 --> 00:24:21.440
Uh is it a billion?
00:24:21.920 --> 00:24:25.599
But it's like there's some more than a billion, more than a billion on chat GPT, wasn't it?
00:24:26.160 --> 00:24:30.480
Yeah, it's gonna be a couple of billion using it in a chat, a chat function.
00:24:30.799 --> 00:24:38.319
Yeah, but that that still means that if there's between seven and eight billion people in the world, there's still a lot of people who've never come across this stuff.
00:24:38.400 --> 00:24:41.279
And actually, how how fast is it going to develop?
00:24:41.359 --> 00:24:45.200
So when they do eventually you know get technology and catch up, etc.
00:24:45.440 --> 00:24:49.599
It's gonna be it's going to be really challenging and interesting in the future.
00:24:49.680 --> 00:24:52.480
This is one of my kind of you know, where does AI take us in 10 years?
00:24:52.640 --> 00:25:00.079
Things like because right now there's only like a seventh or an eighth of the popul the world's population have got access to it.
00:25:00.160 --> 00:25:03.119
What what happens, what happens in most countries where they don't?
00:25:03.440 --> 00:25:14.640
I did have an amusing thought to myself when I was walking the dog, which was about you can imagine going back like four years and being the only person that had Chat GPT and no one else knew about it.
00:25:15.279 --> 00:25:20.000
You'd be like, you are just an unbelievable work machine.
00:25:21.599 --> 00:25:22.880
You just go, yeah, am I?
00:25:22.960 --> 00:25:23.200
Yeah.
00:25:23.519 --> 00:25:25.200
Yeah, while I'm in the pub.
00:25:26.079 --> 00:25:28.000
Yeah, it's something else I've done.
00:25:28.079 --> 00:25:28.240
Yeah.
00:25:28.480 --> 00:25:28.799
Exactly.
00:25:28.880 --> 00:25:29.200
Yeah, yeah.
00:25:29.359 --> 00:25:31.279
So I'm just doing several other things right now.
00:25:31.440 --> 00:25:40.880
Um I I also that that interview that you uh the economist interview you talked about, if AI is conscious, I was kind of quite struck by that whole um which bit?
00:25:42.000 --> 00:25:43.759
Is it every server that it's on?
00:25:43.920 --> 00:25:50.400
Is it every iPhone that's running a local as they're coming out and apparently the new model that's due out probably September going to run local?
00:25:50.799 --> 00:25:52.400
Are they each one conscious?
00:25:52.559 --> 00:25:56.319
Or is it sort of just the overarching global version?
00:25:56.559 --> 00:25:56.799
Yeah.
00:25:57.039 --> 00:26:02.079
So there's a whole bunch of questions, and uh if if that if we end up in that route, but who knows?
00:26:02.559 --> 00:26:04.799
This comes back to something we talked about a couple of weeks ago.
00:26:04.880 --> 00:26:19.359
Um uh that um quite a few of the US uh universities were complaining that all their philosophy professors were being up by um by the big LLMs to uh to deal with these kinds of issues.
00:26:19.680 --> 00:26:22.480
Yeah, that's right, it's a big challenge.
00:26:22.960 --> 00:26:27.920
You also mentioned talking about adoption, uh AI adoption.
00:26:28.319 --> 00:26:31.839
Our ongoing bane of our lives is trying to get people to use the tools.
00:26:32.000 --> 00:26:37.200
I was with a university client yesterday who was responsible for a thing called UK VI compliance.
00:26:37.279 --> 00:26:47.599
That is, the are you allowed to sponsor students, the universities being able to work with international students and provide uh their visa effectively or their sponsorship for their visa.
00:26:47.759 --> 00:26:55.519
Um, and um I showed her knowledge flow and that they have the university has it, and she said, Well, well, why haven't I seen this before?
00:26:55.759 --> 00:26:58.960
This is got this answer answers most of my questions.
00:26:59.279 --> 00:27:02.319
I was like, Well, I can't really I can't really help with that.
00:27:02.400 --> 00:27:08.559
Well, unfortunately, I am here spending my time showing you now, but um I don't really know how to answer your question.
00:27:08.640 --> 00:27:13.200
But that adoption piece down to the basics of I didn't even know he had it.
00:27:13.920 --> 00:27:18.640
Well, I think that's probably true for lots of uh for lots of our customers, is the honest truth.
00:27:18.880 --> 00:27:41.920
And um the reason that it came up it came up for me was uh on Sunday, I think it was Sunday, uh Ned's um video was on uh forward deployed forward deployment engineers, FDEs, which is a relatively new uh job title that's come out of Silicon Valley, even this year, I think, or late last year, or earlier this year.
00:27:42.160 --> 00:27:50.640
But uh OpenAI, Anthropic, and others are all pitching for these people, and the commanding salaries of something in the region$350,000.
00:27:51.440 --> 00:27:52.319
Um it's huge.
00:27:56.880 --> 00:27:59.039
Um but they have to have some skills, Kieran.
00:27:59.119 --> 00:28:00.079
So hang on a minute, but you might have to go.
00:28:02.480 --> 00:28:03.599
But it was fascinating.
00:28:03.680 --> 00:28:12.640
But what he said was these people are being charged out massive rates to big corporations like your Boeings, uh like you Lockheed Martins, all of these people.
00:28:12.720 --> 00:28:15.519
I mean, he's talking about American and East based in Seattle.
00:28:15.599 --> 00:28:17.119
So um that's all fair enough.
00:28:17.200 --> 00:28:26.079
But what he was saying was that the reason they're getting such massive salaries is because the last mile, as it's called with AI, is the hardest piece.
00:28:26.319 --> 00:28:34.640
And we found that with, you know, oh, we can get co-park to that, or we can oh, we can kind of get it to this bit, but we can't get it to do this bit.
00:28:34.880 --> 00:28:48.720
And we've got you know, one customer who remained nameless who, you know, famously is like, oh well, no, it's got to have the spelling mistakes in this box, in this form, in this font, in this colour, or it's or it doesn't work for me.
00:28:48.799 --> 00:28:50.960
It's just like uh back to your comment.
00:28:51.279 --> 00:28:53.039
Oh, I've given you the best toaster in the world.
00:28:53.119 --> 00:28:53.920
But does it do X?
00:28:54.160 --> 00:29:11.039
No, it's a toaster, you know, it's just uh but what what Nate was talking about is the bit that the um uh large LLMs uh and others are working on is how do you embed it into the workflow?
00:29:11.359 --> 00:29:15.519
And this is exactly the comp the conversation we started with like three years ago.
00:29:15.599 --> 00:29:22.400
It's one of the reasons we talk about knowledge flow, is because this is about how you improve how do you inject knowledge into the workflow.
00:29:22.640 --> 00:29:26.400
And what Nate was talking about was were three things really.
00:29:26.640 --> 00:29:30.559
One was uh domain expertise is super key.
00:29:30.640 --> 00:29:34.799
So knowing the problem, whether that's housing, whether that's education, whether it's social work.
00:29:34.880 --> 00:29:41.440
So if you've got somebody who understands that piece, the second then is um around uh technical expertise.
00:29:41.519 --> 00:29:44.720
So can you manipulate the tools to do the things that you actually want?
00:29:45.039 --> 00:29:48.319
And then the third one is what's the process design?
00:29:48.480 --> 00:29:51.279
Now, come back to that customer I talked about.
00:29:51.440 --> 00:29:53.759
Yeah, they didn't start with a blank piece of paper.
00:29:53.920 --> 00:29:57.680
If you'd started with a blank piece of paper, you wouldn't start from here, you wouldn't do it like this.
00:29:57.839 --> 00:30:05.759
You're just trying to cobble, you're trying to shoehorn AI on top of something, and um, and then it's not going to work.
00:30:05.839 --> 00:30:07.599
You need to redo the entire process.
00:30:07.680 --> 00:30:13.200
Or if you if you redo the entire process from scratch, you will end up with a much better experience.
00:30:13.359 --> 00:30:22.480
And I think my one of my other holiday reflections was the number of companies that we come up against where uh one of two things happens.
00:30:22.640 --> 00:30:26.480
Uh the first is the IT guys go, oh, we can do that.
00:30:26.559 --> 00:30:33.119
Uh when when we show it to their IT people guy, and the number that have come back to us and said, actually, our IT people can't do that.
00:30:33.279 --> 00:30:34.799
Please could you help us?
00:30:35.039 --> 00:30:52.400
Uh and then and then the second group is the uh AI snake oil sales people who go, Yeah, yeah, we've uh we've been doing AI for like six minutes now, and we've just put we've changed our name to uh uh uh Fred Blog's AI Limited, and we do AI, and it's like you've learned nothing.
00:30:52.559 --> 00:30:58.240
You've learned what you've done is you've got you've paid you 20 quid a month to Claude, and you're getting Claude to do it all for you.
00:30:58.400 --> 00:31:05.839
You don't really understand how to manipulate these things in order to keep them secure, keep them up to date, how to manage them.
00:31:06.559 --> 00:31:30.400
It's just uh so uh the I I just if you haven't listened to it, I would definitely recommend it's um uh substack video because um it was really interesting to me that he's he's talking about a problem that uh every organization in the world is is kind of coming up against, whether they know it or not, and whether they or they're going to come up against it whether they know it or not.
00:31:30.559 --> 00:31:49.279
But the big organizations are charging uh tens of thousands of dollars uh a week in order to um to to uh to get customers to embed AI into their workflows, and in order to do that, they're paying massive salaries um uh to people who who've got those skills.
00:31:49.680 --> 00:31:50.640
So really interesting.
00:31:51.359 --> 00:31:52.079
Really interesting.
00:31:52.240 --> 00:31:56.000
And I that whole kind of getting in the workflow, I mean, that is exactly, isn't it, where we go.
00:31:56.079 --> 00:32:09.039
And and if you sort of go back into the question of what's the 10-year view, I mean it's it it will be, I am pretty convinced, as much as anyone can say, but we are going to be like entire workflows just being handled.
00:32:09.279 --> 00:32:32.480
And I and I hope that Gartner are right about you'll have kind of teams of one human and a bunch of agents, and and that human needs those skills very likely that you talked about that sort of domain, deep domain knowledge, kind of an understanding of that whole workflow, and enough technical knowledge to know what the AI, or at least the ability to amend things and change the agents so that they are improve and do what they need to do.
00:32:32.720 --> 00:33:02.799
I was um talking also to uh was it I can't remember who it was, but about when we automate agentic email answers or any message answers in our agentic AI systems and how we manage spot checking, I guess, um, of the answers still, because there is that whole process of like if you said, you know, the pet policies for housing, you could say, Well, we're just gonna automate all of those because they're never wrong, and then maybe six months from now they might be wrong and they might continually be wrong.
00:33:05.200 --> 00:33:06.640
You know what looking anymore.
00:33:06.720 --> 00:33:17.680
So I think there's a there's some really interesting, I mean that whole kind of uh explainability, observability, and testing is an area that's still pretty new, and um, I know we're working on it quite a lot.
00:33:17.759 --> 00:33:26.640
We've got Ibby doing quite a lot on automated testing and LLMs as a judge, so use another AI to check another AI, but it's nothing perfect yet in that space.
00:33:26.960 --> 00:33:29.519
No, and Sue Belagodu was interested in that, wasn't she?
00:33:29.680 --> 00:33:33.680
She was saying that you know, if you just stick a human right at the end of the loop, then you've done nothing.
00:33:33.759 --> 00:33:37.680
If they're just rubber stamping stuff at the end, you've done nothing useful at all.
00:33:37.839 --> 00:33:42.240
You need you need to build the human into the workflow in the loop loop.
00:33:42.480 --> 00:33:45.119
So uh she I thought she had some really interesting points on that.
00:33:45.359 --> 00:33:45.680
I agree.
00:33:45.759 --> 00:33:48.799
I think that was really really sensible thought.
00:33:49.119 --> 00:33:59.359
Designing workflow where the human can play a genuine role and needs to guide, direct, nudge rather than here's the finished article, which is so difficult, isn't it?
00:33:59.519 --> 00:34:00.880
So difficult to check.
00:34:01.119 --> 00:34:06.640
On that, did you see Claude's now got um watermarks in all their models from August August the first?
00:34:07.519 --> 00:34:14.480
I did, but I also saw Julia's blog on the leading AI blog, which was like, hang on a jolly minute, it's not as straightforward as that, is it?
00:34:14.639 --> 00:34:21.039
Yeah, well, I because I originally thought in just parsing, I thought, well, that's the end of AI detectors as an as a whole industry.
00:34:21.280 --> 00:34:29.920
Because uh ultimately, if the AI is watermarking it in the first place, then what's the point of turn it in and uh whether it's no uh no was it GPT-free or whatever?
00:34:30.000 --> 00:34:30.320
I can't remember.
00:34:30.400 --> 00:34:37.840
There's all kinds of AI detectors, which none of which are massively reliable, um, or at least in my opinion, are not massively reliable at this point.
00:34:39.519 --> 00:34:43.360
In my humble opinion, allegedly they're not as good as I might have thought they were.
00:34:43.599 --> 00:34:46.400
Yeah, but indeed, yeah.
00:34:46.800 --> 00:34:49.280
But then you've got watermarking that's supposed to be coming over the top of it.
00:34:49.360 --> 00:34:52.559
You won't know what it you won't know and won't find them.
00:34:52.719 --> 00:34:57.440
I'm sure other AIs might find them in time, but you need to kind of know what their patterns are.
00:34:57.599 --> 00:35:05.519
Um in images, they're going to embed sort of hidden pixels basically that humans can't see, but um uh in text, who knows?
00:35:05.760 --> 00:35:09.280
But yeah, the big question then is at what point is it okay to watermark it?
00:35:09.360 --> 00:35:11.360
If and what point is it your work?
00:35:11.519 --> 00:35:12.880
And there's both angles on that.
00:35:12.960 --> 00:35:17.440
There's the I wrote an essay, I just said to the AI, write me an essay and then handed it in.
00:35:17.599 --> 00:35:20.079
Clearly, not my work, watermarked.
00:35:20.239 --> 00:35:22.079
You'd think it would be picked up.
00:35:22.239 --> 00:35:28.320
There's the I wrote the essay and then had it checked for grammar and spelling with the AI and a couple of bits.
00:35:28.480 --> 00:35:33.199
Now it's fully watermarked still, the same outcome from a hand-it-in perspective.
00:35:33.280 --> 00:35:42.480
And then there's the other angle, which is I got the AI to do the work, and then I read and checked it all, and checked the references and all of those things, which is a kind of even greyer area.
00:35:42.639 --> 00:35:51.280
So it's it's a minefield how you're actually going to use watermarks in any sensible categoric that's AI, I don't want to sit, I don't want it near me.
00:35:51.599 --> 00:35:59.039
And it and you would have thought that at some point in time they would have thought about that before they did the watermarking, because that was a bleeding obvious question, aren't they?
00:35:59.440 --> 00:36:07.039
Well, I've made it yeah, especially especially the one where it's like I I've done it and then I've got AI to check it or run I've run AI over it.
00:36:07.119 --> 00:36:10.159
And actually it's my it's my structure, it's my thinking.
00:36:10.239 --> 00:36:16.159
It might have you know offered some suggestions, I might accept them or reject them, but it's still now stamped as the AI work.
00:36:16.400 --> 00:36:23.039
That doesn't that doesn't work in my opinion, in case anyone Yeah, just in case the lawyers are listening.
00:36:24.079 --> 00:36:26.880
Well at least we'll have some more listeners if we get the lawyers listening.
00:36:28.320 --> 00:36:29.840
Not the ones we want, though.
00:36:30.159 --> 00:36:31.199
Yeah, yeah.
00:36:32.400 --> 00:36:35.280
Our audience isn't a lawyer, that's that's that's a good thing.
00:36:35.519 --> 00:36:36.639
Let's hope so.
00:36:36.880 --> 00:36:44.000
Um I have an amusing thing to sort of start to to close on, which made me laugh, which is nothing nothing to do with AI.
00:36:44.159 --> 00:36:45.840
But I was reading only this morning.
00:36:46.000 --> 00:36:49.440
So Batman is being filmed in Glasgow right now.
00:36:49.920 --> 00:36:56.639
The second yeah, a Batman film, and they're using Glasgow's streets as the set for Gotham City right now.
00:36:56.800 --> 00:37:02.159
Literally, as we speak, it's going on, and they've closed these streets, and apparently, this will amused the hell out of you.
00:37:02.320 --> 00:37:10.480
Every morning, the security teams have to go around and find all of the iron brew cans that the locals have hidden around the set in the night.
00:37:13.599 --> 00:37:14.800
Amuse me enormously.
00:37:14.880 --> 00:37:16.000
So uh yeah, good on them.
00:37:16.079 --> 00:37:18.880
The Glaswegians are doing what Glaswegians do best.
00:37:21.280 --> 00:37:29.599
I wondered why Tawny was looking a bit tired out because he's been sneaking out at night planting iron brew cans and bottles everywhere.
00:37:30.400 --> 00:37:30.800
I love it.
00:37:30.880 --> 00:37:36.079
So if uh if and when that batman films out, you have to watch out to see if there's any iron brew knocking around in Gotham City.
00:37:36.719 --> 00:37:37.760
There's bound to be one.
00:37:37.840 --> 00:37:40.559
There's there's there's always something slips through, isn't it?
00:37:40.639 --> 00:37:42.719
So hilarious, absolutely hilarious.
00:37:42.960 --> 00:37:44.079
Yeah, it didn't amuse me.
00:37:44.639 --> 00:37:47.440
I wonder if iron brew paid for that as place product placement.
00:37:47.599 --> 00:37:48.320
Ah, it'd be good.
00:37:48.400 --> 00:37:49.760
Yeah, that would be good if they were, wouldn't it?
00:37:50.159 --> 00:37:51.440
Back to the lawyers again then.
00:37:52.239 --> 00:37:52.800
Yes.
00:37:53.679 --> 00:37:56.800
So anyway, very very amusing, I thought.
00:37:57.039 --> 00:38:02.239
Right, I'm gonna call this the lawyer episode then, if uh rather than the uh keep on buggering on.
00:38:02.480 --> 00:38:03.039
Yeah.
00:38:04.480 --> 00:38:05.119
Very good.
00:38:05.360 --> 00:38:05.679
Right.
00:38:05.840 --> 00:38:11.840
Well, listen, it's great to have you back nearly, although I know you're still not officially back at work until next week.
00:38:11.920 --> 00:38:12.960
So um it's very good.
00:38:13.599 --> 00:38:17.119
But this will go out on Tuesday, and I will then be 60 years old.
00:38:17.199 --> 00:38:18.639
So yeah, yeah.
00:38:19.039 --> 00:38:19.920
That's right, yeah.
00:38:20.000 --> 00:38:22.079
So I am off to put my glad rags on.
00:38:22.159 --> 00:38:23.599
We're having a little bit of a party tonight.
00:38:23.679 --> 00:38:25.039
So um nice one.
00:38:25.119 --> 00:38:27.679
Well, have an amazing time and have a brilliant birthday.
00:38:27.840 --> 00:38:32.719
Hope everyone looks after you suitably well, and then I look forward to your feedback on the present I sent.
00:38:34.480 --> 00:38:36.159
I will give you that next week.
00:38:36.480 --> 00:38:37.119
Great stuff.
00:38:37.360 --> 00:38:38.480
All right, nice to see you, mate.
00:38:38.559 --> 00:38:38.880
Take care.
00:38:39.440 --> 00:38:39.519
Bye.
00:38:39.760 --> 00:38:41.119
Cheers, bye bye, bye.
