S1: Hi folks, and welcome to a Vision Australia Radio special podcast.
S1: And today I'm joined by Professor Toby Walsh. And I've
S1: already forgotten what I'm going to call you, so I'll
S1: just call you, um, chief science person from the Eye
S1: Institute of University of New South Wales, which I probably
S1: just strangled it. Um, so, Toby, thanks for coming on
S1: the program today. And like I did, have seven questions
S1: that I'll get to very, very quickly. But my first one,
S1: and it makes me want to grind my teeth a lot,
S1: is why is there a need in even with AI
S1: itself and the people behind it, to actually try and
S1: make AI systems sound and react like human beings? That's a.
S2: Fantastic question. It's it. And it's a it's a fatal
S2: mistake that we're making all the time because it confuses
S2: people and it messes with people. Um, we give them,
S2: we give them names, often female names. Um, um, you know,
S2: we shouldn't call them Siri or Cortana. We should call
S2: them robot or computer or or or or, you know,
S2: names that certainly aren't gendered. You know, you could call
S2: them Alpha or Omega or, you know, Greek letters of
S2: the Greek alphabet, whatever it is. Yeah. Um, but we
S2: it's a it's a bad human trait. We anthropomorphize, we,
S2: we apply human attributes to things that are not human. Um, no.
S1: Exactly. And because in the in the demo from OpenAI, um,
S1: last week, as we record this week, they had it
S1: doing things like I think it was supposedly flirting with
S1: the person. Um, it was laughing at its own jokes.
S1: And I kept thinking, you're a computer program. This is
S1: all false and not real. And that's why I keep
S1: thinking of, um, I read your book, which is the,
S1: you know, the Faking It, um, excellent book about faking intelligence.
S1: And it this just reminds me that this is just
S1: so up there amongst faking it. I just don't know
S1: why people don't shrink away from this stuff and go, look, guys,
S1: I think we really are getting a bit carried away.
S2: This. The story is actually even worse than that. So
S2: actually today they've actually had to retard the female voice
S2: for the new OpenAI chat bot because, um, they tried
S2: to buy the rights for Scarlett Johansson. She was the
S2: voice on the movie her where it was her voice.
S2: She was nominated for an Oscar, indeed, for the part
S2: where she was the AI operating system. She declined to
S2: license her voice to OpenAI. But the voice that they
S2: had was eerily like her voice. Lots of people said
S2: it was reminded them of the movie, and many of
S2: Scarlett Johansson's friends apparently said they thought it was her.
S2: Right now. Um, because I think she's probably threatened to sue.
S2: She's probably taken a lawyer's less legal, um, action and
S2: the bad publicity. They've actually had to take the voice down. Yeah.
S1: Because, I mean, as a blind person, I've had talking
S1: computers since 1980. So, you know, in one form or another,
S1: I've had computers speak to me. So is it this
S1: thing that I don't know for a, for sighted people,
S1: it's really novel to have a computer quote speak to you.
S1: Or is it the fact that it's supposedly this real
S1: thing behind your computer screen that's really talking to you?
S2: It is a deceit. I mean, it is fooling us
S2: that maybe there's more behind the scenes than there actually is.
S2: As an example, another example, if you use one of
S2: these chatbots like ChatGPT, it slowly types out the answer
S2: to your question. If you actually understand how it works,
S2: it has the answer in a flash. It doesn't actually
S2: have to slowly type out the answer, but they've they've
S2: actually built the interface. So it does that. So you
S2: get the feeling that there's a person behind the screen
S2: that's slowly typing away to you to make it more personalized,
S2: to try and engage you more, to to fool you
S2: more that it's smarter, more intelligent, maybe even more sentient
S2: than it is.
S1: That's right. And a lot of the the nicer the voice,
S1: the more I tend to actually, um, dislike it more
S1: because as far as I'm concerned, you're a computer. Don't
S1: care what you sound like. Give me the information and
S1: make it accurate. And the one of the issues that
S1: I've got, particularly with generative AI, is that if it's
S1: just doing straight, you know, word, sentence, paragraph, page prediction, um,
S1: how can it actually get things so badly incorrect sometimes?
S1: I mean, yes, it's almost like a default. Sometimes. Most
S1: of the time it'll get it correct almost accidentally, I
S1: think sometimes based on its data. But when it gets
S1: things wrong, it really gets things wrong.
S2: Well, because it's, it's that's the thing that we should
S2: remember that it's artificial intelligence. It's not human intelligence. It's
S2: not going to break like human touch. I mean, the
S2: remarkable thing about human intelligence is, is how robust we are.
S2: Whereas computers, people who program computers quickly realize computers are
S2: very brittle. They break and they break very catastrophically. They
S2: don't have all of our common sense. They don't have
S2: our remarkability I can take you, I can drop you
S2: into a new circumstance and you could start doing stuff.
S2: Whereas a computer, you change the input very slightly and
S2: it falls over completely. Um, because it's an artificial, very
S2: different intelligence to human intelligence. And we we're quick, especially
S2: when it sounds like us to think it's going to
S2: be like us, but not realizing. No, actually it's quite
S2: a different type, quite a different flavor of intelligence. I
S2: can remember.
S1: This. I can't remember what Star Trek episode it was,
S1: but I think it was one of the original episodes
S1: with Spock involved because he had to sort of like,
S1: you know, he had to do these, um, his, you know,
S1: mind meld. But he did it with a computer, and
S1: I can remember this computer. It was some sort of
S1: bastardized name of Voyager, because NASA sent out the Voyager
S1: probe and this thing came back, which was this alien spacecraft.
S1: And it was interesting because what they were basically saying
S1: in the episode is that you can have all the
S1: knowledge in the world. It actually doesn't mean you're intelligent.
S1: And I just think it's such a misused word because
S1: I know it's artificial, but I think it's a bit
S1: bold to actually use the word intelligence because it's actually not.
S1: You actually just all it's doing is just looking up facts.
S1: It's not really extrapolating anything. And I just, I just
S1: want to actually have a new name for artificial intelligence
S1: because it's not intelligent.
S2: Well, it nearly got called many other things. It nearly
S2: got called cybernetics in Europe. They call it informatics. Um, yeah.
S2: It's at the end of the day, it's a terrible name.
S2: Intelligence is very poorly defined. So what the heck could
S2: artificial intelligence be? But Spock is a good example because,
S2: you know, he was quite a smart guy, but he
S2: was somewhat lacking in emotional intelligence. And you realize there
S2: are many different facets to intelligence your social intelligence, our
S2: emotional intelligence, our creativity that are very important, really important
S2: to to our interactions with each other. And Spock, despite
S2: he was clearly a very smart cookie. Mm. Was also
S2: quite lacking in his interpersonal skills. Yes.
S1: Yeah. No it was it used to be quite funny.
S1: It was great.
S2: And the same would be the same is true of machines.
S2: They don't have. Um, no. Some cognitive intelligence. They have
S2: some ability to, you know, answer general knowledge questions and
S2: do maths and those things. But they are very severely
S2: lacking in emotional intelligence. And that's likely to be a
S2: significant handicap, that they have a long time going forwards,
S2: in part because they're not they don't have emotions themselves.
S2: I mean, obviously they don't have emotions. Emotions are biochemical.
S2: So they they're electrical devices. So they don't have anything
S2: like that. And, and one of the great things that
S2: we have is that, well, we can reflect on, on
S2: our own emotions. We can say, well, how would I
S2: feel if, um, you know, someone said that to me
S2: and then I could think, oh, I'd be really upset. Um,
S2: and so machines can't do that. They don't have any
S2: insight to reflect and think about how I would feel,
S2: because they have no feelings themselves. So they're going to
S2: be very severely handicapped, certainly that facet of their intelligence. Um,
S2: for a long time. Absolutely.
S1: I, I can remember the first, um, thing I played
S1: on my little Apple TV of speech back in the
S1: early 1980s. Was that horrible, ridiculous psychotherapy program. Eliza. Um.
S1: And I used to try and trick it. I'd say, oh, look, my,
S1: my my bed is feeling very sad today. And it's like, oh,
S1: when was the last time your your bed felt sad?
S1: And it just got more and more ridiculous. And I thought, yeah,
S1: all you're really doing is just word prediction. You're just
S1: doing straight reflection. Um, so we're we're smart speakers these days,
S1: are they? Because I'm assuming, you know, our traditional smart
S1: speakers lie. So the, the upper ones, the Amazon ones
S1: and the Google ones, I'm assuming at the moment they're
S1: still not using large language models. I'm still they've sort
S1: of got a I'm assuming they've got like some sort
S1: of set series of instructions and that's all they're currently
S1: working on at the moment.
S2: Yes. Something like Alexa. Very it's very heavily programmed, very
S2: heavily scripted. Mm. Um, but the future is going to
S2: be that ultimately AI is going to be the operating
S2: system of those devices. And indeed all our devices, your
S2: your smartwatch, your smart phone, your smart home, your smart toaster,
S2: your smart front door, your smart light switch, they're all
S2: going to have, uh, it it is actually when we
S2: come back to Scarlett Johansson, is is going to be
S2: like that movie. Her AI is going to be the
S2: operating system of all of those devices that's going to
S2: be allowed you to interact with them so you can
S2: talk to them. They understand at a much higher level
S2: what you want them to do, and then do that
S2: stuff for you. So it is, um, you know, we're
S2: only at the beginnings of that journey where, um, devices
S2: get upgraded with more and more AI that allows us
S2: to have a richer and richer conversation with. Um, so that,
S2: you know, ultimately, you know, people will, I think, become
S2: somewhat attached to these devices that they're always talking to.
S1: No. Yeah. The one thing that always gets me very
S1: nervous about, um, anything to do with AI is when
S1: it comes to computer vision. Um, because I know in
S1: your book you were talking about, you know, um, you know,
S1: there's lots of data being trained on, on, on medical stuff, on, uh,
S1: doing stuff for radiology and all that sort of stuff.
S1: The problem I have as a blind person is, you know,
S1: whenever I use my smartphone to take a picture of
S1: something in the backyard or out the front and so on,
S1: there's still about a 75 or maybe even 80% chance
S1: of if I was being nice, that what the camera's
S1: actually telling me what the object is is actually incorrect.
S1: And I just thought, is that just the state of
S1: computer vision? Or is the fact that, you know, it
S1: doesn't have enough data to base the fact that you know,
S1: my red garbage bin is not a fire hydrant on
S1: my grass verge, for example?
S2: Yeah, it's it's getting better and better as we train
S2: them on more and more data. Um, you know, the
S2: idea ultimately of computer vision is to be able to
S2: understand the world so that computers can robots, for example,
S2: or autonomous cars can navigate around it. Mm. Uh, and
S2: I'm pretty confident we will get there, um, in part because,
S2: for example, um, we're driving increasing. You know, if you're
S2: driving a Tesla, you're helping to train the next generation
S2: of Tesla. Yeah. Um, and what and the other thing
S2: that's really making a big difference is, interestingly enough, is
S2: the is that, in fact, actually Tesla's do more of
S2: their computer vision training on simulators than they do in
S2: the real world that the simulators now, the car driving
S2: simulators are so good, so realistic. Mm. Um, that they drive,
S2: you know, more than ten times the distance every night
S2: in simulators. And of course, if you're doing it in
S2: a simulator, you can do it faster than real time.
S2: You can you can speed up the world ten times
S2: or 100 times, um, and train the computer systems on those.
S2: And then the other great thing about simulators, of course,
S2: is you can you can do things that wouldn't, you
S2: wouldn't be allowed to do in the real world. You know,
S2: you can have you can practice accidents. You can make
S2: the conditions really difficult. Yeah. Um, you know, you can
S2: put the sun into the eye of the driver and
S2: you make the road wet. You can do things that
S2: you know, where you might be risking the life of
S2: the driver and and cause an accident, and then no
S2: one dies, which is great. And then, of course, what
S2: you can do in a simulator is that you can say, well, okay, um, the,
S2: the computer failed to do it that right that time,
S2: but let's train the algorithm again. Let's try and see
S2: if we can correct it and then run it again
S2: in exactly the same circumstances. You. Mm. Replication of the experiment,
S2: which you can't of course, do in the real world.
S2: You can never replicate exactly those conditions again. No, you
S2: can say let's repeat it until the computer gets it
S2: right and doesn't kill the driver. No. That's true. And
S2: so so being able to train computers and simulators is
S2: actually going to really help us move forwards in leaps
S2: and bounds.
S1: So is there any I mean, there are there any
S1: true level five self-driving cars on the road, or are
S1: there sort of or or back down to the level?
S1: I think it's a level two isn't it? They're not
S1: really self autonomous vehicles at the moment.
S2: 2 to 3. Yes. Um, yeah. I mean, the bad
S2: news there is that unfortunately sighted people are going to get, um,
S2: autonomous driving before people with, um, visual impairment because it's
S2: slowly being put into our cars without us realizing. I
S2: was driving my car the other day, and I realized
S2: I'm driving less. I'm not looking over my shoulder as
S2: often as I. I suspect it's going to be our
S2: children who don't drive cars. They will, but they won't
S2: get around to get their licenses. And in our case,
S2: for people who already can drive. Mm. It's going to
S2: slowly happen to us. Mhm. So um, I imagine at
S2: some point in the future I'm going to go to
S2: the RTA to renew my driving license, and they're going
S2: to say to me, well, Mr. Walsh, we, um, we
S2: checked your computer records and it seems that you haven't
S2: been doing much driving recently, but it's actually been the car,
S2: been doing all the driving, all the assists, all the
S2: autonomous assistance in the car. And you really don't have
S2: the hours under your belt. So you have two choices now, Mr. Walsh.
S2: You can either you can either take your test again
S2: to prove that you can still actually drive the car.
S2: It's not just the all the intelligent assistants, the the
S2: lane following and the automatic braking and all of that
S2: and the automatic steering, the parking. Or we can give
S2: you this new, um, non-driving driving license that, that and
S2: I think I expect well, I it was too painful
S2: getting my license in the first place. I think I'll
S2: get the non-driving license. And they say, oh, by the way, Mr. Walsh,
S2: you'll get a discount on your insurance now.
S3: Yeah. That's right. Yes.
S2: Remove the dangerous human from the loop.
S1: That's right. My my wife often says it's it's actually
S1: the other drivers that, um, are the.
S3: And that is part.
S2: Of the problem. Yes, dealing with the uncertainty of other drivers.
S2: Once vehicles can talk to each other, then there would
S2: be much less uncertainty. But I think it's slowly creeping
S2: up on us. And then there will be it will
S2: happen in special places. So there'll be the, you know,
S2: inner city congestion charging zone where you're only allowed to
S2: go in if you're in an electric car that's autonomous
S2: or the high speed of the highway, you'll only be
S2: able to enter the high speed of the highway where
S2: you can platoon the cars together if you've got the
S2: autonomous aids.
S1: So the only question I've got to with autonomous cars
S1: is it's fine to take you somewhere. But there seems
S1: to be no mention at the moment about how I
S1: could then say, okay, so the car's parked you in
S1: the car park at Woolworths. I'm now going to actually
S1: let you I'm going to guide you to where you
S1: can start entering into the actual shop itself inside the complex.
S1: Is that the sort of stuff that I could actually
S1: do for people that are blind? So your GPS gets
S1: you to the Or the car gets you to the location,
S1: but then, then the AI has then got to get
S1: you out of the car park into the building.
S2: Yeah, yeah, it's the last 100m or the or the
S2: last kilometer. That is a problem. And of course, GPS
S2: doesn't work inside. No. And GPS doesn't have the accuracy or,
S2: you know, really to do that, you know, very high precision. Um,
S2: last 100m, which is exactly where you want AI to
S2: step in, which is exactly why we work on things
S2: like computer vision. Because you have to see the world.
S2: You have to see where the door handle is. You
S2: have to see there's a, you know, another pedestrian coming
S2: out of the door and someone's left, um, a dog
S2: tied to the railings. And you.
S3: Yeah.
S2: Um, that's why you have to treat, teach computers how
S2: to see the world so you can navigate those last 100m.
S3: Mm.
S1: So is there any is there any sort of, like,
S1: 3D modeling.
S3: Or.
S1: Sort of computer vision type stuff where, um, I don't know,
S1: a shopping center could say, well, look, here's a complete
S1: 3D visual map of the whole complex, and then we'll,
S1: we'll actually use that data, imagery, that sort of stuff,
S1: then to guide the person around. So I can say, okay, so, um,
S1: the software or the AI knows that I'm in the building.
S1: I want to go to the storage section in Bulworth's
S1: so I, you can see where I am. Can you,
S1: can you guide me to that spot just based on
S1: the stuff that you write, the stuff that it's actually
S1: sees around me?
S3: Yeah.
S2: The real problem, um, with that is that the world
S2: keeps on changing. So they have high precision maps and
S2: high precision, uh, 3D models of the world. But the
S2: world has always changed. So one of the things that
S2: we try and do in, in AI is what's called Slam.
S2: It's simultaneous location localization and mapping, which is look at
S2: the world, understand where you are, and also map the
S2: world at the same time. So actually the computer vision
S2: is not only working out where you are with perspective to,
S2: you know, the front door and the door handle, but
S2: also working out what is in the world to see actually, oh,
S2: there's a dog over there now, which was obviously not
S2: in my model of the world because it's just stepped
S2: into frame. Mm. Um, so that's something that, you know,
S2: we put a lot of effort in. I trying to
S2: build systems that can actually situate themselves in the world,
S2: but also map the world as they find it, because
S2: the world keeps changing and you can never have, you know,
S2: up to date maps and models of the world. They're
S2: always out of date. You really got to go there
S2: and actually perceive the world and work out what state
S2: is the world now in.
S1: Yeah. And look, that that's my ultimate thing to do
S1: with I mean I can, I can sort of take
S1: or leave self-driving cars. But if I knew I had
S1: a computer vision system that could 100% independently navigate me around, um,
S1: into shopping centers or public infrastructure, um, transport hubs, the airport,
S1: that sort of stuff. That would be absolutely amazing. So
S1: for me, the.
S2: Good news is that's coming, right? Okay.
S3: So it's not.
S1: It's not pie in the sky.
S3: Stuff.
S2: It's not magic because we do it right. We do
S2: it with our two eyes. Stereoscopic vision. Yeah. We managed.
S2: Humans have managed to do that. Um, um, and we
S2: are slowly getting to the point where, increasingly with increasing accuracy,
S2: we can get computers to do the same thing. Mm.
S2: And the great thing about computers, of course, as well,
S2: is that they're not limited to the visual spectrum. So
S2: they can also see the world in microwaves and infrared.
S2: And um, so they can see. And so if it's
S2: low light or bad weather, then they can also still
S2: see the world when, um, uh, where vision alone may,
S2: may make it really challenging problem. So I have the
S2: possibility ultimately of actually helping us to see the world better.
S2: We can see the world because they can do it
S2: even if it was dark or even if it was
S2: pouring with rain.
S3: Okay.
S1: And just finally, has there been much research into how
S1: AI could impact people with a disability moving forward into
S1: the future?
S2: But, I mean, there has been work. Um, not enough work, actually,
S2: I'm sure. Um, and, um, because when I think about
S2: what are the technologies that are going to help people
S2: with limited hearing or with limited vision, they are exactly
S2: I the I is the technology that allows computers to
S2: hear the world and see the world, and then convey
S2: that information to those people who would otherwise be more
S2: isolated than they need be. So. So I, I have
S2: a lot of hope. The problem. The problem, of course,
S2: the fundamental problem is not a technical problem. It's a
S2: societal and a financial one, which is how do we
S2: ensure that there are incentives for the tech companies and
S2: business to do that? Because to begin with, it's going
S2: to cost them money.
S3: Mm.
S2: It's much easier for them to cater for the, you know,
S2: the vast majority of people who are, uh, you know,
S2: normally sighted and, um, normally hearing and not, um, you know,
S2: invest the time and effort and actually use this as
S2: a way of making, um, it more accessible for people
S2: with with disability.
S3: No.
S1: And I get this asked a lot about robotics and
S1: artificial intelligence, but there's some interesting stuff coming out for mobility,
S1: stuff to do for blind and low vision. And one
S1: of the major products is called glide from glide technology.
S1: And what it is, it's basically a well, people say
S1: it looks like a little vacuum cleaner. You've got little
S1: two little robot with a handle on it, and the
S1: blind person hangs onto the handle, and then it steers
S1: you around obstacles. And because it's connected to your smartphone,
S1: you've got an app that will tell you what's around you,
S1: as in businesses and that sort of stuff. But I'm
S1: just wondering how trustworthy is such a system, because I'm
S1: assuming it's using lidar and radar and infrared and all
S1: sorts of amazing stuff, but I just, I just think
S1: at the end of the day, it it it doesn't.
S1: And I come back to that original thing that I
S1: talked about. It doesn't have the level, a level of
S1: human intelligence or thinking outside of the box, if you'll
S1: pardon the pun, if the wheels fall off, for example.
S3: Yes.
S2: Yeah, it's obviously it doesn't replace what those wonderful guide
S2: dogs do. Mhm. Uh, because if you know, I've had
S2: the pleasure to meet some blood blind people with their
S2: dogs and they have a wonderful relationship with their dog. Mm. Um,
S2: and it is about that as much as anything that
S2: they can trust the dog to, to, to guide them across,
S2: you know, a busy road and putting their life in
S2: the hands of the dog, literally. Um, and, uh, that
S2: is a significant technical, um, milestone to me. Mm. But equally.
S2: Mm am also aware of how expensive it is to
S2: train guide dogs and how limited supply they are in. So, um,
S2: I do think, you know, there is a possibility there
S2: that we might be able to provide that mobility that perhaps,
S2: you know, some people can't because we don't have enough blind.
S2: You know, we don't have enough guide dogs to help
S2: people around. It would be wonderful if we had more
S2: and we could afford more. We trained more. But yeah,
S2: I'm not sure that's the world that we're in. And so.
S3: Um.
S2: If it is not perhaps as such a good solution.
S3: Yeah, we might look.
S1: And the other thing too, that I always think about,
S1: like I'm always thinking about the. Yes, but if then
S1: when type stuff all the time. Because to me the
S1: world's not black and white. It's got various shades of gray. And,
S1: you know, it's fine. A self-driving robot type thing might
S1: be fine for just trundling down the footpath and maybe
S1: going around the occasional car that's parked on the footpath,
S1: or it sees a branch and it goes, oh, hang
S1: on a minute. Um, I know my, my, my user is, um, 2.2m.
S1: That branch is two metres. I'll, I'll stop and go
S1: around it or do something like that. The problem is
S1: when you get into the highly unpredictable things. So I
S1: remember I was yelling at my guide dog one day
S1: because she wouldn't go in a straight line. She kept
S1: zigzagging and, um, I got to you and I went
S1: freaking bloody my dog. She was doing all sorts of
S1: nut stuff. And they went, yeah, somebody was actually repainting
S1: the manhole covers, and they were lifted off down that footpath.
S1: And I went, oh, what a good guide dog my
S1: guide dog was.
S3: So you're right.
S2: These, these unpredictable things, these these black swan, these corner cases,
S2: those are going to be the challenging ones. And which
S2: is why you're going to see these things turn up
S2: in more controlled environments. So so suppose you live you
S2: know on an estate, you know, an old person's estate
S2: where it's, you know, a gated community and it's, you know,
S2: it's a much more controlled environment. And then we're trundling
S2: around that environment. Um, you might be quite safe with
S2: a robot where, you know, as soon as you leave
S2: the gates of that community, you're out of the big,
S2: wild world where there are people who inconveniently leave manhole
S2: covers off. Might be better off. Um, you know, using
S2: the using the services of a of a of a
S2: guide dog. Um, so I can see it, you know,
S2: those more constrained settings being the ones where this sort
S2: of technology is first turn up, where you can be
S2: more sure that there are, you know, all of these
S2: black swan events that are waiting to manhole covers, waiting
S2: to trip you up or. Um.
S3: Yeah.
S1: And it just seems with the whole of, um, I
S1: there's no I remember that famous saying I can't remember
S1: it exactly, but something about who will guard those self-same guardians.
S1: And I'm just wondering, is there any sort of fallback
S1: system that says, no, the AI system is completely lost.
S1: The plot. It's wrong. Um, it's got the information wrong.
S1: It's got the orientation mobility wrong. It's got everything wrong.
S1: Now it's time to stop and just put a human
S1: being in charge.
S2: This Custodiet custodes. Who guards the guards?
S3: That's the one.
S2: My, my, my classical education finally got.
S3: Well done.
S2: All of that. Studying my Latin and Greek finally got
S2: used once.
S3: Uh.
S2: Yes. At the end of the day, um, we ultimately
S2: humans have to be in charge because only humans can
S2: be held accountable. So, um, um, we there are plentiful
S2: places where I think we're going to have to make
S2: sure that humans are left with, you know, overall responsibility.
S1: Yeah. No, look, I tend to agree because, um, I mean,
S1: every time I look at I and it mostly gets
S1: it wrong or somebody says to me, this Onam system
S1: is the best thing since sliced bread, and it still
S1: gets it wrong. Um.
S2: You you're right. It gets it's still making mistakes. It's
S2: still not perfect, but it's also it's easy to be
S2: forgetful of how it has advanced. I mean, I remember
S2: speech recognition systems 20 years ago. Mm. Incredibly painful. They
S2: had to be speaker trained. Mm. Um, that you you
S2: had to train them on your voice. You had to
S2: train them. They didn't work in the wild. You had
S2: to use the proper microphone and and quiet environment. And
S2: now people expect to, you know, buy a new smartphone,
S2: open it up, walk down the street and start talking. Right.
S3: True.
S2: And it doesn't do a perfect job of transcribing what
S2: people say, but it does a pretty good job. Mm.
S2: And and you're just thinking of the advance that we've got.
S2: I mean, that was just unthought of 20 years ago
S2: to think the idea that it wouldn't be trained for
S2: your voice, it would work in the wild with all
S2: the street noise and wind around you. And you could
S2: just you could just start talking and it would get,
S2: you know, 90% of the words. Right. Mm. Is pretty,
S2: you know, someone who's been working in the field for
S2: those 20 years, I find that pretty amazing. You know,
S2: it's still not it's still not good enough. It's still
S2: not perfect. You still you still, you know, shouldn't stake
S2: your life on it. Um, but for, you know, wandering
S2: around a town, strange environment, and, you know, a country
S2: where they speak a different language, it's good enough to,
S2: you know, make yourself understood.
S3: Well.
S1: That's right. Yeah. And look, and I know when I
S1: first had a look at the Kurzweil personal reader that, um,
S1: Ray Kurzweil brought out in the mid, the mid late 70s,
S1: I mean, that thing wasn't perfect either. And I was
S1: sitting there going, oh my God, who the who? The
S1: Blazers can afford something that's worth about $55,000 Australian. And
S1: now it's in our pocket. Yeah. Um, and then I
S1: remember when I got my first Apple Tui, the synthesizer
S1: was even worse than the original Daleks on Doctor Who,
S1: because for a long time it kept saying to me
S1: there was an unclosed error on the Apple Tui. And
S1: it wasn't until somebody started said it's actually saying unknown,
S1: but it's actually saying the K as a C, right. Um,
S1: and I thought that was only that was only 40
S1: years ago.
S3: Yeah. Oh another example.
S2: I mean subtitles on the TV now. I mean, it
S2: used to be we people had to transcribe it. The
S2: only way you could get anything reasonable as a subtitle out.
S2: Now it's done. Pretty automatic. Not done perfectly, but it's
S2: good enough to, you know, be able to work out
S2: what's happening if you can't hear the TV.
S1: No, no. Exactly, exactly. And look, I'm I mean, I'm
S1: because I'm an Apple geek. I mean, I'm really looking
S1: forward to, um, what's coming up at the Worldwide Developers
S1: Conference at WWDC to see what Siri morphs into.
S2: Apple has been very slow, though, on the uptake of
S2: AI that's going to change. Um, and the Apple was
S2: the great thing about Apple is they're going to do
S2: it more and more on your device.
S3: Mm.
S1: Exactly. Yeah. And that's what I'm looking forward to because
S1: I really don't want my, you know, my conversations or
S1: whatever else I might be looking at via an image
S1: going out to a cloud somewhere, because you've got no
S1: idea what where the information is going to end up
S1: on the cloud. So the more stuff is done locally,
S1: and to me that's going to be more appropriate.
S2: But that's that's going to be the future. And certainly
S2: Apple was one of the companies that's been, um, you know,
S2: promoting that. But the idea is that increasingly you don't
S2: want to share your data with everyone and anyone. And increasingly,
S2: we'll have the sophisticated AI algorithms will be small enough
S2: and smart enough to run actually on your device. Um,
S2: that solves lots of other problems, and it solves the
S2: privacy problem, but it also solves the latency problem. So
S2: there's lots of places where you don't have the connectivity
S2: you're in, you know, an urban canyon, you're in a tunnel,
S2: whatever it is. Yeah. Can't depend upon, um, the connectivity
S2: to be able to, you know, send the data to
S2: the cloud and have it transcribed or the computer vision
S2: on your car to do stuff. You need to be
S2: able to.
S3: Do it on.
S2: The device. So but that's the future of AI. Increasingly,
S2: it's going to be powerful enough to run on the
S2: limited amount of hardware that you're actually carrying on your person.
S1: No. And look, that's what I'm looking forward to. I mean,
S1: I've got I've got about ten questions that I always
S1: keep checking in with AI every, every three months or so.
S1: And at the moment it's it it's getting better. Um,
S1: the first couple of ones that actually broke dramatically on
S1: my on my questions. Um, I was funny because, um,
S1: when I was going to interview, I, I checked up
S1: and it doesn't think that you're a poker player anymore,
S1: which is cool.
S3: Um.
S1: It knows how many bees are in the in the
S1: webinar now, which is also pretty cool.
S2: Oh, Taramasalata will call it now, though.
S3: All right.
S2: Although I must admit I struggle to spell Taramasalata myself.
S3: But anyway, yeah.
S1: Actually my favorite word at university for philosophy was, um,
S1: reductio ad absurdum. I love that word. Um, I was like, oh,
S1: that's pretty cool. Um, all right. So look, if people
S1: want to find out more about your books because I
S1: know there's there's faking it, there's the 202,062, uh, there's
S1: machines Behaving badly, which I absolutely love. Um, now there's
S1: a fourth book, which, for the life of me, I
S1: can't remember.
S2: It's alive. Artificial intelligence from the piano to killer robots. Actually,
S2: the first book I wrote, which is.
S3: Oh, okay.
S2: About the history of.
S3: AI. Okay, now I've.
S1: Got all of them on Kindle. Have you done anything
S1: with audible? So people that want to sort of sit
S1: back with a glass of wine?
S3: Good news is.
S2: Coming out next month. I have just recorded my, um, uh, book.
S2: I've just been put out by Belinda. Um, Faking It
S2: is coming out in audio, and I have to say,
S2: I'm an absolute convert. I, I think it is the
S2: best version of my book. Um, because I realized that,
S2: you know, I've written it in some places. I write
S2: it with, you know, I'm writing things and I'm annoyed
S2: and upset or excited. And if you're if you read
S2: the page carefully, maybe, hopefully you can tell that in
S2: whether what I've written. Um, but in the audible book,
S2: it's very clear when I, when I spoke the book,
S2: I had the privilege of pleasure of speaking my own book. I,
S2: you know, I could laugh at the jokes in the book.
S2: I can I can express disgust with what the tech
S2: companies are doing with my data. Um, so there's a
S2: lot more information conveyed in the audible book that's not
S2: conveyed in the written book. Maybe.
S1: And, uh, because I, when I was reading the stuff
S1: about the, um, the local autonomous weapon systems, I thought
S1: that's just absolutely appalling. Giving, giving drones the capability to
S1: be able to actually kill people without any human intervention.
S1: That's just getting a bit beyond the pale. Basically it.
S3: Is.
S1: Um, and I just thought, oh, I wonder what you
S1: would have sounded like reading that particular particular paragraph, because.
S2: Next month you could discover.
S3: There you go, I will.
S2: The great thing about the book was I actually only
S2: speak half the book.
S3: Oh, because the.
S2: Book is all about how ChatGPT and examples like that
S2: make mistakes and get things wrong and, um, amuse us and, and, um,
S2: I said to the publisher, I said, well, I could
S2: read this. Um, but why should I read this? Why
S2: not get the computer to read it? So I say
S2: half of it and ChatGPT says the other half.
S1: Yeah. Does it use your voice to do the the
S1: second bit, or does it just use a generic computer
S1: synthesizer voice?
S3: Oh, no.
S2: They've, um, they're getting a very nice, uh, synthesized voice
S2: for that. Um, so the publisher is very excited. They
S2: said this is the first book. They've they've really, um,
S2: embraced the technology. Right. And it's so, so it is
S2: a conversation between me and the computer, and I hope
S2: you enjoy it.