Speaker 1: Welcome to tech Stuff. This is the story. Each week
Speaker 1: on Wednesdays, we bring you an in depth conversation with
Speaker 1: someone who has a front row seat to the most
Speaker 1: fascinating things happening in tech today. A conversation with David Spiegelholter,
Speaker 1: a professor of statistics at Cambridge an author of the
Speaker 1: Art of Uncertainty, How to Navigate Chance, Ignorance, Risk and Luck.
Speaker 1: Spiegelhlter has devoted his life to understanding uncertainty. After all,
Speaker 1: it's one of the most uncomfortable aspects of being human,
Speaker 1: particularly when it comes to our health. Since the nineteen seventies,
Speaker 1: Spiegelhulter has worked on algorithms to assist clinicians and patients
Speaker 1: make better decisions about what treatment options to take for cancer,
Speaker 1: and he has a deeply personal understanding of the topic.
Speaker 1: In nineteen ninety seven, he lost his son Danny to
Speaker 1: cancer at the age of just five, and the epigraph
Speaker 1: to David's book is a quote from the Bible. The
Speaker 1: race is not to the swift, nor the battle to
Speaker 1: the strong, nor bread to the wise, nor riches to
Speaker 1: men of understanding, nor favor to men of skill. But
Speaker 1: time and chance happened to them all of course, with
Speaker 1: the explosion of AI, we now have better and better
Speaker 1: tools to help us understand the world and make informed decisions.
Speaker 1: And in fact, Spiegelhalter was an early pioneer of the technology.
Speaker 1: So that's why I decided to start our conversation. We
Speaker 1: live in this extraordinary moment where technology seems to be
Speaker 1: giving us more of a view around the corner into
Speaker 1: the future than it ever has. How has the march
Speaker 1: of technology impacted your work and your understanding these topics
Speaker 1: over your career?
Speaker 2: Oh a huge amount. I mean, we needn't get into
Speaker 2: the whole Asian methodology, but that's what I was interested in,
Speaker 2: and we couldn't do it because you just couldn't do
Speaker 2: the calculations. You couldn't do the maths. But instead of
Speaker 2: trying to do the maths, you just used brute computing
Speaker 2: force to simulate millions of different possibilities and look at
Speaker 2: their distribution and the algorithms we knew would converge to
Speaker 2: the correct answer if they ran long enough. You had
Speaker 2: to wait till nineteen ninety or so. Just before that
Speaker 2: technology that ability to do such huge simulation exercises was
Speaker 2: on everyone's desktop, and then there was an explosion and
Speaker 2: a complete change in the way statistics was done. Up
Speaker 2: to then people did clever maths and then programmed that
Speaker 2: in and it changed into no, we don't have to
Speaker 2: do any maths. We just have to program in the problem,
Speaker 2: the model that we're trying to solve, present the data
Speaker 2: to it, and send it off. And wait. But what
Speaker 2: I think you might be starting to allude to, which
Speaker 2: I'm sure get onto, is the role of AI. Our
Speaker 2: AI is already changing my I used a lot in
Speaker 2: writing the book, both in the researching and summarizing of
Speaker 2: literature and of course in the coding all the time.
Speaker 2: You know, I always rewrote everything, but I used it
Speaker 2: a lot. I use it all the time in my
Speaker 2: daily word, daily life. But actually, will it be able
Speaker 2: to make predictions? And I am rather skeptical about claims
Speaker 2: both that are sort of you know what, you might
Speaker 2: call it a global level, or a social level, or
Speaker 2: even at a personal level, about our health, about the
Speaker 2: ability of AI to make predictions.
Speaker 1: Your book has the epigraph from the Bible. How did
Speaker 1: you come up with that? Oh?
Speaker 2: By using AI to ask for quotes that use chants
Speaker 2: and things like that.
Speaker 1: Is that true?
Speaker 2: Yeah, I'd actually for that one, I knew that one,
Speaker 2: but otherwise so yeah, I use AI.
Speaker 1: Well why that one? Why was that the first one
Speaker 1: that you used? Oh?
Speaker 2: I think because the whole book, and especially if we're
Speaker 2: the first chapter, which is about my grandfather, was intended
Speaker 2: to give the idea of the utter lack of control
Speaker 2: we have in our lives, and we have an illusion
Speaker 2: of control, which I think actually is not helpful. I
Speaker 2: think to realize that how little control we do have
Speaker 2: in our lives, how much of what happens to us
Speaker 2: is what, for want of a better word, we might
Speaker 2: call chance. In other words, events that will happen to
Speaker 2: us that were unpredictable and that you know, and that
Speaker 2: we had no control over. I think that's rather important
Speaker 2: to realize that. And because the word that appears in
Speaker 2: the book more than almost any other is humility. There's
Speaker 2: almost no mention of the word rationality in the book.
Speaker 2: This is not a book about being rational, It's a
Speaker 2: book about being humble.
Speaker 1: He mentioned your grandfather, and in the book you talk
Speaker 1: about how he survived various battles in World War One.
Speaker 1: You'll talk about your mother being captured by parents in
Speaker 1: the South China. See exactly and they're making it to
Speaker 1: the UK, where you should meet your father.
Speaker 2: Yeah, who then nearly died in the Second World War
Speaker 2: as well.
Speaker 1: He did.
Speaker 2: Yeah, he got TB. I mean it was an illness
Speaker 2: and he was in the hospital for weeks, and he
Speaker 2: was a vat cuated from Jerusalem as he was there
Speaker 2: when he heard the Saint David's Hotel being blown up.
Speaker 2: So then you know, there so much could have happened
Speaker 2: to both of them. Then, as I mentioned in my book,
Speaker 2: the biggest chance event of all is my conception. It's
Speaker 2: not just me, everyone's conceptions. An extraordinarily unlikely of it
Speaker 2: so easily could not have happened. So me realizing that
Speaker 2: and researching the situation of my conception, it really made me.
Speaker 2: You know, it changed my whole attitude to life. In fact,
Speaker 2: it really did. It made me think, God, I'm here
Speaker 2: just by total chants in what I call these micro
Speaker 2: contingencies that just accumulated, and here I am. And so
Speaker 2: the idea somehow that I'm you know, on the earth
Speaker 2: for a purpose, or I'm you know, in any way
Speaker 2: special I find is now for me a complete illusion.
Speaker 1: When did you start getting interested in this relationship between poverty, statistics,
Speaker 1: and medicine.
Speaker 2: Yeah, I was interested in the mathematical aspects of statistics particularly,
Speaker 2: But then there was a job going and the funny
Speaker 2: the job going was in nineteen seventy eight, and it
Speaker 2: was to work on what was then called computer aided diagnosis.
Speaker 2: Well now we've called it AI. So nineteen seventy eight
Speaker 2: was using some basic statistical algorithms what is now called
Speaker 2: naive base simple algorithms. It's still around and used as
Speaker 2: a very basic machine learning algorithm, for example in a
Speaker 2: spam filtering or whatever. And we were doing that in
Speaker 2: the late seventies. So it's one of the first jobs
Speaker 2: to work on algorithms in medicine for both diagnosis and prognosis.
Speaker 2: We were working on the likelihood of someone with head
Speaker 2: injury surviving and so on. And because the computers you
Speaker 2: could even carry them around a thing. They were sat
Speaker 2: in the corner with a huge, great machine with a keyboards.
Speaker 2: It's incredibly clumsy to use, but we were doing that.
Speaker 2: And then in the early eighties I was working on
Speaker 2: more algorithms. Then we got into developments in AI and
Speaker 2: so on. So you know, this stuff is not new.
Speaker 2: It's been around for ages, what was predict addicted. It's
Speaker 2: still going because that's an algorithm for predicting the survival
Speaker 2: of women with breast cancer and men with prostate cancer,
Speaker 2: still available, hugely, widely used. It's a very good statistical algum,
Speaker 2: a regression algorithm. Of course, in practice, any actual clinician
Speaker 2: making a decision with the patient would use much more
Speaker 2: personal information that they might have the patient, because you know,
Speaker 2: for example, physical status doesn't go into the algorithm, and
Speaker 2: yet that might be you know, someone's basic underlying health
Speaker 2: might be incredibly important. So that's when we wrote the
Speaker 2: interface for a predict we try to emphasize not say
Speaker 2: this is the risk of this patient. It's just what
Speaker 2: would expect to one hundred happen to one hundred people
Speaker 2: who ticked the boxes that she did or he did.
Speaker 2: But actually people have tried different, more sophisticated machine learning
Speaker 2: methods and they don't make much difference. And so it's
Speaker 2: about as good as you can do. I think with
Speaker 2: the data that is available, you could always do better
Speaker 2: by collecting more data to and having a bigger, better database.
Speaker 2: It's going to be marginal marginal benefits just from using
Speaker 2: AI with the same data so the real you know,
Speaker 2: benefit in the future, of course, is just by having
Speaker 2: better data.
Speaker 1: So where did you say a few moments ago that
Speaker 1: you doubted that AI would be a useful.
Speaker 2: Oh well, I mean it's going to be marginal, marginal
Speaker 2: benefits just from using AI with the same data.
Speaker 1: What about other things like drug discovery.
Speaker 2: Or oh yeah all that, Yeah that's very important. No, no,
Speaker 2: then it's going to be great, huge, huge benefits there.
Speaker 2: Now I'm talking about predicting what's going to happen to
Speaker 2: me in the future, because I've got prospect cancer so
Speaker 2: I am quite interested in this. And of course when
Speaker 2: I got it, I looked at all the algorithms and
Speaker 2: which were sort of helpful, but they're very broad. All
Speaker 2: they do really is tell you what we would expect
Speaker 2: to happen to It is one hundred people who ticked
Speaker 2: the boxes you've ticked, and of course everyone is so different,
Speaker 2: Everyone's so different, so I know that that will give
Speaker 2: me a broad figure, but it's only a ballpark figure.
Speaker 2: It's still very useful until of my tenure survival, but
Speaker 2: one that I know could be changed by just having
Speaker 2: more information.
Speaker 1: So you spent your career trying to help doctors and
Speaker 1: patients make better decisions about what to do when a
Speaker 1: patient gets sick. But you've also lived this with your
Speaker 1: son Danny, this experience of how to make medical decisions
Speaker 1: in the face of horribly serious illness.
Speaker 2: Oh, that's interesting because I do think about that. We
Speaker 2: did some decisions and I'm not sure, you know, maybe
Speaker 2: we always say, well, maybe if we had taken him
Speaker 2: to these, to Canada or something, and that we could
Speaker 2: have got a different therapy. In a way, that's one
Speaker 2: thing I prefer not to dwell on too much, because
Speaker 2: you know, you don't know. But it has made me
Speaker 2: very aware of the importance of making informed medical decisions,
Speaker 2: and that's what I with my team we worked on
Speaker 2: those on providing decision aids, not in any way to
Speaker 2: encourage people to make any particular decision, but just as
Speaker 2: I mentioned in the book, I don't believe that decision
Speaker 2: theory and all the advances that have been done in
Speaker 2: that can never tell you what to do, because it
Speaker 2: assumes that you know all the possible outcomes, you know
Speaker 2: all the possible options, you know all the probabilities and
Speaker 2: the values, and of course this is totally infeasible apart
Speaker 2: from the simplest sort of gambling type examples. You never
Speaker 2: know any of these things. You never know how you're
Speaker 2: going to feel in the future if something happens, and
Speaker 2: so on. So it's impossible to be rational in those
Speaker 2: situations and use decision theory. But I think it's really
Speaker 2: helpful to try to examine the problem to face up
Speaker 2: to a decision has to be made. One of the
Speaker 2: biggest problems about decisions is that people don't actually make decisions.
Speaker 2: I don't you just go. You just find yourself going
Speaker 2: down a path, and you never just stop and say,
Speaker 2: this is a decision point. There is a branch in
Speaker 2: the road. We have to choose which way to go,
Speaker 2: and sometimes you can recognize those points, but they're few
Speaker 2: and far between, and so would I love to encourage
Speaker 2: people to actually have much more of those decision points.
Speaker 2: This is the time we have to make a decision.
Speaker 2: These are the possibilities, the benefits and harms of the
Speaker 2: options that face you. We're not going to tell you
Speaker 2: what to do. We might be able to put some
Speaker 2: rough probabilities. For example, for women in breast cancer, we've
Speaker 2: got such a lot of data we can actually produce
Speaker 2: reasonably good ten year survival rates and what the benefit
Speaker 2: would be if you had chemotherapy. So, for example, in Cambridge,
Speaker 2: unless you your absolute survival benefit is going to go
Speaker 2: up by three percent with chemotherapy, they don't recommend chema
Speaker 2: therapy because that means essentially that of all the people
Speaker 2: that give chemotherapy, out of thirty people, only one will
Speaker 2: benefit after ten years. Only you get one extra ten
Speaker 2: year survivor for thirty people being given to chemotherapy, which
Speaker 2: can have a really awful effect on people. And by
Speaker 2: producing those numbers you can get a feeling that well,
Speaker 2: you've got to have a reasonable benefit in order to
Speaker 2: take the hit of the tree. So in those situations,
Speaker 2: I think it's really good. You can't do it this
Speaker 2: exactly to some idea of what the benefits might be.
Speaker 2: But on the whole, you know, it's difficult to do
Speaker 2: that in situations where you havn't got all that data
Speaker 2: and all that analysis, all that tech behind.
Speaker 1: It coming up. Is it possible to predict murder? Stay
Speaker 1: with us? Well, it just is remarkable to think that
Speaker 1: kind of Hiding underneath all of these numbers and statistics
Speaker 1: and maths are so many life and death decisions. And
Speaker 1: the other thing I wanted to ask you about was
Speaker 1: your work on one of the most prolific serial killer
Speaker 1: cases of all time, the Herald Shipment case. Just for
Speaker 1: a US audience, can you explain that case and what
Speaker 1: your work on it was.
Speaker 2: Harold Shepman was a family doctor who, over a twenty
Speaker 2: year period, murdered at least two hundred and fifty of
Speaker 2: his patients and possibly up to four hundred without being caught,
Speaker 2: of course, until he finally, rather stupidly forged, rather badly,
Speaker 2: did a rather bad forgery of a will in order
Speaker 2: to inherit some money. Absolute madness. And it was this
Speaker 2: a woman whose daughter was a solicitor and looked at
Speaker 2: this willness, it just didn't believe it. And so suspicions rose,
Speaker 2: and finally he was arrested and they exhumed the last
Speaker 2: fifteen patients that had died, and they all had incredibly
Speaker 2: high levels of diamorphine heroin essentially in their bloodstream. I mean,
Speaker 2: he only got away with it because there were never
Speaker 2: any post mortems. There's many old people and they liked him.
Speaker 2: He was a very trusted family doctor for many people.
Speaker 2: He did a lot of home visits, and that's of
Speaker 2: course when he murdered people. So when when someone went
Speaker 2: back and looked at all the certificates of the time
Speaker 2: of death, for most people, people died all times of
Speaker 2: the day and night, the sort of uniform distribution of
Speaker 2: the twenty four hours, Harold Shipman's deaths had a great,
Speaker 2: huge spike between around one to three in the afternoon
Speaker 2: when he did his home visits.
Speaker 1: And what was your involvement personally with the case.
Speaker 2: There was a public inquiry because the families quite really
Speaker 2: on other people ask how do you get away with
Speaker 2: it for so long? It's an absolute scandal, And the
Speaker 2: public inquiry, I think very sensibly brought in quite a
Speaker 2: substantial team of statisticians to look at the data, which
Speaker 2: like the time of death data, but also the deaths
Speaker 2: of all his patients when they had occurred, how it
Speaker 2: compared with other doctors, how many would have been expected,
Speaker 2: compared with how many ratually observed. And we used sort
Speaker 2: of fairly standard industrial quality control methods to work out
Speaker 2: when you could have spotted with considerable confidence when something
Speaker 2: al was going on. So it's like industrial quality control
Speaker 2: methods spot when a production line is going out a
Speaker 2: bit out of kilter. They would have been developed over decades,
Speaker 2: and we used those for his death rates and worked
Speaker 2: out he could have been caught after about forty deaths,
Speaker 2: or he could have been identified as being odd. In
Speaker 2: other words, someone could have done an investigation. Now, Shipman,
Speaker 2: when the algorithm that we developed was applied to a
Speaker 2: thousand other gps without their knowledge, there were six who
Speaker 2: as bad as Shipman.
Speaker 1: I used many deaths on their watch.
Speaker 2: Exactly why do you think that was? They were really
Speaker 2: good gps who were working in retirement communities and who
Speaker 2: were enabling their patients to die at home rather than
Speaker 2: going to hospital by being really good caring gps, and
Speaker 2: so they were signing a lot of death certificates. And
Speaker 2: these were really good people, but they had very high
Speaker 2: death rates. So I used this as an example all
Speaker 2: the time about how statistics is about correlation not causation.
Speaker 2: You know, if someone has got a high death rate,
Speaker 2: it's an indication that someone perhaps should look at the data,
Speaker 2: but you cannot conclude the cause for that.
Speaker 1: Well, just in the last few weeks, the UK has
Speaker 1: announced an algorithm to predict the likelihood of committing murder.
Speaker 2: Well, those algorithms have been around for ages, but the
Speaker 2: chance of predicting someone, all you'll do is find a
Speaker 2: small change in odds. You're never going to be able
Speaker 2: to predict an event like that. At an individual level.
Speaker 2: You'll be able to predict someone's at someone it's slightly
Speaker 2: increased risk. But there's so much puff befind these algorithms
Speaker 2: to make it. You know, they get headlines, but actually
Speaker 2: I'm deeply skeptical about their actual ability and certainly to
Speaker 2: predict events like murders.
Speaker 1: You spend quite a bit of time working on public
Speaker 1: inquiries and informing a public about various issues, and I
Speaker 1: think you have one of the most interesting, if not
Speaker 1: the most interesting title in British academia Professor for the
Speaker 1: Public Understanding of Risk. Can you talk a little bit
Speaker 1: about what that means?
Speaker 2: Yeah, I think I am the one and only Professor
Speaker 2: for the Public Understanding of Risk because after I retired
Speaker 2: they renamed it when the next person got the funding.
Speaker 2: So this was a fascinating I'd been an academic and
Speaker 2: was doing okay, it'd got a good reputation, but fancied
Speaker 2: a change in direction from the normal business of writing
Speaker 2: papers and all that stuff, and then a philanthropist David Harding,
Speaker 2: a hedge fund manager, wanted to endow a chair in
Speaker 2: Cambridge that was to do with the improving the way
Speaker 2: that statistics and risk was discussed in society because he
Speaker 2: got so fed up with all the stories in the
Speaker 2: news and all the misunderstandings, and so he paid for
Speaker 2: this chair. And if you gave three and a half
Speaker 2: million pounds the University of Cambridge, you could have a
Speaker 2: chair of absolutely anything.
Speaker 1: And he had the good grace not to name it
Speaker 1: after himself.
Speaker 2: Exactly why it was the Winton. It was the Winton
Speaker 2: Professor for the Public Understanding of risk, so which was
Speaker 2: just fine because he was very good. I always give
Speaker 2: my career advice for young people now is to say,
Speaker 2: find a billionaire and get him to give you lots
Speaker 2: of money to do what you feel like, because he
Speaker 2: just gave the money and then completely hands off.
Speaker 1: In their capacity. What was the biggest misunderstanding you encountered
Speaker 1: about how the public understand risk?
Speaker 2: Oh goodness, that's so difficult. I mean you could the
Speaker 2: absolutely standard one, of course, which the media don't help
Speaker 2: with is the difference between absolute and relative risk. So
Speaker 2: you know, the media stories are always full of oh, well,
Speaker 2: if you eat meat, it's going to increase your risk
Speaker 2: of bowel cancer by twenty percent or so on. And
Speaker 2: that's a relative risk. And I think it's actually true
Speaker 2: that actually red meat is some process meat in particular,
Speaker 2: is associated with an increased risk of bow cancer. And
Speaker 2: that's what gets in the headlines increase risk. I've got lovely,
Speaker 2: you know, headlines of the killer bacon soandwich and this
Speaker 2: sort of thing. But when you actually translated, and I
Speaker 2: talk about this all the time to schools audiences, when
Speaker 2: they hear a story like this, they want to know, well,
Speaker 2: you know, is this the big number? Do we care
Speaker 2: about this? And to know that, you have to know
Speaker 2: twenty percent of what, in other words, the baseline risk
Speaker 2: of which there is a twenty percent increase. Now, the
Speaker 2: baseline risk of getting boo cancer, sadly is about six percent,
Speaker 2: about one in sixteen will get it during our lifetime, sadly,
Speaker 2: and a twenty percent increase over those six percentage points
Speaker 2: is about seven percentage points. So that's out of one
Speaker 2: hundred people eating a bacon sandwich every single day of
Speaker 2: their lives, one extra will get bow cancer because of that.
Speaker 2: And that's a complete different way of reframing the story
Speaker 2: to make it look frankly fairly reassuring, especially if you
Speaker 2: like bacon savwiches. So it's a great example of this
Speaker 2: difference between relative risks and absolute risk because percentage, the
Speaker 2: word percentage is used for both. It's just in one
Speaker 2: you're talking about a percentage increase and the other talking
Speaker 2: about percentage points.
Speaker 1: When we come back, we break down the probability that
Speaker 1: AI could lead to human extinction. Stay with us. When
Speaker 1: the consumer internet boomed in the late nineties early two
Speaker 1: thousands and Google came along, you could either click Google
Speaker 1: Search or I'm feeling lucky and I'm feeling Lucky would
Speaker 1: bypass the search results and take you directly to a website.
Speaker 1: And this is basically a way for Google to flex
Speaker 1: and say like, this is how incredibly good we are
Speaker 1: in search. And they've since abandoned the I'm feeling a
Speaker 1: Lucky button, but actually, in parallel, the whole Internet in
Speaker 1: the last two or three years has become an I'm
Speaker 1: feeling Lucky engine in the sense that you now get
Speaker 1: a generative AI response rather than a selection of links
Speaker 1: to follow, or at least you get both. How do
Speaker 1: you see that incredible cultural shift of sort of outsourcing information,
Speaker 1: summarization and predictions to large language models.
Speaker 2: I think it's great. I'm a real fan of the
Speaker 2: AI summary. So as long as you know, just like
Speaker 2: using any large language model, you have to grasp the
Speaker 2: fact that it doesn't know anything at all. You know,
Speaker 2: it is. All it does is string words together and
Speaker 2: comes up with something that sounds plausible. Now maybe in fact,
Speaker 2: as we all know, it comes up if it talks
Speaker 2: about facts, it can be deeply wrong and say all
Speaker 2: sorts of things that are just incorrect. So it has
Speaker 2: to be taken with a huge pinch of salt when
Speaker 2: it's saying anything factual. When it's summarizing an argument, or
Speaker 2: perhaps you know, with a discussion on a topic, I
Speaker 2: think it can be enormously helpful. I mean, if you
Speaker 2: just ask it a fact, you know, what's the capital
Speaker 2: of somewhere, then it'll generally be right. But I think,
Speaker 2: as someone who's who worked on uncertainty in AI forty
Speaker 2: years ago, we thought we'd solved it in nineteen eighty six.
Speaker 1: Well, how do you think you've solved it?
Speaker 2: Oh, because then the model's much more We in the
Speaker 2: mid nineteen eighties, the way of actually handling probability, first
Speaker 2: within rule based systems and then within basian networks was
Speaker 2: really developed. It was extremely successful, but of course that's
Speaker 2: in much smaller networks.
Speaker 1: We're living in this extraordinary moment. I mean, Jeffrey Hinton
Speaker 1: has said there's a thirty percent chance that AI will
Speaker 1: drive human extinction in the next twenty to thirty years.
Speaker 1: There are rogue genetic scientists editing the human gene line.
Speaker 1: There is uncertainty about whether the COVID pandemic was you know,
Speaker 1: something creating a lab or something emerged organically. I muchine
Speaker 1: what you say is timeless. But how do you suggest
Speaker 1: navigating this particular scientific technological moment.
Speaker 2: Yeah, I think again by trying to think coldly about
Speaker 2: it instantly. Jeff, when I mentioned working on AAR in
Speaker 2: the nineteen eighties, I mean I was. I was in
Speaker 2: Cambridge and Jeff was in Cambridge then, and we used
Speaker 2: to think, oh, poor, because Jeff was going around saying, well,
Speaker 2: these neural networks, one day they'll be big enough to
Speaker 2: really be able to act in an intelligent way. And
Speaker 2: we used to.
Speaker 1: Think poor Jeff.
Speaker 2: He's Gary is banging on about his networks again, Why
Speaker 2: didn't he just give up? Because he was right. It
Speaker 2: took a long time, but he was right.
Speaker 1: He was on tech stuff not too long ago. And
Speaker 1: I asked him, how did you count with the number
Speaker 1: thirty percent for the probability that AI will drive humans extinction?
Speaker 1: He said, well, I knew it was more than one
Speaker 1: percent and less than one hundred percent.
Speaker 2: It means a non trivial chance of this happening. Really,
Speaker 2: I think obviously there is a danger of tech. I
Speaker 2: mean in the book, I talk about surveys that have
Speaker 2: been done of people, you know, looking at the chance
Speaker 2: of existential risk to the population into the world in
Speaker 2: general and from tech. And because people do have judgments,
Speaker 2: like Jeff, does you know, I think it probably is
Speaker 2: a non zero. Probably we could argue about how big
Speaker 2: it was.
Speaker 1: How do you measure it?
Speaker 2: Oh, well, well you can't measure it. There's no measurement
Speaker 2: because it's not a number. There's no truth out there,
Speaker 2: so you can't measure it.
Speaker 1: So remember that, so you simulate different The best way
Speaker 1: to approximate with simulation.
Speaker 2: Or I wouldn't believe any simulated futures either. I mean,
Speaker 2: the simulating possible futures is fantastic method. We've used it
Speaker 2: all the time in prediction work, and that's what's done
Speaker 2: in a lot of weather forecasting as well. So but
Speaker 2: it's a good idea. I just don't think you'd you'd
Speaker 2: be so reliant on the assumptions in your models. No,
Speaker 2: these are personal judgments. But just like an intelligence analysts
Speaker 2: will be assessing probabilities even now about what will happen
Speaker 2: in the Russia Ukraine war in a year's time and things.
Speaker 2: So these are judgments that we should all be assessing.
Speaker 2: I think is really valuable to work in separate teams
Speaker 2: to come up with these judgments and the reasons for them.
Speaker 2: So I like this sort of exercise, and I'm glad
Speaker 2: you have put a number on it. I think my
Speaker 2: number would be considerably lower, but you know he knows
Speaker 2: more than I do. But so I think the crucial
Speaker 2: answers once we get to something that's what you might
Speaker 2: call a distinct possibility, what do you do about it?
Speaker 2: You know, where are the controls? You know that you
Speaker 2: need to think about where you don't just sit back
Speaker 2: as casual partisips or that many of us will be
Speaker 2: just an audience, but that's not true of the people
Speaker 2: working in this area, or the regulators, or the people
Speaker 2: who might be able to do something about it. So
Speaker 2: I think it does, as people, of course have said,
Speaker 2: you know, generate the question, well, okay, how can we
Speaker 2: reduce that probability?
Speaker 1: I want to bring us back to the book, I mean,
Speaker 1: which reads as a clarion call to learn to embrace uncertainty.
Speaker 1: I mean, is that a fair characterization. What do you
Speaker 1: hope that your readers will take away from this book?
Speaker 2: Yeah? I mean I always say it's not a self
Speaker 2: help book, although see people do seem to get quite
Speaker 2: a lot from it sometimes of the fact that you know,
Speaker 2: by owning up to uncertainty, first of all, that it
Speaker 2: shouldn't be something to dread. We live with uncertainty all
Speaker 2: the time. We enjoy it. It'd be awful to be
Speaker 2: certain about everything. Can't think of anything worse to live alone.
Speaker 2: And I always ask audiences if I could tell you,
Speaker 2: would you know, want to know when you're going to die?
Speaker 2: And a few people would always just a few, they'd
Speaker 2: like to be able to plan and things, and that
Speaker 2: you know that somebody, but nearly everybody does want to know.
Speaker 2: You don't read. You don't look at you know, on
Speaker 2: a you know on a thriller series. You don't go
Speaker 2: for the last efforts to find out what the conclusion.
Speaker 2: You don't want to know the sports result before you
Speaker 2: see the match, if it's recorded. And so the point
Speaker 2: is that we live with uncertainty. I think we should
Speaker 2: embrace it. It will never go away, but there are ways
Speaker 2: to explore it.
Speaker 1: I want to close with this, David, you said recently,
Speaker 1: my wildest prediction is that people will stop making predictions.
Speaker 2: Mm. Well, that's the one I would love. And what
Speaker 2: I mean by that is predictions where they say what's
Speaker 2: going to happen. And what I want is the Jeff
Speaker 2: Hinton approach where you give probability, so what's going to happen?
Speaker 2: And those probabilities may be good. I think Jeff's a
Speaker 2: bit high. It may not be, but at least we've
Speaker 2: got something we know where they are. He's not saying
Speaker 2: it's going to happen or it's not going to happen.
Speaker 2: I don't care about whether someone thinks something's going to
Speaker 2: happen or not going to I couldn't care less. I
Speaker 2: wouldn't their probabilities of whether it's going to happen. That's
Speaker 2: why sports pundits when they're chatting on if you're just
Speaker 2: chatting casually, you might say, oh, I think this is
Speaker 2: the result. But of course anyone taking sports seriously doesn't
Speaker 2: say who's going to win. Going to look they work
Speaker 2: out the odds, because they're going to be going on
Speaker 2: to the betting exchanges and checking if they can get better. Okay,
Speaker 2: you know if there's differences between the odds they think
Speaker 2: were appropriate the odds being offered by on their betting exchanges.
Speaker 2: So serious sporting people only think in terms of probabilities.
Speaker 1: David, thank you so much for joining us today and
Speaker 1: tex stuff.
Speaker 2: It's been a real pleasure.
Speaker 1: For tech stuff. I'm os Voloscian. This episode was produced
Speaker 1: by Eliza Dennis and Adriana Tapia. It was executive produced
Speaker 1: by me, Karen Price and Kate Osborne for Kaleidoscope and
Speaker 1: Katrina Norvelle for iHeart Podcasts. Jack Insley mixed this episode
Speaker 1: and Kyle Murdoch Rudel theme song. Join us on Friday
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