The innovation show with aidAn mccullen – Act TWO
This episode of The Innovation Show with Aidan McCullen is Act II of Stories, Dice, and Rocks That Think: How Humans Learned to See the Future and Shape It with Byron Reese.
In 17th century France, the mathematical framework known as ‘probability theory’ is born—a science for seeing into the future that we used to build the modern world.
“…if the future really is random, then what can you say about it?…in randomness, we can actually get high degrees of predictability. And to me, that’s a big idea.”
FULL EPISODE TRANSCRIPT
Aidan McCullen [00:00]: Stay hungry, stay foolish. And we’re back for Part Two of Stories, Dice, and Rocks That Think with Byron Reese, part of the Exponential Series here on the Innovation Show. Before we launch into that episode, I want to thank our sponsor Zai, boldly transforming the future of financial services with a suite of embedded products and services, enabling businesses to create multiple payment workflows, and move funds with ease, check out Zai at: www.hellozai.com. Welcome back to part two of this excellent episode with Byron Reese. He is here on the line with me. Before we launch in Byron, I was telling you before we came on air, I understand where you’re going with the book, it’s great because I actually see it like frameworks or lenses that we get given throughout life or that we were fortunate enough to waken to.
And the first was, well, the Great Awakening, so the brain changed for whatever reason that was that we discussed in part one, then there is okay, well, the mind, then we come along, we have art, then we have communication through language, but language, firstly, to think and then to communicate, then to write. And then we have limitations. And we get to a point where yeah, we can only get so far with all that, we need mathematical models, then next, to be able to increase how we can think and how we can plan and how we can predict the future. And then we need data. And then we’re going to get on to part three or act three from the book, which is the fantastic part. And I found it a little bit difficult, I was saying because I don’t really like metrics, and I don’t really like math, etc. So I struggled slightly with act two compared to act one, and I’m certainly going to enjoy act three, I think I’ve earned getting to act three. And I thought that overview would be helpful coming from you to describe what your goal was with the three acts as well.
Byron Reese [02:11]: Yeah, I mean, I guess it reads a little bit like a morning at school, you get there and you got English class, and you’re like, I love it. And then your next class is math, and you’re a little bit like, not my, and then the next class I think is going to be I guess computer science or something, I guess. Any case? That’s exactly right. Act one is about how we learned to imagine the future. But how do you know what’s really going to happen? If you’re just like making stuff up? How likely is that to happen versus that? And that’s the dissection. It’s about probability. And I know that sounds like chloroform in print, like I know that. But it doesn’t have any equations in it. It just has a narrative that tries to walk through like… So, the first question it asks is, why do things happen the way they do? And I would have made that an entire section because I enjoy writing it. I had to make that so dense. And if it’s okay, I’ll just whip through those off the top of my head.
Aidan [03:20]: Yeah, absolutely. I was going to tee you up as well. And if those nuggets are out of place, just let me know, as well. Will we go for that?
Byron [03:32]: Go right ahead.
Aidan [03:33]: Okay, so act two finds us in 17th century France. And yes, you kind of go a little bit back to go forward. But we’re kind of stuck at 1654, essentially. And the mathematical framework known as probability theory was born, a science for seeing into the future that we used to build the modern world. Great place to start over to you.
Byron [03:57]: Okay, well, I’m curious when I explained the so-called problem of points. Was that too mathy? Did you understand that part?
Aidan [04:10]: Yeah, actually, I’d love it if you introduced that as well, the idea of the points and the coin flips, etc. I thought that was great. I think it’s great, I’m just saying for my brain I found that a little bit dense, but it’s fantastic. And it’s so necessary, and you did a great job and it’s not I don’t want people thinking oh, it’s really like mathematical equations and functional theories, etc. It’s not at all its narrative. And it’s really well done. And you go all the way through to eugenics, which is fascinating. So, I loved it. I loved it. I just was a little bit slower reading it is all.
Byron [04:54]: Oh, I understand. Alright. So I’ll go two ways with this one. I want to just set up what I was just talking about. This is, we come out of Act One where we stayed for 50,000 years. And in that 50,000 years, we could imagine the future. And there became different opinions on why it happens the way it does. And as I started to say, I could write a whole section on that. So the first idea that people have about why the future is fated to happen, things are pre-written, somebody decided your future, before you were born or something, and I quote, I think William Blake “some are born to sweet delight, and some are born to Endless Night.” And that’s just how it is. And so that was a pretty dominant belief. Most of the ancient civilizations had gods that assigned your fate to you when you were born. In Greek myths, there were three women who would measure out the string of your life and cut it at the end, and that was it. Like you couldn’t beat that. And so that was one way that people thought the future happened. And then another one would be a doctrine known as necessity, the future had to happen that way, it couldn’t happen any other way. And I’m sure everybody who’s listening has had the same thought that if we live in this mechanical universe, where, a billiard ball hits another one, which hits another one, which hits another one, that’s all knowable, like, if you know the initial condition, then you can know what’s going to happen.
And that’s the doctrine of necessity that things had to happen, the way that they did. That you stubbing your toe in the morning is nothing but the inevitable outcome of a series of events that began 20 billion years ago. And interestingly, the Enlightenment thinkers were rooting for that one, like, that’s what they hoped. Because necessity meant you couldn’t beat the future. But you could know it. And it didn’t require anything supernatural. And maybe even there were even a few laws like Newton had, that would help you kind of predict it. So the science-minded people were kind of rooting for that. And that’s what they wanted there to be. Now, they were really disturbed by Newton. Because Isaac Newton was the quintessential scientist like if you had to pick one, you’d say “Isaac Newton.” I would, he comes up with gravity. And it’s a mystical force that nobody knew why it worked that way. And nobody still really knows why it is that way. They were very suspicious of Newton because it was like, “Oh, you’re reintroducing magic back into our understanding of the future.” I mean, why the universe works the way it does, and they didn’t like that. And the thing about it, well, I won’t go too deep down that hole. And then I told the story of how that view got a real boost at 9:33 am on Friday, April 22nd, 1715. I love that it’s a minute you can point to, what had happened months earlier, Edmund Halley of Halley’s Comet fame had predicted a solar eclipse, a total eclipse. And that wouldn’t be anything if it was like going over the Scottish Highlands or someplace, but it was going over the heart of London. And so for weeks, there were all these like, what are those called playbills? Or what do they call the big posters that say, “the sun’s going to vanish for four and a half minutes on this date!” And lo and behold, it did like they only got it off by about a minute or two.
And the path, they were really close and everybody was like, okay, alright, I’m going to throw in my lot with those people. Then there’s a third reason people think things happen. That’s a strange one. It’s called synchronicity. I still haven’t wrapped my mind around it, as an example, I don’t believe in astrology. But I’m really intrigued by people who do, why do they believe it works? I get it, but what do you think is the mechanism? And then you think about all those years where they read chicken entrails or they looked at spots on the liver of a goat and they could read the future. And, like, why, why would that tell you the future? And the answer is that everything in the universe is connected to everything else. And because of that, you could study the sweat stain on the back of your gym shirt, and predict the future with it, because it is influenced by what influences everything else. But only if you know how to read it. The people that used to read the chicken entrails and the livers, and they worked really hard at it, like they had textbooks and models and tests and all the rest to learn how to do it the right way. And if you think about it, if the moon can cause the oceans on the earth to rise and fall without touching them, is it really so strange that stars could influence our lives? Or that? Oh, I don’t know, a voodoo doll could work, you put a pin in it and somebody 200 miles away goes, ouch. Could that work? That’s called synchronicity. And that’s kind of still with us. In a lot of ways, that view of that if everything’s connected, you can look at one thing and know something about something else. And then those were the kind of the three schools of thought on why things happen. You’ll notice the one that wasn’t in there. And that is randomness, that things just happen for random reasons, and there’s no rhyme or reason. And if that were true, that would seem that we’re in sort of last, because if the future really is random, then what can you say about it? I’m just going to jump to that because that’s my visual aid. And that is, if you had asked me, many years ago, if you flip a coin 1000 times, how many times will it come up heads? Well, I’ve been trained to say, oh, around 500. I guess I’ve known that forever, but only because somebody told me I didn’t ever flip the 1000 coins, right?
And statistically, the amazing thing is that the odds of you getting less than 400 heads or more than 600 heads are one in billions, billions. So if you flip a coin, because I would have said, oh if I flip a coin 1000 times, I don’t know, it’s going to be all over the map. One time, you’re going to get 100 heads, and one time, you’re going to get 800 heads, and one time, you’re going to get 502 no rhyme or reason to it. And that isn’t true. Like, I still have trouble with that. And it’s illustrated with this thing called a Galton board, which you may have seen in a science museum. I’m about to flip it upside down, and these little beads, these little beadies are going to fall through this funnel. And when they do, they’re going to hit a piece of plastic. And they can go to the left or the right, randomly, either one. And then they’re going to hit another piece of plastic or the left to the right, and they’re going to hit another piece of plastic and go from the left to the right. And if you flip it, what happens is every time you flip it, you get a normal curve. And how can that be? Like, why isn’t it a U sometimes? And why isn’t it flat sometimes? And this is something we didn’t know in randomness, we can actually get high degrees of predictability. And to me, that’s a big idea. There is a fourth way that people thought the future unfolded, free will. The future happens because of the choices people make, and the decisions that people make, and people are actually the drivers of the future. Not synchronicity, not the necessity, and not the fates. It’s just us. And then the one that nobody kind of was owning was randomness. So that leads into section two, which says we want to predict the future. And we think we know how it happens. And we want to build science around that. And to do that there were five things about the universe that we didn’t know, that we had to learn in order to invent this math, and I won’t bore you with the five. But I do want to give an example of a math problem nobody could solve. Now, this is called ‘the problem of points.’ It’s a math problem that has been around for hundreds of years. And people talked about it. And they couldn’t solve it. Everybody kind of had their own solutions, but people kind of knew, none of them felt that right.
So let me set the problem up. And let’s just go through it kind of leisurely because, seldom do you get to actually put your mind in the mind of somebody from 400 years ago, especially of Blaise Pascal. Here’s the setup: you have two people playing a game, you have Harry and Tom, and they’re flipping a coin that can come up heads or tails. So H for Harry (heads) and T for tails for Tom. And what they say is, okay, we’re going to play this game, we’re going to flip a coin five times. And Harry gets a point, every time heads come up. And Tom gets a point every time Tails comes up. And whoever has the most points at the end wins the cash pot. And then, after three tosses, the score is two to one. And the game is interrupted. That’s the important part. Why is the game interrupted? I’m not entirely sure. I think they dropped their coin and it rolled under a refrigerator or something. I don’t know. But the game ended. And the question is, how do you split the pot? And what’s the fair way to split the pot? Harry’s ahead, two points to one. And there were supposed to be two more tosses. So I said, How would you solve that? So the first and most common answer was, you can’t, you don’t know what’s going to happen in the future. So there’s no way, it’s not even a math problem. Like that’s something else. But it’s not math, which is what they said. Then other people say, well, the fairest way to split the pot is 50/50. But then Harry’s like, wait a minute, I was winning.
Then other people are like, well, Harry has twice as many points as Tom, he’s got two to one. So he should get two parts of the pot, and Tom should get one part of the pot. And that sort of seemed okay. Until you say, Well, wait a minute. What if they were playing to a million tosses? And the score was two to one? Would you really still say Harry’s lead is so overwhelming, that he deserves twice as much as Tom? As they say, no, no, no. Then other people were like, Well, why don’t we cut the deck another way and say, Well, how many points do each need? So Harry needs one more, and Tom needs two. But again, you get the same problem. So they go through all these things. So nobody could solve it. And so these two guys from Fermat and Pascal, start writing letters to each other to try to solve it. And they do. And they didn’t just solve it. They built the math to solve problems like it. And the way you solve it, is you say, there are a couple of ways but the straightforward way is you can say okay, okay, we’re going to flip the coin two more times. And let’s pretend we’re going to flip the coin two more times, there are only four things that could happen. You could have two heads, two tails, tail then a head, and heads then a tail. In those four things that can happen, and only one of them does Tom when the tail guy when, and in three of them, Harry wins. And that’s the answer. Harry gets three-quarters of it and Tom gets a quarter. And the big kind of mental leap was, nobody really thought about, okay, there’s things that could happen in the future, and I don’t know what’s going to happen, but I’m going to start putting numbers on things in the future.
Like I said, so crazy obvious, that it’s going to rain tomorrow. 80% chance. It’s reflexive. They didn’t have any notion of anything like that. So Pascal went from these letters, and they come up with a solution, but then they build the math for it because you could imagine a harder version of it. There’re three people flipping coins and the score is 182 to 64 to 9. How do you split the pot? So they had to figure the math out. And when they figured this math out, they started sending all these letters around and everybody went crazy. Like, oh my gosh, they finally solved it. And we see how they did it. And then within like five years, we went from nobody being able to solve that problem to not only that, but everybody could solve it. It was like child’s play at that point. I think I put in the book, you don’t even get a smiley face sticker on your homework when you’re 10 years old, for doing that problem, like it doesn’t matter anymore. And so at that point, people started building statistics and probability out so that they could predict what was going to happen. And that, believe it or not, is just the first two chapters of the probability section. I call it a math section that sounds like chloroform and prints like, oh, yeah, I want to read up a book about math. But in the probability section, I guess it doesn’t sound any better. That’s the way that sets up, that’s the beginning of that part.
Aidan [21:08]: Yeah. I loved what you were doing because what it reveals is kind of going, well, we were at this impasse, or so we thought and what it did to me was actually kind of go… And I often ponder this, and I think it’s from doing the show, and you’ve had your own podcast as well. And one of the great things is, that it’s constantly stimulating thought and kindling new ideas for you. And one of the ones that are constantly niggling, like a buzzing fridge for me is, what are the other things that we have not solved that we’re going to look back on someday? And kind of go, oh, man, how did we do that so wrong for such a long time? Death and illness and aging and all these things that we, it was staring us in the face, I often think of poor old Semmelweis and the hand washing, and it was so obvious, but nobody believed them, then everybody washes their hands and child mortality decreases massively. And so many of those, and you say, really, why not earlier, and it was because of the frameworks and the ways of thinking and that was really the message I got from that.
Byron [22:18]: I mean, you’re right about what we don’t know, I remember 20 or 30 years ago, a David Letterman top 10 list. And it was like top 10 headlines from 2020 or something. And one of them was “OatBran: the silent killer,” right? Like one of these really healthy things or “Jogging the silent killer.” So, once all of a sudden, and you have to understand, there was a conceptual leap, undoubtedly, but there was also the fact that Arabic numerals were new in that part of the world. Today, we’re still using Roman numerals, which are notoriously difficult to multiply and divide. They didn’t have all the notation that we had, they didn’t even have the equal sign. They had a dearth of formalness around math. But once these two letters, these, all of these letters were exchanged between Pascal and Fermat everybody kind of went math crazy. And I love this part. So the first thing that happened is well, one of the things that happened is countries used to back in the day, raise money, by selling annuities. Now an annuity is where you come in, you give them some money upfront, let’s say $100. And then every year for the rest of your life, they pay you some small amount $5. And you can imagine that it’s way, a bet that the government is taking out on you like, they kind of want to sell them to sick people. Right? Because if you sign up for an annuity, they’re going to just start sending cards in the mail that say “it’s never too late to start doing drugs.” And they’re just going to try to entice you to take up skydiving and all of that. So the key to pricing an annuity is how long is that person going to live. If two people walk in and one is 20, and one is 80, who do you think going to live longer, like, we know it’s the 20-year-old and if the 20-year-olds going to live longer, then you should charge them more for the annuity and then 80-year-old person charges them very little to get the same amount of money a year because you don’t think you’re going to pay them that long. But here’s the interesting thing, they didn’t know that the older you were, the more likely you were going to die in the next year. Like just nobody knew that.
And on the one hand, it’s forgivable, because we don’t have many people that die young like that. And we’re used to older people passing, if you lived in a world where people died young all the time, a mule is going to kick somebody in the head and kill them. And it could be that 30-year-old or that 40-year-old or that 80-year-old, you don’t know who the mule is going to kick. So all three have equal chances of dying in the next year. That’s what they thought, now, that’s why it’s forgivable, because how would you know? The reason it’s unforgivable is you could actually make a mortality table, which is a prediction of how likely old people are to die next year, by spending one afternoon walking around a cemetery. Because you could walk around the cemetery, and you would look at every headstone and you would say, okay, they died at 53. Okay, and then you go to the next one, okay, they died at 71, and so forth. And when you wrote all that down, you would count them up, and you’d say, Wow, a lot of people died in their 80s. And then the 70 succeeded, and very relatively few people down here. And then like one thing after the other like that, when we started putting the meat on the bones of probability, and that is what slowly kind of built the modern world we have now, which we not only think about the future and the past, but we conceptualize them probabilistically. You noticed when I was going through faith and necessity, and all of that the future happening probabilistically didn’t ever even, like that would not have even made sense to them. At that point, that’s when we started seeing the future probabilistically, which would not have made sense to anybody back in the day.
Aidan [27:10]: I’d love it if you would share a few characters involved in the story. So there was Pascal, for example, and Fermat and then there was also Cardano, who the cryptocurrency is named after. And then there was also Galileo made a little cameo in there as well. And Voltaire I love this story, of Galileo getting asked by one of the Medici family to kind of go, “Hey, I need you to do this job for me.” And he’s like, “ugh, I’ll do it, but begrudgingly,” and then he does it and he doesn’t do it in Classical Latin. And then also Voltaire gamed the system because he understood this system.
Byron [27:54]: Yeah, all of those are a lot of fun. Because what they asked Galileo to do is, figure, if you lived back in the day, there was only one place where probability would touch your life, and that would be in games of chance, and gambling. They did do that. And there are some strange things related to that, that they kind of intuited. But that was the only real thing they had. And so that was right the Medicis went to Galileo, and they’re like, “can you figure out how likely different dice tosses are?” Because they wanted to know, what to bet on and what not to. The Voltaire story is really funny because it’s the kind of thing that if it happened today, it wouldn’t last five minutes. So, the French government decided they needed to raise money and they going to hold a lottery next to annuities. Lotteries were the way they did it. They said, “we’re going to do a lottery.” And then we’re going to do it by essentially neighborhoods in Paris in France. And so there are a lot of little lotteries, relatively small lotteries and if you bought a bond, you got entered in the lottery.
Like a savings bond; if you loaned the government some money, you were entered in this lottery, so not only were you going to get your money back, but you had a chance of winning, and the way they wrote the rules, it didn’t matter how big of a bond you bought, you still got entered in the lottery. I think that was how he made a big chunk of all of his money. I could be wrong on that. But when I read it, it was like cha-ching. It just illustrates how little they kind of could conceive of it, of things like probability. Just how un-mathy the world was a lot of times the people in the books that you rattle off or names we’ve all heard of Voltaire and Galileo and all that. But every now and then somebody just would come up that was just out of nowhere, the haberdashery in London, they also sold buttons and ribbon. And that was his career. And he became the world’s first demographer. And so what he did is, every week in London, they would publish a list of everybody who died and how they died. And it was mainly because they were tracking plagues when you would want to know if there was like some surge of other plagues. So they would track all the deaths. And he went and found those for like the last 100 years, I mean, there were gaps in it. But he found many of them. And he put together all of this stuff as he could actually tell that he made all of these intuitive leaps, like, oh, I estimate the population of London. I can tell more people are moving in and then exiting, I can do all these things.
Aidan [31:12]: The guy, John Grant was his name. I absolutely loved him. This is the haberdashery we’re talking about, the guy who was just curious, and he’s just starting to do this stuff. And what I loved what you did was, you put us in the shoes of those people in that period where everything was so new, and there was so much opportunity, and there was so much opportunity to use your imagination and try things like you were saying, like Newton, for example, putting needles in his eyes and experiments like that. There was so much to discover, and there probably still is, it’s just not as obvious maybe now. And that was one thing it did for me was to just reignite that curiosity and the opportunity for things to discover, which I’m sure we’re going to do in Act Three as well. So, we got to death and taxes, which we all get to, excuse the pun. And that was where Grant, for example, was finding out, the number of deaths and the statistics, childbirths. For example, you say everybody thought everybody had the same probability of death until Grant did this work, which was just fascinating. But we’re at the point now, well, what else is this good for? And one of them was for laws. And I thought about this. Here’s a question for you. What’s the probability of ever hearing this sentence ever in a podcast that goes as follows: “Tycho Brae was a Danish astronomer in the 1500s most famous today for the gold nose that he wore.” I thought that was a great way to tee you up for laws.
Byron [32:55]: Returning to this, to the bell curve, I think we can all agree that it is a product of randomness, right? Like there’s nothing, it’s just beads falling down, bouncing one way or the other. And there are more ways to get to the middle. Now, here’s a big question to mull, so, all of that, the bell curve, is based on randomness. There was a person early on, who kind of didn’t discover the bell curve, but applied it, and was trying to find some data that he could graph. And he graphed the chest sizes of a Scottish regiment. And I think it had to do with them ordering uniforms or something, he had to measure everybody’s chest. And so they had like 1000s of these measurements of different people’s chest sizes. And if you graphed them, you get a bell curve. And it’s like, well, why? I mean, chest size isn’t random. Then another strange thing they noticed was, let’s say you are following an astronomical object, a planet, or something. And every night you take a reading to see where it is, and your readings because you don’t have very good equipment are all over the place. But when you take all of those measurements of where it is and you take the average, and then you graph how wrong the other ones are, they are a bell curve, and you say well wait a minute, I thought bell curves were supposed to be randomness, not error, not mistakes. And so all of a sudden, we started realizing there were these bell curves all over the place. And I think one of the examples I give is about the United States, and whatever reference here I use is 2015. 160 people died at work by being electrocuted. The next year 164 died.
The same number really, right? If you look at automobile casualties in the United States per year, and you pick a year, it’s going to be something like 30,000. And then you pick an adjacent year, and it’s going to be within 500 of that. And so you have to say, how can that be? How can electrocutions at work be the same from year to year? And it is, again, this thing. Why? Well, in order for you to electrocute yourself at work, it’s essentially, your little bead has to end up way over here, you have to do like 10 things wrong, like, it has to be hot, and you didn’t know it’s hot, and you have to not have checked beforehand. 10 things you would have to do wrong. And that’s going to happen in a pretty constant amount of time. And that’s a big, crazy idea you just have to, like wrap your head around. So early on, when they noticed these bell curves, they said, why would the number of murders in France resemble a bell curve? So let’s just graph murders by I don’t know, every October for the last 10 years, they’re going to look like that, there’s going to be a center, and there’s going to be things that are way above it, but very few and way below it are very few, and then a bunch that is close to it. So I know, it’s a lot of stuff. But the crazy thing is that if this is a product of randomness, and this appears in our lives, does that not mean that our lives are random? That’s wow. Because not only was there high predictability in the number of murders, but then if you said, well, I’m just really interested in the number of people who are murdered, and their bodies are thrown in the same river, or something like that. And that too is or I’m just going to look at murders, and I’m just going to look at murder weapons bludgeoned with a blunt instrument, and that’s the same year to year. And then as you start thinking, this is crazy, like, what’s going on there? And that’s what’s going on. And so what people started thinking was, how do we hold anyone responsible?
Like, there are going to be 10 murders next year, no matter what. Like there were 10 this year, 9 last year and 7 last year, and 14 the year before. So you can be about 10 murders, somebody’s got to do them. And if somebody’s got to do them, then how do you punish them for it? We aren’t completely over that way of thinking, by the way. Because if somebody says commits a murder, willfully plan it out, all of that. We say, well, they’re responsible for that, right? So they’re found guilty. 20-something people saw them do it. And they go before the judge for sentencing. And the judge said you’ve got to understand my client grew up poor and wasn’t able to go to school and was abused and had all these bad things happen to them. So we want to ask for leniency. And so the judge is like, well, all right, like you had a hard life. So instead of putting you away for 40 years, we’re going to put you away for 20. Well, what are you saying there? You’re actually saying they’re still responsible, but somehow, their circumstances make them less responsible. And that’s what the prevailing thought of the day is that there are going to be these murderers, and there are going to be the people that do them are going to be like, almost the most misfortunate people, and it’s wrong for us just to blame them, and throw them in jail. And that was kind of the thinking of all of that going on in people’s head when they discovered the normal curve. And then they saw it applied to life, and they realized it’s all about randomness. And that is the story of the normal distribution. But it’s really the story about how we got it. We struggle to this day to understand why it is and what it means. And then I have a hard time connecting the dots that reflect fundamental randomness underneath everything.
Aidan [37:37]: That was Gaussian curves, then we had Bayes theorem 1761. And then what I thought was it again and this is the way I constructed the book in my head, so we’re at this point where now we have a theorem. Now we have models. Now we can think at a different level. But the thing we’re lacking is data. And we need data to fill that. And like you said, we had Gauss looking around for data and he started looking around the cemeteries, then governments, as you say, is their nature, there are bureaucracies that are like, great, now we can have data for everything. And then we can track everything. Because that became really important. And you then go as far as talking about phonology, which I thought was fascinating. So maybe we jump there because again, I love the way you pepper the book with lovely nuggets of information like this.
Byron [40:43]: So there was a guy named Keightley, who was the person who really just went kind of normal curve crazy. And he saw them everywhere. And maybe he was a bit aggressive in where he saw them. But his big problem is he just didn’t have any data to speak of, Scottish regiment chest sizes were one thing. It’s funny because years later, we got a lot of measurements of people. It’s really something because when you think of the chest measurements of the Scottish regiment, they’re the people in the middle, and to Keightley, those are the perfect people. Like that’s the correct measurement, that’s how big your chest size should be. And if you think about it, it’s a really Industrial Revolution way of thinking about things. Because if you’re manufacturing something on an assembly line, you’re not really measuring what’s my best car I made, you’re kind of what’s my average car, like, any variance from the model is considered an error. Even if it’s better, it’s considered an error, I guess that’s the way to think about it. So he saw that, like people in the middle, that was the perfect thing. And later, somebody took a bunch of measurements of men and women and found all the averages, and made two statues of the perfect man, and the perfect woman, based on all those statistical averages. So they made statues of the perfect man and woman, and somehow became regarded as ideals, and anything that varied from them was considered bad. And you could see how that could be not only misunderstood but terribly abused. So enter a guy named Francis Galton whom we named the Galton board.
He was a fascinating guy, like, who did all these adventures and like he did all these things. And in his spare time, he worked on statistics. And he came up with something called regression to the mean. And I didn’t really understand this until I was writing the book. I was fortunate that there’s a Math professor at the University of Texas at Austin, which is where I live, who would meet with me and explain this all to me very slowly. But the way to think about regression to the mean, is if you’re really tall or really smart, or really anything, odds are, your children are going to be less so than you. Odds are, they’re going to be less so. And then their children, even less so. Because things have a tendency to reconverge on that middle to converge on average. And you say, well, why would that be? Why would that be? And the reason is pretty simple. Actually, let’s pretend this is a distribution of people. By height, and if you’re really tall, you’re over here, right? You’re one of these really tall people, your ball bounced to the right, eight times out of 10 or something like that. And so you’re really tall. If you just take all these beads here, all the tall people, and then you feed them back to the top. They’re not going to go to the right like they’re not going to just go there, they’re going to make a new normal curve around the middle. And everything’s getting closer to the mean, closer to the mean. And then if you did it again, well, let’s take the tall people, the few tall offspring of the tall people, and put them back in, it’s going to be a normal curve. And so you can’t beat it, you can’t beat it. And so it’s really interesting that eugenics said you could.
Like what we could do with eugenics is we could improve the species by advantageous breeding. So, we would just basically, only let people up here breed, and we would discourage, however, people from here, and over time, the beads are going to keep moving further and further and they’re going to get taller and smarter and all these things, but they’re not. And I just can’t get over the fact that Galton was the champion of eugenics. Galton created the phrase and the math of regression to the mean, like, as if anybody should have known better. I have a young teenage son and I started to tell him this, and I connected it to Galton, and he’s like, wait a minute, how would he have done that? If he’s still regressing to the mean, guy? How could he say you could make it forever better? And it’s like, exactly, how could he? So he started advocating for this and writing about it. And he wasn’t like, I mean, I put in the book, he’s no Nazi. Like, he really thought like, Oh, this is the smart way forward. Like, I mean, look, there’s no way around the fact it’s a terrible thing. And even his writing about coercing people, and I mean, like, it’s highly problematic at all different levels, but he wasn’t coming from… Anyway, I’ll just say, he should’ve known better. And he didn’t. So that went on for a while. And then different countries passed laws. I mean, sorry, different states in the United States passed laws that allowed them to forcibly sterilize people.
And then that eventually went to the Supreme Court of the United States, where it was upheld. And it was upheld, it was eight to one. There’s only one person who said no, no, no, you can’t force people, you can’t forcibly sterilize people, you can’t do that. And, it was advocated for in the interest of the state, like, the state’s going to have to take care of these offspring that are not favored, the state’s going to have to take care of them. And therefore the state has a compelling interest in sterilizing people, and that went on for a really long time, and the last forced sterilization in the United States happened in 1981, and then, what you and I were chatting about off camera was that’s where the Nazis got the idea was they read Oliver Wendell Holmes’ Supreme Court decision. I mean, that is the person who wrote the decision defending it. At this institution, he shook hands with both John Quincy Adams and John F. Kennedy. Like that’s how long his span was. And he’s the one who wrote this majority decision upholding it. And that’s it. And then finally, the Nazis did it. So the Nazis did it, and they got it, unfortunately, largely from scientific writing. And that’s the sad story of that. We only had one other similar thing, which was like forced lobotomies. It was the same logic, the person who developed that procedure won the Nobel Prize for it. Worst Nobel ever. And so it’s troubling that like the tendency to devalue other people’s life to say it’s worth less for whatever reason. It’s discouraging in that it is not just pervasive, but still very, very common. It somehow reflects that their life’s not worth living is what they would say about these forced sterilizations.
Aidan [49:27]: Loads of stuff in there about even your level of expertise Byron for those of you who don’t know, had a magnificent podcast, he’s paused on it for the moment, but mainly focused on AI and artificial intelligence, machine learning, all those things. And indeed, the Fourth Age focuses heavily on that, and Act Three is we’re going there as well, because what this talks about and I mentioned to you we had this wonderful guest on the show Angela Saini, where we talked about the science of race. You know, it was man-made science. As you talk about, we all came from the same areas, we all had one language, we were one. And we had this awakening, it wasn’t like, some people were smarter than the others, it was just an awakening. But I often think about it, my business is called Edge behavior, and it’s because of the curve, that there’s magic at the edges if you embrace the edges.
And if you think about that, even from a neurodiversity perspective, just because in an organization, the people who are most accepted are in the middle at the top of the curve doesn’t mean that they’re superior in any way, they may be able to speak the most common language of the business, etc. And it often alienates people at the edges, I think that’s just a great tragedy of even the business world. And then the other thing is, it always reminds me when I see those curves of the diffusion of innovations, and it’s just the same curve again, and oftentimes the things at the edges are often ignored until they become normal until they’re accepted by the mean. And this is a great tragedy of so many great innovators and great changemakers, great paradigm shifters. It takes time for the middle to accept the edge.
Byron [51:20]: Act Two started in 1654, and then we ended in 1954. Because what happened is all that math that we were doing, we did with paper, pencils, and side rules, and that’s how we built the modern world, with lots of chalkboards and, and all of that. And we said to ourselves, maybe we can build machines, we built machines, so we didn’t wear our muscles out, can’t we build Thinking Machines that keep us from wearing our minds out doing tedious math like this. So we said let’s build machines that can apply probability and statistics. And let’s hook sensors up to them, so we don’t even have to input data into them. And the hope is that if they collect enough data, and we build them well enough, they can predict the future with unerring accuracy. They can tell us the future with as much confidence as they can tell us the present. And so there was nothing, no new invention, no new technique, like the normal curve or regression to the mean or standard deviation or all of that. What happened is we just learned how to do it better and faster and cheaper and that’s Act three, it’s artificial intelligence.
Aidan [52:47]: Byron, for people who want to follow you, find out about your other books and also win a copy of this. I’ve mentioned I have a copy up for grabs, just sign up to: www.theinnovationshow.io newsletter and you’ll be in with a chance but also do buy a copy. And when you buy a copy preorder like Byron said it really helps the author, it helps get you into the Amazon bump as well. And then if you do buy a copy, leaving a review also helps the algorithm. We live in this algorithmic society now, unfortunately, so that’s so important for the author as well. But Byron, where can people find you?
Byron [53:22]: I’m the easiest person in the world to find, my name is Byron Reese. And I’m www.byronreese.com and email@example.com. My LinkedIn is Byron Reese and the advantage of getting in early and having an unusual name.
Aidan [53:37]: Absolutely pleasure always speaking to you, man, author of Stories, Dice, and Rocks That Think: How Humans Learned to See the Future and Shape it. Friend of the Innovation Show Byron Reese, thank you for your time.
Byron [53:50]: Thank you.
Aidan [53:50]: Another excellent episode of the exponential series here on the Innovation Show with thanks to our sponsor Zai, boldly transforming the future of financial services with a suite of embedded products and services, enabling businesses to create multiple payment workflows and move funds with ease. Check out Zai at www.hellozai.com and see you very soon.
Podcast and Radio Interviews
Best-Selling Author And Futurist Byron Reese On The Power Of Brand Storytelling
As humans, we are hard-wired to tell and share stories. We also gravitate to brands that tell a compelling story that connects with us on an emotional level. So, how do marketers shape narratives that resonate with their audiences? In episode #471, our host Peggy Anne Salz talks with Byron Reese, author of the upcoming book Stories, Dice, and Rocks That Think. He shares insight into how stories have driven the growth and development of human culture, and he outlines tips on how you can tell stories that persuade your customers to take action.
An Interview on NickSav.com discussing how artificial intelligence affects our lives, the difference between narrow AI and general AI, whether being human is something more than a machine, challenges and philosophical questions raised by advancements in AI, why optimism matters and can make all the difference, and how AI might redefine creative work LISTEN
Nonprophets [Super] Forecasting Podcast
An interview with Atief, Robert, and Scott on the NonProphets podcast discussing “The Fourth age”, the skills necessary for an AI filled future, where the fear of what AI will do comes from, and thoughts about consciousness and free will and the implications for robots and AI. LISTEN
An Interview with Michelle Mason on Association Forum discussing “The Fourth Age”, technologies effect on the future of work, the difference between narrow and general AI, and the need to be constantly learning. LISTEN
An Interview with Carla and Tom on Robopsych Podcast discussing “The Fourth Age”, the anxiety of technological change, and what makes us human and comparing and contrasting with what AI could be. LISTEN
The Bad Crypto Podcast
An Interview with Joel and Travis on The Bad Crypto Podcast discussing “The Fourth Age”, what is AI and what will it do to our jobs, how will AI and robots be used for war, and how will AI and robots effect our dignity. LISTEN
Radio New Zealand
An Interview on Radio New Zealand discussing “The Fourth Age”, what we think of ourselves as humans and what that implies for AI, the concept of emergence, and the economic opportunity of AI. LISTEN
An Interview with James Kotecki on the Kotecki on Tech, discussing “The Fourth Age”, the inevitability of technological progress, technological optimism vs pessimism, the disruption of jobs, and conscious computers. LISTEN
An Interview on WorkMinus, discussing Byron’s thoughts on technology removing dehumanizing jobs, amplifying human productivity and other ideas from Byron’s book “The Fourth Age: Smart Robots, Conscious Computers and the Future of Humanity.” LISTEN
DM Radio and Host Eric Kavanagh – Narrow AI: Artificial Intelligence and the Future of Work – Original Air Date: November 29, 2018 The Guests Stefan Groschupf, SalesHero Faisal Abid, Zoom.ai Byron Reese, Gigaom Micah Hollingworth, Broadway.ai About the Discussion Hollywood has it all wrong with respect to Artificial Intelligence (AI), at least for now. LISTEN NOW