Speaker 1: Welcome to Stuff to Blow your Mind from how Stuff
Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind.
Speaker 1: My name is Robert Lamb and Julie Douglas. Julie, you know,
Speaker 1: making decisions in this world can be really taxing. We've
Speaker 1: discussed that before. Even the smallest decisions in life are
Speaker 1: often difficult to make. But put yourself in the in
Speaker 1: the boots in the gold studied jewel encrusted boots of
Speaker 1: a king or or an Elvis, somebody who's whose decisions
Speaker 1: are are are sweeping that change the escape of international
Speaker 1: politics and economics. We're talking about huge ripple effect globally, globally,
Speaker 1: huge political leaders, leaders of multinational companies, policymakers, The decisions
Speaker 1: that they make UH can have catastrophic effects. They can
Speaker 1: they can change the world for the better for the worst.
Speaker 1: So in UH, in ancient times, you know, you would
Speaker 1: have the emperor and he would have like a sourcerer
Speaker 1: or a diviner there too. I was gonna say, an
Speaker 1: oracle or an oracle, you know, there would be some
Speaker 1: sort of magical go to man to bounce these ideas
Speaker 1: off of and be like, hey, I'm trying to figure
Speaker 1: out what to do about this. I don't know this
Speaker 1: protest situation in the streets, um, but I'm not really
Speaker 1: sure what to do. Can you look into your magic
Speaker 1: pool of water and see what the future is going
Speaker 1: to be, um, and tell me what I should do? Now?
Speaker 1: Of course, there's no such thing as magic. There is,
Speaker 1: so I'm sorry atually to tell me that my fortune
Speaker 1: cookies are full of bunk, except the ones where the
Speaker 1: fortune is general advice. But but we we love the
Speaker 1: idea of being able to do that. Mainly we want
Speaker 1: to we want to test our assumptions about what should
Speaker 1: be done versus the outcome of those assumptions in the
Speaker 1: real world. So what you're saying is that if leaders
Speaker 1: had some sort of magic ball, right that they could
Speaker 1: ask a question of and it could it actually spit
Speaker 1: out an answer that was valid, we could uh really
Speaker 1: manage our lives on a global scale in the much cleaner,
Speaker 1: better way. That's the idea. But of course, to power
Speaker 1: that magic eight ball, you would need some pretty intense
Speaker 1: computer technology, and you would you would basically have to
Speaker 1: have a computer model of everything under there. Uh. And
Speaker 1: in the same day, in our attempts to understand global
Speaker 1: climate UH, and just not even global climate, just local
Speaker 1: weather to find out whether whether we should have a
Speaker 1: picnic tomorrow. Can I plan on mo in my yard?
Speaker 1: Should I would bring a raincoat with me to the
Speaker 1: to the train station, that kind of thing. We depend
Speaker 1: on these these climate models, which UHL is we've discussed
Speaker 1: in the past. Creating an an accurate climate model is
Speaker 1: very difficult. There's so many factors involved. It's a it's
Speaker 1: largely chaotic system, and it's it's difficult to judge. Like
Speaker 1: every every day that you're into the future that you look,
Speaker 1: the more flawed the model becomes. But but still we
Speaker 1: we depend on the community computer models for for our
Speaker 1: understanding of what the weather is going to be. And
Speaker 1: if conceivably, if we created a complex enough computer model,
Speaker 1: could we not have a a kind of simulation of
Speaker 1: the world in which to test our ideas. So you
Speaker 1: would have this politician or this policymaker somewhere in a
Speaker 1: position of power, gold stilettos, and she's thinking to herself,
Speaker 1: what should I do? Should I enact this policy or
Speaker 1: this policy? Well, bring me the magic eight ball and
Speaker 1: I will ask it in the magic eight ball will
Speaker 1: then run two scenarios in the in its simulation of
Speaker 1: the world, one in which policy A is enacted and
Speaker 1: one in which policy B is enacted. Okay, but this
Speaker 1: magic eight ball would have to aggregate data of our
Speaker 1: entire existence, right, so we're talking about the economic existence,
Speaker 1: are social um existence, the geographical existence um, you know,
Speaker 1: the physics of our existence. All of this, uh, would
Speaker 1: have to be something that we could get our arms around,
Speaker 1: all this data, right, and and and then we have
Speaker 1: to have a name for this, all this data. We
Speaker 1: call it big data. Big data name for it. And
Speaker 1: it's kind of like imagine, like the way I was
Speaker 1: kind of thinking, because I'm writing about some related issues
Speaker 1: here for work, so I've been been researching, and the
Speaker 1: way I tend to think of it is imagine, imagine
Speaker 1: like this this field, all right, this flat plane, right,
Speaker 1: what's not completely flat. There's some slight bumps in it
Speaker 1: enough to where you have numerous puddles of water, and
Speaker 1: then it starts raining, right, those puddles get bigger and
Speaker 1: bigger until those puddles all meet and then you have
Speaker 1: like a giant lake. And so that's the kind of Uh,
Speaker 1: that's how I like I end up imagining big data
Speaker 1: because every day we create an estimated two point five
Speaker 1: quin trillion bytes of data. So to the point of
Speaker 1: the data in the world today has been created in
Speaker 1: the last two years alone. And when I say big data,
Speaker 1: like this is all these little puddles of data that
Speaker 1: form this giant lake of big day. We're talking about
Speaker 1: everything we'll climate sensors, to your to everyone's Facebook and
Speaker 1: Twitter updates, to digital text, digital video and picture upload.
Speaker 1: Stuff you're putting on Flicker, stuff you're putting on YouTube
Speaker 1: to just to show visions of the world, ideas about
Speaker 1: what the world is doing, online transaction records, cell phone
Speaker 1: GPS signals, all of it is coming together into this
Speaker 1: big picture of big data, right and right now it's
Speaker 1: chaos to us, right, I mean, this is not something
Speaker 1: that we've tried to manage before to get a picture
Speaker 1: of what our lives look like using all of this
Speaker 1: big data, although some people, some institutions have tried to
Speaker 1: do it at a smaller scale, like NASA. Right, it's
Speaker 1: kind of like imagine the dude or a lady who
Speaker 1: starts just picking up buying a book every day and
Speaker 1: doesn't doesn't have any kind of accurate library system going
Speaker 1: in their house. They just bring a book or to home.
Speaker 1: They maybe they read a little bit of one, they
Speaker 1: throw on here, throw on here. Eventually they have an
Speaker 1: entire house just filled with books. But they have no
Speaker 1: system of organization to understand how many books they have,
Speaker 1: how their selection in one category stacks up against another,
Speaker 1: or even kind of like just a general idea of
Speaker 1: what kind of books they like. Um. So we were
Speaker 1: in this household of big data where data is just everywhere,
Speaker 1: on everything, but we we tend to lack a complete
Speaker 1: picture of what that data is telling us. What we're
Speaker 1: getting to is that there is actually in the works
Speaker 1: a a proposal and an enactment of this idea. It's
Speaker 1: a billion a half dollar idea computing system to actually
Speaker 1: try to wrangle all of this data. Right. It all
Speaker 1: falls under the this project known as UH Future I
Speaker 1: C T, which is really three parts. There's a planetary
Speaker 1: nervous system and the idea here is you would um,
Speaker 1: it's like a global sensor network throwing in all sorts
Speaker 1: of socio economic, environmental, technological data from around the world. Again,
Speaker 1: like the big day of the big data in the
Speaker 1: world is from the last two years. Although there it's
Speaker 1: the the timiness of this data as it's rolling in.
Speaker 1: So if you put up enough, put up enough sensors,
Speaker 1: you you hooked into enough existing data networks, you would
Speaker 1: have like a real time picture of what is happening
Speaker 1: in the world and all these different spheres. Okay, and
Speaker 1: the guy who is heading it all up is dirt helping.
Speaker 1: He's the Scientific Coordinator of the Future i c T.
Speaker 1: Which is that what you're talking about, this large scale
Speaker 1: European research program to explore and manage our future. And
Speaker 1: he's talking about the necessity to understand complex global, socially
Speaker 1: interactive systems. He's saying that we live in a global
Speaker 1: world and this requires new tools. Yeah, and uh, if
Speaker 1: i'm engine the first tool the planetary nervous system. Another tool,
Speaker 1: which we'll get into later, is the Global Participatory Platform,
Speaker 1: which you can think of in a way sort of
Speaker 1: like this the the interactive aspect of this project. But
Speaker 1: then the really core thing, the thing that we're going
Speaker 1: to talk in detail about here is the Living Earth simulator.
Speaker 1: And this is exactly what we were talking about, the
Speaker 1: engine that would drive this imaginary eight ball magic eight ball.
Speaker 1: I love this idea because to me it seems like
Speaker 1: a souped up second life. Yeah, where it's or it's
Speaker 1: you can't help. But even though it's it's probably not
Speaker 1: kosher to really talk about the matrix anymore after the
Speaker 1: last two films, but it sounds very matrix like the
Speaker 1: idea that here is here, there's a simulated world where
Speaker 1: we would we would bring in all this data to
Speaker 1: create a model of the world on which we can
Speaker 1: test possible choices. Well, and have you ever seen Google's
Speaker 1: Liquid Earth. I don't think I have. It's really cool.
Speaker 1: It's a it's actually created as like someone's project over
Speaker 1: at Google. So the Skuy's time, he decided to dedicate
Speaker 1: to the Google Earth model that they have, you know
Speaker 1: that consuming on the cities, and he created this highly
Speaker 1: immersive program has eight panels surrounding you. She almost feel
Speaker 1: like you're in a video game. And the idea I
Speaker 1: think is that you know, you can zoom in and
Speaker 1: out and you can see the taj Mahal and you
Speaker 1: can see all the details. Um So when I think
Speaker 1: about this Earth simulator, this Living Earth simulator, I think
Speaker 1: about this sort of immersive situation and where you can
Speaker 1: be on a city street, you can zoom in and
Speaker 1: you'll have all of this data overlaid on top of
Speaker 1: it real time, right, And again, real time is key
Speaker 1: when we're talking about all this data. Um so Dr
Speaker 1: Dirk Um, Dr Dirk, Dr Dirk he Um. He's very
Speaker 1: expressive about all of this. He makes the point that
Speaker 1: in the past, we we didn't really have the data
Speaker 1: to come up with a systemic science of how our
Speaker 1: society works. But now we have this data, right, and
Speaker 1: of course who could have conceived of it right? Right? Right?
Speaker 1: Who could have conceived of it in the past. And
Speaker 1: he's saying it's it's necessary to keep up with globalization,
Speaker 1: technological change. You have all these systems like smashing into
Speaker 1: each other. In fact, he often refers to the living
Speaker 1: or simulator is a knowledge collider. The idea that you
Speaker 1: would take all this data and in the same way
Speaker 1: that the the large hypn collider is is slamming particles
Speaker 1: together to try to understand how the universe works. This
Speaker 1: is about like slamming all this information together and seeing
Speaker 1: what happens. Which I like this idea because although I
Speaker 1: will say that it doesn't sound like it's going to
Speaker 1: be quite that dynamic, because this is this is predictive
Speaker 1: modeling that we're talking about, and as and as we
Speaker 1: discussed in the weather example, predictive modeling is uh imperfect
Speaker 1: at best. Uh. There there are various arguments about what
Speaker 1: can be done with computer modeling of of complex systems.
Speaker 1: There are plenty of arguments that state that you cannot
Speaker 1: form a perfect model of a complex system that you're
Speaker 1: you're never gonna be able to to really get down
Speaker 1: into the exact minusha of it. Uh. It's kind of
Speaker 1: like with our our ability to understand whether you can
Speaker 1: look at data telling you what the weather has been
Speaker 1: like at a particular place, particular times of the year,
Speaker 1: and you can use that and you can create a
Speaker 1: general idea of what the weather is going to be
Speaker 1: in the future. You know, I can see, like say
Speaker 1: March third, We can take March third for Atlanta, Georgia,
Speaker 1: run it all the way through the past as far
Speaker 1: as the recorded data goes, and we can get a
Speaker 1: general idea of what March thirds in the future are
Speaker 1: going to be because it's the their their seasonal aspects
Speaker 1: to all of this. There is currents, but it's not
Speaker 1: that far away from Richard's Almanac, right, or Richard's Almanac,
Speaker 1: which is what two hundred years old or something that
Speaker 1: they've been, um, you know, recording all the weather systems
Speaker 1: to try to predict the best time to plant crops
Speaker 1: and some one and so forth. So yeah, there's it's different.
Speaker 1: Um you know, technologies of course to have are in
Speaker 1: play now that inform us, but it's you know, the
Speaker 1: unpredictability factor is still there. But before we talk about that,
Speaker 1: I just wanted to talk about the impetus for this
Speaker 1: whole creation. This the the future I see because because
Speaker 1: it's we can agree that it's a wonderful idea, but
Speaker 1: what potentially gets the money behind this idea? And uh
Speaker 1: and that's what Yeah, billion and a half dollars. The
Speaker 1: European Commission selected the Living Earth Simulator, which is part
Speaker 1: of this project right as a way to help predict
Speaker 1: economic conditions. And this was brought up by the Greek
Speaker 1: financial crisis because as we know now, it's severely undermine
Speaker 1: the European Union and a lot of people are saying, well, perhaps, uh,
Speaker 1: you know, grease should pull out of the euro Zone.
Speaker 1: And if they were to do that, what would be
Speaker 1: the ramifications? You know, you would have a highly devalued
Speaker 1: currency for Greece. But what does this mean on a
Speaker 1: practical level, for for a global economy. Does this mean
Speaker 1: that trade routes would alter? Would there be less disease? Actually,
Speaker 1: because it would be a less tourist, less people traveling
Speaker 1: to Greece. Um, they're saying, I wish we had to
Speaker 1: magic eight ball to ask about this. Dr Dirk says,
Speaker 1: I have one. I can build one for you. Will
Speaker 1: only cost a billion and a half. There you go. Yeah.
Speaker 1: So they have all these different questions about what would
Speaker 1: happen if this were the scenario, because of course they
Speaker 1: don't want Grease necessarily to pull out and you know
Speaker 1: for the EU to crumble, right, I mean these are
Speaker 1: big stakes. It's like a big economic ginga game. Um, yeah,
Speaker 1: it is. It is um. And And then also you
Speaker 1: know you can use this for for other huge situations
Speaker 1: or high impact and situations. I should say, like for
Speaker 1: instance of volcano eruption. UM could tell you what the
Speaker 1: short term economic growth is going to be, as well
Speaker 1: as the effect it may have on everything from education
Speaker 1: to the distribution of vaccines, political unrest. UM. Disease epidemics
Speaker 1: was another one. How disease is spread across the world,
Speaker 1: how we should be prepared for the spread, and that
Speaker 1: was actually modeled on how the dollar bills are circulated
Speaker 1: in the United States, which is really interesting that they
Speaker 1: use this as a model. And again we'll talk about
Speaker 1: the limitations of using these types of models for other
Speaker 1: instances such as disease epidemics. One of the real world
Speaker 1: systems that this is based on is UH as actually
Speaker 1: the urban traffic the idea of typically congested traffic and
Speaker 1: urban area and how do you how do you figure
Speaker 1: out what's going wrong, how do you combat it? How
Speaker 1: do you deal with the little pockets of unrest and
Speaker 1: change that eventually cascade into just complete gridlock, right, and
Speaker 1: and Dirk helping this is really his knowledge center, you know,
Speaker 1: this is something he's been concentrating on in his career.
Speaker 1: Um in this case it's human and machine traffic patterns.
Speaker 1: Helving actually consulted on a project that model the movement
Speaker 1: of pedestrians during the Hajj in Mecca, resulting in a
Speaker 1: billion dollars of street and bridge rejiggering to prevent deaths
Speaker 1: from trampling. So this is yeah, this is of course
Speaker 1: one of the pillars of Islam. Uh and the the
Speaker 1: idea that if you were able, you take this pilgrimage
Speaker 1: to Mecca to see the holy sites. So it creates
Speaker 1: a lot of challenges just for the infrastructure in Saudi
Speaker 1: Arabia to how do you deal with all these these
Speaker 1: visitors coming to the country to to see these sites
Speaker 1: and do so in a way that doesn't, like you said,
Speaker 1: result in trampling, result in starvation. I'm not starvation, but
Speaker 1: the hydration. I watched an interesting video here just back.
Speaker 1: It was actually put up by the Saudi government that
Speaker 1: it was kind of their their video of saying, hey,
Speaker 1: we got it under control, don't worry when you come
Speaker 1: on the hodge. It's like a public service and yeah, yeah,
Speaker 1: and uh, you know, so it was definitely coming from
Speaker 1: from the said of government, but it but it was
Speaker 1: really interesting because they did go into all these various
Speaker 1: things that they are doing and or have done in
Speaker 1: the past to try and and limit the congestion or
Speaker 1: or or things like the hydration making sure there's plenty
Speaker 1: of water. Yeah, and this is a really cool model.
Speaker 1: But of course there are limitations to this type of model,
Speaker 1: and in particular, if you look at the Hajje or highways,
Speaker 1: everyone is moving in the same direction. Right, This is
Speaker 1: highly predictable, which underlies one of the main criticisms of
Speaker 1: trying to predict the future based on these types of models. Um,
Speaker 1: what we know and can predict is actually far less
Speaker 1: than what we don't actually know, because in real life
Speaker 1: there's not just a northbound lane and a southbound lane
Speaker 1: and turn lane there. It's commendously more complex and every
Speaker 1: every layer of complexity, Um, I mean it just makes
Speaker 1: the overall model that much more are difficult to create. Right.
Speaker 1: And there's actually a term for this. Yes, yes, they're
Speaker 1: called black Swan events. And when we return, we shall
Speaker 1: reveal what the black Swan is. The black Swan we're back.
Speaker 1: The black Swan is, uh, is not a crazy ballerina
Speaker 1: who turns into a bird. It's true, Yeah, just to
Speaker 1: get that out there. Uh, but it is actually an
Speaker 1: incredible theory about not only the auliers who change the world,
Speaker 1: but the way that we try, the way the way
Speaker 1: that we don't understand, um, the importance of those outliers
Speaker 1: in retrospect, and the reason why we we it's called
Speaker 1: the black Swan event for black Swan events is because
Speaker 1: for I don't know, decades hundreds of years. Actually people
Speaker 1: thought that there were no black swans because all that
Speaker 1: had been documented were white swans. Right, so people thought
Speaker 1: really like that there are no black swans, um, there
Speaker 1: are only white swans. And in fact they were so
Speaker 1: confident of this information that black swan became sort of
Speaker 1: this this uh code word for for you know, something
Speaker 1: not existing, right, just be something of medical fantasy. Really, yeah,
Speaker 1: there's there's actually a Latin term that talks about this,
Speaker 1: but low and the whole. Some dude in the eighteen
Speaker 1: hundreds visits Australia documents a black swan, and all of
Speaker 1: a sudden, this this uh certainty, this this absolute idea
Speaker 1: that there were no black swans on the white swans
Speaker 1: was turned on its head. Yeah. It reminds me a
Speaker 1: lot of two events in the last several years, one
Speaker 1: being the maybe half hour or so that it seemed
Speaker 1: possible that we had found Bigfoot and Bigfoot's body was
Speaker 1: in a cooler in the in the middle Georgia. Uh.
Speaker 1: And of course that turned out to be complete bunk bunk.
Speaker 1: But but for a for a very brief time, I
Speaker 1: was like, oh my goodness, what if they what if
Speaker 1: this is it? What if if the the world in
Speaker 1: which Bigfoot is an unproven mythical creature is about to end,
Speaker 1: and I am entering into a new world in which
Speaker 1: Bigfoot is a reality, proven reality, what would that be like?
Speaker 1: Another example would be in the last year. And the
Speaker 1: jury is still out on exactly what these findings means.
Speaker 1: But the findings out of cern regarding faster than light particles.
Speaker 1: The idea, I mean the speed of light is is
Speaker 1: based on our understanding of physics, uh a universal speed limit.
Speaker 1: Nothing can go faster than that, um, because I mean
Speaker 1: it would break the universe, to break our understanding of
Speaker 1: the universe, it would be a black swan. And then
Speaker 1: suddenly we have this finding saying, yeah, we we clocked
Speaker 1: some uh some phimotomic particles going fast in the speed
Speaker 1: of light. And everybody's like, whoa, I doubt it. Uh,
Speaker 1: you know, let's let's do some let's study this, let's uh,
Speaker 1: let's find out if if you're if your findings are
Speaker 1: actually accurate here. But if they are, it changes everything.
Speaker 1: It's it just forces us to complete to enter this
Speaker 1: new world where the rules are are different than we
Speaker 1: originally perceived them to be. Right, there's a book by
Speaker 1: Nacineing Nicholas Talub And he's a distinguished professor of Risk
Speaker 1: Engineering at the Polytechnic Institute that n y u UM.
Speaker 1: He has this book black Swan, and he talks about
Speaker 1: these black Swan events is having three attributes, and the
Speaker 1: attributes are rarity, extreme impact in retrospective and what he
Speaker 1: means by that is the first, for for for it
Speaker 1: to be a black Swan event, it has to be
Speaker 1: an outlier. Right, it's outside of our realm of expectations
Speaker 1: because nothing in the past would have predicted that it existed. Right. Second,
Speaker 1: it carries an extreme impact. The ripples of its existence
Speaker 1: are far reaching. Right, and like the photon, I mean
Speaker 1: the subatomic particle example here definitely lines up with those
Speaker 1: two right if true? If true? Right, that's that's the
Speaker 1: key there. For that third, in spite of its outlier status,
Speaker 1: human nature makes us concoct explanations for its occurrence after
Speaker 1: the after the fact, making it explainable to us and
Speaker 1: seemingly predictable. Okay, So he says that there are examples
Speaker 1: of this all around us, like the two thousand and
Speaker 1: four tsunami, the Rise of Hitler nine eleven, UH, the
Speaker 1: advent of the Internet. He says, there all all black
Speaker 1: Swan events that we didn't expect were outliers, changed our culture,
Speaker 1: um forever right, Uh, changed society, changed world events and uh.
Speaker 1: And also the things that we we scrambled afterwards to
Speaker 1: try to explain their existence, to to try to make
Speaker 1: them feel not so much like these voids of knowledge
Speaker 1: to us, because a lot of people would say, oh, well,
Speaker 1: if we could just only have done this, we would
Speaker 1: have you know, avoided the tsunami or voted nine eleven.
Speaker 1: So he actually says, and this is a quote from
Speaker 1: his book, Um, this is from the intro. He says,
Speaker 1: what did people learn from the nine eleven episode? They
Speaker 1: did they learn that some events, owing to their dynamics,
Speaker 1: stand largely outside the realm of the predictable. No, did
Speaker 1: they learn the built in defect of conventional wisdom? No?
Speaker 1: What they what did they figure out? They learned precise
Speaker 1: rules for avoiding Islamic proto terrorists and tall bill links.
Speaker 1: And this, he says, is a real problem because we
Speaker 1: learned so specifically that we try to apply this model again.
Speaker 1: You know, we're talking about model systems over and over again.
Speaker 1: So he's saying Okay, we learned some sort of lesson
Speaker 1: from there, but it's so specific it deals with strong
Speaker 1: predators and tall buildings, that it doesn't necessarily say that
Speaker 1: we learned a lesson there that helps us to avoid
Speaker 1: terrorism altogether. Right, I mean, and that comes down to
Speaker 1: just to wear of our minds work. And we were
Speaker 1: talking about this before the podcast. Inevitably, when we're talking
Speaker 1: about the way we think about the world, we end
Speaker 1: up falling back on on examples and involve our ancestors,
Speaker 1: and like savor tooth tigers, like the sabre tooth tiger
Speaker 1: attacks you, you learn a lesson, but it's gonna be
Speaker 1: a very specific lesson in these cases, how to avoid
Speaker 1: being eaten? How did you avoid being eaten on this
Speaker 1: particular this particular encounter, right we we He would argue
Speaker 1: that um Talent would argue that our brains really aren't
Speaker 1: built for thinking per se, because if our ancestors had
Speaker 1: stopped to think, you know, that probably would have been
Speaker 1: torn apart by the sabre tooth tiger. Right, Um that
Speaker 1: we are so much more invested in pattern recognition and
Speaker 1: UH predictability models than unpredictability. So he's saying that now
Speaker 1: we live in this entirely complex world, and we can't
Speaker 1: really conceive of all the black swans around us. You know,
Speaker 1: we think about eighteen hundreds. Probably it is a lot
Speaker 1: more simple, Right, you were going to turn some butter milk,
Speaker 1: some cows, um things were you have far less choices. Um,
Speaker 1: it was a far lex complex world. So he's saying, really,
Speaker 1: our brains haven't caught up to it, and that's why
Speaker 1: we are actually blind to all of the black swans
Speaker 1: around us. Well, you know, just think back, you know,
Speaker 1: I'm speaking to everyone, to you the listener, Just think
Speaker 1: back on your own life and and just how how
Speaker 1: little of it you could have possibly predicted you could
Speaker 1: you know, all the all the little things that have
Speaker 1: that have led you to this place in your life,
Speaker 1: that the decisions, the the the bits of of just
Speaker 1: fate and blind luck and uh and here you are.
Speaker 1: But you look back on it and you see it,
Speaker 1: uh the way our perspective on it works. Um, you
Speaker 1: don't see those black swans even in our own personal history. Right.
Speaker 1: But but if you really look closely and you see
Speaker 1: that there probably are a series of random events. Right.
Speaker 1: We we plan to the best of our ability, and
Speaker 1: things happen, and life takes us down different roads, right,
Speaker 1: and you know, in retrospect we can probably explain some
Speaker 1: of those away and apply some sort of pattern to them.
Speaker 1: But Talib would say that it's complete randomness. Um. And
Speaker 1: he says, actually, the world is dominated by black swans
Speaker 1: and that they are the norm. And so this is
Speaker 1: interesting when he says this, because if you if you
Speaker 1: take this up face value, it means that this, uh,
Speaker 1: this Earth simulator is probably not going to work in
Speaker 1: the way that that the European Commission actually wants it to. Yeah,
Speaker 1: it's it wouldn't be a situation where, oh, just occasionally
Speaker 1: you have a black swan that throws the monkey wrench
Speaker 1: into the into our our understanding the world. It's not
Speaker 1: like occasionally, oh occasionally, there's been a lot. Occasionally there'sn't
Speaker 1: there's a Hitler or Einstein that kind of changes the
Speaker 1: way it works. You know, he's saying they're everywhere. That
Speaker 1: that that that the world continues to change at a
Speaker 1: at a at a steady rate based on the actions
Speaker 1: of these various black swans, both individuals and just random
Speaker 1: events in the world around us and these very spheres
Speaker 1: of activity. Right, So it's great to have these predictive models,
Speaker 1: but if you can't build in some sort of system
Speaker 1: for ferreting out black swans, or you don't have a
Speaker 1: system in there, and that says, okay, well, we think
Speaker 1: this is going to happen based on what's you know,
Speaker 1: historically the data stream that's coming in. But if you
Speaker 1: can't run that up against something that says, you know,
Speaker 1: forget it, this might actually not happen, or you have
Speaker 1: five other variables, uh, then sort of it sort of
Speaker 1: discounts the system as a whole. And even if you
Speaker 1: did have the black swan effect or events built in,
Speaker 1: you're still not going to get that one answer that
Speaker 1: they so desperately want. This says this is the right answer,
Speaker 1: because you're gonna get four variables, five variables, ten variables
Speaker 1: spit out at you and you're back at square one.
Speaker 1: And and then this is something that really blew my mind.
Speaker 1: Um when you when you think, imagine you did build
Speaker 1: this just enormously complex, uh model of the world, this
Speaker 1: living Earth simulator. What gets me is the feedback loop
Speaker 1: loops you eventually fall into because because because you're building,
Speaker 1: you're building a model of the world that has access
Speaker 1: to a model of the world, that has access to
Speaker 1: a model of the world, that has access to a
Speaker 1: model of the world, and it just like it just
Speaker 1: it blows my mind to think of that. How would
Speaker 1: that pan out? It just would the complexity would just
Speaker 1: would spiral out forever. Well, and there's another point here
Speaker 1: that even if you did have a couple of answers
Speaker 1: spit out in a in a scenario, right, that seemed like, Okay,
Speaker 1: this is the best course of action. Because you have
Speaker 1: such a complex system and you can't even understand how
Speaker 1: that data came to that conclusion, then you're probably less
Speaker 1: likely to trust it in the first place. Yeah. Yeah,
Speaker 1: And in fact, it lines up interestingly with climate change
Speaker 1: Man Maine climate change and our our attempts to understand it. Uh,
Speaker 1: the scientific findings that have come out arguing, uh, the
Speaker 1: point that hey, humans are are altering global climate and
Speaker 1: here are some things we should do to stop it.
Speaker 1: And and just and just how how little of that
Speaker 1: has been has resonated with the decision makers and with
Speaker 1: the general public in some cases. Right, Right, So they
Speaker 1: have a bunch of information there and they still can't act. Yeah,
Speaker 1: we ask our our our biggest reasoning machine that we
Speaker 1: have available to a science what we should do about
Speaker 1: a given situation, we get an answer for it, and
Speaker 1: not not everyone's gonna listen. So is there gonna be
Speaker 1: any better if we have a a complex simulation of
Speaker 1: of existence that we can turn to our people are
Speaker 1: gonna trust that? And indeed, are people are going to
Speaker 1: trust this supercomputer that has that is using reasoning that
Speaker 1: we can't even fathom, uh, to tell us of what
Speaker 1: we should do? And what if what indeed if if
Speaker 1: it's a suggestion is something that seems nonsensical? Well, and
Speaker 1: there's this whole idea too, that all of this is
Speaker 1: predicated on us even understanding our existence in the first place,
Speaker 1: and how our existence is the fact that you and
Speaker 1: I are sitting here and everybody you guys are listening.
Speaker 1: This is a black Swan event in and of itself.
Speaker 1: And what I mean about that is the odds of
Speaker 1: our existence. Um. There's a Harvard professor Dr Ali Benazzar
Speaker 1: who says, so, what's the probability of you existing? This
Speaker 1: is a quote. It says, it's the probability of two
Speaker 1: million people getting together about the population of San Diego,
Speaker 1: each to play a game of dice with trillion sided dice.
Speaker 1: They each roll the dice and they all come up
Speaker 1: with the exact same number. Say, five hundred and fifty trillion,
Speaker 1: three hundred forty three million, two hundred seventy nine thousand
Speaker 1: and one. That's the number. So, you know, we've talked
Speaker 1: about this before to just in terms of the rare
Speaker 1: Earth theory, about how the fact that life on Earth
Speaker 1: came about in and how there's circumstances were just right.
Speaker 1: But this is a rarity as far as we know, right, Yeah,
Speaker 1: so it's you know, we have a whole podcast devoted
Speaker 1: to this. But but the arguments often come down to, um,
Speaker 1: it's such a rare event that this Earth exists, Does
Speaker 1: that mean that we're special or you know? But but
Speaker 1: we can't think scientifically, we can't view ourselves as special.
Speaker 1: So how do we how do we wrap our heads
Speaker 1: around that one? So yeah, yeah, there you go, There
Speaker 1: you go. And then if this if this simulator, if
Speaker 1: this future set, if it actually it works right, if
Speaker 1: it comes to fruition and it's useful in naturally predicting
Speaker 1: uh black swans really, because that's what the end result
Speaker 1: of that should be. Uh, then What does that mean
Speaker 1: about science? What does that mean about thought experiments? You know,
Speaker 1: if everything is answerable and predictable, is at the end
Speaker 1: of science? I don't know. And and then likewise it
Speaker 1: also brings to mind any kind of corporate situation where
Speaker 1: you have a problem. What's the first thing people do?
Speaker 1: Meeting about it? Committee about it. Let's form a task
Speaker 1: force to to talk about this, uh, this situation and
Speaker 1: come up with some recommendations to what extent would this
Speaker 1: simulation this uh, this Living Earth simulator be a version
Speaker 1: of that where we're like, oh, we have a problem. Um,
Speaker 1: you know, there's a there's a there suffering in the
Speaker 1: world somewhere. What should we do about it? Throw into
Speaker 1: the simulator and then we get the results, and then
Speaker 1: the simulator gives us a list of recommendations and we
Speaker 1: end up following none of them. Right, we don't have
Speaker 1: the budget for that, right because we've we've talked about it.
Speaker 1: We we put the the info into the machine. It
Speaker 1: uh it does spit us out some numbers, so we're good. Right, Well,
Speaker 1: at least we can finally uh let the cat out
Speaker 1: of Shreddinger's box, right, yeah, all right, Well, let's have
Speaker 1: the Robot bring us something to read reading Donald, Thank you.
Speaker 1: There we go. All right, Well, we heard from a
Speaker 1: listener by the name of Dustin. Dustin Wright sentences Hi, Robert,
Speaker 1: Julie and the rest of the STB y M staff.
Speaker 1: I wanted to say thanks for your episode on mesophonium.
Speaker 1: My whole life has been driven nuts by chewing, swallowing
Speaker 1: and smacking. I always thought that my reaction wasn't normal,
Speaker 1: but now I realized that I'm not alone out there.
Speaker 1: It was really fascinating to hear about others triggers. At
Speaker 1: least I know when I expect my triggers and can
Speaker 1: take steps to cope with the overwhelming anxiety. If a
Speaker 1: person's trigger was the crunching of leaves, then that must
Speaker 1: be hell. I really hope that the more research that
Speaker 1: develops on this topic. Thanks for all of your hard
Speaker 1: work and the wonderful show. Yeah, that was really great.
Speaker 1: We've had so many people right in about mesophonia. It's
Speaker 1: really interesting. It seems to be just anecdotally, uh more
Speaker 1: widespread than we saw. Yeah. I mean, I find myself
Speaker 1: thinking about it all the time too. Whenever I let
Speaker 1: petty things sounds annoy me, and I sort of have
Speaker 1: to step back and I'm like, maybe have a touch
Speaker 1: of messophonia. And then I'm like, well be conscious of it,
Speaker 1: don't let it irk you in. And luckily I don't
Speaker 1: have severe enough reactions that that doesn't work. Yeah. Yeah,
Speaker 1: you know what, Chris, this was hard for me because
Speaker 1: I've got a three year old and there was a
Speaker 1: lot of start from around and I was constantly stop.
Speaker 1: All right, here's another one just from listener Neil. Neil
Speaker 1: writes and it says, Hi, Robert and Julie's super podcast again,
Speaker 1: didn't know about Terry Pratchett's sword made out of meteor?
Speaker 1: Do they call him sky swords? He's a responding tore
Speaker 1: away of the Sword podcast. Just a note on kindo.
Speaker 1: The foot stomp happens at the time of the strike
Speaker 1: or cut, not before, or at least not as a warning.
Speaker 1: The stomp uh fumi komi is part of the cut. Typically,
Speaker 1: for a cut to be counted, you need to stomp,
Speaker 1: call out what you were cutting, and of course land
Speaker 1: the cut in the place that you have called out.
Speaker 1: This is all to demonstrate what kN Doka call key
Speaker 1: kin ta ichi, the oneness of the spirit key with
Speaker 1: the sword. Can and the body tie. You can, of
Speaker 1: course foot stomp before a cut, but typically this is
Speaker 1: a thing to get a reaction, either a flinch or
Speaker 1: start from the other kim dooka, which might reveal a
Speaker 1: hesitate to exploit immediately or just put them on edge,
Speaker 1: or an attack which you might counterattack or otherwise blunt.
Speaker 1: I am not sure I would call it a warning,
Speaker 1: since the intention is usually not that charitable. That said,
Speaker 1: kendo is a great martial art, no disrespect to other
Speaker 1: sword arts, and well worth the effort. Fun to boot
Speaker 1: and the gear is cool too. Anyway, thanks for the
Speaker 1: super podcast series, Cheers and Neil Cool. Yeah, I was
Speaker 1: so glad to hear the information. I saw some of
Speaker 1: the kendo in the documentary Reclaiming the Blade, and it
Speaker 1: seemed as though they were stomping as a warning. But
Speaker 1: it's great to share that information. It makes it even
Speaker 1: that much more intriguing of muscial art. Yeah, it's kind
Speaker 1: of it sounds a lot like it's kind of like
Speaker 1: a like a lunch like you see. I mean, I
Speaker 1: not that I've ever been in a fight, but you know,
Speaker 1: you see people in like in a fighting situation, and
Speaker 1: one will sort of like Yeah, but I like this idea,
Speaker 1: this one oneness of the spirit and the sword and um,
Speaker 1: and that's why they did a little foot stomping. I
Speaker 1: also I was thinking that the little foot stoping because
Speaker 1: they have tiny little feet with Kendem Marshall artists have
Speaker 1: tiny little fie. Okay, I'm kidding, um, but but I
Speaker 1: mean it made me think of playing pool, like if
Speaker 1: you were to call out a pocket, but you would
Speaker 1: do it at the same time that you were to
Speaker 1: hit the ball, or kind of like Babe Ruth like
Speaker 1: pointing the field is going to hit the ball. Yeah,
Speaker 1: thank you, sucker. Try just try to get it. Well. Hey,
Speaker 1: we would love to hear what anyone has to say
Speaker 1: about the the idea of a living Earth simulator. Um,
Speaker 1: what do you think about it? Depending do you do?
Speaker 1: You do you think it's a great idea, do you
Speaker 1: think it's at all feasible? And how do you imagine
Speaker 1: a future in which we have access to one? Would
Speaker 1: you want an app on your phone? Yeah, because that
Speaker 1: was one of the things that they talk about, is
Speaker 1: the idea that if we had this simulator in place,
Speaker 1: you would basically be able to get apps where you
Speaker 1: could see like and I imagine someone would be kind
Speaker 1: of fun like. Someone will probably be like, what would
Speaker 1: a zombie apocalypse actually look like? Let's load that in.
Speaker 1: What would it be like? If you know, just any
Speaker 1: kind of crazy scenario you could think of, you could
Speaker 1: conceivably have an app for it to to to load
Speaker 1: in to check. And you can just imagine everyone from
Speaker 1: any like every business in the world would have some
Speaker 1: sort of access to this model so they could test
Speaker 1: their various uh, promotional materials, etcetera. But let us know
Speaker 1: what you think. You can find us on Facebook and Twitter.
Speaker 1: We are blow the Mind on Twitter, and you can
Speaker 1: find us on Facebook just by searching for stuff to
Speaker 1: blow the mind. And you can drop us a line
Speaker 1: at blow the Mind at how stuff works dot com.
Speaker 1: Be sure to check out our new video podcast, Stuff
Speaker 1: from the Future. Join how Stuff Work staff as we
Speaker 1: explore the most promising and perplexing possibilities of tomorrow.