Speaker 1: Get in touch with technology with tech Stuff from how
Speaker 1: stuff works dot com. Hey there, welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer and
Speaker 1: I love all things tech. And in a recent episode
Speaker 1: I mentioned a guy named John von Neuman, and that
Speaker 1: said I should probably do an episode about him, and
Speaker 1: several of you wrote in and urged that I should
Speaker 1: do this sooner rather than later. So today we're going
Speaker 1: to learn more about von Neuman and his numerous contributions
Speaker 1: to science and technology. This is the first part of
Speaker 1: a two part episode. His achievements were remarkable, perhaps made
Speaker 1: even more astonishing by the fact that he only lived
Speaker 1: to his mid fifties, and yet he was an incredibly
Speaker 1: prolific thinker. But he also had his flaws, and I'll
Speaker 1: talk about those as well, because I think it would
Speaker 1: be a disservice to just gloss over them. So while
Speaker 1: he was a genuine whenly intelligent, brilliant man, he had
Speaker 1: some some flaws to his character as well. So John
Speaker 1: von Neumann was born Neuman Ya nash Law Josh margin
Speaker 1: Tie in Budapest, Hungary, in December. And I know, I know,
Speaker 1: I butchered the pronunciation of that, but I'm doing the
Speaker 1: best I can. He was born into a nonpracticing Jewish family,
Speaker 1: so uh, ethnic Jewish family, but not a practicing Jewish family. Now,
Speaker 1: according to the biography, as I read, the household liberally
Speaker 1: mixed in Jewish and Christian traditions together. His father was
Speaker 1: a successful banker. His mother came from a prosperous family,
Speaker 1: so in those biographies they also mentioned that he came
Speaker 1: from a wealthy background. He was the oldest of three boys.
Speaker 1: His younger brothers grew up to be a doctor and
Speaker 1: a lawyer, respectively, and the family would employ governesses to
Speaker 1: look after the children and from them, Vaughn Neuman began
Speaker 1: to learn French and German and English and other languages
Speaker 1: as well. Even as a kid, he was obviously gifted.
Speaker 1: He could talk with his father in Greek and tell
Speaker 1: jokes in Greek. He could memorize an entire page out
Speaker 1: of a phone book in just a few minutes and
Speaker 1: answer questions about who had which number or what a
Speaker 1: person's street address was. They would do this as like
Speaker 1: party tricks. When he was six years old, John von
Speaker 1: Neumann was an apt student in school, and he attended
Speaker 1: the Lutheran High School starting in nineteen thirteen. This was
Speaker 1: one of the best schools in Hungary. He earned the
Speaker 1: award of being the best Mathematician of the fifth class
Speaker 1: in nineteen eighteen and he won Best Hungarian Student in
Speaker 1: Mathematics in nineteen twenty. Now, in between that time there
Speaker 1: were some bumps in the road, but it wasn't due
Speaker 1: to his academics, was due to world politics. In nineteen
Speaker 1: nineteen you had the end of World War One. Hungary
Speaker 1: fell under the governance of a commune. This leader named
Speaker 1: bellah Kun, and he and his Hungarian Soviet Republic moved
Speaker 1: to nationalize a lot of private property in other words,
Speaker 1: sees the property of wealthy individuals in order to redistribute
Speaker 1: that to the rest of the population. Now, the Noumans
Speaker 1: didn't really like the sound of that, and so they
Speaker 1: fled temporarily to Austria. After about a month, they came
Speaker 1: back to Budapest and the Soviet Republic didn't last very long. It,
Speaker 1: I mean it ultimately it fell. But in the wake
Speaker 1: of its failure, that added more problems for this family.
Speaker 1: So namely, the failing government had many Jewish representatives in it.
Speaker 1: It was a largely Jewish government, and so public opinion
Speaker 1: towards Jewish people in general turned very, very negative. So
Speaker 1: it didn't matter that John's family had been in opposition
Speaker 1: to that government. The fact that they were Jewish meant
Speaker 1: that they would receive a lot of the ill will
Speaker 1: of the people. He would go on to study mathematics
Speaker 1: in one university and chemistry at another at the same time.
Speaker 1: Sort of all right, so here's the story behind that.
Speaker 1: His dad didn't want him to pursue a career that
Speaker 1: wasn't going to accumulate wealth, and he felt that an
Speaker 1: advanced degree in pure mathematics wasn't going anywhere. It wasn't
Speaker 1: a real money maker. So he and John sat down
Speaker 1: and together they agreed upon the subject of chemistry. There
Speaker 1: were a lot of rising stars in the world of
Speaker 1: chemistry out of this part of Europe, and so John
Speaker 1: von Neumann agreed he would study chemistry. So he enrolled
Speaker 1: in the University of Berlin. However, at the same time
Speaker 1: he also enrolled in the University of Budapest for mathematics. Now,
Speaker 1: Hungary's universities had really strict limitations on the number of
Speaker 1: Jewish students would be allowed to attend at any one time. However,
Speaker 1: von Neuman's academic record was beyond impressive, so he was
Speaker 1: able to get in. And then he did something pretty
Speaker 1: darn baller. He would attend classes at the University of
Speaker 1: Berlin learning about chemistry, and he would skip nearly all
Speaker 1: of the lectures and classes at the University of Budapest
Speaker 1: in mathematics. He would just come back to the University
Speaker 1: of Budapest to take exams or whenever he was absolutely
Speaker 1: required to be there, and he aced those exams even
Speaker 1: though he wasn't going to the lectures. He graduated with
Speaker 1: a pH d in mathematics from the University of Budapest
Speaker 1: in ninety without really going to lectures there. He was
Speaker 1: twenty three years old. He transferred out of the University
Speaker 1: of Berlin as he was studying chemistry, and he would
Speaker 1: ultimately receive a diploma in chemical engineering in ninety six
Speaker 1: from a school in Zurich, Switzerland. I wish I could
Speaker 1: tell you the name of that school, but I'm looking
Speaker 1: at it and it would do such a terrible job
Speaker 1: with this one. I don't even dare attempt to pronounce it.
Speaker 1: So I'm just gonna leave it be now. When he
Speaker 1: was twenty. When he was still in school, John von
Speaker 1: Neuman published a definition for ordinal numbers, and an ordinal
Speaker 1: number is a way to describe the position of an
Speaker 1: object within a sequence of objects that are inside a set. So,
Speaker 1: for example, if you consider a set to be people
Speaker 1: who are in line for pizza, and there are four
Speaker 1: people ahead of me, I am the fifth person in
Speaker 1: that line or set. So the ordinal number that is
Speaker 1: my designation is five because I'm the fifth person in line.
Speaker 1: Von Neuman's definition of ordinal numbers is the same one
Speaker 1: that we use to this day now. Von Neuman's dissertation
Speaker 1: for his PhD had the title The Axiomatic System of
Speaker 1: set theory. Set theory concerns collections of objects, typically mathematical objects,
Speaker 1: as opposed to you know, like hammers and Set theory
Speaker 1: was established in the late nineteenth century by George Cantor
Speaker 1: in an article titled on a property of the Collection
Speaker 1: of all real algebraic numbers. Basically, this is the theory
Speaker 1: that can be described like this. Sets are collections of
Speaker 1: objects or elements. So in a real world example, the
Speaker 1: classification of mammals includes all animals that are vertebrates, that
Speaker 1: have for that typically give birth to live young, and
Speaker 1: they produce milk for offspring. So a cat fits that definition.
Speaker 1: A cat fits the set of mammals. All cats are mammals. However,
Speaker 1: sets themselves can be objects that belong to larger sets.
Speaker 1: So in this example, mammals is a set, but it's
Speaker 1: also an object. It belongs to the larger set of
Speaker 1: all animals. So a cat belongs to the set mammal
Speaker 1: as well as to the set animals, and mammals are
Speaker 1: a subset of animals. If you've seen a Venn diagram
Speaker 1: in which you have two circles that overlap in some way,
Speaker 1: you've seen a representation of one aspect of set theory.
Speaker 1: So let's give an example of a Venn diagram. Let's
Speaker 1: say we have two circles. One circle represents people who
Speaker 1: love they might be giants, and the second circle represents
Speaker 1: people who love Andrew w K. These circles each represent
Speaker 1: different sets. The overlap, or the intersection of those two
Speaker 1: sets is where you have people who fit both categories.
Speaker 1: They love they might be giants and they love Andrew
Speaker 1: w K. We could even give this group a new name.
Speaker 1: We could call it something else. Like weirdos like Jonathan
Speaker 1: Strickland because I love both, they might be giants and
Speaker 1: Andrew w K. But we could also talk about the
Speaker 1: set difference of this Venn diagram. The set difference for
Speaker 1: the people who love they might be giants would include
Speaker 1: all the people who only love Andrew w K. And
Speaker 1: the opposite would be true for the set difference for
Speaker 1: the people who love Andrew w K. You also have
Speaker 1: symmetric differences. The symmetric difference of these two sets would
Speaker 1: include all the people who only loved one of the
Speaker 1: two bands, but not both. There are many other ways
Speaker 1: you can describe sets, but you get the general idea.
Speaker 1: As for axioms, those are statements that are self evidently true,
Speaker 1: things that are true because of common sense. We can
Speaker 1: declare them to be true. It's about as fundamental as
Speaker 1: you can get. In fact, it is as fundamental as
Speaker 1: you can get with truth. So one of those might
Speaker 1: be parallel lines will never intersect. By definition, parallel lines
Speaker 1: will never intersect. That is an axiom. It is a
Speaker 1: fundamental truth. It's a common sense statement. It's not based
Speaker 1: on earlier or or even more granular statements. So these
Speaker 1: axioms can be used to deduce further conclusions. But doing
Speaker 1: that hand be tricky. If you build deductions on axioms
Speaker 1: and you find that two different deductions you have based
Speaker 1: off the same axiom end up contradicting each other, then
Speaker 1: you've got a problem on your hands. So let's say
Speaker 1: you've got your axiom A. This is your fundamental statement,
Speaker 1: the one that you've declared to be true. Then from A,
Speaker 1: you deduce that because A is true, statement B, which
Speaker 1: is based on A, must also be true. And then
Speaker 1: from statement B you deduce that statement P is also true.
Speaker 1: Now let's get back to A. Let's say that we
Speaker 1: start from A again, and now we're making a different
Speaker 1: deduction and we deduce a new statement. We're calling this
Speaker 1: statement D, and that one must be true. But now
Speaker 1: from statement D we make a deduction, and from statement
Speaker 1: D we deduce that statement P has to be false.
Speaker 1: So this is a problem. You have one line of
Speaker 1: reasoning that states P has to be true because A
Speaker 1: is true, B is true, P is true. Then you
Speaker 1: have another one that says P has to be false
Speaker 1: because A is true, D is true. That means P
Speaker 1: must be false. This is a paradox or a contradictory statement,
Speaker 1: and it means we have to look over the entire system.
Speaker 1: We have to look at the axioms to make sure
Speaker 1: that they are actually sound, and we have to look
Speaker 1: at the process we've used to deduce the truth or
Speaker 1: falsehood of the statements that followed from this axiom. This
Speaker 1: falls into an area of logic that I absolutely loved
Speaker 1: studying in college. Now, I'm no von Neumann, not by
Speaker 1: a long shot, but I got a brag for just
Speaker 1: a second. So when I was in college, I took
Speaker 1: a course in symbolic logic, and I found that my
Speaker 1: professor was teaching directly from the textbook. So I made
Speaker 1: a tough decision. I decided to stop going to classes.
Speaker 1: I only took the exams and I aced the course. Now, granted,
Speaker 1: the version of logic I was studying was the most
Speaker 1: basic version of symbolic logic. It was child's play for
Speaker 1: someone like von Neuman. He would have breathed through the
Speaker 1: class back when he was six years old. So I
Speaker 1: can't brag too much, but it did give me a
Speaker 1: little bit of insight into his mind, at least in
Speaker 1: that aspect. I've got a lot more to say about
Speaker 1: John von Neuman. But first, let's take a quick break
Speaker 1: to thank our sponsor. Set theory would become one of
Speaker 1: many areas that von Neuman would continue to study and
Speaker 1: develop over the course of his life. There's a concept
Speaker 1: in mathematics called the von Neuman universe. In fact, although
Speaker 1: some scholars like Gregory H. Moore have gone on to
Speaker 1: say that this attribution is somewhat misleading, but we'll leave
Speaker 1: that for now, because would otherwise be diving into an
Speaker 1: area of mathematics so far outside of my expertise and
Speaker 1: understanding that I would just be reading from textbooks or
Speaker 1: history books, and I don't think that makes very good podcasting.
Speaker 1: In addition to mathematics and chemistry, the young von Neumann
Speaker 1: was also fascinated by technology and aviation, and it began
Speaker 1: to work in an area that would have a really
Speaker 1: big effect on many different different industries, different careers moving forward.
Speaker 1: That would be game theory. Now, personally, I find the
Speaker 1: term game theory to be a little misleading because it
Speaker 1: undersells what it's all about. You could use game theory
Speaker 1: to describe how people play a game like poker, but
Speaker 1: it's actually way more than that. In psychology, you might
Speaker 1: refer to it as the theory of social situations, and
Speaker 1: it really comes down to how human beings interact with
Speaker 1: one another in specific types of situations. And generally you
Speaker 1: can break it down into two large branches, cooperative game
Speaker 1: theory and non cooperative game theory, and the names kind
Speaker 1: of are self explanatory. Cooperative game theory describes how people
Speaker 1: will work together to achieve a common goal. How will
Speaker 1: they leverage their strengths, how will they compensate for their weaknesses,
Speaker 1: how do they manage to go after this goal together.
Speaker 1: Non Cooperative game theory, you could call it competitive game theory,
Speaker 1: describes how intelligent people will interact with each other as
Speaker 1: they each are working toward achieving their individual goals. Now,
Speaker 1: those individual goals might be the same, so it may
Speaker 1: be that everyone's trying to go after the same prize
Speaker 1: and only one person can get it. Or it might
Speaker 1: be that each person has a different individual goal, and
Speaker 1: it may be that some of those individual goals are
Speaker 1: at conflict with one another. For example, maybe my goal
Speaker 1: is to get a certain trophy and someone else's goal
Speaker 1: is to get a certain medal. But the problem is
Speaker 1: that the when one person achieves one of those goals,
Speaker 1: the path to achieving the other one is cut off,
Speaker 1: so that would be another example. Now, John von Neumann
Speaker 1: was not the first mathematician to suggest using mathematics to
Speaker 1: describe game theory, or to study game theory, or to
Speaker 1: come up with various strategies in game theory. Numerous thinkers
Speaker 1: had worked on various applications, some for specific games like chess,
Speaker 1: before von Neuman had ever come onto the scene. But
Speaker 1: von Neuman's work was some of the first general purpose
Speaker 1: game theory work not dedicated to a specific implementation. His
Speaker 1: scholarship effectively established game theory as its own distinct field
Speaker 1: of study. John von Neumann published his first paper on
Speaker 1: game theory in nineteen twenty eight. It had the title
Speaker 1: Theory of Parlor Games. He recognized that a game like
Speaker 1: poker had a lot more going on than just probabilities.
Speaker 1: So if poker just was reliant upon chance, then you
Speaker 1: could memorize all the possible outcomes of a round of cards,
Speaker 1: and you would have a good chance of being able
Speaker 1: to play your hand to the best of its effectiveness. Right,
Speaker 1: you would know that the odds of someone having a
Speaker 1: better hand would be higher or lower than um any
Speaker 1: given hand that you have and that would help you
Speaker 1: make a decision. However, that does not take into account
Speaker 1: the human element of bluffing. So with bluffing, a person
Speaker 1: can act as if his or her hand is stronger
Speaker 1: than it really is, or maybe they are giving off
Speaker 1: the implication that they aren't working with a very strong
Speaker 1: hand and they're hoping that you will get out of
Speaker 1: the game. There's a lot of psychology in their doubt
Speaker 1: enters into the equation. So von Neyman started to work
Speaker 1: on this idea and he saw how it could be
Speaker 1: applicable to all sorts of stuff, not just games, but
Speaker 1: stuff like economics, and he partnered with an Austrian economist
Speaker 1: who was at print Sston University named Oscar Morgan Stern,
Speaker 1: and together they would publish a book titled Theory of
Speaker 1: Games and Economic Behavior. In the introduction of that book,
Speaker 1: they lay out the fact that economics is a really
Speaker 1: complicated science. There are a lot of contributing factors to
Speaker 1: economic outcomes, and not all of them are identified, let
Speaker 1: alone understood. So out of the factors that we can
Speaker 1: say yes, this definitely impacts economics, we don't necessarily understand how,
Speaker 1: but we know it happens, and then there are others
Speaker 1: that we may not have identified yet. So the authors
Speaker 1: maintained that because of that, because of this uncertainty, this
Speaker 1: lack of knowledge, this gap in our knowledge, it's pretty
Speaker 1: much the case that anyone who claims to have a
Speaker 1: universal theory of economics has got to be wrong because
Speaker 1: we don't have that full understanding of all the factors
Speaker 1: and how they interact with one another in any given situation,
Speaker 1: how they're weighted in any given situation. So in a way,
Speaker 1: this would mirror another big challenge von Neuman would encounter later,
Speaker 1: which would involve predicting the weather. I'll talk a bit
Speaker 1: about that in our next episode. Now, one of the
Speaker 1: central concepts of von Neumann's game theory was called mini max.
Speaker 1: Emil Borel had previously theorized about mini max, and this
Speaker 1: is all about minimizing the possible loss in the event
Speaker 1: of a worst case scenario. So, considering considering a scenario
Speaker 1: where the absolute worst happens, the maximum bad happens, how
Speaker 1: do you minimize the impact to you in that event?
Speaker 1: And this could be applied to all sorts of situations.
Speaker 1: How do you limit the setbacks you're going to suffer
Speaker 1: should the worst happen. There's also a concept called maximn
Speaker 1: This is sort of the opposite. How can you make
Speaker 1: the absolute most gains with the minimum success you might
Speaker 1: have in any given scenario. So these two on steps
Speaker 1: together would become part of game theory, and game theory
Speaker 1: wasn't the only scholarly work von Neumann was pursuing in
Speaker 1: the nineteen twenties. At the same time, he was also
Speaker 1: studying quantum mechanics, which would ultimately form the foundation of
Speaker 1: his book, The Mathematical Foundations of Quantum Mechanics. So I've
Speaker 1: talked about quantum mechanics before, but what the heck doesn't
Speaker 1: actually mean. Well, the simple answer is that it's a
Speaker 1: branch of physics that's concerned with the very very very small.
Speaker 1: We're talking atomic and subatomic levels generally. So at that scale,
Speaker 1: the physics that we observe in our day to day lives.
Speaker 1: The behavior of larger stuff, you know, stuff like tractors
Speaker 1: and puppy dogs and skyscrapers and people. The physics that
Speaker 1: we encounter day to day that breaks down when you
Speaker 1: get down to this atomic and subatomic level. So in
Speaker 1: our day to day world, I cannot walk up to
Speaker 1: a wall and then in an instant appear on the
Speaker 1: other side of the wall. I would have to have
Speaker 1: a door to walk through or a window decline through,
Speaker 1: or I'd have to burst kool aid or hulk like
Speaker 1: through the barrier. There would have to be an opening,
Speaker 1: or I would have to make one. Those are the
Speaker 1: only two options if I want to get onto the
Speaker 1: other side of a wall, or I guess I could
Speaker 1: walk around it if if that's an option, but you
Speaker 1: get what I mean. On the quantum level, however, this
Speaker 1: is not the case. You can actually have a quantum
Speaker 1: particle come up to a barrier and sometimes appear on
Speaker 1: the other side of the barrier as if it had
Speaker 1: just passed through, without even having to pass through. This
Speaker 1: tendency can have consequences in our macro world. So take
Speaker 1: electrons for example. So for convenience sake, we talk about
Speaker 1: electrons inhabiting an orbit around a nucleus of an atom,
Speaker 1: and we usually depict this in some way that makes
Speaker 1: sense to us on a macro scale. And you might
Speaker 1: have a very simple drawing where you've got the the
Speaker 1: very uh icon drawing of an atom where you've got
Speaker 1: the nucleus as a big dot in the center, and
Speaker 1: they have a circle around the nucleus, and around in
Speaker 1: that circle you have a dot that represents an electron.
Speaker 1: So that's sort of saying, in this moment of time,
Speaker 1: the electron is right here. But that's misleading. That's not
Speaker 1: really what we can definitively say. Electrons have wave like properties.
Speaker 1: They don't act just as particles. They also connect as
Speaker 1: a wave, and waves don't just abruptly end when they
Speaker 1: hit a barrier. They actually taper off. If the barrier
Speaker 1: is thin enough, some of the wave will continue through
Speaker 1: the barrier to the other side. Now, the wave represents
Speaker 1: a probability function. Now essentially that tells us the chance
Speaker 1: of the electron inhabiting any part along that wave at
Speaker 1: any given time. So that means there is a probability,
Speaker 1: albeit a small one, that the electron could exist on
Speaker 1: the other side the barrier. Because it's it still exists
Speaker 1: on the other side, it represents a probability, and as
Speaker 1: long as there's a probability, it means that sooner or
Speaker 1: later it'll happen. So that means that if you have
Speaker 1: enough electrons near a barrier like this one, some of
Speaker 1: those electrons will just from probability, appear on the other
Speaker 1: side of the barrier as if it had passed through,
Speaker 1: and we call it electron tunneling. Now there's no actual
Speaker 1: tunnel created, there's no hole made in the barrier. It
Speaker 1: just was a fact of probability. There was a small
Speaker 1: probability that the electron could be on the other side,
Speaker 1: and so sometimes that happens. This is one of the
Speaker 1: big challenges that microprocessor manufacturers make when they miniaturize elements
Speaker 1: on the chips because if the gates, the actual gates
Speaker 1: that are controlling the pathway of electrons are thin enough,
Speaker 1: then it's possible for the electron probability function to overlap
Speaker 1: the barrier, and then you have electrons passing through these
Speaker 1: gates as if they were open even when they're closed,
Speaker 1: and that creates errors. So that's why this has real
Speaker 1: world uh impact, even though we don't see this kind
Speaker 1: of behavior in the macro world. Like I said, I
Speaker 1: can't walk up to a wall and then magically appear
Speaker 1: on the other side of it just because of probability.
Speaker 1: There's zero probability that that will happen. Concepts like these
Speaker 1: are hard to wrap our minds around because we occupy
Speaker 1: a world in which quantum mechanics do not apply. This
Speaker 1: is also or if they do apply, they apply, it's
Speaker 1: such a tiny, tiny amount that it's imperceptible to us.
Speaker 1: So this is why I get grouchy when I see
Speaker 1: people try to use these concepts from quantum mechanics to
Speaker 1: describe or predict stuff in our real world macro environments,
Speaker 1: because it really doesn't apply there, at least not on
Speaker 1: a level that is at all, you know, noticeable. At
Speaker 1: this point, you're either dangerously close to using pseudoscience or
Speaker 1: you've fully jumped into the pseudo science science boat. So
Speaker 1: be where people who try to describe real world scenarios
Speaker 1: in quantum mechanics um you know, methods or approaches. I've
Speaker 1: got more to say about John von Neuman in just
Speaker 1: a second, but first let's take another quick break to
Speaker 1: thank our sponsor. Now, John von Neumann was working on
Speaker 1: quantum mechanics at a really exciting time. Heisenberg had just
Speaker 1: proposed his uncertainty principle. Now that's largely based off the
Speaker 1: fact that matter can act as both a wave and
Speaker 1: a particle, and that would mean there's a limit to
Speaker 1: how precisely. We might know a particle's properties, like an
Speaker 1: electron's position and speed, for example. So the more precisely
Speaker 1: we know one of those two things, the less we
Speaker 1: know about the other. So the more precisely we can
Speaker 1: talk about the electron's position, the less we know about
Speaker 1: its speed, and vice versa. John von Neumann's contributions were
Speaker 1: unsurprisingly related to the application of mathematics when it comes
Speaker 1: to quantum mechanics. Von Neumann's emphasis was on mathematical rigor.
Speaker 1: That is, his approach emphasized the degree to which a
Speaker 1: mathematical representation of a concept and quantum physics is logically sound.
Speaker 1: He wanted the math to be as strong and a
Speaker 1: method of proof as possible to logically support these various
Speaker 1: principles and quantum mechanics. Now, that put his approach in
Speaker 1: contrast with a another physicist named Paul de rac who
Speaker 1: argued for a more pragmatic approach that was less mathematically rigorous,
Speaker 1: but it was also more efficient. It was easier to apply,
Speaker 1: and it would lead to conclusions that were easier to
Speaker 1: understand than these very complicated mathematical formulas. So you had
Speaker 1: these two very different styles coming at quantum mechanics at
Speaker 1: the same time. So John VA Neuman. By the late
Speaker 1: nineteen twenties was already something of an intellectual celebrity, at
Speaker 1: least in academic circles, and he was doing groundbreaking work
Speaker 1: in game theory and quantum mechanics. In ninety nine, he
Speaker 1: was invited to lecture at Princeton University on the subject
Speaker 1: of quantum theory, and he said he would be happy
Speaker 1: to do so, but first he had to attend to
Speaker 1: a small personal matter. That small personal matter was a wedding.
Speaker 1: His wedding, he got married to a woman named Marietta Covechi.
Speaker 1: Now Covechi and von Neumann had known each other since childhood.
Speaker 1: Covechi was a talented economics student at the University of Budapest.
Speaker 1: She was also something of a socialite in Hungary. She
Speaker 1: was known for appearing at parties and being very glamorous.
Speaker 1: Von Neumann was also a fan of the nightlife. Apparently,
Speaker 1: he was quite well known as a patron of the
Speaker 1: cabaret circuit in Berlin. He would teach in the daytime
Speaker 1: and go out for a night on the town in
Speaker 1: the evening, and his love of parties and alcohol would
Speaker 1: follow him as he relocated to the United States. In
Speaker 1: addition to marrying Covechi, von Neumann converted from being a
Speaker 1: nonpracticing Jew to a Catholic. Now this was not an
Speaker 1: indication that he had found religion. He was agnostic through
Speaker 1: most of his life. I'll talk a little bit about
Speaker 1: that in the next episode as well. It was more
Speaker 1: of a practical decision so that he could actually marry Covechi.
Speaker 1: So he converts to Catholicism and then he and his
Speaker 1: newlywed wife move over to the United States. Now, von
Speaker 1: Neuman would become a professor at Princeton, but reportedly it
Speaker 1: was one of the few things in academia that he
Speaker 1: was not great at, or at least people didn't really
Speaker 1: like his style. So the trouble mostly appeared to be
Speaker 1: that von Neuman was super duper wicked smart, and he
Speaker 1: had a phenomenal memory as well, so he could work
Speaker 1: out complex equations in his head, and he would leap
Speaker 1: around the topic quickly, which left a lot of students
Speaker 1: struggling in his wake. They couldn't keep up, they didn't
Speaker 1: weren't able to connect the dots like he was. He
Speaker 1: got a reputation for scribbling out important equations hurriedly on
Speaker 1: a chalkboard and then erasing them before anyone knew what
Speaker 1: they meant or could even copy them down. However, he
Speaker 1: also had a reputation for being able to communicate complicated
Speaker 1: ideas in a very straightforward way in a one on
Speaker 1: one setting that would allegedly make sense even to dullards
Speaker 1: like myself if I had been given the opportunity. Now,
Speaker 1: since von Neuman died decades before I was born, I
Speaker 1: can't actually put this claim to the test, but by
Speaker 1: many accounts, he was talented at explaining complicated ideas to
Speaker 1: people who didn't have the expertise in mathematics to understand
Speaker 1: all of the bells and whistles. In nineteen thirty three,
Speaker 1: he was named a mathematics professor for the Institute for
Speaker 1: Advanced Study in Princeton. That was a brand new department.
Speaker 1: He was one of the six experts in the original
Speaker 1: group of professors. He was also the youngest of those six. Uh.
Speaker 1: Those professors included some really smart people, including one that
Speaker 1: I'm sure you've all heard about. That would be Albert Einstein,
Speaker 1: so he was in really good company. Three was also
Speaker 1: the last year that von Neumann would lecture for a
Speaker 1: term in Germany. He was going back and forth. He
Speaker 1: would do a term in Germany, he would come back
Speaker 1: and do a term in the United States, and so
Speaker 1: on and so forth. The Nazi Party, however, was starting
Speaker 1: to consolidate power in Europe around this time, so Neuman
Speaker 1: withdrew to work solely in the United States. Now, some
Speaker 1: of his peers would leave continental Europe in an effort
Speaker 1: to escape the Nazi regime as it got more powerful,
Speaker 1: but von Neuman had already relocated in an effort to
Speaker 1: find steady employment as an academic. Now I say this
Speaker 1: only because as I was researching von Neumann, I came
Speaker 1: across differing accounts, some of which said, you know, he
Speaker 1: was fleeing the Nazi regime. But from the information I
Speaker 1: could find, it sounded more like he was looking for
Speaker 1: a steady gig and he got one at Princeton, and
Speaker 1: that was the guiding force in his decision. It just
Speaker 1: happened to pre date the rise of Nazis in Europe,
Speaker 1: so he had already left by the time the Nazi
Speaker 1: Party was starting to pick up steam in Germany in
Speaker 1: the mid nineteen thirties. Von Neumann would become interested in
Speaker 1: the problem of hydro dynamic turbulence and the theory of shocks.
Speaker 1: This would become really important the next decade. This area
Speaker 1: of interest was also really complicated. Is so complicated even
Speaker 1: von Neuman's mind couldn't tackle some of these equations because
Speaker 1: hydro dynamics is very counterintuitive, especially when it comes into
Speaker 1: shock waves. So he would need a device to help
Speaker 1: him suss out the more complicated nuances, and that began
Speaker 1: von Neuman's interest in computer science. I'll talk a lot
Speaker 1: more about that in the next episode as well. Now,
Speaker 1: in his personal life, John and his wife Marriott had
Speaker 1: a daughter named Marina, but von Neuman's private life was
Speaker 1: not one of matrimonial bliss. According to biographies I researched,
Speaker 1: he was affectionate towards his daughter, but he wasn't really
Speaker 1: involved in her upbringing at all, or in the care
Speaker 1: of the household in general. He considered that to be
Speaker 1: the work for his wife, and that he was going
Speaker 1: to just dedicate himself to his scholarly work and then
Speaker 1: tying one on occasionally getting rip roaring drunk at parties.
Speaker 1: That was his Those were his two interests. So his
Speaker 1: relationship was strained. Now, Eventually Marriott would leave him and
Speaker 1: the two would divorce. Interestingly, Marriette would go on to
Speaker 1: Mary again. She married a physicist named James Brown Horner Cruper,
Speaker 1: sometimes known as Desmond for some reason. This guy Cooper.
Speaker 1: He was part of the radiation laboratory at m I T.
Speaker 1: And you might remember I talked a lot about that
Speaker 1: particular lab on my episodes about Alfred Loomis. So if
Speaker 1: you want to learn more about that, look into the
Speaker 1: Tech Stuff archives for the Alfred Loomis stories. Now, von
Speaker 1: Neuman would be married a second time. His second wife
Speaker 1: was Clara Dan. Clara was, like von Neuman, from Budapest.
Speaker 1: She was also from a wealthy Jewish family. She was
Speaker 1: born in nineteen eleven. As a teenager, she had become
Speaker 1: a championship figure skater. She also had been married twice already.
Speaker 1: She got married in nineteen thirty one to a man
Speaker 1: named Farrank Ingle, but they were divorced a few years later.
Speaker 1: Her second marriage was in ninety six to a man
Speaker 1: named and Or Rapos. He was still married to her
Speaker 1: when von Neuman struck up a relationship with her, so
Speaker 1: they were technically they were both having affairs because von
Speaker 1: Neuman's marriage had not come to an indiet the divorce
Speaker 1: was still in process, so they end up getting into
Speaker 1: a relationship with each other. Clara ends up divorcing her husband,
Speaker 1: then Mary's von Neumann, and together they immigrate to the
Speaker 1: United States. Clara was a remarkable woman in her own right.
Speaker 1: Absolutely she made significant contributions. She would become the head
Speaker 1: of statistical computing over at Princeton. She would become one
Speaker 1: of the early computer programmers of the Mathematical Analyzer, Numerical Integrator,
Speaker 1: and computer a k A. Maniac. More on that in
Speaker 1: the next episode two. And she was also a tragic figure.
Speaker 1: So John von Neumann died in nineteen fifty seven not
Speaker 1: a spoiler alert, happened decades ago, but we'll talk about
Speaker 1: that more in the next episode two. So after his death,
Speaker 1: she would go on to Mary for a fourth time.
Speaker 1: This time it was to a physicist named Carl Eckhart,
Speaker 1: and in nineteen sixty three, she drove out to a
Speaker 1: secluded beach in California. She walked out into the surf
Speaker 1: and she drowned. The San Diego Coroner's office would rule
Speaker 1: her death a suicide, so a very tragic ending for
Speaker 1: her back to von Neuman to To wrap up this episode,
Speaker 1: I've covered a lot of his work, his early work
Speaker 1: in mathematics. In our next episode, we're going to learn
Speaker 1: more about his involvement in the Manhattan Project. That's the
Speaker 1: of course, the super secret project that was dedicated to
Speaker 1: designing the atomic bomb. We'll also learned about how he
Speaker 1: helped design computer systems, and we'll learn more about some
Speaker 1: of his contributions to tech and science, as well as
Speaker 1: some of what people have generously described as his personality quirks.
Speaker 1: I would call them severe character flaws. We'll talk more
Speaker 1: about those in the next episode. If you want to
Speaker 1: learn more about the show, including how to get in
Speaker 1: touch with me, go over to our website the addresses
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Speaker 1: public dot com slash tech Stuff, And remember every purchase
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