Episode 3: The Experimental Rabbit
THOMAS HAIGH: When I visited the Library of Congress to look through the von Neumann papers there, we found a 30-page document, which held a whole bunch of numbers.
KATIE HAFNER: Meet Thomas Haigh. A history professor at the University of Wisconsin-Milwaukee.
THOMAS HAIGH: So there were two columns with numbers and letters in it, and it wasn't immediately clear what it was, but I took copies of it because it looked like it might be important.
KATIE HAFNER: I go to Tom’s website to see what he’s talking about. It’s a whole bunch of numbers alright. It looks like a piece of scratch paper you might use for a math problem, then throw away. Or wad up a piece of gum in it and then throw it away. It is not something I myself would have pulled out of the archives.
THOMAS HAIGH: Um, if we scroll down…so you can see there’s page after page of this…
KATIE HAFNER: Wow.
THOMAS HAIGH: …and that turned out to be program code. So, hiding in the archives there, pretty much unnoticed, I think by, uh, everybody else who had visited them, we found an incredible amount of material for a computer program run in the 1940s.
KATIE HAFNER: Amazing to have this evidence. To have this, like, documentation.
THOMAS HAIGH: I'm just glad that the archivists, who would have had no idea what it was, thought that it was worth preserving.
KATIE HAFNER: Yeah, exactly. And how did you know that Klára is the one who had written that?
THOMAS HAIGH: We know that it was produced by Klára von Neumann because the handwriting analyst confirmed that it was her handwriting.
KATIE HAFNER: You heard it–Tom Haigh hired a handwriting analyst for the book he co-authored, ENIAC in Action. That’s how thorough a scholar Tom is–and his meticulous scholarship accrued to our benefit, because it meant we got to see Klári at work, right there on the page.
The document filled with numbers turned out to be actual code that Klára Dán von Neumann wrote. We’d read Klári’s memoir, we’d looked at her letters, but here she was telling a computer what to do in a language no one had used before.
I’m Katie Hafner, and this is Lost Women of Science, where we unearth the stories of scientists who haven’t gotten the recognition they deserve. This season is about Klára Dán von Neumann, who wrote some of the earliest computer code.
When we left off, two atomic bombs had just destroyed two cities in Japan, ending World War II. Klári had arrived at Los Alamos, the place where those bombs were created.
And it was here that a new life opened up to her. The world of computer programming.
To understand Klári’s work—her coding—we need to zoom out a bit. We need to tell you the story about a particular computer, the ENIAC. Because before Klári got involved, the ENIAC wasn’t even something that could be programmed in the modern sense. And once you understand what that means, it will become clear why Klári’s involvement was so revolutionary.
This story starts in Los Alamos…at the end of World War II.
THOMAS HAIGH: After the war, Los Alamos kind of downsizes, but the effort to develop new and better nuclear weapons continues.
KATIE HAFNER: With the Cold War coming hot on the heels of World War II, an arms race was underway.
The goal was clear: build bigger better bombs. And to do that, scientists needed to find faster ways to tackle a load of calculations.
At Los Alamos they were still using those punch-card machines we heard about in the last episode. Besides being slow and finicky, they could really only do simple math.
The laboratory at Los Alamos needed something much more powerful.
But this wasn’t a new problem. In fact…
THOMAS HAIGH: And in fact, the mathematical needs of the Los Alamos project during the war are why John Von Neumann was crisscrossing the country, making touch with different groups, developing computers.
In the last episode, we started talking about Johnny’s search for faster technologies. Throughout the war, he’d been on the hunt for computers that could assist in weapons development. Most of the machines he ran across were revolutionary, but they weren’t exactly what he was looking for. But one day…
NIC LEWIS: And in a chance encounter in Maryland, he ran into the mathematician, uh, Herman Goldstine, who happened to be the army liaison for the ENIAC project.
KATIE HAFNER: That’s Nic Lewis, a historian of technology at Los Alamos National Laboratory. You’ve heard him in earlier episodes.
Johnny’s fateful meeting with Herman Goldstine took place at a train station in the summer of 1944. Herman was another mathematician. He’d been working on computing projects for the US Army. A year earlier, in 1943, he’d started working on an exciting new machine. It was called the ENIAC.
NIC LEWIS: The ENIAC was the first electronic digital general purpose computer, and it was being developed for the army at the, uh, university of Pennsylvania.
KATIE HAFNER: When Johnny bumped into Herman at that train station, it was the first time he’d heard of this new electronic computer.
NIC LEWIS: Goldstine later described it. He said that their chat was very convivial up to the point that von Neumann learned about the ENIAC. And then suddenly it turned into the oral exam for a PhD in mathematics.
KATIE HAFNER: And why was Johnny asking so many questions? Because the machine Herman was describing had unprecedented potential. The ENIAC was faster than anything at Los Alamos.
NIC LEWIS: Well the punch card machines could do about 600 multiplications per hour. The ENIAC had the potential to perform the same amount of work in two seconds.
KATIE HAFNER: The ENIAC was so fast because it was all electronic. Information could be transmitted at electronic speeds, which is to say practically the speed of light.
Johnny was keen to get a look. So on August 7th, 1944, shortly after grilling Herman Goldstine, Johnny arrived at the University of Pennsylvania. That’s where the ENIAC was being built.
It was being designed with one purpose in mind: to calculate ballistic trajectories. Remember, we were still in the middle of a war. And all this was happening at the university’s Moore School of Electrical Engineering.
When Johnny first set eyes on it, the ENIAC was on its way to becoming a technological marvel. But by today’s standards, it sure didn’t look or sound like one…
CLAIRE L. EVANS: Oh, my god, uh, loud, loud, loud.
KATIE HAFNER: That’s Claire L. Evans, the author of Broad Band, which tells the story of some of the first users of the ENIAC. She’s describing what the ENIAC was like…what Johnny would have experienced…
CLAIRE L. EVANS: Massive, I mean the size of a room, obviously. Black, steel, with lots of little vacuum tubes that keep fritzing and lots of fans and drawing, lots of power…
KATIE HAFNER: It’s easy to get bogged down in all the hardware – the fans, the vacuum tubes…
But the big innovation of the ENIAC is right there in its name: electronic numerical integrator and computer. Earlier computers were mechanical. To store and manipulate the numbers they were processing, these computers still relied overwhelmingly on moving parts—gears or electromechanical switches. The ENIAC, on the other hand, used electronic signals, transmitted by vacuum tubes.
If you’ve ever looked at the back of a guitar amp, you’ve probably seen these. They look like little light bulbs. All the air’s been sucked out (that’s why they’re vacuum tubes). And that makes them great for sending electrons from one end to the other. You can use vacuum tubes as amplifiers, boosting an electronic signal, or you can use them as switches, turning the electronic signal on or off.
That simple difference between on and off is how we communicate with machines. It’s kind of all they recognize. Punch card machines did it with patterns of holes in the cards, and computers today do it with endless strings of binary code, 1s and 0s, and the ENIAC did it with vacuum tubes. 18,000 of them. A huge rack of these light bulb-looking things ready to be turned on or off.
Stacked together, the vacuum tubes could store and transmit complex information—AKA data.
And because it was all electronic, the ENIAC had the potential to be faster than any other machine around.
THOMAS HAIGH: Now ENIAC is special because it's electronic. It can do 300 multiplications a minute. It can do thousands of additions a minute.
KATIE HAFNER: If you gave it the right constants and the right variables, the ENIAC would be able to tell you where a shell was going to land before it even hit the ground. And John von Neumann was impressed.
But the ENIAC had limitations. Big ones.
There were two primary problems. First: It was hard to operate.
And second: It couldn’t store much data.
Let’s unpack what this means.
We’ll start with operation.
CLAIRE L. EVANS: It was like a whole body gig.
KATIE HAFNER: There was no mouse or keyboard, or even a monitor. To operate the ENIAC, you had to use cables to connect the different components. And there were lots of them. So operators would be…
CLAIRE L. EVANS: …physically patching components together with cables, uh, rewiring control boards, or, or hand punching and managing thousands of punch cards…
KATIE HAFNER: So even though the ENIAC could spit out results unbelievably quickly, telling it what to do could take weeks. The operators untangled the rat’s nest of wires and figured out which cables to plug into which jacks.
Old footage of the ENIAC in operation looks a lot like the old footage of telephone switchboard operators—women moving plugs around on a huge panel of jacks to complete phone calls.
CLAIRE L. EVANS: The first people who were hired to do that job were women.
KATIE HAFNER: Six women, who’ve come to be known as the ENIAC Six, were hired to operate the machine in 1945. This job was seen as something appropriate for women for two reasons. One: Women had already been doing the computational labor that these machines were taking on. And two:
CLAIRE L. EVANS: It wasn't considered to be a very glamorous or important job. It was not seen as being a technical job in any capacity. It was seen as something closer to like a secretarial position or a clerical position, something like being a telephone operator. But of course it was, you know, an enormously complex job and these women were severely underestimated.
KATIE HAFNER: This labor was seen as menial because the ENIAC’s engineers hadn’t really thought about what operating the machine would look like. Constructing the hardware was the hard part, right?
CLAIRE L. EVANS: Operation was really an afterthought. There were no, like, instructions. So the women that were brought on to program these machines who were, you know, highly mathematically proficient, you know, people with mathematics degrees or an aptitude for mathematics who had already been doing differential calculus, like, around the clock in the basement of the war theater in order to create these ballistics trajectories for the war, they had to kind of bootleg their own engineering education as they were sort of demanded like, “hey, put the math on this machine.”
KATIE HAFNER: The women of the ENIAC got to know the machine so well that one said she could track a problem down almost to the individual vacuum tube.
Still, setting up computations was incredibly tedious–the ENIAC Six had to rewire the machine for every new program.
THOMAS HAIGH: The ENIAC team themselves appreciated that this was an inefficient way of setting up a computer,.
KATIE HAFNER: And this leads us to another of the ENIAC’s shortcomings: how it stored information.
Remember, for computers, all the data is just in patterns of what’s on and what’s off. And while the ENIAC was great at sending this data at lightning speeds, it was really inefficient at storing the information. The way it was originally set up, it took 28 vacuum tubes to communicate just one decimal digit to the computer. So all those 18000 tubes, with their state of the art processing speeds, were mostly there to store numbers.
But, after Johnny visited the ENIAC in August of 1944, all of this began to change.
THOMAS HAIGH: When John von Neumann learned of the ENIAC project, visited them, within a few weeks of him arriving, they had put a proposal in to build a new machine.
KATIE HAFNER: In her memoir, Klári has an entire chapter called “The Computer,” where she lays out Johnny’s dream. Here’s Eva Szabo reading from it…
KLARA VON NEUMANN: He wanted to build a fast, electronic, completely automatic “all purpose” computing machine which could answer as many questions as there were people who could think of asking them.
KATIE HAFNER: That’s a tall order. Johnny had a vision for a machine that could go far beyond ballistics trajectories. He wanted a machine that could calculate anything.
THOMAS HAIGH: At the beginning around August, 1944, it was already defined that one of the characteristics of this new machine would be that it had a whole different approach to programming.
KATIE HAFNER: Johnny understood that a computer shouldn’t have to be totally reconfigured and put back together every time it needed to do something new. There had to be a way to store instructions on the machine. This concept of storing instructions was built on the work of many scientists, including the two men who had originally designed the ENIAC at Penn. This is an example of the time-is-ripe phenomenon. I call this the time-is-ripe phenomenon. When all the pieces are in place for a certain discovery, someone is bound to make it–and often, multiple people get there around the same time.
In this case, one of those people was Johnny, and what he articulated is something called “the stored-program concept.”
THOMAS HAIGH: The program instructions would be loaded into memory
KATIE HAFNER: And that idea, of storing instructions on the machine itself…
ANANYO BHATTACHARYA: That's the sort of computer that we all carry around in our pockets.
KATIE HAFNER: Ananyo Bhattacharya is the science writer who recently came out with a biography of John von Neumann called The Man from the Future. ANANYO BHATTACHARYA: All of these machines have something in common, which is called the von Neumann architecture. And this is the idea that you can store programs along with data in memory. Uh, you don't need to rewire the entire machine every time you want to run a new program. And so we can run thousands of different apps on our smart phones without having to unscrew the back of it and start messing around with electronics.
KATIE HAFNER: And Johnny planned to create this new machine right at home, at the Institute for Advanced Study in Princeton. But building a whole new computer would take years. So, in the interim…
THOMAS HAIGH: An obvious idea was, what if we wire up ENIAC so that it essentially simulates a new style computer?
KATIE HAFNER: In other words, they decided to work with what they had…
Luckily, the ENIAC was built with parts called function tables. The function tables were basically a big panel of switches. And just like with the vacuum tubes, you could store lots of information using different on/off patterns. They’re what we today call a read-only memory.
THOMAS HAIGH: And those originally had just been intended to hold numerical constants and look up tables.
KATIE HAFNER: This meant that the function tables held only data values, not the instructions of a full computer program. Operators used the function tables to provide the computer numbers to work with. To tell the computer what to do with these numbers, they still had to wire together parts of the machine by plugging in cables.
THOMAS HAIGH: But then they were like, hey, wait, we have thousands of digits of memory that can be read at higher electronic speeds here. KATIE HAFNER: They decided to repurpose the function tables. They realized they could use the function tables to set up the program instructions in memory. This way, you could give the computer a road map for a lot of what to do right at the start—and making changes to the program just meant turning the switches.
THOMAS HAIGH: And we actually have ourselves a pretty flexible and powerful computer that can run these new style programs.
KATIE HAFNER: Not only would this harness much more memory, it would also make the operation of the machine much, much smoother–because you’re not rewiring anything.
THOMAS HAIGH: So as they go, they make little tweaks and improvements, but you don't change those things from one job to another. All you do is change the switches on the function table.
KATIE HAFNER: In 1948, Johnny’s dream came true. The ENIAC had been reconfigured into an electronic computer that could be easily programmed and store much more information.
And here’s what that meant: The computer gave you responses as fast as you could load instructions onto the function tables. You had all the benefits of the high processing speed of the vacuum tubes, without the painstaking process of patching and rewiring cables for every new problem.
THOMAS HAIGH: And this, finally, is where Klára von Neumann enters the story.
KATIE HAFNER: Klári, like many of the human computers turned computer coders, had been doing calculations during the war. But now, she upgraded to working with machines.
THOMAS HAIGH: We believe her initial involvement was working with Adele Goldstine and others to work out how ENIAC could be converted to a new programming mode.
KATIE HAFNER: Adele Goldstine was married to Herman, the guy who told Johnny about the ENIAC in the first place. Like Klári, Adele had a gift for mathematics. Unlike Klári, Adele had an actual degree in it. Adele was an expert on all things ENIAC. She trained the ENIAC six and she even wrote the user manual for the machine after it became operational. And Klári and Adele…
THOMAS HAIGH: They were sharing an office in Princeton. So in the early summer of 1947, they were both hired as contractors by Los Alamos.
Our understanding of the division of labor is that it finished up that Adele Goldstine was working more on the configuration to convert ENIAC and give it this modern style instruction set. And Klára von Neumann was involved more with attempting to write instructions in that new style.
KATIE HAFNER: Klári gets assigned a task with little precedent: telling a machine that can do anything exactly what to do.
When we come back, we promise we’ll get out of the weeds.
I’m Katie Hafner, and this is Lost Women of Science.
NATHAN ENSMENGER: Klára von Neumann is doing what is probably the most novel aspect associated with this project, which is what we today would call programming.
KATIE HAFNER: That’s Nathan Ensmenger, a historian of technology at Indiana University Bloomington. NATHAN ENSMENGER: And so she is figuring this stuff out in real time at a moment in which no one even understands the categories.
KATIE HAFNER: And what Klári is figuring out is how to write code for the reconfigured ENIAC.
NATHAN ENSMENGER: And the coder’s job was largely one of translation. So translating between this mental plan into the kind of techniques that would be required to control a computer.
KATIE HAFNER: So Klári is a translator, taking this plan of action, written by a mathematician, and turning it into a language of electronic signals that a computer understands.
In this case, the plan she’s working with is called a flowchart.
NATHAN ENSMENGER: It's a visual representation of a computer program.
KATIE HAFNER: We first came across one of these flowcharts at the Library of Congress. It was tucked away in one of the dozens of boxes we sifted through. It was folded several times over and when we opened the massive document, it looked like complete gibberish. Mathematical operations, variables, and arrows all over the page. We had absolutely no idea what it meant, but it did look pretty important.
We later learned that John von Neumann had not only put together this flowchart, but he popularized the use of flowcharts in computer programming. This was a whole new way of using flowcharts.
NATHAN ENSMENGER: And so flow diagrams were used in various kinds of process manufacturing, so think about turning grain into flour, for example.
KATIE HAFNER: Since flowcharts were used in industrial manufacturing, it’s likely that Johnny had come across them when he’d studied chemical engineering in college.
NATHAN ENSMENGER: Engineers had come up with ways of defining the flow of these materials through an industrial process that produced a given output. And he's borrowing from that.
KATIE HAFNER: To write code, Klári was using flowcharts—these visual representations of what a program should do.
And the code she was writing did not look anything like it does today. These days, our computers arrive with built-in programs called compilers. Compilers essentially translate human language into machine language. Think of the classic line of code “print hello world.” You type it in and your computer already has all the instructions to–you guessed it–print “hello world.” But no such shortcuts existed for Klári. Machines weren’t yet fluent in our language, so she had to become fluent in theirs…
THOMAS HAIGH: Klára's original role as a coder is to write down numbers.
KATIE HAFNER: This is what Tom was showing me at the beginning of this episode…a piece of paper with two rows of digits. This is where the word “coder” comes from: Klári’s job was to look up and write down codes: numbers that corresponded to specific instructions for the computer.
These codes were then loaded into the ENIAC by turning switches on the function tables.
The program a coder wrote and set up on the ENIAC could instruct the machine to do several things on its own: Repeat blocks of code, decide which blocks of code to run and reuse a block of code in several parts of the program.
And all of this was done with numbers – coded instructions.
THOMAS HAIGH:And we call that the modern code paradigm.
KATIE HAFNER: And Klári was one of the first programmers to use this modern code paradigm. To do it, she had to know the ENIAC inside and out.
THOMAS HAIGH: They have to be completely down with the level of the machine itself. You know, if you want to access a variable, you don't even have a label for it. You need to manually keep track of what instructions go on what numbers on the function table. You know, it's, it's absolutely down there right with the metal.
KATIE HAFNER: It was incredibly detail-oriented work. But Johnny believed that in creating the flowcharts, he was doing the hard part. As for the coding…
NATHAN ENSMENGER: In the imagination of John von Neumann, this is work that could be done by a relatively mathematically uneducated person that given the right plan, that person could translate his imagination into some kind of working computer program.
KATIE HAFNER: And when Johnny is looking for a technologically uneducated and available person…there’s Klári.
THOMAS HAIGH: Now what we have is this fragment from her memoir…
KATIE HAFNER: Right, right. Yeah.
THOMAS HAIGH: saying that he wanted to perform some kind of experiment…
KLARA VON NEUMANN: For this experiment he needed a guinea pig…
KATIE HAFNER: Right.
THOMAS HAIGH: On, uh, someone who was a mathematical moron, right.
KATIE HAFNER: Right, exactly, yeah.
KLARA VON NEUMANN: The ideal subject was right there within easy reach—namely me.
I became Johnny’s experimental rabbit.
THOMAS HAIGH: And it's filtered through her. So did he really say I'm looking for a mathematical moron to experiment on, or is that her self-deprecating interpretation?
Maybe he said, well, you're very smart and I'm sure you can handle this work. And, uh, you know, it would do you good to have something, like, challenging to, to occupy yourself with.
KATIE HAFNER: And so there's this tension between clearly, I mean, you look at the work, the actual work she did versus this self-deprecating woman.
THOMAS HAIGH: We probably will, will never know exactly what was going on there.
KATIE HAFNER: Tom Haigh is right–we’ll never get the whole story. But it’s important to note that Klári actually did have some mathematical training. At the beginning of 1947, before she was hired as a coder, she’d taken calculus at Columbia University at Johnny’s suggestion. And let’s not forget the years of population research she’d recently done at Princeton.
ANANYO BHATTACHARYA: So some of the mathematics that she came across dealing with—the fluctuations of populations and so on—must have come in handy.
KATIE HAFNER: That’s Ananyo Bhattacharya, the von Neumann biographer, again.
ANANYO BHATTACHARYA: Programming was this completely new discipline, so really everybody was starting on the ground floor as it were. So that must have been quite handy for a smart person, which she clearly was.
KATIE HAFNER: And unsurprisingly, in this new problem solving job, Klári flourishes.
ANANYO BHATTACHARYA: And she says, I learned how to translate algebraic equations into numerical forms, which in turn, then, have to be put into machine language in the order in which the machine was to calculate it. And she describes this as a very amusing and rather intricate jigsaw puzzle that was lots and lots of fun. So she clearly enjoyed this, this whole thing.
KATIE HAFNER: I was curious about this connection between puzzles and code. How much do they actually resemble each other?
A lot of the puzzle connection has to do with the bulk of the coding process: debugging. Code failed all the time — it still does. And the coder’s job is to find that one puzzle piece that actually fits–the instruction that makes the program actually run. But, unlike programmers today, Klári did most of this debugging work at her desk…well before she got to the computer. She must have spent hours poring over her code, figuring out where the problem was and how to fix it.
All those bugs to fix and no search engine, no online forums, no developer community, no technical conferences with birds-of-a-feather breakout sessions, not even—actually, definitely not—“ENIAC coding for DUMMIES.”
NATHAN ENSMENGER: It becomes clear that, that software is hard. That whatever it is that uh, needs to be done to make a computer work, whether that's kind of planning or design or programming or operation, that that is, that is quite difficult.
KATIE HAFNER: Back when Klári was working 75 years ago, the lines between software and hardware weren’t clear. There weren’t even terms for that distinction yet.
And Klári often had to go beyond what was seen as the pretty straightforward task of writing numerical code. At times she even asked for parts of the ENIAC to be built or rewired.
But in the coming decades, the division between the work of building the machine and programming the machine would become more explicit.
JANET ABBATE: It's really much more interesting to rethink history in general—for example, to rethink this relationship between hardware and software.
KATIE HAFNER: That’s Janet Abbate, a historian of technology at Virginia Tech.
JANET ABBATE: …and to rethink ideas about skill and what's simple and what's complicated and what's important and what's routine.
KATIE HAFNER: Janet points out that the story of women in computing is bigger than Klára von Neumann. It’s not about plucking a single woman out of obscurity, but about reconsidering what parts of the work have been valued, when, and for whom.
JANET ABBATE: So I think trying to step back and rethink our assumptions is more important than just kind of rescuing an individual woman here or there and saying, you know, we're going to hold her up as this unique role model in this otherwise male landscape.
KATIE HAFNER: At Lost Women of Science, we think about this a lot.
We don’t just want to put scientists on a pedestal for listeners to stand back and admire. We want to dig into the truth of their stories, relaying their life and work as honestly as we can. But more than that, we look beyond the individual. We use these scientists as a way to investigate the historical context, interrogate the gender dynamics at play, and perhaps transform our understanding of how scientific innovation happens.
And Klári is a great lens through which we can look at this period. She didn’t come to this work looking to make history. She wasn’t single-handedly responsible for iPads, or even the ENIAC. But that’s what makes her a good representative of women in computing, for whom war, or a husband, or luck opened a door and made their brilliant, unlikely contributions possible.
She shows us that their work—the seemingly routine, repetitious work of translating pen and paper calculations to code—was creative and complex. And more broadly, Klári might be a good case study of one of the most crucial forces pushing science forward, and that is chance.
THOMAS HAIGH: So it's kind of just a really interesting personal story that is, I think, probably not typical of the other people in your list of, you know, 200 lost women of science.
KATIE HAFNER: Klári is also a fascinating historical subject because she provides a window into a world obscured by secrecy. As the wife of John von Neumann, her papers were saved. And because she did this work in various locations, often remotely, letters back and forth paint a picture of the work that was happening, even when an official account might not exist.
As Tom tells us, the documentation saved from some of her program code is the most detailed that exists from this time period. This is all there at the Library of Congress, and we got to see it.
And so Klári takes us into this world–her life serves as the spine, the organizing principle, from which we can branch out—into computers and bombs and gender and coding itself–which was undervalued simply because the engineers didn’t understand what it would take.
And of course, when it became clear just how important coding was…
NATHAN ENSMENGER: It gets gendered male. But at this particular moment, this is a space that is open to women and, and will remain so for several decades.
KATIE HAFNER: It was this early period of undervaluation that made the field of coding available to Klári and other women. It’s bittersweet. And of course, this moment was the beginning of the field itself, so the clay was still soft, so to speak. Klári had her hand in shaping what it meant to be a coder.
THOMAS HAIGH: She’s very hands-on with the different stages of the program from writing the code to setting it up to running it, and then eventually going to Los Alamos and briefing the scientists there on what they found and the processes that they used, writing up a report.
NATHAN ENSMENGER: What Klára von Neumann is doing is helping to define what is possible on this new kind of machine.
KATIE HAFNER: So Klári is one of the first people to learn how to communicate with a machine.
Next time on Lost Women of Science, what she tells it to do.
This has been Lost Women of Science. Thanks to everyone who made this initiative happen, including my co-executive producer Amy Scharf, producer Sophie McNulty, associate producer Ashraya Gupta, senior editor Nora Mathison, composer Elizabeth Younan, and the engineers at Studio D Podcast Production.
Thanks also to our voice actors Eva Szabo and Nandor Tary, as well as our many Hungarian translators: Agi Antal, Rick Esbenshade, Charles Hebbert, Laszlo Marcus, Alina Bessenyey Williams, Lehel Molnar.
We’re grateful to Mike Fung, Cathie Bennett Warner, Dominique Guilford, Jeff DelViscio, Meredith White, Bob Wachter, Maria Klawe, Susan Kare, Jeannie Stivers, Linda Grais, Rabbi Michael Paley, Marina von Neumann Whitman, George Dyson, Thomas Haigh, and our interns, Hilda Gitchell, Kylie Tangonan, Leeza Kopaeva, and Giuliana Russo.
Thanks also to the Computer History Museum, to Paula Goodwin, Nicole Searing and the rest of the legal team at Perkins Coie, and to the Institute for Advanced Study, the Library of Congress, and the UCSD Special Collections for helping us with our search.
Many thanks to Barnard College, a leader in empowering young women to pursue their passion in STEM, for support during the Barnard Year of Science.
A special shout out to the Women’s Audio Mission in San Francisco, where this podcast was recorded.
Lost Women of Science is funded in part by the Gordon and Betty Moore Foundation, Schmidt Futures and the John Templeton Foundation, which catalyzes conversations about living purposeful and meaningful lives.
This podcast is distributed by PRX and published in partnership with Scientific American.
You can learn more about our initiative at lost women of science dot org or follow us on Twitter and Instagram. Find us @lostwomenofsci.
Thank you so much for listening. I’m Katie Hafner.
Katie Hafner is a longtime reporter for The New York Times, where she continues to be a frequent contributor, writing on healthcare and technology. Katie is uniquely positioned to tell these stories. Not only does she bring a skilled hand to complex narratives, but she has been writing about women in STEM for nearly 30 years. The author of six works of non-fiction, she is currently the host and executive producer of Our Mothers Ourselves, an interview podcast that celebrates extraordinary mothers.