Guido van Rossum: Python | Lex Fridman Podcast #6

The following is a conversation with Guido and Rossum, creator of Python, one of the most popular programming languages in the world, used in almost any application that involves computers, from web backend development to psychology, neuroscience, computer vision, robotics, deep learning, natural language processing, and almost any subfield of AI. This conversation is part of MIT course on Artificial General Intelligence and the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, iTunes, or your podcast provider of choice. Or simply connect with me on Twitter lexfriedman, spelled F R I D. And now here's my conversation with Guido van Rossum. You were born in the Netherlands in 1956. Your parents and the world around you was deeply impacted by World War II, as was my family from the Soviet Union. So with that context, what is your view of human nature? Are some humans inherently good and some inherently evil? Or do we all have both good and evil within us?

Ouch. I did not expect such a deep one. I. I guess we all have good and evil potential in us, and a lot of it depends on circumstances and context.

Out of that world, at least on the Soviet Union side, in Europe, sort of out of suffering, out of challenge, out of that kind of set of traumatic events often emerges beautiful art, music, literature. In interview I read or heard you said you enjoyed Dutch literature when, when you were a child. Can you tell me about the books that had an influence on you in your childhood?

Well, as a teenager, my favorite writer was. My favorite Dutch author was a guy named Willem Friedrich Hermans, whose writing, certainly his early novels were all about sort of ambiguous things that happened during World War II. I think he was a young adult during that time and he wrote about it a lot. And very interesting, very good books, I thought.

I think in a nonfiction way, no.

It was all fiction, but it was very much set in. In the ambiguous world of resistance against the Germans, where often you couldn't tell whether someone was truly in the resistance or really a spy for the Germans. And some of the characters in his novels sort of cross that line and you never really find out what exactly happened.

And in his novels there's always a good guy and a bad guy. Is the nature of good and evil? Is it clear there's a hero?

It's no. His heroes are often more. His main characters are often anti heroes. And so they're not very heroic. They're often they. They fail at some level to accomplish their lofty goals.

And looking at the trajectory through the rest of your life, has literature, Dutch or English or translation had an impact outside the technical world that you existed In.

I still read novels. I don't think that it's impacts me that much.

Directly doesn't impact your work. It's just, it's a, it's a separate world.

My work is, is highly technical and sort of the, the world of art and literature doesn't really directly have any bearing on it.

You don't think there's a creative element to the design? You know, some would say art design of a language is art.

Uh, I'm not disagreeing with that. I'm just saying that sort of. I don't feel direct influences from more traditional art on my own creativity.

Right, of course you don't feel. Doesn't mean it's not somehow deeply there in your subconscious.

Who knows?

Who knows?

Yeah.

So let's go back to your early teens, your hobbies, or building electronic circuits, building mechanical models. If you can just put yourself back in the mind of that young Guido, 12, 13, 14. Was that grounded in a desire to create a system, so to create something, or was it more just tinkering, just the joy of puzzle solving?

I think it was more the latter, actually. I, maybe towards the end of my high school period, I felt confident enough that I designed my own circuits that were sort of interesting somewhat. But a lot of that time I literally just took a model kit and followed the instructions, putting the things together. I mean, I think the first few years that I built electronics kits, I really did not have enough understanding of sort of electronics to really understand what I was doing. I mean, I could debug it and I could sort of follow the instructions very carefully, which has, which has always stayed with me. But I had a very naive model of like how a transistor works. And I don't think that in those days I had any understanding of coils and capacitors, which actually sort of was a major problem when I started to build more complex digital circuits because I was unaware of the sort of the analog part of how they actually work. And I would have things that the scheme, the schematic looked every. Everything looked fine and it didn't work. And what I didn't realize was that there was some megahertz level oscillation that was throwing the circuit off because I had a sort of two wires were too close or the switches were, were kind of poorly built.

But through that time, I think it's really interesting and destructive to think about because there's echoes of it are in this time now. So in the 1970s, the personal computer was being born. So did you sense in tinkering with these circuits, did you sense the encroaching revolution in personal computing? So if at that point you're situated, would sit you down and ask you to predict the 80s and the 90s, do you think you would be able to do so successfully to unroll this, the process that's happening?

No, I had no clue. I remember, I think in the summer after my senior year, or maybe it was the summer after my junior year. Well, at some point, I think when I was 18, I went on a trip to the Math Olympiad in Eastern Europe. And there was, like, I was part of the Dutch team, and there were other nerdy kids that sort of had different experiences. And one of them told me about this amazing thing called a computer. And I had never heard that word. My. My own explorations in electronics were sort of about very simple digital circuits. And I had the idea that I somewhat understood how a digital calculator worked. And so there is maybe some echoes of computers there, but I never made that connection. I didn't know that when my parents were paying for magazine subscriptions using punched cards that there was something called a computer that was involved that read those cards and transferred money between accounts. I was also not really interested in those things. It was only when I went to university to study math that I found out that they had a computer and students were allowed to use it.

And there was some. You're supposed to talk to that computer by programming it. What did that feel like?

Yeah, that was the only thing you could do with it. The computer wasn't really connected to the real world. The only thing you could do was sort of, you typed your program on a bunch of punched cards, you gave the punched cards to the operator, and an hour later the operator gave you back your printout. And so all you could do was write a program that did something very abstract. And I don't even remember what my first forays into programming were, but they were sort of doing simple math exercises and just to learn how a programming language worked.

Did you sense, okay, first year of college, you see this computer, you're. You're able to have a program and it generates some output. Did you start seeing the possibility of this, or was it a continuation of the tinkering with circuits? Did you start to imagine that one, the personal computer, but did you see it as something that. As a tool, like a word processing tool, maybe, maybe for gaming or something? Or did you start to imagine that it could be going to the world of robotics, like you, you know, the Frankenstein picture that you could create an artificial being. There's like another entity in front of you you did not see.

I don't think I really saw it that way. I was really more interested in the tinkering. It may be not as sort of a complete coincidence that I ended up sort of creating a programming language which is a tool for other programmers. I've always been very focused on the sort of activity of program itself and not so much what happens with. With the program you write.

Right.

I do remember, and I don't remember maybe in my second or third year, probably my second actually, someone pointed out to me that there was this thing called Conway's Game of Life. You're probably familiar with it, I think.

In the 70s, I think is when he came up with it.

So there was a Scientific American column by someone who did a monthly column about mathematical diversions. I'm also blanking out on the guy's name. It was very famous at the time, and I think up to the 90s or so. And one of his columns was about Conway's game of life. And he had some illustrations and he wrote down all the rules and. And sort of there was the suggestion that this was philosophically interesting, that that was why Conway had called it that. And all I had was like the two pages photocopy of that article. I don't even remember where I got it, but it spoke to me. And I remember implementing a version of that game for the batch computer we were using, where I had a whole Pascal program that sort of read an initial situation from input and read some numbers that said do so many generations and print every so many generations. And then out would come pages and pages of.

Sort of things, patterns of different kinds and. Yeah, yeah.

And I remember much later, I've done a similar thing using Python, but I'd sort of. That original version I wrote at the time I found interesting because I combined it with some trick I had learned during my electronics hobbyist times. I essentially, first on paper, I designed a simple circuit built out of logic gates that took nine bits of input, which is the sort of the cell and its neighbors, and produced a new value for that cell. And it's like a combination of a half adder and some other clipping. No, it's actually a full adder. And so I had worked that out and then I translated that into a series of Boolean operations on Pascal integers, where you could use the integers as bitwise values. I could basically generate 60 bits of a generation in eight instructions or so.

Nice.

So I was proud of that.

It's funny that you mentioned, for people who don't know, Khan Conway's Game of life is there's. It's a cellular automata where there's single compute units that kind of look at their neighbors and figure out what they look like in the next generation based on the state of their neighbors. And this is deeply distributed system in concept at least. And then there's simple rules that all of them follow. And somehow out of this simple rule, when you step back and look at what occurs, it's beautiful. There's an emergent complexity. Even though the underlying rules are simple, there's an emergent complexity. Now, the funny thing is you've implemented this and the thing you're commenting on is you're proud of a hack you did to make it run efficiently. When you're not commenting on this is a beautiful implementation, you're not commenting on the fact that there's an emergent complexity that you've coded a simple program. And when you step back and you print out the following generation after generation, that stuff that you may have not predicted would happen is happening.

Right.

And is that magic? I mean, that's the magic that all of us feel when we program. When you create a program and then you run it and whether it's hello world or it shows something on screen, if there's a graphical component, are you seeing the magic in the mechanism of creating that?

I think I went back and forth as a student. We had an incredibly small budget of computer time that we could use. It was actually measured. I once got in trouble with one of my professors because I had overspent the department's budget. It's a different story. I actually wanted the efficient implementation because I also wanted to explore what would happen with a larger number of generations and a larger sort of size of the board. And so once the implementation was flawless, I would feed it different patterns. And then I think maybe there was a follow up article where there were patterns that were like gliders, patterns that repeated themselves after a number of generations, but translated one or two positions to the right or up or something like that. And there were, I remember, things like glider guns. Well, you can. You can Google Conway's Game of Life. It is still. People still go ah and ooh over.

It for a reason, because it's not really well understood why. I mean, this is what Stephen Wolfram is obsessed about, okay? He's just the. We don't have the mathematical tools to describe the kind of complexity that emerges in these kinds of systems. The only way you can do it is to run it.

I'm not convinced that it sort of a problem that lends itself to. To classic mathematical analysis.

No. And so one, one theory of how you create an artificial intelligence or an artificial being is you kind of have to. Same with the Game of Life. You kind of have to create a universe and let it run that. Creating it from scratch in a design way in the, you know, coding up a Python program that creates a fully intelligent system may be quite challenging. That you might need to create a universe just like the Game of Life is.

Well, you might have to experiment with a lot of different universes before there is a set of rules that doesn't essentially always just end up repeating itself in a trivial way. Yeah.

And Stephen Wolfram, who works with these simple rules, says that it's kind of surprising how quickly you find rules that create interesting things. You shouldn't be able to, but somehow you do. And so maybe our universe is laden with rules that will create interesting things that might not look like humans, but emergent phenomena. That's interesting. May not be as difficult to create as we think.

Sure.

But let me sort of ask. At that time, you know, some of the world, at least in popular press, was kind of captivated, perhaps at least in America, by the idea of artificial intelligence that, that these computers would be able to think pretty soon. And did that touch you at all? Did that in science fiction or in reality in. In any way?

I didn't really start reading science fiction until much, much later. I think as a teenager. I. I read maybe one bundle of science fiction stories.

Was it in the background somewhere, like in your thoughts?

That sort of.

The.

Using computers to build something intelligent always fell to me because I had, I felt I had so much understanding of what actually goes on inside a computer. I knew how many bits of memory it had and how difficult it was to program and sort of. I didn't believe at all that you could just build something intelligent out of that. That would really sort of satisfy my definition of intelligence. I think the most influential thing that I read in my early twenties was Goebbel Escherbach. That was about consciousness. And that was a big eye opener in some sense.

In what sense? So, yeah, so on your own brain, did you at the time, or do you now see your own brain as a computer? Or is there a total separation of the way. So, yeah, you're very pragmatically practically know the limits of memory, the limits of this sequential computing or weakly paralyzed computing. And you just know what we have now and it's hard to see how it creates, but it's also easy to see. It was in the, in the 40s, 50s, 60s and now at least. Similarities between the brain and our computers.

Oh yeah. I mean I, I totally believe that brains are computers in some sense. I mean the rules they, they use to play by are pretty different from the rules we, we can sort of implement in, in our current hardware. But I don't believe in like a separate thing that infuses us with intelligence or consciousness or any of that. There's no soul. I've been an atheist, probably from when I was 10 years old, just by thinking a bit about math and the universe and. Well, my parents were atheists. Now I know that you could be an atheist and still believe that there is something sort of about intelligence or consciousness that cannot possibly emerge from a fixed set of rules. I am not in that camp. I totally see that sort of. Given how many millions of years evolution took its time. DNA is a particular machine that sort of encodes information and an unlimited amount of information in chemical form and has figured out a way to replicate itself. I thought that that was maybe it's 300 million years ago, but I thought it was closer to half a billion years ago that that sort of originated and it hasn't really changed. The structure of DNA hasn't changed ever since. That is like our binary code that we have in hardware.

I mean the basic programming language hasn't changed, but maybe the programming itself obviously did sort of.

It happened to be a set of rules that was good enough to, to sort of develop endless variability and sort of the idea of self replicating molecules competing with each other for resources and one type eventually sort of always taking over. That happened before there were any fossils. So we don't know how that exactly happened, but I believe it's clear that that did happen.

Can you comment on consciousness and how you see it? Because I think we'll talk about programming quite a bit. We'll talk about intelligence connecting to programming fundamentally. But consciousness is this whole other thing. Do you think about it often? As a developer of a programming language and as a.

Those are pretty sort of separate topics. My line of work working with programming does not involve anything that goes in the direction of developing intelligence or consciousness, but sort of privately, as an avid reader of popular science writing, I have some thoughts, which is mostly that I don't actually believe that consciousness is an all or nothing thing. I have a feeling that, and I forget what I read that influenced this, but I feel that if you look at a cat or a dog or a mouse, they have some form of intelligence. If you look at a fish, it has some form of intelligence. And that evolution just took a long time. But I feel that the sort of, the evolution of more and more intelligence that led to sort of the human form of intelligence followed the evolution of the senses, especially the visual sense. I mean there is an enormous amount of processing that's needed to interpret a scene and humans are still better at that than computers are. And I have a feeling that there is a sort of the reason that mammals in particular developed the levels of consciousness that they have and that eventually going from intelligence to self awareness and consciousness has to do with sort of being a robot that has very highly developed senses, has a lot of rich.

Sensory information coming in. So the, that's a really interesting thought that whatever that basic mechanism of DNA, whatever that basic building blocks of programming, if you just add more abilities, more high resolution sensors, more sensors, you just keep stacking those things on top. That this basic programming in trying to survive develops very interesting things that start to us humans to appear like intelligence and consciousness.

Yeah. So as far as robots go, I think that the self driving cars have that sort of the greatest opportunity of developing something like that. Because when I drive myself, I don't just pay attention to the rules of the road. I also look around and I get clues from that, oh, this is a shopping district. Oh, here's an old lady crossing the street. Oh, here is someone carrying a pile of mail. There's a mailbox. I bet you they're going to cross the street to reach that mailbox. And I slow down and I don't even think about that. And so there's so much where you turn your observations into an understanding of what other consciousnesses are going to do or what other systems in the world are going to be. Oh, that tree is going to fall. Yeah, I see sort of, I see much more of. I expect somehow that if anything is going to become conscious, it's going to be the self driving car and not the network of a bazillion computers in a Google or Amazon data center that are all networked together to do whatever they do.

So in that sense, so you actually highlight, that's what I work in, autonomous vehicles. You highlight the big gap between what we currently can't do and what we truly need to be able to do to solve the problem. Under that formulation then consciousness and intelligence is something that basically a system should have in order to interact with us humans as opposed to some Kind of abstract notion of consciousness. Consciousness is something that you need to have to be able to empathize, to be able to fear, understand what the fear of death is. All these aspects that are important for interacting with pedestrians. You need to be able to do basic computation based on our human desires and flaws.

Yeah, if you look at the dog, the dog clearly knows. I mean, I'm not a dog owner, but I have friends who have dogs. The dogs clearly know what the humans around them are going to do, or at least they have a model of what those humans are going to do. And they learn. Some dogs know when you're going out and they want to go out with you. They're sad when you leave them alone. They cry, they're afraid because they were mistreated when they were younger. We don't assign sort of consciousness to dogs, or at least not all that much, but also don't think they have none of that. So I think it's, it's. Consciousness and intelligence are not all or nothing.

The spectrum. It's really interesting, but in returning to programming languages and the way we think about building these kinds of things, about building intelligence, building consciousness, building artificial beings. So I think one of the exciting ideas came in the 17th century and with Leibniz, Hobbes, Descartes, where there's this feeling that you can convert all thought, all reasoning, all the thing that we find very special in our brains. You can convert all of that into logic. So you can formalize it, formal reasoning. And then once you formalize everything, all of knowledge, then you can just calculate. And that's what we're doing with our brains is we're calculating. So there's this whole idea that we, that this is possible, that this, they.

Weren'T aware of the concept of pattern matching in the sense that we are aware of it now. They sort of thought you, they, they had discovered incredible bits of mathematics like Newton's calculus and their sort of idealism, their, their sort of extension of what they could do with logic and math sort of went along those lines. And they thought there's like, yeah, logic, there's like a bunch of rules and a bunch of input. They didn't realize that how you recognize a face is not just a bunch of rules, but it's a shit ton of data plus a circuit that sort of interprets the visual clues and the context and everything else, and somehow can massively parallel pattern match against stored rules. I mean, if I see you tomorrow here in front of the Dropbox office, I might recognize you even If I'm.

Wearing a different shirt.

Yeah. But if I. If I see you tomorrow in a coffee shop in Belmont, I might have no idea that it was you or on the beach or whatever. I make those kind of mistakes myself all the time. I see someone that I only know as like, oh, this person is a colleague of my wife's.

Yeah.

And then I see them at the movies and I don't recognize them.

But do you see those? You call it pattern matching. Do you see that? Rules is unable to encode that everything you see, all the pieces of information. You look around this room, I'm wearing a black shirt. I have a certain height. I'm a human. All these. There's probably tens of thousands of facts you pick up moment by moment about this scene. You take them for granted and you accumulate, aggregate them together to understand this scene. You don't think all of that could be encoded to where at the end of the day, you can just put it all on the table and calculate.

Oh, I don't know what that means. I mean, yes, in the sense that there is no there. There is no actual magic there, but there are enough layers of abstraction from sort of from the facts as they enter my eyes and my ears to the understanding of the scene that I don't think that that AI has really covered enough of that distance. It's like if you take a human body and you realize it's built out of atoms. Well, that is a uselessly reductionist view. Right. The body is built out of organs. The organs are built out of cells. The cells are built out of proteins, the proteins are built out of amino acids, the amino acids are built out of atoms. And then you get to quantum mechanics.

So that's a very pragmatic view. I mean, obviously, as an engineer, I agree with that kind of view, but I also. You also have to consider the Sam Harris view of. Well, intelligence is just information processing, like you said. You take in sensory information, you do some stuff with it, and you come up with actions that are intelligent.

He makes it sound so easy. I don't know who Sam Harris is.

Oh, philosopher. So, like, this is how philosophers often think, Right? And essentially, that's what Descartes was. Wait a minute. If there is, like you said, no magic. So he basically says, it doesn't appear like there's any magic, but we know so little about it that it might as well be magic. So just because we know that we're made of atoms, just because we know we're made of organs, the fact that we Know very little how to get from the atoms to organs in a way that's recreatable. Means that you shouldn't get too excited just yet about the fact that you figured out that we're made of atoms.

Right. And the same about taking facts as our sensory organs take them in and turning that into reasons and actions that sort of. There are a lot of abstractions that we haven't quite figured out how to. How to deal with those. I mean, I sometimes I don't know if I can go on a tangent or not.

Please drag you back in.

Sure. So if I take a simple program that parses, say I have a compiler, it parses a program in a sense. The input routine of that compiler, of that parser is a sense, a sensing organ. And it builds up a mighty complicated internal representation of the program. I just saw, it doesn't just have a linear sequence of bytes representing the text of the program anymore. It has an abstract syntax tree. And I don't know how many of your viewers or listeners are familiar with compiler technology, but there's fewer and fewer these days, right? That's also true. Probably people want to take a shortcut, but they're sort of. This abstraction is a data structure that the compiler then uses to produce outputs that is relevant, like a translation of that program to machine code that can be executed by hardware. And then that data structure gets thrown away when a fish or a fly sees, sort of gets visual impulses. I'm sure it also builds up some data structure. And for the fly, that may be very minimal. A fly may have only a few. I mean, in the case of a fly's brain, I could imagine that there.

Are.

Few enough layers of abstraction that it's not much more than when it's darker here than it is here. Well, it can sense motion because a fly sort of responds when you move your arm towards it. So clearly its visual processing is intelligent. Well, not intelligent, but it has an abstraction for motion. And we still have similar things, but much more complicated in our brains. I mean, otherwise you couldn't drive a car if you. If you didn't have an incredibly good abstraction for motion.

Yeah, in some sense the same abstraction for motion is probably one of the primary sources of our. Of information for us. We just know what to do. I think we know what to do with that. We've built up other abstractions on top.

We build much more complicated data structures based on that, and we build more persistent data structures. We sort of, after some processing, some information sort of gets stored in our Memory pretty much permanently and is available on recall. I mean there are some things that you sort of, you're conscious that you're remembering it. Like you give me your phone number. I, well, at my age I have to write it down, but I could imagine I could remember those seven numbers or 10, 10 digits and reproduce them in a while if I sort of repeat them to myself a few times. So that's a fairly conscious form of memorization. On the other hand, how do I recognize your face? I have no idea. My brain has a whole bunch of specialized hardware that knows how to recognize faces. I don't know how much of that is sort of coded in our DNA and how much of that is trained over and over between the ages of year went three. But, but, but somehow our brains know how to do lots of things like that that are useful in our interactions with, with other humans with, without really being conscious of how it's done anymore.

Right. So our actual day to day lives, we're operating at the very highest level of abstraction. We're just not even conscious of all the little details underlying it. There's compilers on top of, it's like turtles on top of turtles or turtles all the way down. It's compilers all the way down. But that's essentially. You say that there's no magic. That's what I, what I was trying to get at I think is with Descartes started this whole train of saying that there's no magic. I mean there's others before him.

Well, didn't Descartes also have the notion though that the soul and the body were, were fundamentally separate?

Yeah, I think he had to write in God in there for political reasons. So I don't actually, I'm not historian, but there's notions in there that all of reasoning, all of human thought can be formalized. I think that continued in the 20th century with Russell and with Gaetho's incompleteness theorem. This debate of what are the limits of the things that could be formalized. That's where the Turing machine came along and this exciting idea, I mean underlying a lot of computing that you can do quite a lot with a computer, you can, you can encode a lot of the stuff we're talking about in terms of recognizing faces and so on, theoretically in an algorithm that can then run on a computer. And in that context, I'd like to ask programming in a philosophical way. So what does it mean to program a computer? So you said you write a Python program or compiled a C program that compiles just some bytecode, it's forming layers. You're programming a layer of abstraction that's higher. How do you see programming in that context? Can it keep getting higher and higher levels of abstraction?

I think at some point the higher level of levels of obstruction will not be called programming and they will not resemble what we, we call programming at the moment. There will not be source code. I mean there will still be source code sort of at a lower level of the machine. Just like there are still molecules and electrons and sort of proteins in our brains. But. And so there's still programming and system administration and who knows what keeping to keep the machine running. But what the machine does is, is a different level of abstraction in a sense. And as far as I understand, the way that for last decade or more people have made progress with things like facial recognition or the self driving cars is all by endless, endless amounts of training data. Where at least as a layperson, and I feel myself totally as a layperson in that field, it looks like the researchers who publish the results don't necessarily know exactly how their algorithms work. And I often get upset when I sort of read sort of a fluff piece about Facebook in the newspaper or social networks and they say, well, algorithms. And that's like a totally different interpretation of the word algorithm. Because for me, the way I was trained or what I learned when I was 8 or 10 years old, an algorithm is a set of rules that you completely understand that can be mathematically analyzed and you can prove things. You can like prove that Aristosthenes sieve produces all prime numbers and only prime numbers.

Yeah. So I don't know if you know who Andre Karpathi is.

I'm afraid not.

So he's head of AI at Tesla now, but he was at Stanford before and he has this cheeky way of calling this concept software 2.0. So let me disentangle that for a second. So kind of what you're referring to is the traditional, traditional the algorithm, the concept of an algorithm, something that's there, it's clear, you can read it, you understand it, you can prove its functioning is kind of software 1.0. And what software 2.0 is, is exactly what you describe, which is you have neural networks, which is a type of machine learning that you feed a bunch of data and that neural network learns to do a function. All you specify is the inputs and the outputs you want. And you can't look inside, you can't analyze it, all you can do is train this function to map the Inputs to the outputs by giving a lot of data in that sense, programming becomes getting a lot of cleaning, getting a lot of data. That's what programming is in this. Well, there would be programming 2.0, 2.0 to programming 2.0.

And I wouldn't call that programming. It's just a different activity. Just like building organs out of cells is not called chemistry.

Well, so let's just step back and think sort of more generally, of course. But you know, it's like as a parent teaching your kids, things can be called programming in that same sense that that's how programming is being used. You're providing them data, examples, use cases. So imagine writing a function not by. Not with. For loops and clearly readable text, but more saying, well, here's a lot of examples of what this function should take, and here's a lot of examples of when it takes those functions, it should do this and then figure out the rest. So that's the 2.0 concept. And the question I have for you is like, it's a very fuzzy way. This is the reality of a lot of these pattern recognition systems and so on. It's a fuzzy way of programming. What do you think about this kind of world should be called something totally different than programming? If you're a software engineer, does that mean you're designing systems that are very. Can be systematically tested, evaluate it, they have a very specific specification. And then this Other fuzzy software 2.0 world, machine learning world, that's something else totally. Or is there some intermixing that's possible?

Well, the question is probably only being asked because we don't quite know what that software 2.0 actually is. And it sort of. I think there is a truism that every task that AI has, has tackled in the past, at some point we realized how it was done and then it was no longer considered part of artificial intelligence because it was no longer necessary to. To use that term. It was just, oh, now we know how to do this. And a new field of science or engineering has been developed. And I don't know if sort of every form of learning or sort of controlling computer systems should always be called programming. I don't know, maybe I'm focused too much on the terminology, but I expect that there just will be different concepts where people with sort of different education and a different model of what they're trying to do will develop those concepts.

Yeah, and I guess if you could comment, another way to put this concept is I think, I think the kind of functions that neural networks Provide is things as opposed to being able to upfront prove that this should work for all cases you throw at it all you're able it's the worst case analysis versus average case analysis. All you're able to say is it seems on everything we've tested to work 99.9% of the time, but we can't guarantee it and fails in unexpected ways. We can even give you examples of how it fails in unexpected ways. But it's like really good most of the time. Yeah, but there's no room for that in current ways. We think about programming.

Programming 1.0 is actually sort of getting to that point too where the sort of the ideal of a bug free program has been abandoned long ago by most software developers. We only care about bugs that manifest themselves often enough to be annoying. And we're willing to take the occasional crash or outage or incorrect result for granted because we can't possibly. We don't have enough programmers to make all the code bug free. And it would be an incredibly tedious business. And if you try to throw formal methods at becomes even more tedious. So every once in a while the user clicks on a link and somehow they get an error and the average user doesn't panic, they just click again and see if it works better the second time, which often magically it does. Or they go up and they try some other way of performing their task. So that's sort of an end to end recovery mechanism. And inside systems there is all sorts of retries and timeouts and fallbacks. And I imagine that that sort of biological systems are even more full of that because otherwise they wouldn't survive.

Do you think programming should be taught and thought of as exactly what you just said I come from is kind of. You're, you're almost denying that fact.

Always in, in sort of basic programming.

Education.

The sort of the programs you're. You're having students write are so small and simple that if there is a bug you can always find it and fix it. Because the sort of programming as it's being taught in some even elementary middle schools, in high school, introduction to programming classes in college, typically it's programming in the small. Very few classes sort of actually teach software engineering building large systems. I mean every summer here at Dropbox we have a large number of interns. Every tech company on the west coast has the same thing. These interns are always amazed because this is the first time in their life that they see what goes on in a really large software development environment and everything they've learned in college was almost always about a much smaller scale. And somehow that difference in scale makes a qualitative difference in how you do things and how you think about it.

If you then take a few steps back into decades 70s and 80s, when you're first thinking about Python or just that world of programming languages, did you ever think that there would be systems as large as underlying Google, Facebook and Dropbox when you were thinking about Python?

I was actually always caught by surprise by every stage of it. Yeah, pretty much every stage of computing.

So maybe just because you've spoken in other interviews, but I think the evolution of programming languages are fascinating, especially because it leads from my perspective towards greater and greater degrees of intelligence. I learned the first programming language I played with in Russia was with the turtle logo.

Logo, yeah.

And if you look, I just have a list of programming languages, all of which I've now played with a little bit. And they're all beautiful in different ways, from Fortran, Cobalt, Lisp, Algol 60, Basic, Logo, Again, C, there's a few object oriented, came along in the 60s, Simula, Pascal, small talk. All of that leads all the classics. The classics, yeah. The classic hits, right? Scheme built, that's built on top of Lisp on the database side, SQL, C, and all that leads up to Python, Haskell too. That's before Python, matlab, these kind of different communities, different languages. So can you talk about that world? I know that Python came out of abc, which actually never knew that language. Having researched this conversation, went back to abc and it looks remarkably. It has a lot of annoying qualities, but underneath those, like all caps and so on, but underneath that, there's elements of Python that are quite. They're already there.

That's where I got all the good stuff.

All the good stuff. But in that world, you're swimming. These programming languages. Were you focused on just the good stuff in your specific circle, or did you have a sense of what is everyone chasing? You said that every programming language is built to scratch an itch. Were you aware of all the itches in the community? And if not, or if. Yes, I mean, what it should be trying to scratch with Python?

Well, I'm glad I wasn't aware of all the itches because I would probably not have been able to do anything. I mean, if you're trying to solve.

Every problem at once, you'll solve nothing.

Well, yeah, it's, it's too overwhelming. And so I had a very, very focused problem. I wanted a programming language that sat somewhere in between shell scripting and C and now arguably there is like one is higher level, one is lower level and Python is sort of a language of an intermediate level, although it's still pretty much at the high level end. I was thinking about much more about I want a tool that I can use to be more productive as a programmer in a very specific environment. And I also had given myself a time budget for the development of the tool and that was sort of about three months for both the design like thinking through what are all the features of the language syntactically and semantically and how do I implement the whole pipeline from parsing the source code to executing it.

So I think both with the timeline and the goals, it seems like productivity was at the core of it as a goal. So like for me in the 90s and the first decade of the 21st century, I was always doing machine learning, AI programming for my research was always in C. And then the other people who are a little more mechanical engineering, electrical engineering are matlaby, they're a little bit more matlab focused, those are the world and maybe a little bit Java too, but people who are more interested in emphasizing the object oriented nature of things. But then in last 10 years or so, especially with the oncoming of neural networks and these packages that are built on Python to interface with neural networks, I switched to Python and it's just I've noticed a significant boost that I can't exactly because I don't think about it, but I can't exactly put into words why I'm just much, much more productive just being able to get the job done much, much faster. So how do you think whatever that qualitative difference is? I don't know if it's quantitative, it could be just a feeling. I don't know if I'm actually more productive. But how do you think about. Yeah, well that's right. I think there's elements. Let me just speak to one as that I think that was affecting my productivity is C was I really enjoyed creating performant code and creating a beautiful structure where everything that, you know, this kind of going into this, especially with the newer and newer standards of templated programming, of just really creating this beautiful formal structure that I found myself spending most of my time doing that as opposed to getting it parsing a file and extracting a few keywords or whatever the task I was trying to do. So what is it about Python? How do you think of productivity in general as you were designing it now sort of through the decades, last three decades, what do you think it Means to be a productive programmer. And how did you try to design it into the language?

There are different tasks and as a programmer it's useful to have different tools available that sort of are suitable for different tasks. So I still write C code, I still write shell code, but I write most of my things in Python. Why do I still use those other languages? Because sometimes the task just demands it and well, I would say most of the time the task actually demands a certain language because the task is not write a program that solves problem X from scratch, but it's more like fix a bug in existing program X or add a small feature to an existing large program. But even if you sort of, if you're not constrained in your choice of language by context like that, there is still the fact that if you write it in a certain language, then you sort of, you, you have this balance between how long does it time does it take you to write the code and how long does the code run. And when you're in sort of in the phase of exploring solutions, you often spend much more time writing the code than running it because every time you've sort of, you've run it, you see that the output is not quite what you wanted and you spend some more time coding. And a language like Python just makes that iteration much faster because there are fewer details. There is a large library, sort of, there are fewer details that you have to get right before your program compiles and runs. There are libraries that do all sorts of stuff for you. So you can sort of very quickly take a bunch of existing components, put them together and get your prototype application running. Just like when I was building electronics, I was using a breadboard most of the time. So I had this like sprawled out circuit that if you shook it, it would stop working because it was not put together very well. But it functioned. And all I wanted was to see that it worked and then move on to the next next schematic or design or add something to it. Once you've sort of figured out, oh, this is the perfect design for my radio or light sensor or whatever, then you can say, okay, how do we design a PCB for this? How do we solder the components in a small space? How do we make it so that it is robust against say, voltage fluctuations or mechanical disruption? I mean, I know nothing about that when it comes to designing electronics, but I know a lot about that when it comes to, to writing code.

So the initial, initial steps are efficient, fast, and there's not much stuff that gets in the way, but you're kind of describing, from a, like Darwin described the evolution of species, right? You, you're, you're observing of what is about true about Python. Now if you take a step back, if the art of, if the act of creating languages is art and you had three months to do it, initial steps, so you just specified a bunch of goals, sort of things that you observe about Python, perhaps you had those goals, but how do you create the rules, the syntactic structure, the features that result in those? So I have, in the beginning, and I have follow up questions about, through the evolution of Python too. But in the very beginning, when you're sitting there creating the lexical analyzer, whatever.

Evolution was still a big part of it because I sort of, I said to myself, I don't want to have to design everything from scratch. I'm going to borrow features from other languages that I like.

Oh, interesting. So you basically. Exactly, you first observe what you like.

Yeah. And so that's why if you're 17 years old and you want to sort of create a programming language, you're not going to be very successful at it because you have no experience with other languages. Whereas I was in my, let's say, mid-30s, I had written parsers before, so I had worked on the implementation of abc. I had spent years debating the design of ABC with its authors, with its designers. I had nothing to do with the design, it was designed fully as it ended up being implemented when I joined the team. But so you borrow ideas and concepts and very concrete sort of local rules from different languages, like the indentation and certain other syntactic features from abc. But I chose to borrow string literals and how numbers work from C and various other things.

So Ian, then if you take that further. So you've had this funny sounding but I think surprisingly accurate or at least practical title of benevolent dictator for life for quite, for the last three decades or whatever, or no, not the actual title, but functionally speaking. So you had to make decisions, design decisions maybe. Let's take Python 2. So Python releasing Python 3 as an example. It's not backward compatible to Python 2 in ways that a lot of people know. So what was that deliberation, discussion, decision like? Yeah, what was the psychology of that experience? Do you regret any aspects of how that experience undergone that?

Well, yeah, so it was a group process really at that point, even though I was BDFL in name and certainly everybody sort of respected my position as the creator and the current sort of owner of the language design, I was Looking at everyone else for feedback, sort of. Python 3.0 in some sense was sparked by other people in the community pointing out, oh, well, there are a few issues that sort of bite users over and over. Can we do something about that? For Python 3, we took a number of those Python wards, as they were called at the time, and we said, can we try to make small changes to the language that address those wards? In the past, we had always taken backwards compatibility very seriously, and so many Python words in earlier versions had already been resolved because they could be resolved while maintaining backwards compatibility or sort of using a very gradual path of evolution of the language in a certain area. And so we were stuck with a number of words that were widely recognized as problems, not like roadblocks, but nevertheless sort of things that some people trip over. And you know that that's always the same thing that, that people trip over when they trip. And we could not think of a backwards compatible way of resolving those issues.

But it's still an option to not resolve the issues, right?

And so, yes, for, for a long time we had sort of resigned ourselves to, well, okay, the language is not going to be perfect in this way and that way. And we sort of certain of these. I mean, there are still plenty of things where you can say, well, that particular detail is better in Java or in R or in Visual Basic or whatever. And we're okay with that because, well, we can't easily change it. It's not too bad. We can do a little bit with user education, or we can have static analyzer or warnings in the parser or something. But there were things where we thought, well, these are really problems that are not going away, they are getting worse in the future, we should do something.

About it, should do something. But ultimately there is a decision to be made, right?

Yes.

So was that the toughest decision in the history of Python you had to make as the benevolent dictator for life? Or if not, what are there? Maybe even on the smaller scale, what was the decision where you were really torn up about?

Well, the toughest decision was.