vtt/episode_006_small.vtt

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 The following is a conversation with Guido Van Rossum, creator of Python,

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 one of the most popular programming languages in the world, used in almost any application

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 that involves computers, from web backend development to psychology, neuroscience,

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 computer vision, robotics, deep learning, natural language processing, and almost any

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 subfield of AI. This conversation is part of MIT course on artificial general intelligence

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 and the artificial intelligence podcast. If you enjoy it, subscribe on YouTube, iTunes,

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 or your podcast provider of choice, or simply connect with me on Twitter at Lex Friedman,

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 spelled F R I D. And now here's my conversation with Guido Van Rossum.

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 You were born in the Netherlands in 1956. Your parents and the world around you was deeply

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 impacted by World War Two, as was my family from the Soviet Union. So with that context,

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 what is your view of human nature? Are some humans inherently good and some inherently

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 evil, or do we all have both good and evil within us?

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 Ouch, I did not expect such a deep one. I guess we all have good and evil potential in us,

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 and a lot of it depends on circumstances and context.

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 Out of that world, at least on the Soviet Union side in Europe, sort of out of suffering,

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 out of challenge, out of that kind of set of traumatic events, often emerges beautiful art,

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 music, literature. In an interview I read or heard, you said you enjoyed Dutch literature

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 when you were a child. Can you tell me about the books that had an influence on you in your

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 childhood? Well, as a teenager, my favorite writer was, my favorite Dutch author was

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 a guy named Willem Friedrich Hermans, whose writing, certainly his early novels, were all about

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 sort of ambiguous things that happened during World War Two. I think he was a young adult

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 during that time, and he wrote about it a lot and very interesting, very good books, I thought,

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 I think. In a nonfiction way? No, it was all fiction, but it was very much set in the ambiguous

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 world of resistance against the Germans, where often you couldn't tell whether someone

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 was truly in the resistance or really a spy for the Germans, and some of the characters in his

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 novels sort of crossed that line, and you never really find out what exactly happened.

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 And in his novels, there's always a good guy and a bad guy, in the nature of good and evil,

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 is it clear there's a hero? No, his main characters are often antiheroes, and so they're

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 not very heroic. They fail at some level to accomplish their lofty goals.

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 And looking at the trajectory through the rest of your life, has literature,

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 Dutch or English or translation had an impact outside the technical world that you existed in?

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

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 It doesn't impact your work. It's a separate world. My work is highly technical and sort of

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 the world of art and literature doesn't really directly have any bearing on it.

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 You don't think there's a creative element to the design of a language's art?

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 I'm not disagreeing with that. I'm just saying that I don't feel

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 direct influences from more traditional art on my own creativity.

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 All right, of course, you don't feel doesn't mean it's not somehow deeply there in your subconscious.

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 So let's go back to your early teens. Your hobbies were building electronic circuits,

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 building mechanical models. If you can just put yourself back in the mind of that

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 young widow, 12, 13, 14, was that grounded in a desire to create a system? So to create

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 something? Or was it more just tinkering? Just the joy of puzzle solving?

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 I think it was more the latter, actually. Maybe towards the end of my high school

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 period, I felt confident enough that I designed my own circuits that were sort of interesting.

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 Somewhat. But a lot of that time, I literally just took a model kit and followed the instructions,

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 putting the things together. I mean, I think the first few years that I built electronics kits,

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 I really did not have enough understanding of electronics to really understand what I was

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 doing. I could debug it and I could follow the instructions very carefully, which has always

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 stayed with me. But I had a very naive model of how a transistor works. I don't think that in

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 those days, I had any understanding of coils and capacitors, which actually was a major problem

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 when I started to build more complex digital circuits, because I was unaware of the analog

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 part of how they actually work. And I would have things that the schematic looked, everything

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 looked fine, and it didn't work. And what I didn't realize was that there was some

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 megahertz level oscillation that was throwing the circuit off, because I had a sort of,

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 two wires were too close or the switches were kind of poorly built.

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 But through that time, I think it's really interesting and instructive to think about,

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 because there's echoes of it in this time now. So in the 1970s, the personal computer was being

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 born. So did you sense in tinkering with these circuits, did you sense the encroaching revolution

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 in personal computing? So if at that point, you're sitting, we'll sit you down and ask you to predict

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 the 80s and the 90s, do you think you would be able to do so successfully to unroll this,

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 the process? No, I had no clue. I, I remember, I think in the summer after my senior year,

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 or maybe it was the summer after my junior year. Well, at some point, I think when I was 18,

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 I went on a trip to the math Olympiad in Eastern Europe. And there was like, I was part of the

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 Dutch team. And there were other nerdy kids that sort of had different experiences. And one of

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 them told me about this amazing thing called a computer. And I had never heard that word.

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 My own explorations in electronics were sort of about very simple digital circuits. And I,

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 I had sort of, I had the idea that I somewhat understood how a digital calculator worked. And

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 so there is maybe some echoes of computers there, but I didn't, didn't, I never made that connection.

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 I didn't know that when my parents were paying for magazine subscriptions using punched cards,

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 that there was something called a computer that was involved that read those cards and

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 transferred the money between accounts. I was actually also not really interested in those

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 things. It was only when I went to university to study math that I found out that they had a

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 computer and students were allowed to use it. And there were some, you're supposed to talk to that

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 computer by programming it. What did that feel like? Yeah, that was the only thing you could do

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 with it. The computer wasn't really connected to the real world. The only thing you could do was

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 sort of, you typed your program on a bunch of punched cards. You gave the punched cards to

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 the operator. And an hour later, the operator gave you back your printout. And so all you could do

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 was write a program that did something very abstract. And I don't even remember what my

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 first forays into programming were, but they were sort of doing simple math exercises and just to

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 learn how a programming language worked. Did you sense, okay, first year of college, you see this

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 computer, you're able to have a program and it generates some output. Did you start seeing the

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 possibility of this? Or was it a continuation of the tinkering with circuits? Did you start to

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 imagine that one, the personal computer, but did you see it as something that is a tool

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 to get tools like a word processing tool, maybe maybe for gaming or something? Or did you start

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 to imagine that it could be, you know, going to the world of robotics, like you, you know, the

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 Frankenstein picture that you could create an artificial being. There's like another entity

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 in front of you. You did not see it. I don't think I really saw it that way. I was really more

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 interested in the tinkering. It's maybe not a sort of a complete coincidence that I ended up

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 sort of creating a programming language, which is a tool for other programmers. I've always been

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 very focused on the sort of activity of programming itself and not so much what happens with

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 what happens with the program you write. I do remember, and I don't remember, maybe in my second

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 or third year, probably my second, actually, someone pointed out to me that there was this

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 thing called Conway's Game of Life. You're probably familiar with it. I think in the 70s,

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 I think, as long as you came up with it. So there was a Scientific American column by

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 someone who did a monthly column about mathematical diversions and also blinking out on the guy's

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 name. It was very famous at the time and I think up to the 90s or so. And one of his columns was

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 about Conway's Game of Life and he had some illustrations and he wrote down all the rules

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 and sort of there was the suggestion that this was philosophically interesting, that that was why

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 Conway had called it that. And all I had was like the two pages photocopy of that article.

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 I don't even remember where I got it. But it spoke to me and I remember implementing

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 a version of that game for the batch computer we were using where I had a whole Pascal program

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 that sort of read an initial situation from input and read some numbers that said,

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 do so many generations and print every so many generations and then out would come pages and

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 pages of sort of things. Patterns of different kinds and yeah. Yeah. And I remember much later

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 I've done a similar thing using Python, but I sort of that original version I wrote at the time

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 I found interesting because I combined it with some trick I had learned during my electronics

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 hobbyist times. I essentially first on paper I designed a simple circuit built out of logic gates

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 that took nine bits of input, which is the sort of the cell and its neighbors

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 and produce the new value for that cell. And it's like a combination of a half adder and some

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 other clipping. No, it's actually a full adder. And so I had worked that out and then I translated

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 that into a series of Boolean operations on Pascal integers where you could use the integers as

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 bitwise values. And so I could basically generate 60 bits of a generation in like eight instructions

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 or so. Nice. So I was proud of that. It's funny that you mentioned so for people who don't know

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 Conway's Game of Life is a cellular automata where there's single compute units that kind of

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 look at their neighbors and figure out what they look like in the next generation based on the

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 state of their neighbors and this is deeply distributed system in concept at least. And then

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 there's simple rules that all of them follow and somehow out of the simple rule when you step back

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 and look at what occurs, it's beautiful. There's an emergent complexity, even though the underlying

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 rules are simple, there's an emergent complexity. Now the funny thing is you've implemented this

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 and the thing you're commenting on is you're proud of a hack you did to make it run efficiently.

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 When you're not commenting on what like this is a beautiful implementation.

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 You're not commenting on the fact that there's an emergent complexity

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 that you've quoted a simple program and when you step back and you print out the

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 following generation after generation, that's stuff that you may have not predicted what

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 happened is happening. And is that magic? I mean that's the magic that all of us feel when we

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 program. When you create a program and then you run it and whether it's Hello World or it shows

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 something on screen if there's a graphical component, are you seeing the magic and the

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 mechanism of creating that? I think I went back and forth. As a student, we had an incredibly

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 small budget of computer time that we could use. It was actually measured. I once got in trouble with

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 one of my professors because I had overspent the department's budget. It's a different story.

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 But so I actually wanted the efficient implementation because I also wanted to explore

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 what would happen with a larger number of generations and a larger sort of size of the

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 board. And so once the implementation was flawless, I would feed it different patterns and then I

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 think maybe there was a follow up article where there were patterns that were like gliders,

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 patterns that repeated themselves after a number of generations but translated one or two positions

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 to the right or up or something like that. And there were, I remember things like glider guns.

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 Well, you can Google Conway's Game of Life. People still go on over it. For a reason because

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 it's not really well understood. I mean this is what Stephen Wolfram is obsessed about.

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 We don't have the mathematical tools to describe the kind of complexity that emerges

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 in these kinds of systems. And the only way you can do is to run it.

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

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 No. And so one theory of how you create an artificial intelligence or an artificial being

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 is you kind of have to, same with the game of life, you kind of have to create a universe

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 and let it run. That creating it from scratch in a design way in the, you know, coding up a

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 Python program that creates a fully intelligent system may be quite challenging that you might

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 need to create a universe just like the game of life is. Well, you might have to experiment with

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 a lot of different universes before. There is a set of rules that doesn't essentially always just

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 and repeating itself in a trivial way. Yeah. And Steve Wolfram, Stephen Wolfram works with

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 these simple rules, says that it's kind of surprising how quickly you find rules that

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 create interesting things. You shouldn't be able to, but somehow you do. And so maybe our universe

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 is laden with rules that will create interesting things that might not look like humans, but

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 you know, emergent phenomena that's interesting may not be as difficult to create as we think.

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 Sure. But let me sort of ask, at that time, you know, some of the world, at least in popular press,

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 was kind of captivated, perhaps at least in America, by the idea of artificial intelligence,

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 that these computers would be able to think pretty soon. And did that touch you at all? Did

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 that in science fiction or in reality, in any way? I didn't really start reading science fiction

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 until much, much later. I think as a teenager, I read maybe one bundle of science fiction stories.

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

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 That sort of the using computers to build something intelligent always fell to me,

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 because I felt I had so much understanding of what actually goes on inside a computer.

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 I knew how many bits of memory it had and how difficult it was to program and sort of

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 I didn't believe at all that that you could just build something intelligent out of that,

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 that that would really sort of satisfy my definition of intelligence. I think the most

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 the most influential thing that I read in my early 20s was Gödel Escherbach.

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 That was about consciousness and that was a big eye opener, in some sense.

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 In what sense? So on your own brain, did you at the time or do you now see your

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 own brain as a computer? Or is there a total separation of the way? So yeah, you're very

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 pragmatically, practically know the limits of memory, the limits of this sequential computing,

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 or weekly paralyzed computing, and you just know what we have now and it's hard to see

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 how it creates, but it's also easy to see it was in the in the 40s, 50s, 60s, and now

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 at least similarities between the brain and our computers. Oh yeah, I mean, I

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 I totally believe that brains are computers in some sense. I mean, the rules they they use to

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 play by are pretty different from the rules we we can sort of implement in in our current

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 hardware. But I don't believe in like a separate thing that infuses us with intelligence or

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 consciousness or any of that. There's no soul. I've been an atheist probably

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 from when I was 10 years old, just by thinking a bit about math and the universe.

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 And well, my parents were atheists. Now, I know that you you you could be an atheist and still

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 believe that there is something sort of about intelligence or consciousness that cannot possibly

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 emerge from a fixed set of rules. I am not in that camp. I totally see that

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 that sort of given how many millions of years evolution took its time. DNA is is a particular

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 machine that that sort of encodes information and an unlimited amount of information in in

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 chemical form and has figured out a way to replicate itself. I thought that that was maybe

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 it's 300 million years ago, but I thought it was closer to half a half a billion years ago that that's

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 sort of originated and it hasn't really changed that the sort of the structure of DNA hasn't

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 changed ever since that is like our binary code that we have in hardware. I mean, the basic

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

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 It happened to be a set of rules that was good enough to to sort of develop

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 of endless variability and and sort of the the idea of self replicating molecules

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 competing with each other for resources and and one type eventually sort of always taking over

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 that happened before there were any fossils. So we don't know how that exactly happened, but

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 I believe it it's it's clear that that did happen and can you comment on consciousness and how you

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 see it? Because I think we'll talk about programming quite a bit. We'll talk about,

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 you know, intelligence connecting to programming fundamentally, but consciousness consciousness

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 is this whole other other thing. Do you think about it often as a developer of a programming

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 language and and as a human? Those those are pretty sort of separate topics.

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 Sort of my line of work working with programming does not involve anything that that goes in the

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 direction of developing intelligence or consciousness, but sort of privately as an avid reader of

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 popular science writing. I have some thoughts which which is mostly that

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 I don't actually believe that consciousness is an all or nothing thing. I have a feeling that and

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 and I forget what I read that influenced this, but I feel that if you look at a cat or a dog or a

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 mouse, they have some form of intelligence. If you look at a fish, it has some form of intelligence

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 and that evolution just took a long time. But I feel that the the sort of evolution of

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 more and more intelligence that led to to sort of the human form of intelligence

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 follow the the evolution of the senses, especially the visual sense.

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 I mean, there is an enormous amount of processing that's needed to interpret a scene. And humans are

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 still better at that than than computers are. Yeah, and so and and I have a feeling that

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 there is a sort of the reason that that like mammals is in particular developed the levels of

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 consciousness that they have and that eventually sort of going from intelligence to to self

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 awareness and consciousness has to do with sort of being a robot that has very highly developed

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 senses. Has a lot of rich sensory information coming in. So the that's a really interesting

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 thought that that whatever that basic mechanism of DNA, whatever that basic building blocks of

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 programming is you if you just add more abilities, more more high resolution sensors, more sensors,

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 you just keep stacking those things on top that this basic programming in trying to survive

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 develops very interesting things that start to us humans to appear like intelligence and

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 consciousness. Yeah, so in in as far as robots go, I think that the self driving cars have that sort

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 of the greatest opportunity of developing something like that because when I drive myself, I don't

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 just pay attention to the rules of the road. I also look around and I get clues from that Oh,

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 this is a shopping district. Oh, here's an old lady crossing the street. Oh, here is someone

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 carrying a pile of mail. There's a mailbox. I bet you they're gonna cross the street to reach

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 that mailbox. And I slow down. And I don't even think about that. Yeah. And so there is there is

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 so much where you turn your observations into an understanding of what other consciousnesses

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 are going to do or what what other systems in the world are going to be Oh, that tree is going to

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 fall. Yeah, I see sort of I see much more of I expect somehow that if anything is going to

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 become conscious, it's going to be the self driving car and not the network of a bazillion

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 computers at in a Google or Amazon data center that are all networked together to

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 to do whatever they do. So in that sense, so you actually highlight because that's what I work in

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 is an autonomous vehicles, you highlight the big gap between what we currently can't do and

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 what we truly need to be able to do to solve the problem. Under that formulation, then consciousness

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 and intelligence is something that basically a system should have in order to interact with us

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 humans, as opposed to some kind of abstract notion of a consciousness consciousness is

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 something that you need to have to be able to empathize to be able to

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 to fear the understand what the fear of death is. All these aspects that are important for

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 interacting with pedestrians need to be able to do basic computation based on our human

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 desires and if you sort of Yeah, if you if you look at the dog, the dog clearly knows, I mean,

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 I'm not the dog owner, my brother, I have friends who have dogs, the dogs clearly know

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 what the humans around them are going to do or at least they have a model of what those humans

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 are going to do when they learn the dog some dogs know when you're going out and they want to go

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 out with you, they're sad when you leave them alone, they cry. They're afraid because they were

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 mistreated when they were younger. We don't assign sort of consciousness to dogs or at least

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 not not all that much but I also don't think they have none of that. So I think it's it's

30:45.280 --> 30:48.960
 consciousness and intelligence are not all or nothing.

30:50.160 --> 30:54.320
 The spectrum is really interesting. But in returning to

30:56.000 --> 31:00.560
 programming languages and the way we think about building these kinds of things about building

31:00.560 --> 31:05.440
 intelligence, building consciousness, building artificial beings. So I think one of the exciting

31:05.440 --> 31:13.360
 ideas came in the 17th century. And with liveness, Hobbes, Descartes, where there's this feeling that

31:13.360 --> 31:23.120
 you can convert all thought all reasoning, all the thing that we find very special in our brains,

31:23.120 --> 31:28.800
 you can convert all of that into logic. You can formalize it, former reasoning. And then once

31:28.800 --> 31:33.280
 you formalize everything, all of knowledge, then you can just calculate. And that's what

31:33.280 --> 31:39.120
 we're doing with our brains is we're calculating. So there's this whole idea that we that this is

31:39.120 --> 31:45.920
 possible that this but they weren't aware of the concept of pattern matching in the sense that we

31:45.920 --> 31:53.840
 are aware of it now. They sort of thought you they had discovered incredible bits of mathematics

31:53.840 --> 32:04.720
 like Newton's calculus. And their sort of idealism there, their sort of extension of what they could

32:04.720 --> 32:16.480
 do with logic and math sort of went along those lines. And they thought there's there's like,

32:16.480 --> 32:23.920
 yeah, logic, there's there's like a bunch of rules, and a bunch of input, they didn't realize that how

32:23.920 --> 32:33.520
 you recognize a face is not just a bunch of rules, but is a shit ton of data, plus a circuit that

32:34.560 --> 32:42.800
 that sort of interprets the visual clues and the context and everything else. And somehow

32:42.800 --> 32:53.120
 how can massively parallel pattern match against stored rules? I mean, if I see you tomorrow here

32:53.120 --> 32:58.320
 in front of the Dropbox office, I might recognize you even if I'm wearing a different shirt. Yeah,

32:58.320 --> 33:04.240
 but if I if I see you tomorrow in a coffee shop in Belmont, I might have no idea that it was you

33:04.240 --> 33:11.920
 or on the beach or whatever. I make those mistakes myself all the time. I see someone that I only

33:11.920 --> 33:17.840
 know as like, Oh, this person is a colleague of my wife's. And then I see them at the movies and

33:18.640 --> 33:26.400
 I don't recognize them. But do you see those you call it pattern matching? Do you see that rules is

33:28.880 --> 33:34.880
 unable to encode that to you? Everything you see all the piece of information you look around

33:34.880 --> 33:39.520
 this room, I'm wearing a black shirt, I have a certain height, I'm a human all these you can

33:39.520 --> 33:45.440
 there's probably tens of thousands of facts you pick up moment by moment about this scene,

33:45.440 --> 33:49.760
 you take them for granted and you accumulate aggregate them together to understand the scene.

33:49.760 --> 33:53.760
 You don't think all of that could be encoded to weren't at the end of the day, you just put

33:53.760 --> 34:02.160
 it on the table and calculate. Oh, I don't know what that means. I mean, yes, in the sense that

34:02.160 --> 34:10.880
 there is no, there is no actual magic there, but there are enough layers of abstraction from sort

34:10.880 --> 34:19.440
 of from the facts as they enter my eyes and my ears to the understanding of the scene that I don't

34:19.440 --> 34:31.200
 think that that AI has really covered enough of that distance. It's like if you take a human body

34:31.200 --> 34:40.960
 and you realize it's built out of atoms, well, that that is a uselessly reductionist view, right?

34:41.760 --> 34:46.640
 The body is built out of organs, the organs are built out of cells, the cells are built out of

34:46.640 --> 34:54.240
 proteins, the proteins are built out of amino acids, the amino acids are built out of atoms,

34:54.240 --> 35:00.800
 and then you get to quantum mechanics. So that's a very pragmatic view. I mean, obviously as an

35:00.800 --> 35:06.720
 engineer, I agree with that kind of view, but I also you also have to consider the the with the

35:06.720 --> 35:13.120
 Sam Harris view of well, well, intelligence is just information processing. Do you just like

35:13.120 --> 35:17.840
 you said you take in sensory information, you do some stuff with it and you come up with actions

35:17.840 --> 35:25.680
 that are intelligent. That makes it sound so easy. I don't know who Sam Harris is. Oh, it's

35:25.680 --> 35:30.240
 philosopher. So like this is how philosophers often think, right? And essentially, that's what

35:30.240 --> 35:37.040
 Descartes was is, wait a minute, if there is, like you said, no magic. So you basically says it

35:37.040 --> 35:43.280
 doesn't appear like there's any magic, but we know so little about it that it might as well be magic.

35:43.280 --> 35:48.240
 So just because we know that we're made of atoms, just because we know we're made of organs,

35:48.240 --> 35:54.320
 the fact that we know very little how to get from the atoms to organs in a way that's recreatable

35:54.320 --> 36:01.280
 means it that you shouldn't get too excited just yet about the fact that you figured out that we're

36:01.280 --> 36:09.600
 made of atoms. Right. And and and the same about taking facts as our our sensory organs take them

36:09.600 --> 36:19.440
 in and turning that into reasons and actions that sort of there are a lot of abstractions that we

36:19.440 --> 36:30.000
 haven't quite figured out how to how to deal with those. I mean, I sometimes I don't know if I can

36:30.000 --> 36:38.880
 go on a tangent or not. Please. Drag you back in. Sure. So if I take a simple program that parses,

36:40.880 --> 36:47.760
 say I have a compiler, it parses a program. In a sense, the input routine of that compiler

36:48.320 --> 36:57.120
 of that parser is a sense, a sensing organ. And it builds up a mighty complicated internal

36:57.120 --> 37:03.920
 representation of the program it just saw it doesn't just have a linear sequence of bytes

37:03.920 --> 37:10.640
 representing the text of the program anymore, it has an abstract syntax tree. And I don't know how

37:10.640 --> 37:18.800
 many of your viewers or listeners are familiar with compiler technology, but there is fewer and

37:18.800 --> 37:26.720
 fewer these days, right? That's also true, probably. People want to take a shortcut, but there's sort

37:26.720 --> 37:35.200
 of this abstraction is a data structure that the compiler then uses to produce outputs that is

37:35.200 --> 37:41.280
 relevant like a translation of that program to machine code that can be executed by by hardware.

37:45.360 --> 37:53.360
 And then that data structure gets thrown away. When a fish or a fly sees

37:53.360 --> 38:03.920
 these sort of gets visual impulses. I'm sure it also builds up some data structure and for

38:03.920 --> 38:11.920
 the fly that may be very minimal, a fly may may have only a few. I mean, in the case of a fly's

38:11.920 --> 38:20.720
 brain, I could imagine that there are few enough layers of abstraction that it's not much more

38:20.720 --> 38:28.000
 than when it's darker here than it is here. Well, it can sense motion, because a fly sort of responds

38:28.000 --> 38:35.440
 when you move your arm towards it. So clearly, it's visual processing is intelligent, or well,

38:35.440 --> 38:43.600
 not intelligent, but is has an abstraction for motion. And we still have similar things in in

38:43.600 --> 38:48.880
 but much more complicated in our brains. I mean, otherwise, you couldn't drive a car if you,

38:48.880 --> 38:52.880
 you couldn't sort if you didn't have an incredibly good abstraction for motion.

38:54.560 --> 39:00.160
 Yeah, in some sense, the same abstraction for motion is probably one of the primary sources of

39:00.160 --> 39:06.160
 our of information for us, we just know what to do. I think we know what to do with that.

39:06.160 --> 39:11.200
 We've built up other abstractions on top. We build much more complicated data structures

39:11.200 --> 39:17.280
 based on that. And we build more persistent data structures, sort of after some processing,

39:17.280 --> 39:24.240
 some information sort of gets stored in our memory, pretty much permanently, and is available on

39:24.240 --> 39:31.680
 recall. I mean, there are some things that you sort of, you're conscious that you're remembering it,

39:31.680 --> 39:37.840
 like you give me your phone number, I, well, at my age, I have to write it down, but I could

39:37.840 --> 39:44.480
 imagine I could remember those seven numbers or 10, 10 digits, and reproduce them in a while.

39:44.480 --> 39:53.120
 If I sort of repeat them to myself a few times. So that's a fairly conscious form of memorization.

39:53.120 --> 40:00.880
 On the other hand, how do I recognize your face? I have no idea. My brain has a whole bunch of

40:00.880 --> 40:07.120
 specialized hardware that knows how to recognize faces. I don't know how much of that is sort of

40:07.120 --> 40:15.200
 coded in our DNA and how much of that is trained over and over between the ages of zero and three.

40:16.240 --> 40:23.120
 But somehow our brains know how to do lots of things like that that are useful in our interactions

40:23.120 --> 40:30.080
 with other humans without really being conscious of how it's done anymore.

40:30.080 --> 40:35.920
 Right. So our actual day to day lives, we're operating at the very highest level of abstraction.

40:35.920 --> 40:40.720
 We're just not even conscious of all the little details underlying it. There's compilers on top

40:40.720 --> 40:45.040
 of, it's like turtles on top of turtles or turtles all the way down. It's compilers all the way down.

40:46.160 --> 40:52.800
 But that's essentially, you say that there's no magic. That's what I, what I was trying to get at,

40:52.800 --> 40:58.640
 I think, is with Descartes started this whole train of saying that there's no magic. I mean,

40:58.640 --> 41:03.280
 there's others beforehand. Well, didn't Descartes also have the notion, though, that the soul and

41:03.280 --> 41:09.520
 the body were fundamentally separate? Yeah, I think he had to write in God in there for

41:10.320 --> 41:16.480
 political reasons. So I don't actually, I'm not historian, but there's notions in there that

41:16.480 --> 41:22.720
 all of reasoning, all of human thought can be formalized. I think that continued in the 20th

41:22.720 --> 41:30.880
 century with the Russell and with Gato's incompleteness theorem, this debate of what are

41:30.880 --> 41:35.280
 the limits of the things that could be formalized? That's where the Turing machine came along.

41:35.280 --> 41:40.800
 And this exciting idea, I mean, underlying a lot of computing, that you can do quite a lot

41:40.800 --> 41:46.320
 with a computer. You can, you can encode a lot of the stuff we're talking about in terms of

41:46.320 --> 41:52.400
 recognizing faces and so on, theoretically, in an algorithm that can then run on the computer.

41:52.400 --> 42:01.120
 And in that context, I'd like to ask programming in a philosophical way.

42:02.960 --> 42:08.160
 What, so what does it mean to program a computer? So you said you write a Python program

42:08.880 --> 42:16.800
 or compiled a C++ program that compiles to somebody code. It's forming layers.

42:16.800 --> 42:22.400
 You're, you're, you're programming in a layer of abstraction that's higher. How do you see programming

42:22.960 --> 42:27.760
 in that context? Can it keep getting higher and higher levels of abstraction?

42:29.680 --> 42:35.120
 I think at some, at some point, the higher level of levels of abstraction will not be called

42:35.120 --> 42:44.800
 programming and they will not resemble what we, we call programming at the moment. There will

42:44.800 --> 42:53.600
 not be source code. I mean, there will still be source code sort of at a lower level of the machine,

42:53.600 --> 43:04.480
 just like there's still molecules and electrons and sort of proteins in our brains. But, and so

43:04.480 --> 43:11.520
 there's still programming and system administration and who knows what keeping to keep the machine

43:11.520 --> 43:17.600
 running. But what the machine does is, is a different level of abstraction in a sense. And

43:18.160 --> 43:25.120
 as far as I understand the way that for last decade or more people have made progress with

43:25.120 --> 43:32.000
 things like facial recognition or the self driving cars is all by endless, endless amounts of

43:32.000 --> 43:42.240
 training data where at least as, as, as a layperson and I feel myself totally as a layperson in that

43:42.240 --> 43:52.240
 field, it looks like the researchers who publish the results don't necessarily know exactly how,

43:52.240 --> 44:02.480
 how their algorithms work. And I often get upset when I sort of read a sort of a fluff piece about

44:02.480 --> 44:10.000
 Facebook in the newspaper or social networks and they say, well, algorithms. And that's like a totally

44:10.000 --> 44:18.640
 different interpretation of the word algorithm. Because for me, the way I was trained or what I

44:18.640 --> 44:25.200
 learned when I was eight or 10 years old, an algorithm is a set of rules that you completely

44:25.200 --> 44:31.600
 understand that can be mathematically analyzed. And, and, and you can prove things, you can like

44:31.600 --> 44:37.920
 prove that Aristotle's Civ produces all prime numbers and only prime numbers.

44:39.120 --> 44:45.360
 Yeah. So the, I don't know if you know who Andre Capati is. I'm afraid not. So he's a

44:45.360 --> 44:52.880
 head of AI at Tesla now, but he was at Stanford before, and he has this cheeky way of calling

44:53.520 --> 45:01.200
 this concept software 2.0. So let me disentangle that for a second. So kind of what you're

45:01.200 --> 45:06.560
 referring to is the traditional, traditional, the algorithm, the concept of an algorithm,

45:06.560 --> 45:10.320
 something that's there, it's clear, you can read it, you understand it, you can prove it's

45:10.320 --> 45:19.280
 functioning as kind of software 1.0. And what software 2.0 is, is exactly what you describe,

45:19.280 --> 45:24.560
 which is you have neural networks, which is a type of machine learning that you feed a bunch

45:24.560 --> 45:31.600
 of data, and that neural network learns to do a function. All you specify is the inputs and

45:31.600 --> 45:38.560
 the outputs you want, and you can't look inside. You can't analyze it. All you can do is train

45:38.560 --> 45:43.840
 this function to map the inputs, the outputs by giving a lot of data. In that sense, programming

45:43.840 --> 45:49.360
 becomes getting a lot of cleaning, getting a lot of data. That's what programming is in this.

45:49.360 --> 45:52.880
 Well, that would be programming 2.0. 2.0 to programming 2.0.

45:53.680 --> 45:57.680
 I wouldn't call that programming. It's just a different activity, just like

45:58.400 --> 46:01.600
 building organs out of cells is not called chemistry.

46:01.600 --> 46:10.560
 Well, so let's just step back and think sort of more generally, of course, but it's like

46:12.560 --> 46:20.000
 as a parent teaching your kids, things can be called programming. In that same sense,

46:20.000 --> 46:26.320
 that's how programming is being used. You're providing them data, examples, use cases.

46:26.320 --> 46:37.200
 So imagine writing a function not with for loops and clearly readable text, but more saying,

46:37.760 --> 46:43.840
 well, here's a lot of examples of what this function should take, and here's a lot of

46:43.840 --> 46:48.880
 examples of when it takes those functions, it should do this, and then figure out the rest.

46:48.880 --> 46:57.600
 So that's the 2.0 concept. And the question I have for you is like, it's a very fuzzy way.

46:58.320 --> 47:02.560
 This is the reality of a lot of these pattern recognition systems and so on. It's a fuzzy way

47:02.560 --> 47:09.520
 of quote unquote programming. What do you think about this kind of world? Should it be called

47:09.520 --> 47:17.840
 something totally different than programming? If you're a software engineer, does that mean

47:17.840 --> 47:24.800
 you're designing systems that are very can be systematically tested, evaluated, they have a

47:24.800 --> 47:31.440
 very specific specification, and then this other fuzzy software 2.0 world machine learning world,

47:31.440 --> 47:35.920
 that's that's something else totally? Or is there some intermixing that's possible?

47:35.920 --> 47:47.200
 Well, the question is probably only being asked because we we don't quite know what

47:47.200 --> 47:57.920
 that software 2.0 actually is. And it sort of I think there is a truism that every task that

47:58.960 --> 48:05.440
 AI has has tackled in the past. At some point, we realized how it was done. And then it was no

48:05.440 --> 48:14.160
 longer considered part of artificial intelligence because it was no longer necessary to to use

48:14.160 --> 48:25.120
 that term. It was just, oh, now we know how to do this. And a new field of science or engineering

48:25.120 --> 48:36.320
 has been developed. And I don't know if sort of every form of learning or sort of controlling

48:36.320 --> 48:41.920
 computer systems should always be called programming. So I don't know, maybe I'm focused too much on

48:41.920 --> 48:53.120
 the terminology. I but I expect that that there just will be different concepts where people with

48:53.120 --> 49:05.600
 sort of different education and a different model of what they're trying to do will will develop those

49:05.600 --> 49:14.800
 concepts. Yeah, and I guess, if you could comment on another way to put this concept is, I think,

49:16.480 --> 49:22.720
 I think the kind of functions that neural networks provide is things as opposed to being able to

49:22.720 --> 49:29.760
 upfront prove that this should work for all cases you throw at it. All you're able, it's the worst

49:29.760 --> 49:36.400
 case analysis versus average case analysis, all you're able to say is it seems on everything

49:36.400 --> 49:43.840
 we've tested to work 99.9% of the time, but we can't guarantee it and it fails in unexpected ways.

49:43.840 --> 49:48.400
 We can even give you examples of how it fails in unexpected ways. But it's like really good

49:48.400 --> 49:55.200
 most of the time. Yeah, but there's no room for that in current ways we think about programming.

50:00.160 --> 50:03.600
 Programming 1.0 is actually sort of

50:06.160 --> 50:14.080
 getting to that point to where the sort of the ideal of a bug free program

50:14.080 --> 50:25.440
 has been abandoned long ago by most software developers. We only care about bugs that manifest

50:25.440 --> 50:33.840
 themselves often enough to be annoying. And we're willing to take the occasional crash or

50:33.840 --> 50:45.760
 outage or incorrect result for granted, because we can't possibly we don't have enough programmers

50:45.760 --> 50:50.880
 to make all the code bug free and it would be an incredibly tedious business. And if you try to

50:50.880 --> 50:59.120
 throw formal methods at it, it gets it becomes even more tedious. So every once in a while,

50:59.120 --> 51:07.200
 the user clicks on a link in and somehow they get an error. And the average user doesn't panic,

51:07.200 --> 51:15.360
 they just click again and see if it works better the second time, which often magically it does.

51:16.320 --> 51:24.240
 Or they go up and they try some other way of performing their tasks. So that's sort of an

51:24.240 --> 51:33.440
 end to end recovery mechanism and inside systems, there is all sorts of retries and timeouts and

51:34.720 --> 51:41.520
 fallbacks. And I imagine that that sort of biological systems are even more full of that

51:41.520 --> 51:50.320
 because otherwise they wouldn't survive. Do you think programming should be taught and thought of

51:50.320 --> 51:59.440
 as exactly what you just said before I come from is kind of you're you're always denying that fact

51:59.440 --> 52:12.240
 always in in sort of basic programming education, the sort of the programs you're, you're having

52:12.240 --> 52:22.240
 students write are so small and simple that if there is a bug, you can always find it and fix it.

52:22.960 --> 52:29.520
 Because the sort of programming as it's being taught in some even elementary middle schools

52:29.520 --> 52:36.880
 in high school, introduction to programming classes in college, typically, it's programming in the

52:36.880 --> 52:45.280
 small. Very few classes sort of actually teach software engineering building large systems. I

52:45.280 --> 52:52.480
 mean, every summer here at Dropbox, we have a large number of interns, every tech company

52:53.440 --> 53:00.880
 on the West Coast has the same thing. These interns are always amazed because this is the

53:00.880 --> 53:09.120
 first time in their life that they see what goes on in a really large software development environment.

53:10.480 --> 53:20.320
 And everything they've learned in college was almost always about a much smaller scale and

53:20.320 --> 53:26.880
 somehow that difference in scale makes a qualitative difference in how you how you

53:26.880 --> 53:33.120
 do things and how you think about it. If you then take a few steps back into decades,

53:34.000 --> 53:39.040
 70s and 80s, when you're first thinking about Python or just that world of programming languages,

53:39.840 --> 53:45.680
 did you ever think that there would be systems as large as underlying Google, Facebook and Dropbox?

53:46.480 --> 53:54.000
 Did you when you were thinking about Python? I was actually always caught by surprise by

53:54.000 --> 53:58.240
 every sort of this. Yeah, pretty much every stage of computing.

53:59.440 --> 54:06.800
 So maybe just because you spoke in other interviews, but I think the evolution of

54:06.800 --> 54:11.920
 programming languages are fascinating. And it's especially because it leads from my

54:11.920 --> 54:17.440
 perspective towards greater and greater degrees of intelligence. I learned the first programming

54:17.440 --> 54:26.720
 language I played with in Russia was with the turtle logo logo. Yeah. And if you look, I just

54:26.720 --> 54:31.520
 have a list of programming languages, all of which I've played with a little bit. And they're all

54:31.520 --> 54:38.320
 beautiful in different ways from Fortran, Cobalt, Lisp, Algal 60, basic logo again, C

54:38.320 --> 54:48.240
 as a few object oriented came along in the 60s, Simula, Pascal, small talk, all of that leads

54:48.240 --> 54:55.360
 all the classics, the classics. Yeah, the classic hits, right? Scheme built that's built on top of

54:55.360 --> 55:03.680
 Lisp on the database side SQL C plus plus and all that leads up to Python, Pascal to

55:03.680 --> 55:10.720
 and all that's before Python, MATLAB, these kind of different communities, different languages.

55:10.720 --> 55:17.040
 So can you talk about that world? I know that sort of Python came out of ABC, which actually

55:17.040 --> 55:22.720
 never knew that language. I just having researched this conversation went back to ABC and it looks

55:22.720 --> 55:29.680
 remarkably, it has a lot of annoying qualities. But underneath those like all caps and so on.

55:29.680 --> 55:34.880
 But underneath that, there's elements of Python that are quite they're already there.

55:35.440 --> 55:39.280
 That's where I got all the good stuff, all the good stuff. So but in that world,

55:39.280 --> 55:43.680
 you're swimming in these programming languages, were you focused on just the good stuff in your

55:43.680 --> 55:50.240
 specific circle? Or did you have a sense of what, what is everyone chasing? You said that every

55:50.240 --> 55:59.520
 programming language is built to scratch an itch. Were you aware of all the itches in the community

55:59.520 --> 56:04.880
 and if not, or if yes, I mean, what itch we try to scratch with Python?

56:05.600 --> 56:12.240
 Well, I'm glad I wasn't aware of all the itches because I would probably not have been able to

56:12.880 --> 56:17.040
 do anything. I mean, if you're trying to solve every problem at once,

56:18.000 --> 56:27.760
 you saw nothing. Well, yeah, it's, it's too overwhelming. And so I had a very, very focused

56:27.760 --> 56:36.480
 problem. I wanted a programming language that set somewhere in between shell scripting and C.

56:38.480 --> 56:48.480
 And now, arguably, there is like, one is higher level, one is lower level. And

56:49.680 --> 56:56.800
 Python is sort of a language of an intermediate level, although it's still pretty much at the

56:56.800 --> 57:10.160
 high level. And I was I was thinking about much more about I want a tool that I can use to be

57:10.160 --> 57:19.040
 more productive as a programmer in a very specific environment. And I also had given myself a time

57:19.040 --> 57:27.600
 budget for the development of the tool. And that was sort of about three months for both the design

57:27.600 --> 57:31.920
 like thinking through what are all the features of the language syntactically.

57:33.760 --> 57:42.080
 And semantically, and how do I implement the whole pipeline from parsing the source code to

57:42.080 --> 57:51.200
 executing it. So I think both with the timeline and the goals, it seems like productivity was

57:51.200 --> 57:59.520
 at the core of it as a goal. So, like for me, in the 90s, and the first decade of the 21st

57:59.520 --> 58:06.400
 century, I was always doing machine learning AI, programming for my research was always in C++.

58:06.400 --> 58:12.160
 Wow. And then the other people who are a little more mechanical engineering,

58:12.160 --> 58:18.640
 electrical engineering, are Matlabby. They're a little bit more Matlab focused. Those are the

58:18.640 --> 58:26.480
 world and maybe a little bit Java too, but people who are more interested in emphasizing

58:26.480 --> 58:33.440
 the object oriented nature of things. So within the last 10 years or so, especially with the

58:33.440 --> 58:38.400
 oncoming of neural networks and these packages that are built on Python to interface with

58:39.200 --> 58:45.760
 neural networks, I switched to Python. And it's just, I've noticed a significant boost that I

58:45.760 --> 58:50.400
 can't exactly, because I don't think about it, but I can't exactly put into words why I'm just

58:50.400 --> 58:57.520
 much, much more productive, just being able to get the job done much, much faster. So how do you

58:57.520 --> 59:02.640
 think whatever that qualitative difference is, I don't know if it's quantitative, it could be just

59:02.640 --> 59:08.000
 a feeling. I don't know if I'm actually more productive, but how do you think about? You probably

59:08.000 --> 59:14.480
 are. Yeah, well, that's right. I think there's elements. Let me just speak to one aspect that

59:14.480 --> 59:23.920
 I think that was affecting our productivity is C++ was, I really enjoyed creating performant code

59:24.800 --> 59:29.840
 and creating a beautiful structure where everything that, you know, this kind of going

59:29.840 --> 59:34.560
 into this, especially with the newer and newer standards of templated programming of just really

59:34.560 --> 59:41.280
 creating this beautiful, formal structure that I found myself spending most of my time doing that

59:41.280 --> 59:46.160
 as opposed to getting it parsing a file and extracting a few keywords or whatever the task

59:46.160 --> 59:51.680
 goes trying to do. So what is it about Python? How do you think of productivity in general as

59:51.680 --> 59:57.440
 you were designing it now? So through the decades, last three decades, what do you think it means

59:57.440 --> 1:00:01.920
 to be a productive programmer? And how did you try to design it into the language?

1:00:03.200 --> 1:00:10.240
 There are different tasks. And as a programmer, it's, it's useful to have different tools available

1:00:10.240 --> 1:00:17.680
 that sort of are suitable for different tasks. So I still write C code. I still write shell code.

1:00:18.720 --> 1:00:22.080
 But I write most of my, my things in Python.

1:00:22.080 --> 1:00:30.960
 Why do I still use those other languages? Because sometimes the task just demands it.

1:00:32.400 --> 1:00:38.880
 And, well, I would say most of the time, the task actually demands a certain language because

1:00:38.880 --> 1:00:44.640
 the task is not write a program that solves problem x from scratch, but it's more like

1:00:44.640 --> 1:00:52.400
 fix a bug in existing program x or add a small feature to an existing large program.

1:00:56.320 --> 1:01:04.560
 But even if, if you sort of, if you're not constrained in your choice of language

1:01:04.560 --> 1:01:15.360
 by context like that, there is still the fact that if you write it in a certain language, then you

1:01:15.360 --> 1:01:26.080
 sort of, you, you have this balance between how long does it time? Does it take you to write the

1:01:26.080 --> 1:01:38.560
 code? And how long does the code run? And when you're in sort of, in the phase of exploring

1:01:38.560 --> 1:01:45.760
 solutions, you often spend much more time writing the code than running it, because every time

1:01:46.560 --> 1:01:52.880
 you've sort of, you've run it, you see that the output is not quite what you wanted. And

1:01:52.880 --> 1:02:05.520
 you spend some more time coding. And a language like Python just makes that iteration much faster,

1:02:05.520 --> 1:02:13.600
 because there are fewer details. There is a large library, sort of there are fewer details that,

1:02:13.600 --> 1:02:20.480
 that you have to get right before your program compiles and runs. There are libraries that

1:02:20.480 --> 1:02:27.040
 do all sorts of stuff for you. So you can sort of very quickly take a bunch of

1:02:28.000 --> 1:02:36.560
 existing components, put them together and get your prototype application running just like

1:02:37.120 --> 1:02:44.640
 when I was building electronics, I was using a breadboard most of the time. So I had this like

1:02:44.640 --> 1:02:52.240
 sprawl out circuit that if you shook it, it would stop working because it was not put together

1:02:52.800 --> 1:03:00.160
 very well. But it functioned and all I wanted was to see that it worked and then move on to the next

1:03:01.040 --> 1:03:07.280
 next schematic or design or add something to it. Once you've sort of figured out, oh, this is the

1:03:07.280 --> 1:03:13.920
 perfect design for my radio or light sensor or whatever, then you can say, okay, how do we

1:03:13.920 --> 1:03:20.560
 design a PCB for this? How do we solder the components in a small space? How do we make it

1:03:20.560 --> 1:03:32.800
 so that it is robust against, say, voltage fluctuations or mechanical disruption? I mean,

1:03:32.800 --> 1:03:37.280
 I know nothing about that when it comes to designing electronics, but I know a lot about

1:03:37.280 --> 1:03:45.200
 that when it comes to writing code. So the initial steps are efficient, fast, and there's not much

1:03:45.200 --> 1:03:54.080
 stuff that gets in the way. But you're kind of describing from like Darwin described the evolution

1:03:54.080 --> 1:04:01.920
 of species, right? You're observing of what is true about Python. Now, if you take a step back,

1:04:01.920 --> 1:04:09.680
 if the act of creating languages is art, and you had three months to do it, initial steps,

1:04:12.480 --> 1:04:17.040
 so you just specified a bunch of goals, sort of things that you observe about Python. Perhaps

1:04:17.040 --> 1:04:23.440
 you had those goals, but how do you create the rules, the syntactic structure, the features

1:04:23.440 --> 1:04:29.200
 that result in those? So I have, in the beginning, and I have follow up questions about through the

1:04:29.200 --> 1:04:35.440
 evolution of Python, too. But in the very beginning, when you're sitting there, creating the lexical

1:04:35.440 --> 1:04:45.680
 analyze or whatever evolution was still a big part of it, because I sort of I said to myself,

1:04:46.480 --> 1:04:52.800
 I don't want to have to design everything from scratch. I'm going to borrow features from

1:04:52.800 --> 1:04:57.440
 other languages that I like. Oh, interesting. So you basically, exactly, you first observe what

1:04:57.440 --> 1:05:04.800
 you like. Yeah. And so that's why if you're 17 years old, and you want to sort of create a programming

1:05:04.800 --> 1:05:11.840
 language, you're not going to be very successful at it. Because you have no experience with other

1:05:11.840 --> 1:05:25.280
 languages. Whereas I was in my, let's say mid 30s. I had written parsers before. So I had worked on

1:05:25.280 --> 1:05:32.000
 the implementation of ABC, I had spent years debating the design of ABC with its authors,

1:05:32.000 --> 1:05:36.560
 it's with its designers, I had nothing to do with the design, it was designed

1:05:37.600 --> 1:05:42.400
 fully as it was ended up being implemented when I joined the team. But so

1:05:44.480 --> 1:05:52.000
 you borrow ideas and concepts and very concrete sort of local rules from different languages,

1:05:52.000 --> 1:06:00.240
 like the indentation and certain other syntactic features from ABC. But I chose to borrow string

1:06:00.240 --> 1:06:10.400
 literals and how numbers work from C and various other things. So in then, if you take that further,

1:06:10.400 --> 1:06:17.280
 so yet, you've had this funny sounding, but I think surprisingly accurate and at least practical

1:06:17.280 --> 1:06:23.280
 title of benevolent dictator for life for quite, you know, for the last three decades or whatever,

1:06:23.280 --> 1:06:30.800
 or no, not the actual title, but functionally speaking. So you had to make decisions, design

1:06:30.800 --> 1:06:40.240
 decisions. Can you maybe let's take Python two, so Python releasing Python three as an example.

1:06:40.240 --> 1:06:47.680
 Mm hmm. It's not backward compatible to Python two in ways that a lot of people know. So what was

1:06:47.680 --> 1:06:53.120
 that deliberation discussion decision like? Yeah, what was the psychology of that experience?

1:06:54.320 --> 1:07:01.360
 Do you regret any aspects of how that experience undergone that? Well, yeah, so it was a group

1:07:01.360 --> 1:07:10.640
 process really. At that point, even though I was BDFL in name, and certainly everybody sort of

1:07:11.200 --> 1:07:20.240
 respected my position as the creator and the current sort of owner of the language design,

1:07:21.680 --> 1:07:24.880
 I was looking at everyone else for feedback.

1:07:24.880 --> 1:07:35.600
 Sort of Python 3.0 in some sense was sparked by other people in the community pointing out,

1:07:36.880 --> 1:07:47.360
 oh, well, there are a few issues that sort of bite users over and over. Can we do something

1:07:47.360 --> 1:07:55.280
 about that? And for Python three, we took a number of those Python words as they were called at the

1:07:55.280 --> 1:08:04.880
 time. And we said, can we try to sort of make small changes to the language that address those words?

1:08:06.000 --> 1:08:14.080
 And we had sort of in the past, we had always taken backwards compatibility very seriously.

1:08:14.080 --> 1:08:20.000
 And so many Python words in earlier versions had already been resolved, because they could be resolved

1:08:21.040 --> 1:08:28.960
 while maintaining backwards compatibility or sort of using a very gradual path of evolution of the

1:08:28.960 --> 1:08:36.400
 language in a certain area. And so we were stuck with a number of words that were widely recognized

1:08:36.400 --> 1:08:45.120
 as problems, not like roadblocks, but nevertheless, sort of things that some people trip over. And you

1:08:45.120 --> 1:08:53.120
 know that that's always the same thing that that people trip over when they trip. And we could not

1:08:53.120 --> 1:09:00.640
 think of a backwards compatible way of resolving those issues. But it's still an option to not

1:09:00.640 --> 1:09:06.960
 resolve the issues. And so yes, for for a long time, we had sort of resigned ourselves to well,

1:09:06.960 --> 1:09:14.720
 okay, the language is not going to be perfect in this way, and that way, and that way. And we sort

1:09:14.720 --> 1:09:20.400
 of certain of these I mean, there are still plenty of things where you can say, well, that's

1:09:20.400 --> 1:09:32.800
 that particular detail is better in Java or in R or in visual basic or whatever. And we're okay with

1:09:32.800 --> 1:09:39.760
 that because well, we can't easily change it. It's not too bad, we can do a little bit with user

1:09:39.760 --> 1:09:50.080
 education, or we can have static analyzer or warnings in in the parse or something. But there

1:09:50.080 --> 1:09:55.200
 were things where we thought, well, these are really problems that are not going away, they're

1:09:55.200 --> 1:10:03.280
 getting worse. In the future, we should do something about it. Do something. But ultimately, there is

1:10:03.280 --> 1:10:10.480
 a decision to be made, right? Yes. So was that the toughest decision in the history of Python you

1:10:10.480 --> 1:10:17.600
 had to make as the benevolent dictator for life? Or if not, what are other maybe even on a smaller

1:10:17.600 --> 1:10:23.360
 scale? What was the decision where you were really torn up about? Well, the toughest decision was

1:10:23.360 --> 1:10:30.400
 probably to resign. All right, let's go there. Hold on a second, then let me just because in the

1:10:30.400 --> 1:10:35.040
 interest of time too, because I have a few cool questions for you. And let's touch a really

1:10:35.040 --> 1:10:40.480
 important one because it was quite dramatic and beautiful in certain kinds of ways. In July this

1:10:40.480 --> 1:10:47.760
 year, three months ago, you wrote, now that PEP 572 is done, I don't ever want to have to fight so

1:10:47.760 --> 1:10:53.360
 hard for a PEP and find that so many people despise my decisions. I would like to remove myself

1:10:53.360 --> 1:10:59.280
 entirely from the decision process. I'll still be there for a while as an ordinary core developer.

1:10:59.280 --> 1:11:06.240
 And I'll still be available to mentor people possibly more available. But I'm basically giving

1:11:06.240 --> 1:11:12.800
 myself a permanent vacation from being BDFL benevolent dictator for life. And you all will

1:11:12.800 --> 1:11:20.720
 be on your own. First of all, just this, it's almost Shakespearean. I'm not going to appoint a

1:11:20.720 --> 1:11:27.600
 successor. So what are you all going to do? Create a democracy, anarchy, a dictatorship,

1:11:27.600 --> 1:11:35.120
 a federation. So that was a very dramatic and beautiful set of statements. It's almost,

1:11:35.120 --> 1:11:41.120
 it's open ended nature, called the community to create a future for Python. This is kind of a

1:11:41.120 --> 1:11:48.080
 beautiful aspect to it. Wow. So what and dramatic, you know, what was making that decision like?

1:11:48.080 --> 1:11:52.400
 What was on your heart, on your mind, stepping back now, a few months later,

1:11:52.400 --> 1:12:00.800
 taking it to your mindset? I'm glad you liked the writing because it was actually written pretty

1:12:00.800 --> 1:12:12.320
 quickly. It was literally something like after months and months of going around in circles,

1:12:12.320 --> 1:12:24.000
 I had finally approved PEP 572, which I had a big hand in its design, although I didn't

1:12:24.000 --> 1:12:34.000
 initiate it originally. I sort of gave it a bunch of nudges in a direction that would be

1:12:34.000 --> 1:12:42.160
 better for the language. So sorry, just to ask, is async IO, is that the one or no? No, PEP 572 was

1:12:42.160 --> 1:12:48.240
 actually a small feature, which is assignment expressions. Oh, assignment expressions, okay.

1:12:49.120 --> 1:12:55.840
 That had been thought there was just a lot of debate where a lot of people claimed that

1:12:55.840 --> 1:13:03.440
 they knew what was Pythonic and what was not Pythonic, and they knew that this was going to

1:13:03.440 --> 1:13:10.480
 destroy the language. This was like a violation of Python's most fundamental design philosophy.

1:13:10.480 --> 1:13:15.920
 And I thought that was all bullshit because I was in favor of it. And I would think I know

1:13:15.920 --> 1:13:22.560
 something about Python's design philosophy. So I was really tired and also stressed of that thing.

1:13:22.560 --> 1:13:31.360
 And literally, after sort of announcing, I was going to accept it. A certain Wednesday evening,

1:13:31.920 --> 1:13:39.760
 I had finally send the email, it's accepted. Now let's just go implement it. So I went to bed

1:13:40.320 --> 1:13:48.480
 feeling really relieved. That's behind me. And I wake up Thursday morning, 7am. And I think,

1:13:48.480 --> 1:13:59.520
 well, that was the last one. That's going to be such such a terrible debate. And that's

1:13:59.520 --> 1:14:05.600
 going to be that's the last time that I let myself be so stressed out about a PEP decision.

1:14:06.480 --> 1:14:13.200
 I should just resign. I've been sort of thinking about retirement for half a decade. I've been

1:14:13.200 --> 1:14:22.080
 joking and sort of mentioning retirement, sort of telling the community, some point in the

1:14:22.080 --> 1:14:30.240
 future, I'm going to retire. Don't take that FL part of my title too literally. And I thought,

1:14:30.240 --> 1:14:38.560
 okay, this is it. I'm done. I had the day off. I wanted to have a good time with my wife. We

1:14:38.560 --> 1:14:48.240
 were going to a little beach town nearby. And in, I think maybe 15, 20 minutes, I wrote that thing

1:14:48.240 --> 1:14:53.600
 that you just called Shakespearean. And the funny thing is, I get so much crap for calling you

1:14:53.600 --> 1:15:00.320
 Shakespearean. I didn't even I didn't even realize what a monumental decision it was.

1:15:00.320 --> 1:15:08.640
 Because five minutes later, I read that a link to my message back on Twitter, where people were

1:15:08.640 --> 1:15:17.360
 already discussing on Twitter, Guido resigned as the BDFL. And I had, I had posted it on an internal

1:15:17.360 --> 1:15:22.880
 forum that I thought was only read by core developers. So I thought I would at least

1:15:22.880 --> 1:15:30.560
 have one day before the news would sort of get out. The on your own aspects, I had also an

1:15:30.560 --> 1:15:39.120
 element of quite, it was quite a powerful element of the uncertainty that lies ahead. But can you

1:15:39.120 --> 1:15:45.280
 also just briefly talk about, you know, like, for example, I play guitar as a hobby for fun.

1:15:45.280 --> 1:15:50.720
 And whenever I play, people are super positive, super friendly. They're like, this is awesome.

1:15:50.720 --> 1:15:56.320
 This is great. But sometimes I enter as an outside observer, enter the programming community.

1:15:57.120 --> 1:16:04.000
 And there seems to some sometimes be camps on whatever the topic. And in the two camps,

1:16:04.000 --> 1:16:08.880
 the two or plus camps, are often pretty harsh at criticizing the opposing camps.

1:16:11.520 --> 1:16:18.400
 As an onlooker, I may be totally wrong on this. Yeah, holy wars are sort of a favorite activity

1:16:18.400 --> 1:16:23.200
 in the programming community. And what is the psychology behind that? Is, is that okay for

1:16:23.200 --> 1:16:28.400
 a healthy community to have? Is that, is that a productive force ultimately for the evolution

1:16:28.400 --> 1:16:35.840
 of a language? Well, if everybody is batting each other on the back and never telling the truth,

1:16:35.840 --> 1:16:45.120
 yes, it would not be a good thing. I think there is a middle ground where sort of

1:16:48.640 --> 1:16:56.960
 being nasty to each other is not okay. But there there is is is a middle ground where there is

1:16:56.960 --> 1:17:06.880
 is healthy ongoing criticism and feedback that is very productive. And you mean at every level,

1:17:06.880 --> 1:17:13.840
 you see that I mean, someone proposes to fix a very small issue in a code base.

1:17:16.240 --> 1:17:22.480
 Chances are that some reviewer will sort of respond by saying, well, actually,

1:17:22.480 --> 1:17:31.520
 you can do it better the other way. When it comes to deciding on the future of the Python

1:17:31.520 --> 1:17:38.800
 core developer community, we now have, I think, five or six competing proposals for a constitution.

1:17:40.960 --> 1:17:46.320
 So that future, do you have a fear of that future? Do you have a hope for that future?

1:17:46.320 --> 1:17:54.880
 I'm very confident about that future. And by and large, I think that the debate has been very

1:17:54.880 --> 1:18:06.000
 healthy and productive. And I actually when when I wrote that resignation email, I knew that that

1:18:06.000 --> 1:18:11.920
 Python was in a very good spot and that the Python core development community that the group of

1:18:11.920 --> 1:18:21.200
 50 or 100 people who sort of write or review most of the code that goes into Python, those people

1:18:22.400 --> 1:18:31.200
 get along very well most of the time. A large number of different areas of expertise are

1:18:31.200 --> 1:18:42.240
 represented at different levels of experience in the Python core dev community, different levels

1:18:42.240 --> 1:18:49.040
 of experience completely outside it in software development in general, large systems, small

1:18:49.040 --> 1:19:00.000
 systems, embedded systems. So I felt okay, resigning because I knew that that the community can

1:19:00.000 --> 1:19:08.240
 really take care of itself. And out of a grab bag of future feature developments, let me ask if

1:19:08.240 --> 1:19:15.360
 you can comment, maybe on all very quickly, concurrent programming parallel computing,

1:19:15.920 --> 1:19:23.520
 async IO, these are things that people have expressed hope, complained about, whatever

1:19:23.520 --> 1:19:31.360
 I have discussed on Reddit, async IO, so the parallelization in general, packaging, I was totally

1:19:31.360 --> 1:19:36.400
 close on this, I just use pip install stuff, but apparently, there's pip end of poetry, there's

1:19:36.400 --> 1:19:41.760
 these dependency packaging systems that manage dependencies and so on, they're emerging, and

1:19:41.760 --> 1:19:47.920
 there's a lot of confusion about what's what's the right thing to use. Then also, functional

1:19:47.920 --> 1:19:57.760
 programming, the ever, are we going to get more functional programming or not, this kind of idea,

1:19:58.560 --> 1:20:07.440
 and of course, the GIL connected to the parallelization, I suppose, the global interpreter

1:20:07.440 --> 1:20:12.240
 lock problem. Can you just comment on whichever you want to comment on?

1:20:12.240 --> 1:20:22.640
 Well, let's take the GIL and parallelization and async IO as one one topic.

1:20:25.280 --> 1:20:35.840
 I'm not that hopeful that Python will develop into a sort of high concurrency, high parallelism

1:20:35.840 --> 1:20:44.480
 language. That's sort of the way the language is designed, the way most users use the language,

1:20:44.480 --> 1:20:50.080
 the way the language is implemented, all make that a pretty unlikely future.

1:20:50.080 --> 1:20:56.000
 So you think it might not even need to really the way people use it, it might not be something

1:20:56.000 --> 1:21:02.400
 that should be of great concern. I think I think async IO is a special case, because it sort of

1:21:02.400 --> 1:21:14.320
 allows overlapping IO and only IO. And that is is a sort of best practice of supporting very

1:21:14.320 --> 1:21:24.000
 high throughput IO, many connections per second. I'm not worried about that. I think async IO

1:21:24.000 --> 1:21:30.080
 will evolve. There are a couple of competing packages, we have some very smart people who are

1:21:30.080 --> 1:21:39.120
 sort of pushing us in sort of to make async IO better. Parallel computing, I think that

1:21:40.320 --> 1:21:46.160
 Python is not the language for that. There are there are ways to work around it.

1:21:47.120 --> 1:21:54.960
 But you sort of you can't expect to write an algorithm in Python and have a compiler

1:21:54.960 --> 1:22:01.200
 automatically paralyze that what you can do is use a package like NumPy and there are a bunch of

1:22:01.200 --> 1:22:10.080
 other very powerful packages that sort of use all the CPUs available, because you tell the package,

1:22:10.720 --> 1:22:17.200
 here's the data, here's the abstract operation to apply over it, go at it, and then then we're

1:22:17.200 --> 1:22:23.440
 back in the C++ world. But the those packages are themselves implemented usually in C++.

1:22:23.440 --> 1:22:26.960
 That's right. That's where TensorFlow and all these packages come in where they parallelize

1:22:26.960 --> 1:22:32.800
 across GPUs, for example, they take care of that for you. So in terms of packaging, can you comment

1:22:32.800 --> 1:22:43.760
 on the packaging? Yeah, my packaging has always been my least favorite topic. It's a really tough

1:22:43.760 --> 1:22:57.440
 problem because the OS and the platform want to own packaging. But their packaging solution is not

1:22:57.440 --> 1:23:04.240
 specific to a language. Like, if you take Linux, there are two competing packaging solutions for

1:23:04.240 --> 1:23:16.000
 Linux, or for Unix in general. And but they all work across all languages. And several languages,

1:23:16.000 --> 1:23:25.600
 like Node, JavaScript, and Ruby, and Python all have their own packaging solutions that only work

1:23:25.600 --> 1:23:34.400
 within the ecosystem of that language. Well, what should you use? That is a tough problem.

1:23:34.400 --> 1:23:43.520
 My own own approach is I use the system packaging system to install Python, and I use the Python

1:23:43.520 --> 1:23:50.240
 packaging system then to install third party Python packages. That's what most people do.

1:23:50.240 --> 1:23:58.160
 10 years ago, Python packaging was really a terrible situation. Nowadays, Pip is the future.

1:23:58.160 --> 1:24:05.360
 There is there is a separate ecosystem for numerical and scientific Python, Python based on

1:24:05.360 --> 1:24:11.280
 Anaconda. Those two can live together. I don't think there is a need for more than that.

1:24:11.280 --> 1:24:16.800
 Great. So that's that's packaging. That's, well, at least for me, that's that's where I've been

1:24:16.800 --> 1:24:22.240
 extremely happy. I didn't I didn't even know this was an issue until it was brought up. Well, in the

1:24:22.240 --> 1:24:27.920
 interest of time, let me sort of skip through a million other questions I have. So I watched the

1:24:27.920 --> 1:24:33.840
 five hour five five and a half hour oral history. They've done with the computer history museum.

1:24:33.840 --> 1:24:38.480
 And the nice thing about it, it gave this because of the linear progression of the interview, it

1:24:38.480 --> 1:24:47.040
 it gave this feeling of a life, you know, a life well lived with interesting things in it.

1:24:47.040 --> 1:24:52.960
 Sort of a pretty, I would say a good spend of of this little existence we have on earth.

1:24:52.960 --> 1:24:59.920
 So outside of your family, looking back, what about this journey are you really proud of?

1:24:59.920 --> 1:25:10.240
 Are there moments that stand out accomplishments ideas? Is it the creation of Python itself

1:25:10.240 --> 1:25:15.040
 that stands out as a thing that you look back and say, damn, I did pretty good there?

1:25:17.600 --> 1:25:21.760
 Well, I would say that Python is definitely the best thing I've ever done.

1:25:21.760 --> 1:25:34.000
 And I wouldn't sort of say just the creation of Python, but the way I sort of raised Python,

1:25:34.000 --> 1:25:41.440
 like a baby, I didn't just conceive a child, but I raised a child. And now I'm setting the child

1:25:41.440 --> 1:25:48.800
 free in the world. And I've set up the child to to sort of be able to take care of himself.

1:25:48.800 --> 1:25:55.760
 And I'm very proud of that. And as the announcer of Monty Python's Flying Circus used to say,

1:25:55.760 --> 1:26:01.200
 and now for something completely different, do you have a favorite Monty Python moment or a

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 moment in Hitchhiker's Guide or any other literature show or movie that cracks you up when you think

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 about it? Oh, you can always play me the Parrots, the dead Parrot sketch. Oh, that's brilliant.

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 Yeah, that's my favorite as well. Pushing up the daisies. Okay, Greta, thank you so much for

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 talking to me today. Lex, this has been a great conversation.