YouSaid · the spoken record

Travis Oliphant

lines on the record: 265
first: 2021-09-23
most recent: 2021-09-23
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“So I would say it didn't really resolve itself. It sort of started a journey that I'm continuing on. I'm still on, I would say. I don't think it resolved itself. But I will say.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Make some money right to figure it out. But I didn't really care. I mean, I was never, I've never been driven by money, just need it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, and that money is just a bartering tool. So, this is the first time I've encountered any of this concept, right? And the fact that, oh, this is actually really critical. Like, it's so critical to our prosperity. We're dangerously not learning about this, not teaching our children about this, you know.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Of the Socialist Commonwealth. It was basically in response to the Bolshevik Revolution in 1917. And his basic argument was it's not going to work to not have private property. You're not going to be able to come up with prices. The bureaucrats aren't going to be able to determine how to allocate resources without a price system. And a price system emerges from people making trades. And it can only make trades if they have authority over the thing they're trading. And that creates information flow that you just don't have. If you try to top down it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“They want their economists to back them up, right? Or to be their magicians. The magicians in Pharaoh's court, right? The people that kind of say, hey, this is, you should listen to me because I've got the expert who says this. And so it gets really muddled, right? But I was looking at it from a scientist going, what is this space? What does this mean? How does paras get fed? What is money? How does it work? And I found a lot of writings I really loved. I found some things that I really loved. And I learned from that, it was writings from people like Von Miss. He wrote a paper in 1920 that still should be read more than it is. It was the economic calculation problem.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, it's a problem. And so during my PhD at the same time, this is because in 98, 99, at the same time, I was in a library. I was reading books on capitalism. I was reading books on Marxism. I was reading books on, you know, what is this thing? What does it mean? And I encountered a, basically, I encountered a set of writings from people that said they were the inheritors of Adam Smith. Been Adam Smith for the first time, right? Which is the wealth of nations and kind of this notion of emergent societies and realize, oh, there's this whole world out here of people. And the challenge with economics is also political, like, because economics, you know, people, different parties running for office, they want their economic friends”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, exactly. There's got to be. Well, so that actually led me to a study of economics because at the time I was ignorant. And it really was. And I'm actually. I'm embarrassed for educational system that they could let me, and I was valedorian in my high school class, and I did super well in college. And academically, I did Right. But the fact that I could do that and then be clueless about this key part of life, it led me to go, there's a problem. I should have learned this in fifth grade. I should have learned this in eighth grade. Like everybody should come out with a basic knowledge of economics.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And like right now, today, you know, I'm still in the middle of that. That's actually the early days of me exploring this question. Because as I was writing SciPy, I mean, as an aside, I also had, so I had three kids at the time. I have six kids now. I got married early, wanted a family. I had three kids. And I remember reading Richard Stallman's post. And I was a fan of Stallman. I would read his work. I liked this collective ideas he would have. Certainly the ideas on IP law, I read a lot of his stuff. But then he said, you know, Okay, well, how do I make money with this? How do I make a living? How do I pay for my kids? All this stuff was in my mind. Young graduate student making no money thinking I got to get a job. And he said, well, you know, I think just be like me and don't have kids, right? That's just don't, don't.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“It's like, or whatever No, it's really that connection. It's something I'm in the middle of right now the business of open source. And how do you connect the ethos of cooperative development with the necessity of creating profits?”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Coding and doing it well and not using MATLAB. I remember being driven by, you know, I like MATLAB, but I didn't like the fact that So, I'm not opposed to proprietary software. I'm actually not an open source zealot. I love open source for what it brings, but I also see the role for proprietary software. What I didn't like was the fact that I would develop code and publish it and then effectively telling somebody here to run my code, you have to have this proprietary software.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so John Hunter was, you know, he wasn't a student at the time, but he was an actor. He was working in Quantfield. And he said, we need better plotting. So he just went out and said, cool, I'll make a new project and we'll call it Matplotlib. And he released in 2001. About the same time that SciPod came out, and it was separate library, separate install, use numeric, sci-py use numeric. And so SciPy, you know, 2001 we released SciPy and then N-thought created a conference called SciPy, which brought people together to talk about the space. An EthConference is still ongoing. It's one of the favorite conferences of a lot of people because it's. It's changed over the years, but early on it was a collection of 50 people who care about.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“It was a programming language that had a plotting tool Disciplin, you had integration to Dislin. I ended up using Dislin plus some of the plotting from Yorick linked to from Python. Anyway, it was a... People don't plot that way now, but this is before SciPy was trying to add plotting. It didn't have much success. Really, the success of plotting came from John Hunter, who had a similar experience to my experience, my kind of maverick experience as a person just trying to get stuff done and kind of having more time than money, maybe, right?”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I just got an offer to go and teach at my albumater. So I took that 10-year track position And SciPot, and kind of then I started work on SciPy as a professor too. I've got the mail clinic, graduated, wrote my thesis using sci-fi, wrote, you know, there's images that were created. Now the plotting tool I used was something from Yorick, actually. It was a plotting PLT from a plotting language that I used.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Early on, it's fine because there's nobody there, and so it works. But then as you get more successful and more people use it, all of a sudden, oh, there's this scale at which this doesn't work anymore. And we have to come up with different approaches. So SciPy came out officially in 2001 was the first release most of the time. I remember the days of getting that release ready. It was Windows installer and there was bugs on how the Windows compiler handled complex numbers and you were chasing segmentation faults. It's a lot of work. There's a lot of effort had nothing to do with my area of study. At the same time, I had just gotten an offer. So he wondered if I wanted to come down and help him start that company with his friend. And at the time, I was like, I was intrigued, but I was squaring a path, an academic path.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“As to which too many cooks in the kitchen, you can do amazing infrastructure work. When it comes down to bringing it all together into a single deliverable, that actually requires a little more product management. That is not, that doesn't really emerge from the same dynamic. So it struggle to get almost too many voices. It's hard to have everybody agree. Consensus doesn't really work at that scale. You end up with politics. You end up with the same kind of things that's happened in large organizations trying to decide on what to do together. So consensus building was still challenging at scale as more people came in.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“We saw Saipay as a way to make an RD environment for Python. Use Python depended on numeric. So numeric was the array library we depended on. Then from there, extend it with a bunch of modules that allowed for. And at the time, the original vision of SciPy was to have plotting, was to have REPLIN environment and kind of a whole, really a whole data environment that you could then install and get going with. And that was kind of the thinking. It didn't really evolve that way, right? It sort of had a, for one, it's really hard to do massive scale projects with open source collectives. Actually, there's sort of an intrinsic cooperation limit.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So that's the origin of the sci-py brand. It came from multi-pack and a whole bunch of modules I'd written, plus a few things from some other folks, and then pulled together in a single installer. SciPy was really a distribution of Python. Masquerade is a library.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“99 doing a lot of work there. 98 doing a lot of work there. 99 kind of spending more time on my PhD helping people use the tools, thinking about what do I want to go from here. There was a company, there was a guy actually, Eric Jones and Travis Vott. They were two friends who founded a company called Nthot that's here in Austin, still here. And they, Eric contacted me at the time when I was a graduate student still. And he said, hey, why don't you come down? We want to build a company we're thinking of a scientific company and we want to take what you're doing and kind of add it to some stuff that he'd done, he'd written some tools. And then Pierre Peterson had done FDI. It's come together and build, pull this all together and call it SciPy.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And so that's, but I actually did that. This happened at the same time. That's why kind of what you're working on and what you're interested in, they're coinciding. I was definitely scratching my own itch. In terms of building stuff, which helped in the sense that I was using it for me, so at least I had one user. I had one person. It was like, well, no, this is better. I like this interface better. And I had the experience of MATLAB to guide some of what those APIs might look like. But you're just doing yourself. You're building all this stuff. But the Windows installer, it was the first time I realized, oh, yeah, the binary installer really helps people. And so that led to spending more time on that side of things. So around 2000, so I graduated my PhD in 2000, end of year, end of 2000.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes. Yes, it was on Linux. I was a Linux developer doing it on Munix box. I mean, at the time, I was actually getting into, I had a new hard drive. I did some kernel programming to make the hard drive work. I mean, not programming, but modification to the kernel so I could actually get a hard drive working. I love that aspect of it. I was also at school. I was building a cluster. I took Mac computers and you put Yellowdog Linux on them. At the Mayo Clinic, they were just all these Macs that were older. They were just getting rid of. And so I kind of got permission to go grab them together. I put about 24 of them together in a cluster, in a cabinet, and put Yellow Dog Linux on them all. And I wrote a C++ program to do MRI simulation. That was what I was doing at the same time for my day job, so to speak. So I was loving the whole process. At the same time, I was, oh, I need an ordinary differential equation. That's why ordinary differential equations were key was because that's the heart of a block equation for simulating MRI.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So 99, there was this thing called multipack, and that's when a high school student, no he was a high school student at the time, a guy named Robert Kern, took that package and made a Windows installer, right? And then, of course, a massive increase of usage”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And that's not true across the board, but a lot of that's there. But here in this world, I was getting responses from people all over the world. You know, I remember Piero Peterson in Estonia was one of the first people. And he sent me back this make file. Because the first thing it is, yeah, your build thing stinks. And here's a better make file. Now, it was a complex makefile. I don't think I never understood that make file, actually. It worked and it did a lot more. And so I think, thanks, this is cool. And that was my first kind of engagement with community development. But, you know, the process was he sent me a patch file. I had to upload a new tarball. And I just found I really loved that. And the style back then was here's a mailing list. It wasn't as, there certainly weren't the tools that are available today. It was very early on. But I really started the whole year. I think I did about seven packages that year, right? And then by the end of the year, I collected them into a thing called multi-pack.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Was the, you know, here I'm writing papers, I'm giving conferences, and I get people to say hello, but yeah, good job. But mostly it was you're viewed with, it's competitive. You publish a paper and people are like, oh, it wasn't my paper I was starting to see that sense of academic. Life where it was so much, I thought there was a cooperative effort, but it sounds like we're here just to one up each other.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Frictionless is actually really key to grow in any community. Any friction point, you're just going to lose some people. Right now, sometimes you may want to intentionally do that if you're early enough on that need of help. You need people who have the skills, you might actually, it's helpful. You don't necessarily have too much users as opposed to contributors if you're early on. Anyway, Sci-Fi started in 98, but it really emerged as this collection of modules that I was just putting on the net. People were downloading. And I think I got 100 users, right? By the end of that year. But the fact that I got 100 users and more than that, people started to email me with fixes. And that was actually intoxicating, right? That was the...”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“It's compiled, ready to install. In fact, a lot of the journey from 98, even through 2012, when we used to, when I started Anaconda, was about that. It's why, you know, it's really the key as to why a scientist with dreams of doing MRI research ended up starting a software company that... Installs software.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Energized by that act. So it's energized by that already Right? And I can't deny that. I was. I sort of had this very, I like that part of science, that part of sharing. And then all of a sudden, oh, wait, here's something, and here's something I could do. And then I slowly over years learned how to share better so that you could actually engage more people faster. One of the key things was actually giving people a binary they could install, right? So it wasn't just your source code. Good luck.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I would say I was inspired. I'd been inspired by Linux. I'd been inspired by Linnis and him making his code available. And I was starting to use Linux at the time. I went, this is cool. So I'd kind of been previously primed that way. And generally, I was into science because I liked the sharing notion. I like the idea of, hey, let's, if collectively we build knowledge and share it, we can all be better off.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Writing an extension module to connect runge cut integration to Python and making an ordinary additional equation solver and then releasing that as a package, so we call ODE pack, I think I called it then, quad pack. And then I just made these packages. Eventually that became multi-pack because they're originally modular. You can install them separately. But a massive problem in Python was actually just getting your stuff installed. At the time, releasing software for me, like today it's people think, what does that mean? Well, then it meant some poorly written web page. I have some bad web page up and I put a tarball, just a gzip tarball of source code. Was the release”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“NetLive, which has hundreds of Fortran routines that people had written in the 60s and the 70s and the 80s in Fortran 77, fortunately, it wasn't Fortran's 16s. It had been ported to Fortran 77. Unfortunate 77 is actually a really great language. Fortunate 90 probably is my favorite Fortran because it's got complex numbers, got arrays, and it's pretty high level. Now, the problem with it is you'd never want to write a program in Fortune 90 or Fortun 77, but it's totally fine to write a subroutine in. And then Fortran kind of got a little off course when they tried to compete with C++. But at the time, I just want libraries that do something like, oh, here's an order inference equation. Here's integration. Here's Runj cutta integration. Already done. I don't have to think about that algorithm. And you could, but it's nice to have somebody who's already done one and tested it. And so I sort of started this journey in 98, really. Look back at the mailing list, there's sort of this productive era of me.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Easily in C. Easily for me because I knew enough C. And then Guido had written a language. I mean, the hard part of extending Python was something called the way memory management works. And you have to reference counting. And so there's a tracking of reference counting. You have to do manually. And if you don't, you have memory leaks. And so that's hard. Plus then see, you know, it's much more, you have to put more effort into it. It's not just I have to now think about pointers and have to think about stuff that is different. I have to kind of, you're like putting a new cartridge in your brain. Like you're okay, I'm thinking about MRI. Now I'm thinking about programming. And there are distinct modules you end up having to think about. So it's harder. And when I was just in Python, I could just think about MRI and high-level writing. But I could do that. And that kind of, I liked it. I found that to be enjoyable and fun. And so I ended up, oh, well, let me just add a bunch of stuff to Python to do integration. Well, and the cool thing is that the power of the internet, just looking around and I found, oh, there's this.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Ambitious is the wrong word and eager and probably more time than since. I was a poor graduate student. My wife thinks I'm working on my PhD and I am. But part of a PhD that I loved was the fact that it's exploratory. You're not just taking orders, fulfilling a list of things to do. You're trying to figure out what to do. And so I thought, well, I'm writing tools for my own use and a PhD. So I'll just start this project. And so 99, 98 was when I first started to write. Which is a C. So I wrote C code to extend Python so that it paused in Python I could write things more easily. That combination kind of hooked me. It was the idea that I could, here's this powerful tool I can use as a scripting language and a high-level language to think about, but that I can extend easily”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, fantastic. So SciPy was effectively. Here I am using Python. To do stuff that I previously used MATLAB to use. And I was using numeric, which is an array library that made a lot of it possible. But there's things that were missing. Like I didn't have an ordinary differential equation solver, I could just call, right? I didn't have integration. I wanted to integrate this function. Okay, well, I don't have just a function I can call to do that. These are things I remember being critical things that I was missing. Optimization. I just want to pass a function to an optimizer and have it tell me what the optimum value is. Those are things like, well, why don't we just write a library that adds these tools? And I started to post on the mailing list. And there had previously been, you know, people have discussed. I remember Conrad Hinson saying, wouldn't it be great if we had this optimizer library or David Ash would say this stuff? And I'm, you know, I'm a...”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I think that's actually why. Probably. I mean, I like the complex numbers were there. I love that. The fact that I could write ND array constructs and that reduction was there. Very simple to write summations and broadcasting was there. I could do addition of whole arrays. So that was cool. Those are some things I loved”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“There's a J, there's a one J, right? And the fact they went the engineering route of J is interesting. I don't think that's entirely favoring engineers. I think it's because I is so often used as the index of a for loop.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Now, there are some warts there still. It wasn't perfect. There are some areas where I'm like, it would be better if this were different or if this were different. Some of those things got added to the language too. I was really grateful for some of the early pioneers in the Python ecosystem back because Python got written in 91 is when the first version came out. But Guido was very open to users. And one of the sets of users were people like Jim Huganen and David Asher and Paul Dubois and Conrad Hinson. These were people that were on the mailing list. And they were just asking for things like, hey, we really should have complex numbers in this language.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“That A time Somewhere readable. Then it was sort of I could take executable English And translate it to Python more easily. Like, I didn't have to go. There was no translation layer. As an engineer or as a scientist, I could think about what I wanted to do. And then the syntax wasn't that far behind it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“No, it isn't. And I've thought about that at such some length. I think definitely the fact that I could read it later. I could use it productively without becoming an expert. Other languages I had to put more effort into.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“NumPy is an inheritor of the traditions that I would say APLJ was another version that was what it did is not have the glyphs, just have short characters, but still a Latin keyboard could type them. And then numeric inherited from that in terms of let's add arrays plus broadcasting plus methods, reduction, even some of the language like rank is a concept that's in that was in Python is still in Python. Number of dimensions, right? That's different than, say, the rank of a matrix, which people think of as well. It came from that tradition, but NumPy is a very pragmatic, practical tool. NumPy inherited for numeric, and we can get to where NumPy came from, which is the current array. Least current as of 2016-2017. Now there's a ton of them over the past two or three years. We can get into that too.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Using these tools, you could construct, and then you start to think at that level, you think in n dimensions is something I like to say, and you start to think differently about data at that point. Really helps”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“The challenge of APL is APL had very dense not only glyphs, like new characters, new glyphs, but they even had a new keyboard because to produce those glyphs, this was back in the early days of computing when, you know, Querty keyboard maybe wasn't as established. We could have a new keyboard. No big deal. But it was a big deal and it didn't catch on. And the language APL, very much like Perl, if people would pride themselves on how much could they write the game of life in 30 characters of APL APL has characters that mean summation and they have adverbs, you know, they would have adjectives and these things called adverbs, which are like methods, like reduction would be an adverb on an ad operator, right?”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“They've gotten better, but there's still a whole culture of folks that doesn't programming. That's systems programming or web programming or lists and maps. And what about an n-dimensional array? Oh, yeah, that's just an implementation detail. Well, you can think that, but then actually, if you have that as a construct, you actually think differently. APL was the first language to understand that. It was in the 60s”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“MATLAB, you did a little bit more. Matt Lab, I did a lot before that, exactly. So I was comfortable in. Those three languages were really the tools I used during my studies and schooling. But to your point about language helping you think, one of the big things about MATLAB is it was and APL before it. I don't know if you're a, you remember APL. Nope. APL is actually the predecessor of array based programming, which I think is really an underappreciated. If I talk to people who are just steeped in computer programming, computer science, like most of the people that Microsoft has hired in the past, for example, Microsoft as a company generally did not understand Array-based programming. Culturally, they didn't understand it. So they kept missing the boat, kept missing the understanding of what this was.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“There's nuance to that statement, but it certainly is more accessible, so more people could actually, as a scientist, as somebody or an engineer who was trying to solve another problem besides point programming, I could still use this language and get things done and be happy about it. I was also comfortable in C. That”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Python was allowed you not to have to be an expert. You didn't have to take all this brain energy. You could leverage what I say, you could leverage your English language center. You're using all the time. I've wondered about other languages, particularly non Latin based languages. Latin-based languages with the characters are at least similar. I think people have an easier time, but I don't know what it's like to be a Japanese or a Chinese person trying to learn a different syntax. What would computer programming look like in that? I haven't looked at that at all, but it certainly doesn't, you know, leveraging your Chinese language center. I'm not sure Python or any program language does that. But that was a big deal. The fact that it was accessible, I could be a scientist. What I really liked is many programming languages really demand a lot of you. And you can get a lot.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, exactly. And it feels good, and it's really selective. It means you have to be an expert in Perl to understand it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Personally, like it was occasional annoyances, but I really like the fact that it didn't have all this extra characters, right? These extra characters didn't show up in my visual field when I was just trying to process understanding a snippet of code.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Open minded. It's a good question. I was open minded, so I was cognizant of the concern. And it definitely has specific challenges, you know, cut and pasting. For example, you're cut and pasting code and if your editors aren't supportive of that or you're putting into a terminal, and particularly in the past when terminals didn't necessarily have the intelligence to manage it now. Now iPython and Jupyter notebooks handle it just fine. There's really no problem. But in the past, it created some challenges, formatting challenges, also mixed tabs and spaces if editors weren't. You weren't clear on what was happening. You would have these issues. So there were really concrete reasons about it that I heard and understood. I never really encountered a problem with it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Now, you know, I'm not saying, hey, this may work. I like this. This is something I can retain without becoming an expert per se. And so that led me to go, I'm going to push more into this. And then 98 was kind of when I started to fall in love with Python, I would say.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And then I had the experience. I did some stuff in Python, and I was just doing my PhD. So I was out, my focus was on, I was actually doing a combination of MRI and ultrasound and looking at a phenomenon called elastography, which is you push waves into the body and observe those waves. Like you can actually measure them. And then you do mathematical inversion to see what the elasticity is. And so that's the problem I was solving is how to do that with both ultrasound and MRI. I needed some tool to do that with. So I was starting to do Python in 1977. In 1998, I went back, looked at what I'd written, and realized I could still understand it, which is not the experience I've had when doing Perl in 95, right? I'd done the same thing and then I look back and I forgot what it was even saying.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“As you build more and more and more abstractions, because I often think about, you know, we have a world that's built on these abstractions that were they the only ones possible? Certainly not, but they led to now it's very hard to do it differently Like there's an inertia that's very hard to push away from. That has implications for things like the Julia language, which you have heard of, I'm sure. And I've met the creators and I like Julia. It's a really cool language, but they've struggled to kind of Against just the tide of this inertia, people are using Python. And there's strategies to approach that. But nonetheless, it's a phenomenon. And sometimes, so I love complex numbers and I love to raise. So I looked at Python.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source