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

“Yeah, yeah. There's several comments there. We are working on something now called data-apis.org. Data-api.org. You can go there today. And it's our answer. It's my answer. It's not just me. It's me and Rolf and Athen and Aaron. And a lot of companies are helping us at QuanSite Labs. It's not unifying all the arrays. It's creating an API that is unified So we do care about this and are trying to work through it. I actually had the chance to go and meet with the TensorFlow team and the PyTorch team and talk to them after exiting Anaconda, just talking about, because the first year after leaving Anaconda in 2018, I became deeply aware of this and realized that, oh, this split in the array community that exists today makes what I was concerned about in 2005 pretty parochial.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, exactly. Well, the problem was, I didn't realize that the Platonic form has a lot of. Edges. They're like, oh, we should cut those out before we present it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“It's less work to make it better and to keep it maintained. And where that's impacted, things, for example, is the GPU. Like all of a sudden, GPU is starting added and we don't have them in NumPy. Like NumPy should just work on GPUs. The fact that we have to download a whole other object called Kupi to have arrays on GPUs is just an artifact. There's no fundamental reason for it”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“It's usability primarily. The cost isn't really efficiency. It's the fact that clumsy to create new types. It's hard to, and then one of the challenges you want to create new types. You want a quaternion type, or you want to add a new posit type, or you want to, so it's hard. Now, if we'd have done that well, when Numba came on the scene where we could actually compile Python code, it would integrate with that type system much cleaner. And now all of a sudden you could do gradual typing more easily. You could actually have Python when you add Numba plus better typing. Could actually be a, you'd smooth out a lot of rough edges in terms of the...”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Python 1, all classes, new objects were one. So if you as a user wrote a class, it was an instance of a single Python type called the class type. In Python 2, he used a metatyping hook to actually go, oh, we can extend this and have users write classes that are new types. So he was able to have your user classes be actual types. And the Python type system got a lot more rich. I barely understood that at the time that NumPy was written. And so I essentially in NumPy created a type system that was Python in one era. It was every detail is an instance of the same type as opposed to having new Dypes be really just Python types with additional metadata.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, what about these lower precision types, these larger precision types? So we had them in NumPy so that you could have a collection of them, but then have an object in Python that was one of them. And there's questions about, like, in retrospect, I wouldn't have created those of it and improved the type system, like made the type system actually a Python type system as opposed to currently it's a Python one level type system. I don't know if you know the difference between Python 1, Python 2. It's kind of technical, kind of depth, but Python 2, one of its big things that Guito did, it was really brilliant. It was he actually.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Good question. So, I think there's technical questions and social questions right there. First of all, I wrote NumPy as a service, and I spent a lot of time doing it, and then other people came help make it happen. Numpy succeeded because the work of a lot of people understand that I'm grateful for the opportunity, the role I had I could play, and I'm grateful that things I did had an impact, but they only had the impact they had because the other people that came to the story. And so they were essential. The way data types were handled, the way data types, we had array scalars, for example, that are really just a substitute for a type concept. So we had array scalars or actual Python objects so that there's for every for a 32-bit float or a 16-bit float or a 16-bit integer. Python doesn't have a natural, it just has one integer, as one float.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And I hold him in esteem. I'm grateful for him. I think still exists. They're doing great work. Helping scientists, they still run the SciPy conference. The, they have an R&D platform. They're selling now that's a tool that you can go get today, right? So Nthought has played a role in the SciPy in supporting the community around SciPy, I would say. They ended up not being able to build a tool suite to write GUI applications. Like that's where they could actually make that the business could work. And so supporting SciPy and NumPy itself wasn't as possible. They tried. I mean, it was not just because it was just because the business aspect. And then I wanted to build a company that could do, that could get venture funding, right? Better for worse. I mean, that's a longer story. We could talk a lot about that.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“It didn't end great. Unfortunately, Eric and I aren't friends now. I still respect him. I wish we were. He didn't like the fact that Peter and I started Anaconda, right? That was not, I mean, so there's two sides of that story, so I'm not going to go into it, right?”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I agree. There'd be things to do. I've thought about that and really had thought about a couple of books or a couple of things that could be done there. And I just haven't, right? I try to hire a ghostwriter this week this year too to see if effect would help, but it didn't. Part of my problem is I've been so excited by a number of things that stems in from that. So I came here, worked at Nthot for four years, graciously, you know, Eric made me president and we started to work closely together. We actually helped him buy out his partner.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Needing the stuff in the book, right? And so they kind of ask, hey, can we just use the stuff in your book? And at that point, said, yeah, I'll just open it up. So, but it has served its purpose. And the money that I made actually funded my grad student. Like it was actually, you know, I paid him $25,000 a year out of that money.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“The fact that PayPal existed and had a way to get the money, the distribution was simple. This is pre-Amazon book stuff. So it was just published in a website. It was the popularity of sci-fi emerging and getting company usage. I ended up not letting it go the five years and not trying to make the full amount because A year and a half later, I was at Enthon. I had left academia as at Enthought and I kind of had a full time job. And then actually, what happened is the documentation people, there's a group that said, hey, we want to do documentation for SciPy as a collective. And they were essentially.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I released it on this. And it's actually interesting because one of the people who also thought that was interesting ended up being Chris White, who was the director of DARPA project that we got funding through at Anaconda. And the reason he even called us back is because he remembered my name from this book and he thought that was interesting. And so even though we hadn't gone to the demo days, we applied and the people said, yeah, nobody ever gets this without a coming to the demo day first. This is the first time I've seen it. But it's because I knew Chris had done this and had this interaction. So it did have impact. I was actually really, really pleased by the result. I mean, I ended up in three years. I made 90,000. So sold 30,000 copies by myself. I just put it up on, you know, use PayPal and sold it. And those are my first taste of kind of, okay, this can work to some degree. And all over the world, right? From Germany to Japan to, it was actually, it did work. And so I appreciated the.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Kind of my ideas around IP law and stuff. I love the idea you can share something and you can spread it. The fact that you have a thing and copying is free. But the creation is not free. So, how do you fund the creation and allow the copying? And in software, it's a little more complicated than that because creation is actually a continuous thing. It's not like you build a widget and it's done. It's sort of a process of emerging and continuing to create. But I wrote the book and had this market determined price thing. I said, look, I think I said $250,000. If I make $250,000 from this book, I'll make it free. So as soon as I get that much money, or I said five years, right? So there's like time limit. That's forever. That's really cool. I didn't know this.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“No. Just kidding. No, I haven't really had any interaction with him personally. Like I said, but there were a few, but actually surprisingly not. There was actually a lot of people like, no, it's fine. You know, you can charge for a book. That's no big deal. We know that's a way you can try to make money around open source. So what I did, I did an interesting way. I said, well,”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And so a lot of people ended up, but I said, look, I need to, so I'm going to charge for it. And I got some flack for that, not that much, just probably five angry messages people yelling at me saying I was bad guy for charging for this book.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And it was not a, I mean, it's not a page turner. It's not a book you pick up and go, oh, this is great over the fire. But it's where you could find the details, like how did all this work?”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So NumPy was just emerging. One of the things I've done, it's worth mentioning because it emphasizes the exploratory nature of my thinking at the time. I said, well, I don't know how to fund this thing. I've got a graduate student I'm paying for and I've got no funding for him. And I had done some fundraising from the public to try to get public fundraising for my lab. I didn't really want to go out and just do the fundraising circuit the way it's traditionally done. So I wrote a book and I said, I'm going to write a book and I'm going to charge for it. It was called Guide to NumPy. And so ultimately Numpie became documentation driven development because I basically wrote the book and made sure the stuff worked or the book would work. So it really helped actually make NumPy become a thing. So writing that book.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Capital markets and company, because again, I fell in love with the notion, oh, profit seeking on its own is not a bad thing. It's actually a coordination mechanism for allocating resources that in an emergent way, right? That respects everybody's opinions, right? So this is actually powerful. So I say all the time when I make a company and we do something that makes profit, what we're saying is, hey, we're collecting the world's resources and voluntarily people are asking us to do something they like. And that's a huge deal. And so I really like that energy. So that's what I came to do and to learn and to try to figure out. And that's what I've been kind of stumbling through for the past 14 years.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Translational dance has been lost a bit. And there's a lot of reasons for that. I'm certainly not an expert on this stuff. I can opine like anybody else. Realized that I wanted to explore entrepreneurship, which I know, and really figure out. And it's been a driving passion for 20 years, 20, 25 years, how do we connect?”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Sort of, it's my entrepreneur world, right? I left academia and went to entrepreneur world in 2007. So I moved here in 2007, kind of took a leap, knew nothing really about business, knew nothing about a lot of stuff there. For a long time, I've kept some connections to a lot of academics because I still value it. I still love the scientific tradition. I still value the essence and the soul and the heart of what is possible. Don't like. A lot of the administration and the kind of, we can go into detail about why and where and how this happens, what are the challenges.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Value broadly physically freedom, but I also understand the power of communities, like the power of collective behavior. And so what's that balance, right? That makes sense. By the time I was just, I got to go out and explore this entrepreneur world. So I left academia. I said, no thanks, called my friend Eric, here his company was going. I said, hey, could I join you and start this trend? And at that time, they were using SciPy a lot. They were trying to get clients. And so I came down to Texas. And in Texas where I...”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I agree there's a lot of stories here that are kind of during this journey because this is sort of the start of this journey in 2005 2006 so My 10-year committee, I applied for tenure in 2006, 2007, it came back. I split the department. I was very polarizing. I had some huge fans and then some people said, no way. Right. So I was a polarizing figure in the department. It went all the way up to the university president. Ultimately, my department chair had the sway. And they didn't say no. They said, come back in two years and do it again. And I went, at that point, I was like, I had this interest in entrepreneurship, this interest in not the academic circles, not the like, how do we make industry work? So I do have to give credit to that exploration of economics because that led me, oh, I had a lot of opinions. I was actually very libertarian at the time. And I still have some libertarian trends, but I'm more of a, I'm more of a collectivist libertarian.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“You need some support. People need support. And I needed just encouragement. And they were helping encouraged by contributing. And once the big thing for me was when John Hunter. He had previously done kind of a simple thing called numerics to kind of between numeric and numerate. He had a little high level tool that would just select each one. Matplotlib. In 2006, he finally said, we're going to just make NumPy the dependency of Matplotlib. As soon as he did that, and I remember specifically when he did that, I said this, okay, we've done it, that was when I knew he had a success. Before then, it was still unsure. But that kind of sort of roller coaster. And then 2006 to 2009. And then I've been floored by what it's done. Like, I knew it would help. I had no idea how much it would help.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“West are to use that almost like a data frame, except it's an array of records, and data frame the challenge is, okay, if you want to augment it at another column, you have to insert, you have to do all this memory movement to insert a column. Whereas data frames became, oh, I'm going to have a loose collection of arrays. So it's a record of arrays that is the heart of a data frame. And we thought about that back in the memory days, but Wes ended up doing the work to build it. And then also the operations that were relevant for data processing. What I noticed is just that each of these little things creates just another tick, another up. So NumPy ultimately took a little while, about six months in, people started joining me. Francesca, Alted, Robert Kern, Charles Harris. And these people are many of the unsung heroes, I would say, people who are, you know, they don't, they sometimes don't get the credit they deserve because they were critical both to support, like, you know, it's hard and you want.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Worried about the credit, you're not worried about where you're going to get, you're worried about. I later realized that I have to worry a little bit about credit, not because I want the credit, because I want people to understand what led to the results. It's not about me. It's I want to understand this is what led to the result. So I think doing, and this is what had no impact on the result. Let's promote this, just like you said, I want to promote the attributes that help make us better off. How do we make more of Wes McKinney? Like Wes McKinney was critical to the success of Python because of his creation of pandas, which is the roots of that were all the way back and numerae and numpy where NumPy created an array of records.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Python, I mean, the sci-fi community particularly, like I said, we wanted to build this big thing, but ultimately we didn't. What happened is we had Mavericks and champions like John Hunt who created Matt Plotlib. We had Fernando Perez who created iPython. And so we sort of inspired each other of this selfless, the stewardship mentality as opposed to ownership mentality, but stewardship and community focused. Community focused but intentional work, like not waiting for everybody else to do the work, but you're doing it for the benefit of others and not worried about what you're going to get.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, right. Exactly. It's a good question. How do we teach this? How do we encourage it? How do we lift it? Because so much of it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“In the United Passion that you'll do it. It can't be just like a perfunctory. Oh, yes, I'll listen to you, and then I'm not really that excited about it. So it really is an aspect. It's a philosophical, like there's a filia, there's a love of esteeming of others that's actually at the heart of what it's sort of a life philosophy for me, right? That I'm constantly pursuing. And that helped, absolutely helped.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And BIPY.array or something that turned out to be problematic because Numeric already had a little mini library of linear algebra and some functions. And it had enough momentum, enough users that nobody wanted to, they wanted backward compatibility. One of the big challenges of NumPy was I had to be backward compatible with both numeric and numerae in order to allow both of those communities to come together. There was a ton of work in creating that backward compatibility that also created echoes in today's object. Like some of the complexity in today's object is actually from that goal of backward compatibility with these other communities, which if you didn't have that, you'd do something different, which is instructive because a lot of things are there. What is that there for? It's like, well, it's a remnant. It's an artifact of its historical existence.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So it had an associated library of math operations. And effectively SciPy became an even larger set of math operations. So the key for me was I was going to write NumPy and then move SciPy to depend on NumPy. In fact, early on, one of the initial proposals was that we would just write SciPy and it would have the numeric object inside of it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“That created challenges for later. And I didn't know it at the time, didn't understand how important that was. And in many cases, didn't know what to do. There was pieces of design of NumPy. I didn't know what to do until five years ago. Now I know what they should have been, but I didn't know at the time and I couldn't get the help. Anyway, so I wrote it. It took about four months to write the first version, then about 14 months to make it usable. But it was that first four months of intense writing, coding, getting something out the door that worked. It was definitely challenging. And then the big thing I did was create a new type object called D-type. That was probably the contribution. And then the fact that I added not just broadcasting, but advanced indexing.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Because that is important because you can get over your skis You can definitely get over your skis. And in fact, this almost got me over my skis, right? I would say, well, in retrospect, I hate looking back. I can tell you all the flaws with NumPy, right? We want to go into it. There's lots of stuff that I'm like, oh man, that's embarrassing. That was wrong. I wish I had somebody slop me with a wet fish there. Like I needed, like what I'd wished I'd had was somebody. With more experience, and certainly library writing and array library. I wish I had me. I could go back in time and go do this, do that. There's a Morton Bean. Because there's things we did that are still there, that are problematic.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Sometimes my wife hears these stories and she's like, You did what? I thought we were going to, I thought you were actually on a path to make sure we had resources and money. But again, there's an aspect I'm very hopeful person. I'm an optimistic person by nature. I love people. I learned that about myself later on. Part of my religious beliefs actually lead to that. And it's why I hold them dear, because it's actually how I feel about what leads me to these attitudes, sort of this hopefulness and this sense of, yeah, it may not work out for me financially. But that's not the ultimate gain. That's a thing, but it's not the scorecard for me. And so I just wanted to be helpful and I knew, and partly because these sci-pi conferences, because the mailing list conversations, I knew there was a lot of need for this, right? And so I had this, it wasn't like I was alone in terms of no feedback. I had these people who knew, but it was crazy. Like people who at the time said, yeah, we didn't think you'd be able to do it. We thought it was crazy.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“It is possible, especially in the early days, the longer it goes, the harder, right? And the more energy in the factions, the harder. But in the early days, it is possible. And it's extremely helpful. And there's a willingness there. But the challenge is there's usually not a willingness to fund it. Not a willingness to walk into a field saying, I'm going to do this. And here I am. I have five kids at home now.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“This is Paul de Bois, David Asher, Conrad Hinson, and myself. I got credit because I wrote this chapter, which is all the CAPI of numeric, like all the C stuff. So I said, probably the one to do it. Nobody else is going to do this. So it's sort of out of a sense of duty and passion. Knowing that I don't think my academic, I don't think the department here is going to appreciate this, but it's the right thing to do.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“There, yeah, it was very, they were very focused. And so Is not great. And so I happened, you know, fate, I had a class I had signed up for. I was trying to build an MRI system. So I had a kind of a set of a digital radio class as a digital MRI class. And I had people sign up. Two people signed up. Then they dropped. And so I had nobody in this class. And I didn't have any other courses to teach. And I thought, oh, I've got some time. And I'll just write a merger of numeric number. Like I'll basically take the numeric code base at the features numeray was adding and then kind of come up with a single array library that everybody can use. So that's where NumPy came from was my thinking, hey, I can do this. And who else is going to? Because at that point, I'd been around the community long enough and I'd written enough C code. I knew the structures. And in fact, my first contribution in America had been writing the CAPI documentation that went in the first documentation for NumPy, for numeric, sorry.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I got really like, oh man, this is not good. We're not cooperating now. We're sort of redoing each other's work and we're just this young community. So that's what led me, even though I knew it was risky because I was on a tenure track position. 2004 I got reviewed. They said, hey, things are going okay. You're doing well. Paper's coming out. But you're kind of spending a lot of time in this open source stuff. Maybe do a little less of that and a little more of the paperwriting and grant writing, which is naive, but it was definitely the time, you know, the thinking.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I knew what it was because it was used in segmentation a lot. And in fact, I had wanted to do something like that in Python, in sci-fi, but just had never gotten around to it. So when it came out that it worked only on a numeray, and sci-py needed numeric, and so we effectively had the beginning of this split. Numeric enumerate didn't share data, they were just two. So you could have a gigabyte of numeric numerae data and gigabyte of numeric data and they wouldn't share it. And so you have these Of these scientific libraries written on top, I got really bugged by that.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Started working on a replacement to numeric called numeray. In 2004, a package called NDIMIG. It was an image processing library. That was written for Numaray. And it had in it a morphology tool. I don't know if you know about morphology is. It's open, dilations. There's sort of this, as a medical imaging.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Want to do this, but numeric actually has some challenges in terms of the array doesn't have enough types. We need more operations, you know, broadcasting to be a little more settled. They wanted record arrays. They wanted record arrays are like a data frame, but a little bit different. But they want a more structured data. So he had called me even early on then and they said, Way, would you want to work on something to make this work? And I said, yeah, I'm interested, but I'm going here. And we'll see if I have time. So in the meantime, while I was teaching and sci-fi was emerging, and I had a student, I was constantly while I was teaching trying to figure out a way to fund this stuff. So I had a graduate student, my only graduate student, a Chinese fellow Lu Hongze is his name, great guy. He wrote a bunch of stuff for iterative linear algebra, like got into writing some of the iterative linear algebras tools that are currently there in sci-fi and they've gotten better since, but this is in 2005, kept working on sci-fi. But Perry has”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Nothing I had gotten anywhere. And I realized, okay, this is not working. So I put away the transparencies and I turned around and just started using the chalkboard. And what it did is it slowed me down. The chalkboard just slowed me down and gave people time to process and to think and then that me made me focus. My writing wasn't great on their chalkboard, but I really loved that part of like the teaching. So that entered sci-fi's world in terms of we always understood that there's a didactic aspect of sci-fi, kind of how do you take the knowledge and then produce it. The challenge we had was the scope. Like ultimately sci-fi was everything, right? And so 2001 when it first came out, people were starting to use it. No, this is cool. This is a tool we actually use. At the same time, 2001 time frame, there was a little bit of like the Hubble Space Telescope, the folks at Hubble had started to say, hey, Python, we're going to use Python for processing images from Hubble. And so Perry Greenfield was a good friend and running that program. And he had called me before I left BOU and said, you know,”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Where do teachers do that? And I agree. And that was kind of what was inspiring me, but you also have to. I cannot say I was articulate of some of the greatest teachers. I was, you know, like one classic example, when I first taught at BYU, my very first class, it was overheads, transparency is overheads. Before projectors were really that common. So transparency is I'm writing my notes out. I go in, room's half dark, I just blaring through these transparencies. Here it is, here it is, here it is. And I gave a quiz after two weeks. Nowhere knew anything.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I would speak too high of a level. Like, I definitely had a calibration problem coming out of graduate work. Where I hate to be condescending to people. Like, I really have a ton of respect for people fundamentally. My fundamental thing is I respect people. Sometimes that can lead to a, I was thinking they had more knowledge than they did. And so I would just speak at a very high level.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Sipo is fairly niche. We stayed connected all while I was a student, sorry, a professor. I went to BYU and started to teach electrical engineering, all the applied math courses. I loved teaching single processing, probability theory, electromagnetism. If you look at Rate, my professor, which my kids love to do, I wasn't, I got some bad reviews because people.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Document is essential. Yeah. So that was actually a so we thought about several things. One is we wanted plotting, we wanted interactive environment. We wanted good documentation. These are things we knew we wanted. The reality is those took about. 10 years to evolve given the fact that we didn't have a big budget, it was all volunteer labor. It was sort of When Nthot got created and they started to try to find projects, people would pay for pieces, and they were able to fund some of it. Not nearly enough to keep up with what was necessary. And no criticism, just simply the reality. I mean, it's hard to start a business and then do consulting and then also promote an open source project that's still fairly new.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I would say so. I would think it's basically accessibility to scientists. Like, give them scientists and engineers tools that they don't have to think a lot about programming. So give them really good building blocks. Give them functions that they want to call. And sort of just the right length of spelling. There's one tradition in programming where it's like, you know, make very, very long names. Right. And you can see it in some programming languages where the names get tick half the screen. In the Fortune world, characters had to be six letters early on, right? And that's way too much, too little. But I was like, I liked to have names that were informative but short.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Wrong more than right, exactly. And so I'm like, oh, this is stuff I would read a ton about early on. So I don't, I feel like I'm with you. Like I want the same thing. I want to be able to, and honestly, not for personally, I've been happy. I've been happy. I feel like I don't have any. I mean, we've been done reasonably okay, but I've had to pursue it. Like, that's really what started my. Trajectory from academia. Is reading that stuff led me, oh, entrepreneurship matters. I love software, but we need more entrepreneurs, and I want to understand that better. So once I kind of had that virus infect my brain, even though I was on a trajectory to go to a tenure track position at a university and I was there for six years, I was kind of already out the door when I started. And we can get into that.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Mechanism. I totally agree. I would love to talk about some of the ideas I have because I actually came across, I think I've come up with some interesting notions that could work, but they'll require anything that will work takes time to emerge. Things don't just turn overnight. That's definitely one thing I've also understood and learned any fixes, that's why it's kind of funny. We often give credit to, oh, this president gets elected. And oh, look how great things have done. And I saw that when I had a transition at Anaconda, when a new CEO came in, right? And it's like the success that's happening, there's an inertia there.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I went in eyes wide open. Like, I knew that there were problems with giving stuff away and creating the market externalities, the fact that, yeah, people might use it and I might not get paid for it and I'll have to figure something else out to get paid. At least I can say I'm not bitter that a lot of people have used stuff that I've written and I haven't necessarily benefited economically from it. I've heard other people be bitter about that when they write or they talk. Like, oh, I should have got more value out of this. And I'm also, I want to create systems that let people like me who might have these desires to do things, let them benefit. So it actually creates more of the same.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source