Elixir meets machine learning

Today we’re sharing a special crossover episode from The Changelog podcast https://changelog.com/podcast here on Practical AI. Recently, Daniel Whitenack joined Jerod Santo to talk with José Valim, Elixir creator, about Numerical Elixir. This is José’s newest project that’s bringing Elixir into the world of machine learning. They discuss why José chose this as his next direction, the team’s layered approach, influences and collaborators on this effort, and their awesome collaborative notebook that’s built on Phoenix LiveView. Sponsors: O'Reilly Media https://www.oreilly.com/changelog – Learn by doing — Python, data, AI, machine learning, Kubernetes, Docker, and more. Just open your browser and dive in. Learn more and keep your teams’ skills sharp at oreilly.com/changelog https://www.oreilly.com/changelogSnowplow Analytics https://snowplowanalytics.com/ – The behavioral data management platform powering your data journey. Capture and process high-quality behavioral data from all your platforms and products and deliver that data to your cloud destination of choice. Get started and experience Snowplow data for yourself at snowplowanalytics.com https://snowplowanalytics.comChangelog++ https://changelog.com/++ – You love our content and you want to take it to the next level by showing your support. We’ll take you closer to the metal with no ads, extended episodes, outtakes, bonus content, a deep discount in our merch store (soon), and more to come. Let’s do this https://changelog.com/++! Fastly https://www.fastly.com/?utm_source=changelog&utm_medium=podcast&utm_campaign=changelog-sponsorship – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com https://www.fastly.com/?utm_source=changelog&utm_medium=podcast&utm_campaign=changelog-sponsorship. Featuring: José Valim – Website https://dashbit.co, GitHub https://github.com/josevalim, X https://x.com/josevalimJerod Santo – GitHub https://github.com/jerodsanto, LinkedIn https://www.linkedin.com/in/jerodsanto, Mastodon https://changelog.social/@jerod, X https://x.com/jerodsantoDaniel Whitenack – Website https://www.datadan.io/, GitHub https://github.com/dwhitena, X https://x.com/dwhitenaShow Notes: Nx (Numerical Elixir) is now publicly available https://dashbit.co/blog/nx-numerical-elixir-is-now-publicly-availableAxon https://github.com/elixir-nx/axon/Livebook https://github.com/elixir-nx/livebookJax https://github.com/google/jaxLivebook demo on YouTube https://www.youtube.com/watch?v=RKvqc-UEe34Erlang Ecosystem Foundation https://erlef.orgBooks “Genetic Algorithms in Elixir” by Sean Moriarity https://www.amazon.com/dp/168050794XUpcoming Events: Register for upcoming webinars here https://practicalai.fm/webinars!

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:02Speaker 00

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• 0:21Speaker 00

  This episode is brought to you by our friends at O'Reilly. Many even know O'Reilly for their animal textbooks and their conferences, but you may not know they have an online learning platform as well. The platform has all their books, all their videos, and all their conference talks. Plus, you can learn by doing with live, online training courses and virtual conferences, certification practice exams, and interactive sandboxes and scenarios to practice coding alongside what you're learning. They cover a ton of technology topics: machine learning, AI, programming languages, DevOps, data science, cloud, containers, security, and even soft skills like business management and presentation skills. You name it, it is all in there. If you need to keep your team or yourself up to speed on their tech skills,

• 1:05Speaker 00

  then check out O'Reilly's online learning platform. Learn more and keep your team's skills sharp at O'Reilly.com/changelog. Again, O'Reilly.com/changelog.

• 1:23Speaker 00

  What's up? This is Adam Stokoviac, editor in chief here at Changelog, and you are listening to Practically I, a weekly podcast that makes artificial intelligence practical, productive, and accessible to everyone. This is where conversations around AI, machine learning, and data science happen. And today, we have a special episode, a crossover episode from the Changelog Podcast for you. On a recent episode, Daniel White-Nag joined Jared Santo talking with Jose Valim, Elixir creator, about numerical Elixir. This is Jose's newest project that's bringing Elixir into the world of machine learning. They discuss why Jose chose this as his next direction, the team's layered approach, influences and collaborators on this effort, and their awesome collaborative notebook that's built on Phoenix Live.

• 2:15Speaker 01

  All right, I'm joined by Jose Valim, creator of Elixir and frequent guest on the changelog. I think this is your fourth time on the show. Welcome back.

• 2:23Speaker 02

  Thank you. Thanks for having me again.

• 2:24Speaker 01

  Excited to have you. Lots of interesting stuff going on in your neck of the woods. And I'm also joined by, hey, that's not Adam. That is Practical AI co-host Daniel Whitenack. What's up?

• 2:33Daniel Whitenack?

  Practical Al.

• 2:34Speaker 01

  Yeah.

• 2:36Daniel Whitenack?

  Yeah. Practical AI, sometimes it looks like with the font on Zoom, it looks like Practical Al.

• 2:42Speaker 01

  Yes.

• 2:42Daniel Whitenack?

  So when we record on our podcast, normally I'm known as Practical Al.

• 2:47Speaker 01

  Well, welcome to the show. I'd have a Tool Time reference, you know, you'd be my Al Bundy for the show, but that would be too old for most people to get that one.

• 2:55Daniel Whitenack?

  Yeah.

• 2:56Speaker 01

  And I would say you can be my Adam, I'll be your Chris Benson, and we'll co-host this sucker. How about that?

• 3:00Daniel Whitenack?

  That sounds wonderful. I'm excited to be here.

• 3:03Speaker 01

  Well, I had to call in the big guns because I know very little about this space. In fact, almost everything I know about the world of artificial intelligence, I learned from producing Practical AI and by listening to Practical Al do his thing each and every week. So that's why Daniel is here. I do know a thing or two about Elixir, but nowhere near as much as Jose. And here we're at the intersection of those two worlds. So kind of an exciting time. And we're here first to talk about NX. So Jose, what is this NX thing here? Here, tell us about.

• 3:37Speaker 02

  All right. So NX stands for Numerical Elixir. And back in November last year, we started working on this. I can tell more about the story later. But the important thing is that in February, we finally unveiled NX, which is a library, but also this idea of a collection of libraries of to improve Elixir. So we can start doing machine learning, data science, numerical computing, and so on. So I'll just give an overview of what we have out so far. So everybody's on the same page, and then we'll expand on that. So we start with NX,

• 4:18Speaker 02

  which is the it's a library itself, is the idea in the library itself. And the main abstraction in NX, as we would expect, is multi-dimensional tensors. So you can do, when I announced the next, one of the things that I did was that I gave a talk. And in this talk, I built a MNIST classifier, new network classifier for the MNIST dataset from scratch, just using an X. And so, you know, you can work with multi-dimensional arrays, tensors. And for those who are not familiar, why multi-dimensional arrays and tensors? One simple example I like to give is like, for example, if you take an image, if you need to represent that image,

• 5:00Speaker 02

  if you need a data structure to represent that image, you can represent that with tensor. And it's going to be a three-dimensional tensor, where one of the dimensions is going to be the height, the other is going to be the width, and then the third dimension is for the channels, like RGB and so on. So, and then, you know, if you can represent the data like this, you're going to send this tensor for data networks and for neural networks. And at the end, it's going to tell, hey, is this a dog or a cat? Or more complex things. So that's where we started, that was the first building block that we built. And one of the things that people ask a lot is that, you know, like Elixir is a functional programming language. And functional programming language, they promote a lot of immutability.

• 5:45Speaker 02

  They promote immutability a lot, which means like, if you have a multi-dimensional tensor, like, you know, if you have a 50 megabytes image and you need to do something with it, you need to transform this image, each transformation that you do is going to copy the whole image in memory, you know, and do a new copy. So you are allocated like 50 megabytes every step along the way. So to solve this, what we did, and this is an idea that we've seen elsewhere. For example, in the Python community, we have Jacks. So a lot of inspirations in the next come from Jacks. So the way we solve this in an X is that we have this thing called numerical definitions. And what numerical definitions are, is that they are a subset of Elixir

• 6:29Speaker 02

  that can compile and is guaranteed to run on the GPU. And that's how we can have, you know, numerical computing Elixir, machine learning and neural networks, because we can effectively look at your Elixir code and say, hey, I'm going to get all of these, compile it, run on the GPU, and it's going to be really, really fast. So those are the two building blocks. We can come back to this and talk about, a lot about those things later. And then we released two bindings for an X. So one is The XLA, the XLA is a binding for the Google XLA, which stands for Accelerated Linear Algebra. So if we're using TensorFlow, what is running the things in TensorFlow is Google XLA. So they're using Google XLA to compile it to run on the GPU, to run on the CPU as efficiently as possible.

• 7:14Speaker 02

  So we have bindings for that. We are also now working on bindings for PyTorch, to be more precise, LibTorch. So PyTorch for Facebook, they have the LibTorch, which is the C library. We are wrapping that as well. And two months later, so that was in February, we released two other libraries. So one is Axon. So we started with the building block, which was tensors, multi-dimensional arrays, numerical definitions. So we released Axon, which is a high-level library for building neural networks. And we just announced Live Book 2, which is interactive and collaborative code notebooks for Elixir. So that's kind of what we have released in the last two months. And it's just the beginning. There's still a lot of things we want to do,

• 7:58Speaker 02

  but we're really starting on working on this ecosystem and build it up.

• 8:02Daniel Whitenack?

  So Jose, I'm curious from the AI perspective, and I'm going to have to admit for listeners that I know almost nothing about Elixir, except what I've learned on the Changelog podcast from you in previous episodes. So I'm curious, like, from the community standpoint, what was really driving your motivation to spend so much time on these things? And I see, like, and we can dig into the individual components, but like you're saying, that like the main components that I think could make this very functional, it sounds like, are there and are being built. But from the community standpoint, were people requesting this? Were people trying to sort of roll their own sort of neural network stuff in Elixir? From your perspective, what sort of led up to that side of things?

• 8:47Speaker 02

  Okay, that's a great question. And to give some context, so one of the things, like, going way, way back, it always started because of like the Erling-Virtue machine. The only reason that Elixir as a programming language exists is because of the Erling-Virtue machine. And the Erling-Virtue machine was built by Ericsson, which is a telecommunications company for building like concurrent, distributed, and fault-tolerant software. I'm not going to expand on that. Like, you can check Elixir on the website. But all these, like, Steams for my love for their virtual machine. So when I created Elixir, I was like, I want to have as many people as possible building on this platform because I love it. And I think other people, they are really going to love it and enjoy it too.

• 9:30Speaker 02

  So I've created Elixir and I've always thought, I think like in terms of like programming languages, I really think like that Python is a really stellar example of like tackling a bunch of different problems. So I always had in mind that, you know, I want that for Elixir and for their virtual machine, for their ecosystem. I think we can grow diverse to solve all the different kinds of problems. So I come from a web background. I was a member of the Rayoscar team almost like a life ago. When I started with Elixir, I had this like obvious web background and that was the first like, let's say dimension that Elixir took off with the Phoenix Web Framework.

• 10:12Speaker 02

  So people started using Elixir more and more for the web. Elixir was already a good natural fit for building distributed systems or anything regarding the network due to the Erlang heritage. But it was like, I always want to try to expand this. And the first time I expanded this was back in 2016, we released abstractions for like data pipelines and data ingestion. So if you need to consume like kills and you need to do that very efficiently, we released libraries for that and that brought Elixir to a new domain, which was like data processing. And there are like some very nice use cases on our website. For example, how change.org, for example, is using data abstractions that we wrote

• 10:54Speaker 02

  back then to process, you know, like because when somebody, if you have a petition that one million people signed, you need to send them an update. Now you have to send an email to a million people. How are you going to do that? So we started that segment and then the community started to grow. So people start bringing Elixir in the Erlang virtual machine for embedded. So there is the NERX framework. People started bringing that to AutoVid streaming. And then there's always the question like, you know, why not numerical computing? Why not machine learning? So I always had this interest, like, you know, I, I feel like it's part of my responsibility, part of my job to try to broaden the domains and the areas of the language. The community is also doing that a lot for a bunch of areas. But you know, if there's something where I feel like, hey, this is a good opportunity,

• 11:39Speaker 02

  we can do it, then why not? Then let's do it. And this always started, just to finish giving more context, when Prag Prog, I always had this interest. Actually, like, my thesis, my master thesis was in test classification. And I use in test classification, but that was like 11 years ago. So, you know, like, there was deep learning, we're not talking about deep learning at the time yet. I think everything was still support vector machines or kind of state of the art. But I never fall back, but I always had this interest. So in October last year, Prag Prog announced a book, which is genetic algorithms for Elixir. And then I was like, hey, apparently there's somebody who knows things about AI and machine

• 12:22Speaker 02

  learning in the Elixir community, and he's shown, shown morality. I sent him an email and I was like, hey, I think the platform could be good for us to do everything in machine learning. He said like, I agree, uh, let's work on it. And we started working on it. So it was basically, you know, like, it's kind of like, you know, why not? If we can make it happen, let's make it happen. And, uh, we will try to figure out how we are going to, you know, let's build this. And then later we will continue working on how to package and how to sell this to people and say like, hey, what are the benefits of having those two words like joining together and working together?

• 12:57Speaker 01

  So if we stay big picture, but we do a bit of a comparison, trying to understand exactly your aim here. If I was a happy NumPy slash PyTorch like that, you know, Python data scientist kind of a person, are you hoping that maybe someday the NX-based and Elixir-based tooling would draw me over to Elixir? Are there aspects of it that's, it's gonna be well positioned better than Python? Or are you more just saying, well, let's bring this area of computing to existing Elixirists and hope to, you know, give them more tools? Or are you also thinking from the other direction?

• 13:35Speaker 02

  Honestly, I never tried to look at it that much ahead. So for me, like, my goal right now is that, for example, If imagine that you're building an application Elixir and then you need to resort to do something with machine learning or data science or like, oh, I need to go to Python to solve this problem.

• 13:53Speaker 01

  Right.

• 13:53Speaker 02

  If we have a tooling, so you don't have to go there and you can stay within the community, I would already consider that a tremendous victory, just because that was not an option in the past. So if people there are starting to make this choice, I would already be very, very happy. And I would be like, you know, like, mission accomplished.

• 14:14Speaker 01

  Gotcha.

• 14:14Speaker 02

  And then we'll see big steps.

• 14:16Speaker 01

  Hmm. Daniel, what tools do you use in your day to day work?

• 14:20Daniel Whitenack?

  Yeah, I like the framing of how you just framed it, Jose, because actually my toolset, my team's toolset, we develop models in Python using TensorFlow and PyTorch. But typically, in terms of the products that we're building or, you know, what we're developing, we're developing either API servers or, you know, something. For the most part, we're doing that in Go. So a lot of times what happens is exactly what you were saying. So we're happy writing our API handlers in Go and everything's nice and wonderful. And then we basically just have to call into Python to do an inference potentially.

• 15:05Daniel Whitenack?

  Now, there is new stuff coming onto the scene in the Go community as well to try to support that same sort of workflow where, like, I would love to not do that. Like, if I was working in Go and I didn't have to call into Python, that would be super cool. And I think that's still developing. So I totally get what you're saying. Like, if you're working in Elixir, then it would be great for those developers to not have to do this sort of awkward call into Python for inferencing. It's awkward and always managing that and monitoring it and all of that is sort of dicey. Also, though, I think that there is this sense in the Python community,

• 15:44Daniel Whitenack?

  or well, I'll say the AI community, that Python sort of consumes this whole world, but I don't think necessarily. necessarily out of like particularly one good reason why it should consume that whole world. Because it's kind of like all these scientists or like grad students working on computational science and working on AI. They're like, well, all our stuff that our advisor wrote is in FORTRAN. I don't want to like write FORTRAN, so I'm going to write this Python stuff that wraps around my FORTRAN. And then like people just start writing Python a lot because it's like pretty easy to get into. And they so they do all their scripting in that. And eventually like the science world just sort of

• 16:26Daniel Whitenack?

  started latching into Python and building things there. I don't think it's necessarily like the best tools for AI will be built using Python. Actually, I think like a lot of my frustrations in life are because of, you know, working in Python. And I'm not trying to bash that because it's it's also great, like you're saying. I think there is an opportunity for both sides of things. I guess is what I'm getting at.

• 16:50Speaker 01

  That's interesting to hear that. Jose, one of the things that you did with Elixir, which I appreciated, and I think a lot of people appreciated because you got a lot of people loving and using the language, right? Is you took all these things that influenced you and that you appreciated, and you brought them together. With the beam, you know, your love for Erlang was the reasoning, right? But then you went to your language design and you designed the language and you pulled in ideas from Ruby and ideas from Perl and ideas from functional languages. I'm not sure which ones, but you've told this story before and you can probably reiterate all your influences. And you kind of made this, what I think is a really beautiful language out of it. But it was based on your history, your knowledge, your taste, what you liked. Here you are doing numerical

• 17:35Speaker 01

  stuff, right? And you're doing data science-y stuff. And I just wonder like, how do you acquire that taste? How do you acquire that knowledge? Do you just know every domain very, very well? Or how do you learn this stuff? I know you said in your back in school, you were doing some of the stuff or statistical things. But how have you come up to speed on what would be an awesome way to do numerical Elixir?

• 17:55Speaker 02

  Yeah, so this time it has really been Sean and Jacko. So all the part of like deep learning and how things should work. Sean, he really the one leading it. But like the main seed that led to this was actually Jekyll Cooper. So when I started talking with Sean by email, before we started working together, I sent a tweet, I don't remember, but it was asking about like some references. And then he pointed me to the Jax library in Python, which a lot of people are taking it like to be the next big library, potentially replace TensorFlow. That's what some people speculate, right? But it's from Google. There's a lot of traction behind it. And then I was reading the docs for Jax.

• 18:37Speaker 02

  So we were saying though, like, hey, you know, like, Elittor is a functional programming language. And as a functional programming language, everything is immutable. So work with multi-dimensional data would actually be very expensive. But then I'm reading the docs for Jax, which is a Python library. And then they have quotes like, "Jax is intended to be used with a functional style of programming." And then they say, "Unlike NumPy arrays, Jax arrays are always immutable." And then I was like, "What is happening here?" So it was like this reference, like, "Hey, it's functional." Right? Like, so that's like my spider senses, they were like tingling, like, okay, wait, wait, wait. So there is something here. That's when Sean and I, like, we jumped with both feet and we're like, okay,

• 19:21Speaker 02

  there's really something here. And the whole idea in there is because the way that Jax works and the way that numerical definitions in the next works is that when you're doing all the operations in your neural network, like, hey, you know, we need to multiply those tensors, we need to calculate softmax, we need to do the sum, when you're doing all those computations, you're actually not doing those computations at the moment. What you're doing is that you're building a computation graph with everything that you want to do in that neural network. And then they get this computation graph. And when you call that function with a particular tensor with certain dimensions of certain size, it emits highly specialized code for that particular type of tensor, for that particular graph.

• 20:06Speaker 02

  And that's why everything is functional, because what you're doing is building a graph, you're not doing any computations. And then you compile that around the GPU. When we saw this idea, it was like, hey, everything can be functional. And, you know, when it started, it was like a bunch of happy accidents, you know, a book being published. So I like to say like, I really have a thank you like for Prag Prog. Because, you know, if they did not publish this book, if somebody read the proposal that Sean sent to Prag Prog and say, hey, we don't need genetic algorithms book for Elixir, maybe none of this would have started. And then somebody pointed us to Jax, so it was all those things happening. And that kind of like gave me a path to, for us to explore and come out of this. And I really think, so I said, like, you know, we said, we are going to start working.

• 20:50Speaker 02

  And as we build the tools, we are going to, to try to find like what advantages Elixir can have compared to other programming languages, for example. And it turned out that, as I kept saying, what I thought would be a negative aspect, which is immutability, really turned out to be, to be a feature, right? And it's really interesting because there are some pitfalls in Jax, for example. So if you go to the Jax documentation, they have a long list of pitfalls. So there are some pitfalls in the Jax documentation that they do not happen in the Elixir implementation in an X, uh, because everything is immutable. So the way that Jax works is that in Python, they call it the tape pattern.

• 21:31Speaker 02

  So basically, as you're calling methods in an object, it is requiring all the methods that you call. In Ruby, we know it as method missing. But there are some operations in Python that they cannot be recorded. So for example, if you are setting a property, for example, in the object, or if you pass that object to a conditional, you don't know that that object is being used in a conditional. So Jax cannot record that structure in your code. So they have like some pitfalls, like, hey, you know, you have to be careful. Or if you have a for loop, if you have a for loop in Jax, what it's going to do is that it's going to unroll the loop and that can lead to very large GPU code. But in NX, everything is immutable, so we don't have those operations in the first place.

• 22:12Speaker 02

  And because we have macros, I can actually rewrite the if to be a if that runs in the GPU. So this is really cool. So in NX, when you go to the numerical definitions and you look at the code, that code, no pitfalls, is going to run on the GPU, is going to be sent to the GPU. It's effectively a subset of the literature on the GPU. So yeah, so you know, it started with this small tip and then it kind of spread from there.

• 22:43Speaker 00

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• 23:46Speaker 01

  So sitting on top of NX is Axon, which is NX-powered neural networks. You want to give us the skinny on that tool, Jose?

• 23:56Speaker 02

  Yeah, so it's pretty much what the name says. It's neural networks built on top of NX. And so Shum is the one, so a lot of those things, Shum is the person behind it. So Axon, EAXL, it's all Shum's work. And what he did for Axon is that he built All of the building blocks of a neural network is built just using functions. They are regular numerical definitions, they are regular numerical definitions. And numerical definitions are regular Lixer functions. So he just built a bunch of functions and then you can compose them together to build the neural networks. And so he did like, he built all of this. It was really funny because I, I think we can still find it in the repo.

• 24:39Speaker 02

  He created the initial issue, which I think had like a hundred checkboxes, which was just like all the, the functions that you use, like all the initialization functions, optimizers, layers, activations, everything that you have in, in a neural network that you usually use. So he listed all those, then he implemented most of those. And then he came up with a higher level, it's still inside X on the higher level API. So you can say, hey, you know, I have a neural network that is going to be this dense layer and this convolutional layer and this activation and this. And I want to train it and, and you're done. So, you know, the same level of API convenience that you would expect from like Keras or from BitTorrent

• 25:22Speaker 02

  is there in X on, but the building blocks as well. That's what X on is about, a little bit, you know, out of my, my reach of my understanding. And it's kind of funny because I can run the basic examples, but I still don't have a GPU. And then if you got a convolutional like neural network, if you're going to train it with all the GPU, it's going to take a lot of time. So I, I cannot run some of the examples, but Sean, he added already a good amount of examples to, to, to the repo story. So, you know, we have like some very classical data sets that people use in machine learning, like MNIST, CIFAR, I don't know if I'm pronouncing those correctly, Daniel, but you probably know what I mean.

• 26:03Speaker 02

  The fashion MNIST and so on. And he has examples of, and then no algorithm was like ResNet and this kind of stuff. And there are examples already in the repo story. And for those things, running in Lixer and compiling and running on the GPU, which is very exciting.

• 26:17Speaker 01

  Don't you have a GitHub sponsors or a donation button, man? Let's get this man a GPU. Someone's got to get you a GPU. Come on.

• 26:26Speaker 01

  The world would be a better place if Jose Valim owned a GPU. I'm gonna put it on record.

• 26:30Speaker 02

  Yeah, I was really like in a, just in a side, I was like, I'm going to buy a Linux machine then so I can have the GPU. And then Apple came out and was like, oh, we have TensorFlow running on M1. But they released just like the compiled executables and not the source code. So I'm like, do I buy a new machine that is going to take space in my house? And then three months later, Apple is just going to, the thing is going to be merging TensorFlow and I'm never going to use it again. So in this way, like, so I'm just like, I'm suffering for like the judicial paralysis. I'm like, you know, should, should, should I invest on this thing or not?

• 27:07Speaker 01

  Well, you've come to the right place. This is Daniel's expertise right here. This guy, he builds these things in his house.

• 27:13Daniel Whitenack?

  Unfortunately, it's all crazy right now. I know we, we ordered a server and like, we had to switch the GPUs because of like, I don't know if you saw Nvidia's. They kind of got mad that everybody was putting consumer cards in their enterprise servers. And so that all got switched up, which I understand their, their business. But yeah, that whole world is crazy right now in terms of actually getting your hands on, on something as well.

• 27:39Speaker 01

  Supply shortages and everything?

• 27:41Daniel Whitenack?

  Yeah, yeah, it's insane. Just scrolling through this, like, I'm, I'm pretty excited to, to try this on my, you know, little workstation with a GPU. I think it's cool that, again, I'm coming not from an Elixir standpoint, but I recognize the API. Like, it's very Karis-like, this high-level API that you're talking about where you're, oh, I've got a dense layer. I've got, you know, a dropout layer, whatever it is. That like instantly makes sense to me. I feel like I could take this API and create like my model definition fairly easily. And I, I really like that. Being a Python user and coming from that outside world, like it, it makes me want to, to play with this.

• 28:21Daniel Whitenack?

  If it was a totally like different sort of looking API, I think I would have, I would be sort of nervous to dive in. But I also see like, you have your model struck, you have your layers, you have your high-level API and you talk- Talk about it like it's just an Elixir struct. And so serializing it to multiple formats is possible. And we're talking about the model itself. So I don't know a ton about Elixir structures, but this sort of serializing it to multiple formats is really like interesting to me. Because at least from my perspective, what I'm seeing is a lot of sort of push for interoperability in the AI world. Where like people like publish their model that they wrote in PyTorch on PyTorch hub.

• 29:06Daniel Whitenack?

  And like then like I'm over here with TensorFlow, but I can pull it down and like convert it using something like Onyx tools or something and use it in TensorFlow. Or maybe there's all sorts of frameworks out there. And I think people are generally realizing it's not going to be one that wins the day, but interoperability is really, really important. If we're going to release models and expect people to be able to use them. So I don't know. Was that sort of factoring in your mindset as you're thinking about how to represent models in Axon?

• 29:37Speaker 02

  Yeah, definitely. When Sean was working with it from the design, he was, we were thinking, you know, how can we get an Onyx model, load that into an Elixir data structure? So we can get that and send to the GPU and have that running on the GPU. And it goes back, you know, to what we were talking about a while ago, that I think like the first users of this, maybe I'm wrong, and I'll be very, very glad to be wrong. But I think the first users, they're going to be, hey, we have our data scientists that are already super familiar with this tooling in Python that is very productive, very useful for them. And it's harder to convince them to migrate. But hey, we are running Elixir in production. And I just want to bring that model and run directly from Elixir.

• 30:20Speaker 02

  And I think that's very important for that use case. So and then I mean, the whole purpose of interoperability. One of the things that I think it's really worth talking about that I think with these ideas, so you know, a lot of people, they think about Elixir, they think about Web, but Elixir is also really good thanks to the NERF framework for embedded. And I think there is a lot of potential in this area of, you know, having machine learning neural networks running on the edge, and NERF can help with that, and that can be an interesting application as well. And that requires kind of the same ideas because you're not going to train on the device, right? So you need to build the model elsewhere and do all the steps and then bring that into the device.

• 31:05Speaker 02

  So sterilization is there, and I think it's a matter of time until... A lot of those things, we are working on them, as you know. It's like, we also started a machine learning working group in their mega ecosystem foundation, so people interested in this. So it's something that we plan to work, but if somebody is really excited about this, so if you're listening to the show like, "Hey, you know, I want to try this out, and maybe I can implement like on its sterilization, and you would like to work with us in the PR," it's definitely welcome. We can have a link to the Airline Ecosystem Foundation, the machine learning working group in the foundation. So we have a Slack, people can join, can talk to us. And there's a lot of work to be done, and this sterilization is definitely going to play a big part of it, yeah.

• 31:49Daniel Whitenack?

  So how long have you both been working on Axon? Because it just seems like there's so much, like, there's so much implemented. Like, you were talking about, you know, "Hey, we need all of these different layers implemented that people know about." Typically, I see libraries like maybe that have a new API for machine learning or something. It seems like it takes them so long to sort of add operations and add, you know, support for different layers and such. And I'm wondering, like, what was your thought process and approach to building this in a way that you could come out of the gates, supporting as much of that as possible?

• 32:29Speaker 02

  To give you an idea, so Sean has been working on it on his free time, okay? And he started working on Axon as soon as we announced the net. So he has been working on it for two months of his free time. And it already has a bunch of stuff. If you check like the the Rhythm, you know, it already has the I'm not going to to be able to say everything, but the dense layers, dropout, convolutional layers, a bunch of optimizers, like seven, eight. So he has been able to add those things really, really fast. And I think one of the reasons for that is because the foundations are just functions, we're just building functions on top of functions. So it's it's very easy to to compose.

• 33:11Speaker 02

  And the other thing is also that I think like one of the reasons I'm speculating here, to be clear, I think maybe one of the reasons why some of those libraries, it takes a lot of time for them to implement a layer, is because they are implementing everything, right? They are going maybe like from Python all the way down to the C code and implementing or or C++ code and implementing that. While for us, it's a very layered approach where Axon just works about an X, and X is the tensor abstraction, and then we have the tensor compiler stuff that compiles for XLA. And working in those different layers, when you're working at Axon or in Axon, you are really at a high level. You're not really worrying about C, C++ any of that.

• 33:56Speaker 02

  You're just saying, hey, you know, what are the tensor operations that I need to do? And I think like that's why he was able to to be so productive in building, you know, all of those features in this short timeframe. And I think adding new like new activations, layers, they're relatively straightforward. What I think what takes more time is in discussion is when we need to change the topology, because that requires to think about how the the struct is going to represent that. So for example, if you have a GAN or a recurring neural network, now you have to think like, you know, oh, if it's recurring, now you need to get the data, feed it back inside. So you have to think how you're going to model that. But it's mostly, it's just that, you know, at the high level representation.

• 34:42Speaker 02

  So that's kind of how things have been structured.

• 34:45Speaker 01

  Yeah, I cloned the repo and his first commit was January 25th of 2021. It's pretty amazing. With a few to follow. And it was funny because like the first commits are like, add some functions, more functions, adding some even more common functions. So he's just like cranking out these functions like you said.

• 35:01Speaker 02

  Yeah. So that was in January. Okay.

• 35:03Speaker 01

  Yeah. So a couple of months.

• 35:04Speaker 02

  Yeah. But while working on that, he was still working on XLA and Anax with me. So we started in November. So in November, it was Sean and I, we were working part-time. So it took us about three months to release Anax and XLA. And then Sean, uh, he's still working with Anax and XLA. And then he's focused after we announced it, February, he changed it to be on Exxon until we, we announced it. And now we are probably all like, kind of going back and forth between projects. So because there's still a bunch of things that we want to build in Anax. So one of the things that I really want to work on is streaming. Because, uh, so Wieder is really good for streaming and I want to have a very good abstraction.

• 35:49Speaker 02

  So, you know, we can start streaming data to be inferred like into the GPU. So you don't have to load everything into memory. Or for example, if you have a webcam or a camera that is your embedded device or you are getting from WebRTC or something like that, and you want to send that straight to the GPU and you stream it. So we can do all this kind of, uh, of stuff, uh, interesting stuff that I think we can do. So yeah, so we are going to be jumping back and forth on that.

• 36:18Speaker 01

  I think it speaks to the power of a solid abstraction too. And like a layered approach when done well, when you get to those higher layers, like you said, unless you have to change the topology. If you're just adding and building on top and not having to drill down through each time, then you can move relatively fast. There's probably also an aspect of this where. It seems like Axon's API is trying to be familiar. And so a lot of times, at least for me, the slow part of software is like getting that API figured out, you know, and like rewriting that API so that it's better. And maybe there's a step up because of all these other projects that have come before that makes it familiar to Daniel and other people who are working in this world.

• 36:58Speaker 02

  Exactly. That's a very good point. And I think on the Axon side, one of the inspirations, I think it's there is a project Think AI in Python, which is a functional approach.

• 37:08Daniel Whitenack?

  Yeah, there's a team in Europe that writes the Spacy library, which is an NLP library. And I think that their main like backbone for that is Think.

• 37:19Speaker 02

  I see, yeah. So that has been one of the inspirations as well. And I think there's PyLightning or Lightning Torch or something like that has also. So yeah, that's, that's a very good point, you know? So if you can look at what people are doing, say, hey, this is what I think it's good. This is what I think it's going to fit very nicely at what we do. That speeds up the process, uh, considered as well.

• 37:52Speaker 01

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• 38:37Daniel Whitenack?

  I mean, there's just such diversity in the AI world in terms of the types of models that people are building. But there is a fairly consistent, like if you look at the implementations, whether it's TensorFlow or PyTorch or, you know, these other frameworks, you can kind of get a pretty quick sense of how they're building their architecture, looking into the source code. And I mean, I'm just looking at some of the layers that are implemented in Axon. And like I said, I think you've done a good job at, like, I don't know how to read Elixir. I can sort of get the sense of what's happening here. And I think that's a testament to like, yeah, like following some of the inspiration,

• 39:19Daniel Whitenack?

  the good inspiration that's already out there in the world. And also, I think it'll be easier for people, maybe that do want to jump and, you know, experiment in Elixir from the Python world and they want to add their own cool layers into Axon, it's going to be a lot easier for them to jump in and do that, I think, if they feel like they're not in a total foreign world, they recognize some of these components and all of that. So I definitely think that that's a good call. I know that some of like data science, AI world kind of operates with a weird set of tooling that includes these things like called notebooks and other things.

• 39:58Daniel Whitenack?

  I know I saw like, there's even some functionality related to like interactive coding and cells and that sort of thing too, isn't there?

• 40:07Speaker 02

  Yeah, so there is a separate project. Another person has been working on this project, Jonathan Klosko. When Sheldon and I started talking like, hey, you know, we want to build this foundation for machine learning, numerical computing, and then we mapped a bunch of things that we have to do. And there are a bunch of things that we have not started working on yet. So for example, we don't have an equivalent to data frames. So that's an open question that has to be solved. We don't have plotting libraries yet. But one of the things that we want to do was this idea of the interactive and collaborative notebook. And to give a bit more context, Daniel, so we have, we have the Phoenix web framework in Elixir. And the Phoenix web framework, I think, two years ago, launched something called LiveView,

• 40:51Speaker 02

  which makes it really easy for you to build interactive, real-time applications, but on the server, so without having to write JavaScript, which, if you're not a JavaScript developer, that can be a plus. And because the logic is on the server, it allows you to do like collaborative, because if you have multiple people collaborating on the text, on a text, right? Like, the server is the one that knows where people are, what they should do, how the text should change. So it's really good for building these kind of applications. The elevator pitch is not correct, but there was one line somewhere that it says like, "React on the server," this way you can think about things like view. And I said, "I want to do this. We want to build this notebook thing as well," which we called LiveBook. So that's the LiveBook project.

• 41:35Speaker 02

  And the way it started was very funny, so we have a project called XDoc, which generates documentation for Elixir, and you're really proud of it. We think that our documentation just looks great, and it's standardized. All the projects in the community, they generate this documentation with XDoc. It has a bunch of great features. And somebody, some time ago, opened up, he said, "Hey, you know, this project's using jQuery. jQuery is huge. We probably don't need to use jQuery anymore." So somebody opened up this issue tracker. I was like, "Sure, sounds a good idea if somebody wants to do it." And then, out of nowhere, somebody sends up a request. They didn't ask if they should do it, right? They just sent up a request, "Replace jQuery by JavaScript." And I was like, "This is great. I reviewed the code. The code was flawless.

• 42:18Speaker 02

  I reviewed like the best of my power since it's JavaScript." And then I, I, I went to check, and then I was like, "Oh, Jonathan, he, he lives in Krakow, which is where I live. He goes to, to H, H, which is where my wife studied. This is very interesting." And it's like, "Oh, he has like some Phoenix experience." And he's still a student, and I was like, "You know what? Maybe he wants to work with us on this LiveBook thing." So I sent him an email, like, "Hey, you know, you want to talk?" You know, at this time, we had not announced the next yet, but we have announced some benchmarks comparing like code running on the GPU and not on the GPU, which was like 4,000 times faster or something like that. And then I told him like, hey, do you want to work with us? And then he's like, sure, but you know, I'm a student, like, no problem. You're going to work part-time. So he started in January, working on LiveBook.

• 43:03Speaker 02

  And the idea, so, and then we started talking to some people. So there was, at about the same time, uh, John, another, another Jonathan John, he had released something like a notebook for Elixir as well, a, a very bare bones one. So he had some experience from Python, we brought him in, like, hey, you know, if you're going to do this, how are you going to do it? What are the benefits? And then we're like, okay. So one of the things that we want to do is that we want to, to leverage the fact that, you know, it's very easy to build collaborative and attractive applications in Elixir. So it needs to be collaborative from day one, and it is. So I, I gave a, I, there is a video on YouTube of me announcing LiveBook. And it's really cool because it shows how LiveBook works, it shows Exxon as well.

• 43:47Speaker 02

  So there are some good examples. And so we're like, hey, it needs to be collaborative from day one. And we really want to be interactive because one of the things, so for those who are not familiar with Elixir, like, the Elixir runtime, it's very easy to extract a lot of information from the runtime, like, what your code is doing, or we break our code into lightweight threads of execution, so you can inspect each of them. So we said, okay, we wanted to be interactive, not only for people that are working with like, machine learning and neural computing, but if you want to get data out of an Elixir system, like a production system, and try to say like, hey, where, where is my bottleneck? You should be able to do all that, you should be able to, uh, interact with a live system as well, and interact with your neural network that is training.

• 44:32Speaker 02

  So, uh, this feature is not there yet, but it's part of our vision. And then I said, well, and what do people complain about in notebooks? That's always part of the research, right? So if you go like to, to Jupiter, uh, what people usually complain?

• 44:44Speaker 01

  A lot. What don't they complain about?

• 44:48Speaker 02

  What we heard was like, well, the format that it writes to disk, it's not good to disk, it's not easy to version control, right? So how are we going to solve that? The dependencies are not explicit, so and the evaluation order is not clear as well. So how we can solve all those things? So, you know, we brought our set of inspirations, we bought the problems and we started working on how we want to solve this. And then a couple of weeks ago, we announced it, maybe one or two weeks ago, we announced LiveBook. Or maybe it was last week, anyway, it's there. You can really see our vision is not complete. You can see the important parts in there of like, you know, it's fully reproducible. The evaluation order is clear. Your dependencies need to be explicitly listed.

• 45:31Speaker 02

  So everybody who get a notebook knows exactly what they need to install and the notebook is going to install it for you. John, he created a format called Live Markdown, which is a subset of Markdown that we use for the notebooks, which is really great because now if we change a notebook, we are just changing a Markdown file, which means you can put it on version control. People can actually review your changes without having to, you know, spin an instance of that thing and make that work. So for us, it's a step again into this ecosystem. And I think there is a bunch of things that we want to explore and try out and really try to be like a a very modern approach to, you know, for interactive and collaborative notebooks.

• 46:15Speaker 02

  And there are other things like happening in space. So there is Jupyter notebooks, there is also Pluto, J Al, call me from the Julia folks. There is also DeepNote, which is a software as a service. So we're kind of looking at everything and coming up with our own takes and ideas as well.

• 46:32Daniel Whitenack?

  That's awesome. I'm glad that when you looked at this, you like took that perspective of like not we need notebooks. People love notebooks, but what's what's wrong with them? Because I think there have been a lot of, there's notebook kernels for, you know, all sorts of different things for Jupiter, but they all suffer from similar issues. And of course I love Jupiter and it's powerful and people use it with great success. I think after people have used it for so long, they've seen these consistent issues. I think, you know, the whole managing state thing that you mentioned and the execution flow is probably the top one on my list. So now you're really tempting me to try out. It also seems like you release something cool every week.

• 47:15Speaker 01

  Right.

• 47:16Daniel Whitenack?

  I don't know how that works. I don't release something cool every week, so I'm feeling really deficient right now.

• 47:21Speaker 01

  I'm with you.

• 47:21Speaker 02

  I don't have anything new to release for now.

• 47:25Daniel Whitenack?

  Until next week.

• 47:26Speaker 01

  Dang it. What you need to do is find some really talented university students and get them to, you know, inspire them to work on some stuff for you.

• 47:32Daniel Whitenack?

  I guess so, yeah.

• 47:34Speaker 02

  Yeah, so yeah, so, uh, yeah, Jonathan has been excellent into this, you know, like, and it was like his first LiveView application. So, uh, I think it's both a testament to Jonathan and to LiveView, the fact that he could build this thing in three months while still studying, working part-time. And go check it out. Go check the video. I think I'm really excited about LiveBook. It's really interesting. And so, for example, we just merged like auto completion. So when you're writing code, there is now auto completion as you would get from VS Code, various Monaco editor. And everything's like collaborative, right? Like if we have two, if we have multiple people working on it, it changes our broadcast. And based on this idea that it's built on LiveView,

• 48:16Speaker 02

  where you don't have to write JavaScript, like the whole thing, including all the Monaco extensions that we had to do. So it had like Galaxy Lexor and so on. It's like 2,000 lines of JavaScript. That's it. For, for everything that it does work. Only thing about the notebook is that, in my opinion, it was a very different approach to how we approached like NX, an Exxon. It's like, hey, you know, like for NX and Exxon, we're like, okay, let's build this and see where this leads us. But for Notebook, it was like, this is an era that AlexiR is really good at, and I really want to have our take on this. I think we can make this. Hours, like our version of this, how our vision, our understanding of this.

• 48:58Speaker 02

  And of course, that requires looking around, but it was a very different thought process. Just like, hey, I think we can build this and I think we can build this great because we have great tools for that. And just to make it clear, like it, it out of the box, it works distributed as well. So for example, if you have a bunch of people using notebooks for some reason, and you want to start like five machines in production and have people connect to those machines from anywhere they want, it just work out of the box. There's no need for external dependencies, you don't need to bring Grad, you don't need to bring a database. So everything was really built using like the, the, again, like if you go to the beginning of the talk, we're talking about the Erlingvort machine, right? And they're building telecommunication systems, right? Imagine you have this platform and you can build collaborative notebooks, right?

• 49:42Speaker 02

  So that was kind of our idea, our take.

• 49:45Speaker 01

  How does it do that? Because it looks like it only runs on like local host. Maybe there's like a way to, how do you tell it? Hey, I've got 10 nodes that I want you to run across. Is that just configuring Phoenix?

• 49:59Speaker 02

  So by default, we run it on local host. By default, if we're running our machine, you don't want to expose that and have somebody access the notebook.

• 50:06Speaker 01

  Yeah, it's like an eval, it's like a public facing eval, right?

• 50:09Speaker 02

  Yes, right? Imagine if you are at an Elixir call, somebody would just be, who is running notebooks here that I can't? Right now, I think we just need to tweak the configuration file. But one of the things that we are working on, we are going to get to the release, we're going to ship both Docker images and a command line executable. Then we'll have flags for all this kind of stuff, you know? Like, hey, do this. And, and most likely what people they want to do is that they want to say, hey, you know, I am deploying this to Kubernetes. So I'm going to use something that uses the Kubernetes DNS manager to connect the nodes. So in Elixir, you would use something like Pure Edge or LibCluster that figure out the topology, connects everything for you.

• 50:54Daniel Whitenack?

  Yeah, and I can definitely confirm that people will want to spin these things up everywhere. Now, I'm not surprised when I hear this, but the first time I started hearing production notebooks, and I was like, how do you have a production notebook? It's a notebook. Like, how are you running a notebook in production? But this is like so pervasive, people are like, oh, this is my production notebook, and this is my, you know, dev notebook and all of these things. I don't know if I go that far because I'm like, I don't know how to support a notebook in production, but it is such a pervasive idea. It's cool to see that as a piece of this. And of course, there's other things too, like you mentioning, you know, pandas and other things. So for people that aren't familiar in Python,

• 51:38Daniel Whitenack?

  there's a library called pandas, which deals with tabular data, and you can do all sorts of cool like data munging stuff. So yeah, it's cool to hear you say that those things are on your mind. And because you release a cool thing every week, you know, maybe that will be next week or the following one.

• 51:56Speaker 02

  Yeah, right now, like I think we are going to tackle graphing, graphs, because it's part of the notebooks. But I'm hoping for like the data frame stuff. Other people are going to step in, and we are having a bunch of related discussions on the earlier ecosystem foundation, machine learning working group, and this kind of stuff. If you want to talk about like, and there is, and sure, like machine learning, right? And then we can talk about neural networks. And there's like so much work to be done and so many things to explore. So people that are excited, like jumping, and you're going to have a feast, right? Because like we didn't talk about like, hey, clustering, you know, forests and classifiers, aggressions. And then we can talk about linear algebra.

• 52:40Speaker 02

  There is just so many things in the ecosystem that one can build and explore, that there is a lot of work to do. And we hope like people, they'll get more and more excited, and they are going to join us in this journey.

• 52:51Daniel Whitenack?

  Yeah, it seems like if you've got the graphing thing going and you're talking about Elixir having this sort of natural abilities with web development with Live Book and the other things here, you know, a big thing in the AI world is monitoring your training runs with a bunch of cool graphs with, you know, something like a tensor board or something like that. So it seems like, yeah, there's like, that would enable a lot of things. It'd be pretty sweet to have your, you know, your training run going in Axon. You kick it off from a Live Book and then you can pull up a, you know, an interface to see all your nice training

• 53:31Daniel Whitenack?

  plots and all those things. And that's all happening in a really nice unified, robust way.

• 53:38Speaker 02

  Yeah, that's definitely something, you know, that we explore at some point. Probably tensor board integration as well. It's something that we are bound to have.

• 53:47Speaker 01

  Yeah, it seems like Live Book really could be your marketing machine. You know, it could be like your way in for all the disillusioned notebook sharers out there who've had, you know, like Daniel said, they can do a lot of stuff with Jupiter notebooks or existing tooling, but there's pain points with collaboration with all these things. I mean, the fact that one of your headlines is sequential evaluation. To me, that seems like, isn't that how everything works? It says code sells run in a specific order, guaranteeing future users of the same Live Book.

• 54:16Daniel Whitenack?

  Not so quick, Jared.

• 54:17Speaker 01

  I'm like, that's a feature? Isn't that just like how things work?

• 54:23Daniel Whitenack?

  I mean, it's kind of the wonderful thing about Jupiter notebooks and the really hard thing about them because like it's similar. Like if you go back in history, I don't know if any, either of you ever used Mathematica, but it's a similar idea. Like you have these cells of execution. It's really wonderful for like experimentation, right? Because you can, oh, you did this, but when you're in experimentation, you expect things to fail almost all the time, right? So you don't expect to have like a script that runs and you unit test it and blah, blah, blah. You expect to try something and fail and fail over and over and over until you like tweak it enough to where it works. And so that's great in the notebook environment. If you can tweak things like that, the problem is then like.

• 55:08Daniel Whitenack?

  Oh, what were the 4 million things that I did tweak to get this to go? And what state is saved like in my notebook? Like, I could get it to work and then reboot it and run it from top to bottom, and it's not going to work again, right? So it's the good thing and the bad thing.

• 55:25Speaker 02

  Yeah, and I'm pretty sure it's like this feature, let's say, this sequential evaluation is going to be a limitation. At some point, people will be like, hey, I started training my neural network, but now I want to do something else in the same notebook while the neural network's training. How can I do that? So we'll have to come up with ways of like branching, but we want to be very explicit on the model. Like, so we'll say, hey, you can branch here. Or, well, we have been calling internally because everything is organized in sections. We have, maybe I can set up some asides. So aside, they fork from a particular point, they branch from a particular point and execute the code based on those bindings.

• 56:08Speaker 02

  So, or from, it's basically the state of the notebook from that moment on, without ignoring the other side. So it's something we'll have to tackle. And if you look at the issue, there are a bunch of things that we have been thinking about. So for example, one of the things that I want to do. So we have the idea, so when you persist in notebooks, you're persisting to the file system. So on the issues, like for example, pluggable file systems, and I want to make GitHub a file system. So, you know, you can easily like persist your notebooks to GitHub, and that works transparently from LiveBook, without you having to say, hey, I need to clone and stuff like that. We can work directly on the repo story, and I think that's going to be a boon for collaboration as well. Or not collaboration, I mean, a different kind of collaboration, right?

• 56:53Speaker 02

  You put on GitHub so somebody can fork and play with it.

• 56:56Daniel Whitenack?

  I know there is like this thing in the Python world called Binder. So essentially, you could create a GitHub repo with a notebook, and then you click on the little badge, and it just pops up a hosted version of that notebook that will run. So you can like give it a Docker image or something with all the dependencies. For someone like me, if there was like that tie-in with GitHub, and I could just launch a notebook and try like Axon, that's like, I feel like people would just latch on to that so quickly. Then the barrier is not like, oh, like, Elixir's... Sort of new to me as a Python person, so I need to figure out the tool chain. But really, what I want to do is I just want to like quick shift, enter through a few cells and see how it works. And that's, that's very powerful.

• 57:40Speaker 02

  Yeah, that's a very good point, something for us to look into, yeah.

• 57:43Speaker 01

  Well, you guys have done a lot, but there's a lot left to do. What's the best place to get involved? Like you said, fertile ground, what do you say, hop in and have a feast or something? If you're interested in the space and an Elixir, it sounds like there's lots of ways to get involved and to build out a lot of the stuff that's lacking. So is there a discourse forum or is there a Slack? Is there a community around this? Is it just you and the dash bit folks working on it? What, what's the situation there?

• 58:08Speaker 02

  Everything, we have the Elixir dash and X organization on GitHub. But a lot of discussion is happening in the Erlang ecosystem foundation. We have the machine learning working group. So if you go to the E F website, you can get all the working groups there. You're going to find machine learning and then you can create an account. It's free and then you can join this Slack and we'll be there. So that's where we are usually chatting things. Originally, a lot of those things, they were kept confidential, like live book. But now everything, at least everything that dash bit was working on, it's out in the public. We don't have anything, no more secret projects. So that's the place to go and where we're talking about things. We have a monthly meeting where we meet and discuss and exchange ideas. So that's definitely the place.

• 58:52Speaker 01

  Is NX bringing machine learning tools to Erlang or their other Erlang, but not Elixir efforts in this space? You understand what I'm saying?

• 59:03Speaker 02

  Yeah.

• 59:03Speaker 01

  Like, is this the first time in Erlang, the beam-based tooling around numerical computation is happening? Or is there like Erlang-only things that have been going on?

• 59:12Speaker 02

  I think it's the first time for the, the ecosystem. And yeah, and because you can call, you know, Elixir from Erlang with no performance cost whatsoever.

• 59:21Speaker 01

  Yeah, it's pretty cool, right?

• 59:22Speaker 02

  You can just call, like the numerical definitions, they don't work in Erlang because they, they translate the Elixir AST, not the Elixir AST, but they translate like the Elixir execution to the GPU. That wouldn't work with Erlang, but everything that we are building on top, like Axon. Because you just build the abstraction so somebody could go get Axon, call it from Erlang, build a neural network from Erlang and like just run it and it should just work.

• 59:48Speaker 01

  That's cool. Daniel, anything else from your side of the fence you want to ask Jose about before we let him go?

• 59:52Daniel Whitenack?

  I'm just super excited about this. Hopefully there is some crossover from the Python world. It seems to me like the timing is such that people in the AI world very much are more open to trying like things outside of the Python ecosystem than they once were. And so, yeah, that's my hope and I definitely want to play around with this and appreciate your hard work on this and I'm excited to try it out and also share it with with our Practical AI community.

• 1:00:21Speaker 02

  Awesome. And I'm really glad that you are having me on the show and I was able to share all those ideas and this work that we have been doing.

• 1:00:31Speaker 01

  Oh, you're welcome back anytime. All the links to all the things are in your show notes. So if you want to check out Jose's live book demo on YouTube, we got the link to that. We'll hook you up with a link to the Erlang Ecosystem Foundation if you want to get involved. Of course, Axon and NX are linked up as well. So that's all. Thanks, everybody, for joining us and we'll talk to you again next time.

• 1:00:53Speaker 01

  Thank you for listening to Practical AI. We appreciate your time and your attention. If you enjoyed this episode, help us out by spreading the word. Think of a friend, think of a colleague, somebody who would benefit from listening to it and send them a link. We'd really appreciate it. Practical AI is hosted by Chris Benson and Daniel Whitenack. It's produced by Jared Santo with music by Breakmaster Cylinder. Thanks again to our sponsors, Fastly, Linode, and Launch Darkly. That's our show. We hope you enjoyed it and we'll talk to you again next week.