
123 segments available
Chris Lattner is a legendary software and hardware engineer, leading projects at Apple, Tesla, Google, SiFive, and Modular AI, including the development of Swift, LLVM, Clang, MLIR, CIRCT, TPUs, and Mojo. Please support this podcast by checking out our sponsors: - iHerb: https://lexfridman.com/iherb and use code LEX to get 22% off your order - Numerai: https://numer.ai/lex - InsideTracker: https://insidetracker.com/lex to get 20% off EPISODE LINKS: Chris's Twitter: https://twitter.com/clattner_llvm Chris's Website: http://nondot.org/sabre/ Mojo programming language: https://www.modular.com/mojo Modular AI: https://modular.com/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 2:20 - Mojo programming language 12:37 - Code indentation 21:04 - The power of autotuning 30:54 - Typed programming languages 47:38 - Immutability 59:56 - Distributed deployment 1:34:23 - Mojo vs CPython 1:50:12 - Guido van Rossum 1:57:13 - Mojo vs PyTorch vs TensorFlow 2:00:37 - Swift programming language 2:06:09 - Julia programming language 2:11:14 - Switching programming languages 2:20:40 - Mojo playground 2:25:30 - Jeremy Howard 2:36:16 - Function overloading 2:44:41 - Error vs Exception 2:52:21 - Mojo roadmap 3:05:23 - Building a company 3:17:09 - ChatGPT 3:23:32 - Danger of AI 3:27:27 - Future of programming 3:30:43 - Advice for young people SOCIAL: - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Reddit: https://reddit.com/r/lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
Chris Lattner discusses the dual forces shaping the future of computing: the rapid advancement of hardware and the explosion of innovation in AI. He emphasizes that as technology evolves, it will become increasingly complex, necessitating a universal platform that can adapt without requiring constant code rewrites.
"- On one access, you have more hardware coming in. On the other hand, you have an explosion of innovation in AI. And so what happened with both TensorFlow and PyTorch is that the explosion of innovati..."
Lattner introduces Mojo, a new programming language designed for AI that serves as a superset of Python. He explains the vision behind Mojo, which aims to make machine learning infrastructure more accessible and user-friendly for researchers and developers, while maintaining high performance.
"It's been, I think two years since we last talked, and then in that time, you somehow went and co-created a new programming language called Mojo. So it's optimized for AI. It's a superset of Python. L..."
In a light-hearted discussion, Lattner reveals that Mojo allows file extensions to be emojis, challenging traditional norms in programming. He argues for the use of Unicode in file extensions, highlighting the visual appeal and modern relevance of emojis in coding.
"and then having the world's power and the innovation of all the smart people in the world get unleashed on something can be quite different. And so really where Mojo came from was, starting from a pro..."
Lattner shares the mixed reactions to using emojis as file extensions in Mojo. He discusses the technical challenges faced, such as compatibility with Git, while expressing optimism about the future acceptance of this innovative approach in programming.
"- Well, and like you take a step back and look at what file extensions are, right, they're basically metadata, right? And so why are we spending all the screen space on them and all this stuff? Also, ..."
Lattner clarifies that Mojo is primarily designed for AI applications, addressing the challenges in AI infrastructure. He explains how Mojo aims to simplify programming for AI while also being a general-purpose language that can handle a variety of programming tasks.
"For example, in Swift, you can do that for sure. So an example we gave at Apple was the dog cow. - Yeah. - So that's a classical Mac heritage thing. And so you use the dog and the cow emoji together, ..."
Lattner introduces Modular, the company behind Mojo, which aims to enhance AI capabilities and streamline the software stack for AI applications. He discusses the evolution of AI tools and the need for a cohesive software solution to meet modern demands.
"- So optimize through simplification by having one thing. So you mentioned Modular. Mojo is the programming language. Modular is the whole software stack. - So just over a year ago, we started this co..."
Lattner explains the unique compilation features of Mojo, which combines interpreted, JIT compiled, and statically compiled elements. He emphasizes the importance of maintaining Python's dynamic features while ensuring high performance for AI applications.
"- So Mojo is a programming language that allows you to do the higher level programming, the low-level programming, like do all kinds of programming in that spectrum that gets you closer and closer to ..."
Lattner discusses how Mojo incorporates dynamic metaprogramming features similar to Python, allowing for expressive APIs and operator overloading. He highlights the challenge of implementing these features efficiently for hardware acceleration.
"And so I think that TensorFlow and PyTorch and these systems embracing Python really took and helped Python grow, but I think that the major thing underlying it is that Python's like the universal con..."
Chris Lattner discusses the integration of dynamic metaprogramming features from Python into Mojo, emphasizing the challenge of maintaining efficiency on embedded processors and GPUs. He explains how Mojo allows for compile-time metaprogramming, enabling Python-style expressive APIs while optimizing performance.
"And so if you look at something like PyTorch or TensorFlow or, I mean, even a simple use case, like you define a class that has the plus method, right, you can overload the dunder methods, like dunder..."
Lattner critiques C++ metaprogramming, highlighting its complexity and the accidental power of templates. He contrasts this with Mojo's approach, aiming to simplify the programming experience by unifying runtime and compile-time programming, making it easier for developers to use metaprogramming effectively.
"- [Chris] Yep. - But it's really messy. - It's super messy. It was accidentally, I mean, different people have different interpretations. My interpretation is that it was made accidentally powerful. I..."
Lattner explains the complexities involved in implementing Mojo's compiler, which integrates an interpreter for metaprogramming. He discusses how this innovative design allows for a unified programming model that enhances both runtime and compile-time capabilities.
"And so, again, one of our goals with Mojo is to make things really easy to use, easy to learn, and so there's a natural stepping stone. And so as you do this, you say, okay, well, I have to do program..."
The conversation shifts to the significance of metaprogramming in machine learning. Lattner describes how frameworks like PyTorch and TensorFlow abstract compute problems, and how Mojo's auto-tuning feature can optimize performance by adapting to different hardware configurations.
"Like, they grow macros or something, right? List right? - Yes. - I know your roots, right? (Lex chuckling) You know, and this is a powerful thing, right? And so, you know, if you go back to list, one ..."
Lattner elaborates on the concept of auto-tuning in Mojo, explaining how it automates the optimization of algorithms for various hardware setups. He emphasizes the importance of making programming more accessible by allowing computers to handle complex tuning tasks.
"- Maybe can you describe auto-tuning and how do you pull off, I mean, I guess adaptive compilation is what we're talking about is metaprogramming. How do you pull off- - Yes. - auto-tuning? I mean, is..."
Lattner discusses the potential performance improvements Mojo offers over Python, citing examples of speed increases due to its compiler-based architecture. He highlights how Mojo can significantly reduce latency and improve efficiency in machine learning applications.
"A lot of the algorithms that you run are actually the same, but the parameters, these magic numbers you have to fill in end up being really fiddly numbers that an expert has to go figure out. And so w..."
In this segment, Lattner compares Mojo's compiler to CPython, explaining how Mojo's design eliminates the overhead associated with Python's interpreter. He details how this shift leads to substantial performance enhancements, making Mojo a compelling alternative for developers.
"- So for auto-tuning, for like tile size, you mentioned 120f for TPU. You would specify like a bunch of options to try, just in the code- - Yeah. Yep. - Just simple statement, and then you could just-..."
Lattner explains how Mojo takes advantage of modern hardware capabilities, such as parallel processing and vectorization. He discusses the importance of optimizing code to utilize these features effectively, which can lead to significant performance improvements.
"So first of all, when we say that, we're talking about what's called CPython, it's the default Python that everybody uses. When you type Python 3, that's like typically the one you use, right? CPython..."
Lattner addresses the role of types in Mojo, explaining how they differ from Python's runtime types. He emphasizes Mojo's flexibility in allowing developers to progressively adopt types, enhancing optimization without sacrificing the dynamic nature of the language.
"- Before I talk about some of the interesting stuff with parallelization and all that, let's first talk about, like, the basics. We talked the indentation, right? So this thing looks like Python. It's..."
In a light-hearted exchange, Lattner and Fridman discuss the cultural pressures surrounding strict typing in programming. Lattner argues for the usefulness of types while acknowledging the freedom that Mojo offers, allowing developers to choose their level of type enforcement.
"- So it's kind of optional, it's not strict typing, like, you don't have to specify type. - [Chris] Exactly. - Okay, so it's starting from the thing that Python's kinda reaching towards right now with..."
In this segment, Chris Lattner explores the optional nature of typing in Mojo compared to Python. He argues that while strict typing can reduce errors, it shouldn't be a requirement for all developers. The conversation touches on the balance between flexibility and the need for structure in large codebases, particularly in team environments.
"why do you have to rewrite it? Well, you have to rewrite it to deploy. Well, why do you wanna deploy? Well, you care about performance, or you care about predictability, or you want, you know, a tiny ..."
Chris explains the differences in type implementation between Mojo and Python, focusing on how Mojo enforces type correctness at compile time. He contrasts this with Python's more lenient approach, where types are often treated as hints. This segment delves into the implications of these differences for performance and error handling in programming.
"- And, you know, people can ignore Mojo. And if it's not their thing, that's cool. But if they wanna use Mojo, I don't want them to have to rewrite all their code. - Yeah, I mean, this, okay, the supe..."
In this segment, Chris Lattner introduces the concept of structs in Mojo, which offer static typing for high performance, contrasting them with Python's dynamic classes. He discusses the benefits of having a more efficient implementation of basic types and how Mojo aims to provide a robust foundation for numerical types, especially in AI applications.
"can't use 'em for performance. And so it's really- - I mean, that's a big one, right? So you can't utilize for the compilation, for the just-in-time compilation, okay. - Yep, yep, exactly. And this al..."
Chris elaborates on the concept of value semantics in Mojo, which allows collections to behave like proper values, reducing bugs and improving code reliability. He discusses the challenges of implementing this feature efficiently and how it can enhance performance without the overhead of traditional copying methods.
"And so that's really cool because, you know, to me as a idealizing compiler language type of person, what I wanna do is I wanna get magic out of the compiler and put in the libraries. Because if someb..."
Chris shares insights into the early development of Mojo and the importance of community feedback. He discusses the challenges of balancing rapid development with quality assurance and the roadmap for future features. This segment highlights the collaborative nature of Mojo's development and the excitement surrounding its potential.
"- So one of the exciting things we should mention here is that this is new and fresh. This cake is unbaked. It's almost baked. You can tell it's delicious, but it's not fully ready to be consumed. - Y..."
In this concluding segment, Chris reflects on the historical tensions between different programming communities, particularly between systems engineers and machine learning practitioners. He expresses a vision for Mojo to bridge these gaps and foster collaboration, emphasizing the shared goals of creating beautiful and functional programming tools.
"if you work with the community, right? And so, yes, it can be frustrating, can be challenging for lots of people involved. And, you know, if you, I mean, you mentioned our Discord. We have over 10,000..."
Chris Lattner discusses the concept of value semantics in the Mojo programming language, explaining how it allows collections like arrays and dictionaries to behave like proper values. This design principle helps prevent bugs by ensuring that when you pass an array to a function, you get a logical copy, allowing for safer data manipulation without unintended side effects.
"And so you pass that pointer to an array around to things. If you pass into a function, they may take that and scroll away in some other data structure. And so you get your array back and you go to us..."
Lattner contrasts Mojo's value semantics with Python's approach to object handling, particularly in database classes. He explains how Python requires developers to be aware of object ownership and the potential for bugs when passing objects around, leading to the need for defensive copying to avoid unintended modifications.
"You have to be careful to implement it in an efficient way. - Yeah, is there a performance hit that's significant? - Generally not if you implement it the right way, but it requires a lot of very low-..."
In this segment, Chris Lattner elaborates on Mojo's lazy copying mechanism, which allows for efficient data handling by only creating copies when necessary. This approach minimizes performance hits while maintaining the integrity of data across different functions and operations.
"This is- - You just kinda have to know that. - You just have to kinda know that, right? And so you roll out version one of the database. You just kinda have to know that. Of course, Lex uses his own d..."
Lattner explains the concept of ownership in Mojo, drawing parallels with Rust and Swift. He discusses how Mojo allows developers to pass references to objects without copying them, which is crucial for managing complex data types like atomic numbers and database handles in systems programming.
"so I've worked with it in Swift, is you say, okay, well, let's provide value semantics. And so we wanna provide the view that you get a logically independent copy, but we wanna do that lazily. And so ..."
Chris Lattner addresses the complexities involved in systems programming, particularly when dealing with unique types that cannot be copied or moved. He emphasizes the importance of designing a language that can handle these complexities while providing a user-friendly experience.
"And so it's, again, it's not novel research at all, right? The magic is getting the design right so that you can do this in a reasonable way, right? And so there's a number of components that go into ..."
In this segment, Lattner reflects on the evolution of programming languages, highlighting how Mojo incorporates ideas from various languages like Rust and C++. He discusses the importance of adapting to new programming paradigms while maintaining usability for developers.
"so that you get the power of something like the Rust programming language, but you don't have to deal with it when you don't want to, which is a major thing in terms of teaching and learning and being..."
Lattner shares his thoughts on concurrency in programming, particularly the challenges it presents in terms of trust and reliability. He discusses the significance of atomic operations and how they relate to concurrent programming, emphasizing the need for robust design to manage shared resources.
"Like, once you create, it has to be where it was, right? Now, if you look at many other examples, like a database handle, right, so, okay, well, what happens? How do you copy a database handle? Do you..."
Chris Lattner discusses the challenges of deploying machine learning models, particularly the divide between researchers and deployment teams. He highlights the complexities involved in getting models into production and the need for a more integrated approach to streamline the process.
"And so it's not that anybody's right or wrong, it's about how do we build one system that scales? - By the way, the idea of an atomic number has been something that always brought me deep happiness, b..."
In this segment, Lattner compares TensorFlow and PyTorch, discussing their respective strengths and weaknesses in terms of usability and deployment. He notes how PyTorch has gained popularity among researchers due to its ease of use, while TensorFlow excels in deployment capabilities.
"enabling unparalleled efficiency. By the way, the use of unparalleled in that sentence... Anyway. (Chris chuckling) Enabling unparalleled efficiency, scale, and the reliability for the largest workloa..."
Lattner explains how Modular aims to address the complexities in machine learning deployment by providing a platform that simplifies the process. He discusses the challenges faced by teams when transitioning models from research to production and how Modular seeks to bridge that gap.
"And so I think that we as an industry have been struggling. And if you look at what deploying a machine learning model today means is that you'll have researchers who are, I mean, wicked smart, of cou..."
Chris Lattner concludes by discussing the overarching challenge of complexity in the AI industry. He emphasizes the need for innovative solutions to manage the intricate systems involved in machine learning, from hardware to software, and the importance of rethinking traditional approaches.
"but you've got Python on one side and C++ on the other side. Python doesn't really deploy. I mean, can theoretically, technically in some cases, but often a lot of production teams will wanna get thin..."
Chris Lattner discusses the pervasive complexity in AI and machine learning systems, highlighting the challenges faced by researchers and developers. He emphasizes that this complexity is often an unseen enemy in the industry, affecting hardware, modeling, and deployment. Lattner argues for a reevaluation of existing systems to simplify and innovate, enabling faster progress in AI technology.
"in terms of their capabilities and gigantic scale, many researchers using PyTorch, right? And so PyTorch doesn't have those same capabilities. And so what Modular can do is it can help with that. Now,..."
Lattner explains how Modular aims to address the complexities in AI by leveraging their team's extensive experience with various systems like TensorFlow and PyTorch. He emphasizes the need for a first-principles approach to rethink existing technologies, allowing for better integration and innovation in machine learning frameworks.
"And why is that? Well, it's because, again, these systems have been built up over many years. They haven't been rethought, there hasn't been a first principles approach to this. And so what Modular's ..."
Chris Lattner articulates Modular's mission to transition AI technology from research to practical applications. He discusses the importance of building a programming language that can handle complex algorithms and hardware, aiming to make advanced AI capabilities accessible to a broader range of developers and researchers.
"in terms of all this really cool technology across compilers and systems and runtimes and heterogeneous computing, like all this kinda stuff. And like, all this technology has been, you know, I wouldn..."
Lattner shares his philosophy on hardware innovation in AI, arguing that specialization is essential as Moore's law reaches its limits. He discusses the diverse needs of AI applications, from supercomputers to mobile devices, and the importance of unlocking new hardware innovations to enhance AI capabilities.
"Like, we've built auto-tuning in many systems. We've built programming languages, right? And so like, have implemented C++, have implemented Swift, have implemented many of these things. And so, you k..."
Chris Lattner delves into the challenges posed by the increasing complexity of AI systems, particularly the need for effective compilers. He explains how the industry has shifted towards viewing AI development as a compiler problem, which can exclude many talented individuals who are not compiler specialists.
"but I have sympathy for the poor software people, right? I mean, again, I'm a generally software person too. I love hardware, but software people wanna build applications and products and solutions th..."
Lattner discusses how Modular aims to reintroduce programmability into AI development, allowing a wider range of developers to contribute to the stack. He emphasizes the need for a system that accommodates both algorithmic innovation and hardware advancements without requiring deep compiler knowledge.
"of a general purpose programming language that can then be compiled across all these kinds of hardware. - Well, so it's not really about a programming language. I mean, the programming language is a c..."
In this segment, Chris Lattner explains the concept of heterogeneous runtimes, where different types of processors work together in a system. He illustrates how modern devices, like smartphones, utilize multiple specialized components to optimize performance and efficiency in AI applications.
"and much more general research hackability together, right? - And that's enabled by the ASICs. That's enabled by certain hardware. So it's like- - Well. - Where's the dance between, I mean, there's se..."
Lattner elaborates on the optimization challenges in AI, particularly how to effectively utilize different hardware components for various tasks. He discusses the importance of understanding communication costs and operation times to make informed decisions about where to allocate computational resources.
"And on the other hand, you have, I don't know how many pieces of hardware there are out there. It's a lot, (chuckles) okay? It's not even hundreds. It's probably thousands, okay? And across all of edg..."
Chris Lattner discusses the challenge of managing complexity in computing systems, particularly in smartphones with multiple specialized processors. He emphasizes the need for a unified approach to leverage the strengths of different systems while minimizing the complexity that arises from their interactions.
"multi-layer abstraction of hierarchical parallelism, hierarchical, asynchronous communication and making that, again, my enemy, is complexity. By getting that away from being different specialized sys..."
Lattner explains the optimization problem in computing, focusing on how to effectively schedule tasks across different processors. He introduces the concept of auto-tuning, where algorithms are used to optimize performance by intelligently distributing workloads among CPUs, GPUs, and neural net accelerators.
"do matrix operations over there, right? But then you realize you get a little bit more complicated because you can do matrix multiplication on a GPU, you can do it on a neural net accelerator, you can..."
In this segment, Lattner explores how machine learning can be applied to optimize computing tasks. He discusses the challenges of searching complex optimization spaces and how genetic algorithms and reinforcement learning can be utilized to enhance performance in computing systems.
"Let's actually do a search of the space to decide, well, what is actually better, right? Well, then you get into this problem where you realize this is not a small space. This is a many-dimensional hy..."
Chris Lattner shares insights on how Mojo aims to unlock the full potential of hardware by addressing memory bottlenecks in machine learning tasks. He highlights the importance of efficient data handling and the need for scalable solutions that can adapt to evolving hardware capabilities.
"- You started and did a little bit of a whirlwind overview of how you get the 35,000x speed up or more over Python. Jeremy Howard did a really great presentation about sort of the basic, like, looking..."
Lattner introduces the concept of kernel fusion as a method to improve performance in machine learning applications. He explains how combining operations can reduce memory access and enhance computational efficiency, particularly in the context of modern accelerators.
"but you get bottleneck sending data back and forth to memory, whether it be local memory, or distant memory, or disk, or whatever it is. And that bottleneck, particularly as the training sizes get lar..."
In this segment, Lattner discusses the future of Mojo as a superset of Python, emphasizing its potential to run Python code with significant performance improvements. He outlines the challenges of integrating with CPython and the vision for seamless compatibility with existing Python libraries.
"each version of the chip comes out with new features and they change things so that it takes more time or less to do different things. And you can't rewrite all the software whenever a new chip comes ..."
Chris Lattner concludes by discussing the importance of creating a unified programming ecosystem that simplifies the development process. He emphasizes the need for tools that can handle the complexity of modern computing while providing developers with the flexibility to optimize their code effectively.
"It's built by different teams, it's built by different hardware vendors, it's built by different systems, it's built by different internet companies. They're trying to solve their problems, right? And..."
Chris Lattner explains how Mojo achieves full compatibility with CPython by utilizing its existing interpreter and objects. He discusses the complexities of working with CPython's low-level details, including reference counting and pointer management, and how Mojo simplifies the interaction between Python and C code, allowing for performance enhancements while maintaining compatibility.
"So it's running its own bike codes, and that's how it provides full compatibility. And then it gives us CPython objects, and we use those objects as is. And so that way we're fully compatible with all..."
In this segment, Lattner discusses the rationale behind Mojo's development as an alternative to improving CPython. He highlights the significant performance advantages Mojo offers, particularly in AI and GPU applications, compared to the incremental improvements being made to CPython. Lattner emphasizes the importance of Mojo's design in addressing the limitations of Python.
"that's really important to you, you can move it to Mojo. You get massive performance benefits on that and other advantages. You know, if you like static types, it's nice if they're enforced. Some peop..."
Chris Lattner shares insights on the future of converting Python code to Mojo, discussing the potential for automation in this process. He explains the current limitations and the vision for creating tools that will facilitate a smooth transition for developers, allowing them to incrementally adopt Mojo while maintaining their existing Python codebases.
"And again, we're not working forwards from making Python a little bit better. We're working backwards from what is the limit of physics? - What's the process of importing Python code to Mojo? Is there..."
Lattner delves into the technical and philosophical challenges of making Mojo a superset of Python. He reflects on the complexities of maintaining compatibility with Python's dynamic features while introducing new capabilities. This segment highlights the importance of thoughtful design in ensuring Mojo can coexist with Python without fragmenting the community.
"Mojo just gives you superpowers, right? And so if you wanna stay with Python, that's cool, but the tooling should be actually very beautiful and simple because we're doing the hard work of defining a ..."
In this segment, Chris Lattner draws parallels between Mojo and Swift, discussing the lessons learned from Swift's transition from Objective-C. He emphasizes the need for gradual adoption of new features and the importance of integrating with existing codebases to avoid disruption. Lattner shares his vision for how Mojo can evolve while respecting the Python ecosystem.
"And it also means we don't wanna repeat the Python 2 to Python 3 transition. Like we want people to be able to adopt this stuff quickly. And so by doing that work, we can help lift people. - Yeah, the..."
Lattner recounts his experiences with Clang and the challenges of ensuring compatibility with legacy code. He discusses the trade-offs between creating a theoretically perfect language and addressing the messy realities of existing codebases. This segment underscores the importance of practical solutions in software development.
"Back in time, project I worked on is called Clang. Clang, what it is a C++ parser, right? And when I started working on Clang, it must have been like 2006 or something, less, or 2007 something, 2006 w..."
Chris Lattner speculates on the potential for AI to assist in modernizing legacy codebases. He discusses how AI tools could help developers transition to newer languages like Mojo, addressing the complexities of maintaining and updating existing software systems.
"and argue with them and try to get them to rewrite their code, right? - Yeah. - And- - [Lex] 'Cause that breaks a lot more things. - Yeah. The reality is like nobody actually even understands how the ..."
In this segment, Lattner shares his conversations with Guido van Rossum, the creator of Python, regarding Mojo. He discusses Guido's concerns about community fragmentation and the importance of ensuring that Mojo complements Python rather than competing with it. Lattner emphasizes the collaborative spirit of Mojo as part of the Python family.
"- You mentioned Guido, the benevolent dictator for life of Python. What does he think about Mojo? Have you talked to him much about it? - I have talked with him about it. He found it very interesting...."
Lattner outlines his vision for how Mojo can integrate with the existing Python ecosystem, particularly in relation to popular libraries like NumPy and TensorFlow. He discusses the importance of creating Python interfaces for Mojo types to facilitate smooth transitions for developers and ensure that both communities can coexist and benefit from each other.
"- I would not say that. I think that would drive a lot of people really crazy. - Because of the PTSD of the 3.0, 2.0. - I'm willing to annoy people about Emacs versus Vim or- - Not that one. - [Chris]..."
Chris Lattner discusses the importance of integrating Mojo with Python, emphasizing the need for seamless communication between the two languages. He envisions a future where popular Python packages like NumPy and TensorFlow can interface with Mojo, allowing developers to transition smoothly without abandoning their existing Python codebases.
"or a new thing in Swift, or you build a new thing in Mojo, then you want it to be end up being used on the other side, (chuckles) right? And so then you need to work on integration back the other way...."
Lattner explains how Mojo is designed to complement existing machine learning frameworks like TensorFlow and PyTorch rather than replace them. He highlights the challenges faced by developers in rewriting code and how Mojo aims to enhance performance and usability in AI applications without forcing a complete overhaul of existing systems.
"- So for a package, port it to Mojo, and then create a Python interface. - [Chris] Yep. - So when you're on these packages, NumPy PyTorch TensorFlow. - Yeah. - How do they play nicely together? So is ..."
Reflecting on his experience with Swift for TensorFlow, Lattner shares insights into the challenges of adopting new programming languages in the machine learning space. He discusses the importance of eager execution and how the timing of technology adoption can significantly impact success.
"I love Python, I love TensorFlow, I love PyTorch, right? This is about making the world better because we need AI to go further. - But if I have a process that trains a model and I have a process that..."
Lattner explores the evolution of programming languages in the context of machine learning and AI. He compares Mojo and Julia, discussing their different focuses and the challenges of gaining traction in a Python-dominated landscape.
"- You're saying this is an irrational set of life choices I make or what? (chuckles) (Lex laughing) - Did you go to the desert and did you meditate on it? Okay, all right. No, it was bold. It was bold..."
Chris Lattner articulates the reasons behind Python's popularity, including its extensive package ecosystem, ease of integration, and low startup time. He emphasizes Python's role in machine learning and its appeal to new programmers, making it a staple in educational settings.
"of different projects, which I'll skip over, right? One of which was this Swift for TensorFlow project, right? And so that project was a research project. And so the idea of that is say, okay, well, l..."
Lattner discusses the potential future of Mojo and the challenges it faces in gaining adoption. He highlights the importance of solving real problems for developers and the need for low adoption costs to encourage users to transition from established languages like Python.
"Second thing is that, you know, a lesson learned is that Swift is a very fast and efficient language, kind of like Mojo, but a different take on it still, really worked well with eager mode. And so ea..."
In this segment, Lattner reflects on the psychological aspects of programming, particularly the excitement of achieving significant performance improvements. He acknowledges that while some developers enjoy the thrill of learning new languages, many prefer to focus on productivity and efficiency.
"I wish it did, of course, right, but, you know, it's a research project. And so you're there to learn from it. - Well, it's interesting to think about the evolution of programming as we come up with t..."
Chris Lattner discusses the Mojo programming language and its potential impact on the programming ecosystem. He emphasizes that Mojo is not meant to replace existing languages like Python but to coexist within a diverse ecosystem of programming languages, each with its unique strengths and trade-offs.
"Like, I'm not saying Python is bad. Like, I hope Python, CPython, like, all these implementations 'cause Python ecosystems, not just CPython, it's also a bunch of different implementations with differ..."
Lattner explores the excitement programmers feel when they achieve significant performance improvements, such as making code run ten times faster in Mojo. He warns, however, that this pursuit of optimization can lead to excessive time spent on tweaking code rather than focusing on broader project goals.
"for why you should try Mojo would be. So practically speaking- - [Chris] Yeah. - it seems like, so there's entertainment. There's the dopamine hit of saying, holy, this is 10 times faster. This little..."
In this segment, Chris Lattner speculates on the future of Mojo and its adoption among programmers. He suggests that while some may dive deep into rewriting their code, the more common path will be gradual adoption, where developers find new ways to simplify and enhance their existing codebases.
"It's very hard to predict, of course, but, you know, if you look 10 years from now and Mojo's super successful. - [Chris] Yeah. - What do you think would be the thing where people like try and then us..."
Lattner reflects on the rapid evolution of computing over the past two decades, highlighting the shift from single-core to multi-core CPUs and the rise of AI. He argues that as technology continues to advance, programming languages like Mojo must adapt to keep pace with increasingly complex hardware.
"the people that don't actually care about the language, that just care about getting stuff done, those people do like learning new things, right? - [Lex] Yeah. - And so you talk about the dopamine rus..."
Chris Lattner discusses his vision for Mojo as a universal programming platform that can adapt to the evolving landscape of hardware and software. He emphasizes the importance of creating a system that allows developers to scale their applications without the need for constant rewrites.
"in the deep end and rewrite everything and, like, whatever, that's cool, right? But I think the middle path is actually the more likely one where it's, you know, you come out with a a new idea and you..."
In this segment, Lattner addresses the challenges of getting developers to adopt Mojo, particularly those who are already entrenched in other languages. He acknowledges that while some may be resistant to change, others will see the benefits of adopting new technologies that enhance their workflow.
"Today, we have CPU, GPUs, graphics. And AI, because it's so important, because the compute is so demanding because of the smart cameras and the watches and all the different places that AI needs to wo..."
Lattner outlines the key advantages of Mojo, including performance improvements and portability across devices. He discusses how Mojo aims to simplify the development process while providing robust features that cater to both performance and safety.
"a few major libraries that go Mojo first? - Well, so I mean, the Modular engines on Mojo. (chuckles) So again, come back to, like, we're not building Mojo because it's fun. We're building Mojo because..."
Reflecting on his experience with Swift, Lattner shares insights into the importance of iterative development and community feedback. He emphasizes the need to launch Mojo with realistic expectations and to prioritize quality over speed in its evolution.
"to rethink these things. - So the two big gains are that there's a performance gain and then there's the portability to all kinds of different devices. - And there's safety, right? So you talk about r..."
Chris Lattner introduces Mojo Playground, a cloud-based platform for developers to experiment with Mojo. He explains the current limitations and the team's commitment to improving the user experience while gathering valuable feedback from early adopters.
"and iterating and evolving things. And so you look at this LLVM thing that I worked on 20 years ago, and you look at MLIR, right? And so a lot of the lessons learned in LLVM got fed into MLIR, and I t..."
In this segment, Lattner discusses the importance of setting realistic expectations for Mojo's capabilities at launch. He reflects on past experiences with Swift and the lessons learned about managing developer expectations and ensuring a smooth transition for users.
"people wanna download it around locally. So we're working on that right now. And so- - So that's the goal, to be able to download locally to it. - Yeah, that's what everybody expects. And so we're wor..."
Lattner shares insights into the overwhelming positive response to Mojo since its launch. He discusses the excitement within the developer community and the importance of addressing feedback to refine the language and its features.
"I signed up to work for Apple because I love Objective-C. Why is there a new thing?," right? - Yeah. - And so now what that meant practically is that the push from launch to first of all the fall, but..."
Chris Lattner articulates the vision behind Mojo, emphasizing its potential to address deep-rooted issues in the Python ecosystem. He highlights the team's commitment to creating a language that is not only powerful but also accessible to a broader audience.
"If you're interested in what we're doing, we'll do it in an open way and we can do it together, but don't use it in production yet. Like, we'll get there, but let's do it the right way. And I'm also s..."
Lattner acknowledges the significant influence of Jeremy Howard in the development of Mojo. He praises Howard's contributions to the machine learning community and his advocacy for making advanced programming concepts more accessible.
"in a sense, right? - Yeah. - And so, I mean, if you go back in time when we started Modular, which is just not yet a year and a half ago, so it's still a pretty new company, new team, small but very g..."
In this segment, Lattner discusses the pragmatic approach to machine learning that Jeremy Howard embodies. He emphasizes the importance of balancing performance with accessibility, making advanced techniques approachable for a wider audience.
"I think people are excited about ownership and taking a step beyond Rust, right? And there's people that are very excited about that and there's people that are excited about, you know, just like I ma..."
Chris Lattner discusses Jeremy Howard's refreshing and pragmatic view on machine learning, emphasizing the balance between efficiency and accessibility. He highlights how Jeremy dives deep into the mechanics of machine learning, focusing on understanding rather than just experimenting, which ultimately aims to make AI more practical and useful for everyone.
"It's all his fault. - Well, there's a kinda very refreshing, pragmatic view that he has about machine learning that I don't know if it, it's like this mix of a desire for efficiency, but ultimately gr..."
Lattner explains the challenges faced in AI research, particularly the fragility of systems that often only work under ideal conditions. He notes that Jeremy Howard's efforts push the boundaries of AI, but the research often involves navigating complex and fragile systems that can fail outside of the 'happy path.'
"And so Jeremy has been really pushing the limits. And one of the things I'll say about Jeremy, and there's many things I could say about Jeremy, 'cause I'm a fanboy of his, but it fits in his head, an..."
The conversation shifts to the complexities of Python packaging, with Chris Lattner acknowledging the significant pain points developers face. He discusses the challenges of building, distributing, and managing dependencies in Python, especially when integrating C code, which complicates the packaging process.
"And as you say, he then brings it and teaches people- - [Lex] Teaches it. - And his mission is to help lift, you know, his website says "making AI uncool again," like it's about, like, forget about th..."
Lattner shares his thoughts on how Mojo could address the packaging challenges faced by Python developers. He suggests that Mojo's new tools and technologies might allow for a reevaluation of packaging systems, potentially simplifying the process and improving the developer experience.
"- So from the perspective of, if you want to create your own package, and then - Yes, yeah. - or you wanna build on top of a bunch of other people's packages and then they get updated and things like ..."
The discussion touches on the difficulties of discovering packages within Python's ecosystem. Lattner expresses surprise at how challenging it is to explore and find packages, despite the vastness of the ecosystem, highlighting a need for better search and discovery mechanisms.
"- So almost a tangent to that question from the user perspective of packages. It was always surprising to me that it was not easier to sort of explore and find packages, you know, with, with PIP insta..."
Lattner reflects on the challenges of decentralized open-source communities, comparing them to centralized platforms like GitHub. He discusses how centralized solutions can help solve discovery problems and build a more cohesive community, suggesting that similar solutions could benefit package management in Mojo.
"like search and discovery as YouTube calls it. - Well, I mean, it is kinda funny because this is one of the challenges of these like intentionally decentralized communities. And so- - Yeah. - I don't ..."
Chris Lattner elaborates on the complications that arise when integrating C with Python, noting that the lack of a package manager for C adds to the challenges. He suggests that Mojo could help reduce the complexity of this integration, making it easier for developers to work across both languages.
"They can go figure that out. - But the point is to have an interface that's usable, that's successful to people of all different skill levels and- - So, well, and again, like what are the benefit of s..."
The conversation shifts to function overloading, with Lattner explaining its significance in Mojo compared to Python. He discusses how Mojo's type system allows for different behaviors based on input types, enhancing safety and predictability in programming.
"- So any kinda packages that are hybrid in nature would be a natural fit to move to Mojo, which- - Which is a lot of them, by the way. - Yeah. - So a lot of them, especially that are doing some intere..."
Lattner contrasts dynamic languages like Python with typed languages, explaining how the latter can provide more predictable behavior. He discusses the challenges of retrofitting typing into existing Python code and how Mojo aims to address these issues with its design.
"Why does Python not have function overloading? - So I can speculate. So Python is a dynamic language. The way it works is that Python and Objective-C are actually very similar worlds if you ignore syn..."
Chris Lattner introduces the distinction between 'def' and 'fn' in Mojo, where 'fn' represents a stricter function definition. He explains how this strictness can enhance predictability and error checking, catering to developers who prefer more control over their code.
"and a function for strings. Well, even if you could put it in that dictionary, you'd have to have the collar do the dispatch. And so every time you call the function, you'd have to say like, is it an ..."
Lattner emphasizes the significance of API design in programming languages, drawing lessons from his experience at Apple. He discusses how effective API design can mitigate the challenges posed by exceptions and enhance the usability of programming languages like Mojo.
"that program Python might be interested in is def versus fn. So it's two different ways to define a function. - Yep. - And fn is a stricter version of def. What's the coolness that comes from the stri..."
The discussion delves into the concept of zero-cost exception handling in programming languages, particularly C++. Lattner critiques this approach and contrasts it with newer languages like Mojo, which treat exceptions similarly to return values, improving performance and API design.
"but you don't have to use it. And this is a way that Mojo is both compatible 'cause defs work the same way that defs have already always worked, but it provides a new alternative that gives you more c..."
Lattner explains the complexities of exception handling during compilation, discussing how errors propagate through functions. He highlights the implications of different handling strategies on API design and performance, particularly in the context of compiled languages.
"And if you typo something, it doesn't matter. It just tells you it's fine, right? And so I think that the trades are very different if you're building a, you know, large scale production system versus..."
In this segment, Chris Lattner explains how Mojo's compiler manages error handling seamlessly behind the scenes. He discusses the complexities involved in ensuring that errors are handled correctly without disrupting the flow of the program. This insight into compiler design showcases the innovative approaches taken in Mojo to enhance programming efficiency.
"- Yeah, yeah. So the way to think about it is, think about a function that doesn't return anything, just as a simple case, right? And so you have function one calls function two, calls function three,..."
Lattner and Fridman discuss the concept of nested functions and closures in programming languages. Lattner shares his thoughts on the benefits and complexities of allowing nested functions, highlighting their utility in certain programming patterns. This segment dives into the nuances of function design and its impact on code readability and maintainability.
"- Exactly. And so the competitor does all this for you. And I mean, one of the things, if you dig into how this stuff works in Python, it gets a little bit more complicated because you have finally bl..."
Chris Lattner outlines the future features planned for Mojo, including tuple support and keyword arguments. He emphasizes the importance of building a robust programming language that meets the needs of developers while avoiding unnecessary syntactic sugar. This segment provides a glimpse into the strategic direction of Mojo and its commitment to enhancing developer experience.
"actually really great and for certain things, right? And so these are also called closures. Closures are pretty cool and you can pass callbacks. There's a lot of good patterns. And so- - So speaking o..."
Lattner discusses the implementation of traits in Mojo, which allow for the definition of abstract types and generics. He explains how this feature enhances the expressivity of the language and draws parallels with other languages like Swift and Rust. This segment highlights the significance of traits in modern programming and their role in type safety.
"and I'm not gonna go into ring theory or something, but the, you know, you can say it's arithmetic. Like if you can add, subtract, multiply, divide it for example, right? And so what you're saying is ..."
In this segment, Chris Lattner explains Mojo's approach to memory management, focusing on how values are destroyed automatically to optimize memory usage. He contrasts this with traditional methods in languages like C++ and discusses the benefits of early destruction of variables. This insight into memory management showcases Mojo's innovative design choices aimed at improving performance.
"And so we're making incredible progress. By the way, Mojo's only, like, seven months old. So that's another interesting thing. - Well, I mean part of the reason I wanted to mention some of these thing..."
Lattner shares his philosophy on language design, emphasizing the importance of building a solid foundation for Mojo. He discusses the iterative process of gathering community feedback and making design decisions that prioritize usability and performance. This segment reflects on the broader implications of language design in the context of modern programming.
"If you look at C++, the way this works is that if you define a variable or a set of variables within a function, they get destroyed in a last in, first out order. So it's like nesting, okay. This has ..."
Chris Lattner talks about the importance of community engagement in the development of Mojo. He highlights the need for feedback on new features and the iterative nature of language design. This segment underscores the collaborative approach taken by the Mojo team to ensure the language meets the needs of its users.
"It's the same thing as the Swift team, right? And this is where one of the reasons we released Mojo much earlier is so we can get feedback and we've already like renamed a keyword data community feedb..."
In this closing segment, Lattner reflects on the future of programming languages and the role of Mojo in shaping that future. He discusses the challenges and opportunities that lie ahead for developers and the importance of innovation in programming language design. This segment encapsulates the vision for Mojo and its potential impact on the programming landscape.
"- I mean, it's funny you come back to the flame emoji file extension, right? You know, we have the option to use the flame emoji, which just even that concept, 'cause for example, the people at GitHub..."
Chris Lattner discusses the concept of syntactic sugar in the Swift programming language, emphasizing its potential distractions from core abstractions. He highlights the importance of engaging with the Python community to ensure that new features align with broader design principles.
"Swift has a lot of syntactic sugar, which may may be a good thing, maybe not, I don't know. But because it's such an easy and addictive thing to do, sugar, like make sure blood get crazy, right? Like,..."
Lattner shares insights on building a strong team culture at Modular AI. He emphasizes the importance of creating an inclusive environment and having a clear vision to avoid conflicts and ensure that team members can work effectively together.
"And syntactic sugar just makes all that more complicated. So. - And yeah, list comprehension. Is that yet to be implemented? Yeah. And my favorite d I mean, I dictionaries. - Yeah, there's some basic ..."
Chris Lattner addresses the challenges of recruiting specialized talent in the tech industry. He explains how Modular AI focuses on building an amazing culture to attract the best programmers, despite the competitive landscape of big tech.
"And I've built a lot of teams, a lot of different places. If you zoom in from the big problem into recruiting, well, so here's our problem, okay. I'll be very straightforward about this. We started Mo..."
Lattner discusses the importance of understanding customer pain points when building products. He reflects on his experiences at Apple and how focusing on solving real problems leads to better product development.
"And so what we've done is we've said, okay, well, let's build an amazing culture. Start with that. That's usually not something a company starts with. Usually you hire a bunch of people and then peopl..."
Chris Lattner shares his philosophy on product development, emphasizing the need for conviction and focus on core problems. He illustrates this with examples from Apple's history, highlighting the importance of patience and understanding in the development process.
"And so we have a clear vision of what we're doing. And so I kind of grew up at Apple in my engineering life, right? And so a lot of the Apple DNA rubbed off on me. My co-founder Tim also is like a str..."
Lattner discusses the necessity of offering competitive salaries and benefits to attract top talent. He highlights the advantages of being a remote-first company while acknowledging the challenges that come with it.
"Yeah. But famously, if you go back to the iPhone, for example, right? iPhone 1, every, I mean, many people laughed at it because it didn't have 3G, it didn't have copy and paste, right? And then a yea..."
Chris Lattner reflects on the value of in-person collaboration and brainstorming sessions. He discusses the challenges of remote work and the need for occasional face-to-face interactions to foster team bonding and creativity.
"and we get the whole company together periodically, and then we get work groups together and we plan and execute together. - And there's like an intimacy to the in-person brainstorming. Yeah, I guess ..."
Lattner shares insights on the challenges of remote work, including communication barriers and the loss of spontaneous interactions. He emphasizes the importance of creating systems to maintain team cohesion and collaboration.
"that happens that's Zoom. - And you're bonding. Yeah. - You're bonding, you're bonding. But through that bonding, you get the excitement. There's certain ideas are like complete. And you'll see that i..."
Chris Lattner discusses his philosophy on team composition, advocating for diversity in skills and perspectives. He explains how balanced teams lead to better problem-solving and innovation.
"- What about people that are kinda specialized in very different aspects of the stack working together? What are some interesting challenges there? - Yeah, well, so I mean, I mean, there's lots of int..."
Lattner reflects on the liberating experience of building Modular AI from scratch. He contrasts this with his previous roles, emphasizing the excitement of working on projects that directly address customer needs.
"And so I think that's really important. - That's team. But what about building a company as ambitious as Modular? So what are some interesting questions there? - Oh, I mean, so many. Like, so one of t..."
Chris Lattner explores the implications of large language models (LLMs) in programming. He discusses how LLMs can assist in code generation and the philosophical questions they raise about creativity and innovation in software development.
"And so that's also pretty exciting and quite nice, but one of the ways that this manifests is it makes it easier to make decisions. And so one of the challenges I've had in other worlds is it's like, ..."
Lattner shares his optimistic view on the future of programming with LLMs, suggesting they will enhance productivity by automating routine tasks. He emphasizes that human creativity and problem-solving will remain essential.
"- This is awesome. So I have to ask you about one of the interesting developments with large language models is that they're able to generate code recently. Really? Well, yes. To a degree that maybe, ..."
Chris Lattner discusses the potential for integrating LLMs with the Mojo programming language. He speculates on how LLMs could assist in learning and using Mojo, while also addressing the challenges of ensuring clarity in programming languages.
"There's mistakes. They're interesting that you learn from, but I just, it would love to get your opinion first high level. Yeah. Of what you think about this impact of large language models when they ..."
Lattner contemplates the future of LLMs in compiler technology. He discusses the balance between efficiency and the potential for LLMs to enhance the coding process, while also considering the implications of using LLMs in production environments.
"And just like, you know, I think we all try to scale through delegation and things like this, delegating rote things to an LLVM I think is an extremely valuable and approach that will help us all scal..."
Chris Lattner addresses the philosophical implications of AI deployment, discussing both the potential benefits and risks. He reflects on the broader societal impacts of AI and the importance of responsible development.
"or the human and the compiler, roughly, right? And so I think the LMS will have no problem learning whatever keyword we pick. - Maybe the fire emoji is gonna, oh. - Maybe that's gonna break it. It doe..."
Chris Lattner discusses the dual nature of large language models (LLMs) in creative tasks versus production code. He highlights the potential of LLMs to enhance creative brainstorming while cautioning against their reliability for critical coding tasks. Lattner suggests that future research could focus on building more reliable systems that balance creativity with correctness.
"I mean, I think that would be interesting. - Is that wise? - Well, well, I mean, it would be very expensive. So compilers run fast and they're very efficient and LMS are currently very expensive. Ther..."
Lattner humorously proposes that advanced AI systems could be developed using Mojo, emphasizing its suitability for scalable AI training and inference. He reflects on the philosophical implications of AI's rapid advancement and the potential benefits and risks associated with its deployment in society.
"and that could be interesting. But if you've chased that rabbit hole down, the question then becomes, how do you express your intent to the machine? And so maybe you want LLLM to provide the spec, but..."
Chris Lattner shares his thoughts on the challenges and opportunities presented by AI technologies. He expresses a calm perspective on the future of artificial general intelligence (AGI), acknowledging the complexities of AI deployment while emphasizing the slow pace of human adaptation to these changes.
"So it's just, I have to ask you about the big philosophical question about human civilization. So folks like Eli Kowski are really concerned about the threat of AI. - Yeah. - Do you think about the go..."
In this segment, Lattner contrasts optimistic and pessimistic views on AI's impact on civilization. He advocates for a positive outlook, suggesting that AI could unlock new potentials and improve lives by automating repetitive tasks, rather than posing a threat to humanity.
"- Well, that's in the physical space. I do think in the digital spaces, the stuff that's built on top of LLMs that runs, you know, the millions of apps that could be built on top of them, and that cou..."
Lattner envisions a future where programming becomes more accessible through simplified tools and technologies. He emphasizes the need to reduce complexity in programming languages and AI integration, allowing more people to engage with these technologies and fostering innovation.
"to improve our lives, and I want that to go faster. - So what do you think the future of programming looks like in the next 10, 20, 30, 50 years? That alums, LLMs and with Mojo, with Modular, like you..."
Chris Lattner offers practical advice for young individuals interested in programming. He encourages them to pursue their passions, build projects, and embrace learning through hands-on experience. Lattner also highlights the value of specialization and the importance of carving unique paths in the tech industry.
"And so one of the things that I'm a little bit concerned about is right now the big companies are investing huge amounts of money and are driving the top line of AI capability forward really quickly. ..."
In the closing segment, Lex Fridman expresses gratitude to Chris Lattner for his insights and contributions to the field of AI and programming. Lattner acknowledges Fridman's curiosity and commitment to fostering thoughtful discussions about technology and its implications for society.
"- And now the herd is using Python. So if you wanna be a rebel, go check out Mojo and help Chris and the rest of the world fight the arch nemesis of complexity 'cause simple is beautiful. - There we g..."