Chris Lattner: Future of Programming and AI | Lex Fridman Podcast #381

The video is about Chris Lattner, a brilliant engineer who has made significant contributions to the field of computing, including the creation of the LLVM compiler infrastructure project, the Swift programming language, and key contributions to TensorFlow and TPUs as part of Google. He is now co-creator of a new full-stack AI infrastructure for distributed training, inference, and deployment on all kinds of hardware called Modular, and a new programming language called Mojo that is a superset of Python with the performance of C++ in many cases. The conversation with Chris Lattner focuses on the vision for Mojo and how it aims to make machine learning more accessible, understandable, and usable by normal people and researchers.

This video by Lex Fridman was published on Jun 2, 2023.
Video length: 03:34:04.

The video is about Chris Lattner, a brilliant engineer who has made significant contributions to the field of computing.

He discusses his vision for the future of programming and AI, and his new project, Modular, which is a full-stack AI infrastructure for distributed training, inference, and deployment on all kinds of hardware. He also talks about his new programming language, Mojo, which is a superset of Python that offers the performance of C++ in many cases. Lattner explains that Mojo is optimized for AI and is designed to be accessible, usable, and understandable by normal people and researchers.

He also discusses the challenges of programming new hardware and the importance of creating a programming language that can take advantage of the power and innovation of all the smart people in the world.

• Chris Lattner is a brilliant engineer who has made significant contributions to the field of computing.
• He is the co-creator of a new full-stack AI infrastructure for distributed training, inference, and deployment on all kinds of hardware called Modular, and a new programming language called Mojo.
• The conversation with Chris Lattner focuses on the vision for Mojo and how it aims to make machine learning more accessible, understandable, and usable by normal people and researchers.
• Mojo is a superset of Python with the performance of C++ in many cases, demonstrating over 30,000 X speed up over Python.
• Mojo code has demonstrated over 30,000 X speed up over Python if you love machine learning and Python.
• The explosion of innovation in AI has led to it's not just about multiplication and convolution, these things have now like 2,000 different operators.
• There is a lot of hardware coming in on the other hand, but it's a lot part of my thesis that computing goes if you look out 10 years from now it's not going to get simpler.
• Mojo is optimized for AI and is a super set of Python, allowing for more accessible, understandable, and usable machine learning.
• The conversation with Chris Lattner is mind-blowing and gets pretty technical at times.

Chris Lattner: Future of Programming and AI | Lex Fridman Podcast #381 - YouTube

Section 1: Introduction

• The video is about Chris Lattner, a brilliant engineer who has made significant contributions to the field of computing.
• He is the co-creator of a new full-stack AI infrastructure for distributed training, inference, and deployment on all kinds of hardware called Modular, and a new programming language called Mojo.
• The conversation with Chris Lattner focuses on the vision for Mojo and how it aims to make machine learning more accessible, understandable, and usable by normal people and researchers.

Section 2: The Vision for Mojo

• The vision for Mojo is to build a universal platform that can continue to get weirder as physics progresses.
• The goal is to help lift people's scale and allow them to do things with it without having to rewrite their code every time a new device comes out.
• Mojo is a superset of Python with the performance of C++ in many cases, demonstrating over 30,000 X speed up over Python.
• Mojo code has demonstrated over 30,000 X speed up over Python if you love machine learning and Python.

Section 3: The Challenges of AI

• The explosion of innovation in AI has led to it's not just about multiplication and convolution, these things have now like 2,000 different operators.
• There is a lot of hardware coming in on the other hand, but it's a lot part of my thesis that computing goes if you look out 10 years from now it's not going to get simpler.

Chris Lattner: Future of Programming and AI | Lex Fridman Podcast #381 - YouTube

Section 4: Conclusion

• The conversation with Chris Lattner is mind-blowing and gets pretty technical at times.
• Mojo is optimized for AI and is a super set of Python, allowing for more accessible, understandable, and usable machine learning.

Section 1: File Extensions

• File extensions are metadata that indicate the type of file.
• They are not essential for understanding the content of a file.
• Emojis are a type of file extension that are used to add color and visual appeal to text.
• Emojis are not supported on all operating systems, such as Windows.
• Git, a version control system, does not escape emojis correctly, which can cause issues when displaying them in the file explorer.

Section 2: Emojis in Code

• Some programming languages, such as Swift, allow emojis to be used as variable names.
• The internet has made emojis a popular choice for naming variables, but this can be difficult to review and understand in code.
• Mojo, a new programming language, is designed to support emojis as part of the code.
• Mojo is built on top of Modular, an AI-first software stack that aims to solve the problems of AI infrastructure.

Section 3: Mojo and Modular

• Mojo is a programming language that is designed to be a superset of Python with the performance of C++ in many cases.
• Modular is the software stack that supports Mojo and other AI-related technologies.
• Modular is focused on building AI infrastructure and solving the problems of AI deployment and scalability.
• Mojo is designed to be a general-purpose programming language that can be used for a variety of tasks, including AI and GPU/CPU optimization.

Section 4: Mojo and AI

• Mojo is designed to be an AI-first programming language that is built on top of Modular, an AI-focused software stack.
• Mojo is designed to simplify the world of AI by providing a single language and optimization tool for a variety of tasks.
• Mojo is designed to be a general-purpose programming language that can be used for a variety of tasks, including AI and GPU/CPU optimization.
• Mojo is designed to be a superset of Python with the performance of C++ in many cases, making it a powerful tool for AI development.

Mojo Programming Language

• Mojo is a programming language that allows for higher-level programming of low-level programming.
• It is a superset of Python with the performance of C++ in many cases.
• Mojo aims to make machine learning more accessible, understandable, and usable by normal people and researchers.
• It is designed to be intuitive and feel like writing natural language English.
• Mojo has a rich ecosystem of packages and humans that do it, making it viral and growing.

Python

• Python is a popular programming language known for its simplicity and ease of use.
• It has a large ecosystem of packages and humans that do it, making it viral and growing.
• Python is well-suited for machine learning, with TensorFlow and PyTorch embracing it.
• Python's universal connector helps bring together different systems and build larger systems without having to understand how they work.
• One of the problems with Python is that it can be slow, but this is usually what people complain about.

Indentation in Programming

• Indentation is a design decision in programming that can be controversial.
• Tabs and spaces are two common ways to format code, but indentation is often preferred.
• Indentation can make code more readable and easier to understand.
• Modern large-scale code bases often use automatic formatters to ensure consistent indentation.
• Python style indentation is popular and can lead to fewer errors due to formatting issues.

Mojo's Syntax

• Mojo is a superset of Python, meaning it includes all of Python's syntax and features.
• Mojo also has some new syntax and features that are specific to it.
• Mojo's syntax is designed to be intuitive and easy to understand.
• Mojo's syntax is similar to Python's, but with some differences that make it more efficient.
• Mojo's syntax is designed to be more performant than Python's, while still being easy to read and understand.

Mojo's Indentation

• Mojo uses Python-style indentation, which is popular and easy to read.
• Mojo's indentation is designed to be more beautiful and consistent than other languages.
• Mojo's indentation is based on the idea of grouping, making it easier to understand the code's structure.
• Mojo's indentation is designed to be more efficient than other languages, while still being easy to read and understand.
• Mojo's indentation is based on the idea of reducing clutter and making the code more readable.

Section 1: Introduction

• The video is about Chris Lattner, a brilliant engineer who has made significant contributions to the field of computing.
• The conversation with Chris Lattner focuses on the vision for Mojo and how it aims to make machine learning more accessible, understandable, and usable by normal people and researchers.

Section 2: Python and Mojo

• Python is a popular AI language that is not done yet, and Mojo is an extension of Python that aims to add features that are missing from Python.
• Mojo is an interpreted language that is also compiled and statically compiled, making it suitable for a variety of use cases.
• Mojo has powerful meta programming features that allow for dynamic and expressive programming, making it suitable for machine learning use cases.

Section 3: Compile Time Meta Programming

• Mojo uses compile time meta programming, which allows for expressive APIs and overloaded operators.
• Automatic differentiation and eager mode in pytorch and tensorflow use dynamic and powerful features at runtime, but Mojo can lift these features to run at compile time.
• Mojo's compile time meta programming allows for more efficient programming in specific cases, such as on embedded processors or GPUs.

Section 4: Templates and Code Instantiation

• Mojo has templates and code instantiation features that allow for efficient programming at compile time.
• Some modern languages, such as Zig, have similar features that allow for efficient programming at runtime.
• One of the problems with C++ is that its meta programming system, templates, is a completely different universe from the normal runtime programming world.

Compiler Design

• Mojo is a new approach to compiler design that builds on existing technologies like MLIR.
• It includes other technologies like caching and other interpreters and JIT compilers.
• Mojo has an interpreter inside the compiler, which unifies the standard model of programming languages with the runtime model.
• This makes programming languages more composable and easier to understand, reason about, and use.
• Mojo also has meta-programming features, which are powerful and make it simpler, more consistent, and easier to use.

Machine Learning

• Machine learning is a meta-programming problem because it requires mapping compute across different ways.
• Auto tuning and adaptive compilation are useful features in machine learning because they allow for better performance and efficiency.
• Auto tuning turns out that hardware systems and algorithms are really complicated, and most programmers don't want to know the details.
• Auto tuning allows people to build more abstracted but also more portable code.
• Auto tuning does this by carving algorithms into blocks and doing those blocks in parallel, and then finding the best way to do it on the system.

Compiler Optimization

• The compiler has an interpreter that is used for meta-programming.
• The interpreter that meta-programming now has to take the code and run it on a Target machine.
• Part of the compilation is machine-specific.
• Ultimate performance is important for a couple of reasons.
• Reducing the latency of a model leads to better products.

Auto-tuning

• Auto-tuning is used for tasks like towel size and TPUs.
• It is a simple statement in the code that can be set and forgotten.
• It helps in a whole bunch of different ways.
• It helps in handling the problem of not everybody caring about performance.
• It helps in taking advantage of all the new features without breaking the old thing.

Python vs. C

• Python is an interpreter and slower than C.
• Moving to a compiler and getting the interpreter out of the loop is two to five to ten X speed up.
• C Python started to lay out data in a way that keeps the memory representation of all objects the same.
• Modern computers don't like chasing pointers very much.
• Reference counting is another way of keeping track of memory in Python.

Mojo

• Mojo is a superset of Python with the performance of C++ in many cases.
• It can provide a 35,000 X speed up over Python.
• It is the default Python that everybody uses when you type python3.
• It is an interpreter and slower than C.
• It is a new programming language that is being developed.

Section 1: Introduction to Mojo

• Mojo is a new programming language created by Chris Lattner.
• It is a superset of Python with the performance of C++ in many cases.
• Mojo allows developers to take advantage of powerful features built into modern hardware.
• It has nice features that make it easy to parallelize code.
• Mojo is designed to make machine learning more accessible, understandable, and usable by normal people and researchers.

Section 2: Basics of Mojo

• Mojo is a typed language, unlike Python which has dynamic types.
• Mojo allows for multiple types, including objects and integers.
• The compiler can use the type of a variable to optimize code completion.
• Mojo allows developers to progressively adopt types into their programs.
• Types in Mojo are not strict, but they can reduce errors and improve performance.

Section 3: Mojo's Approach to Typing

• Mojo's approach to typing is different from Python's.
• Mojo uses a different approach to inject types into its programs.
• The importance of types is recognized in Mojo, but it is not strict typing.
• Mojo's approach to typing is seen as a rebellion against the idea that there is only one right way to do things.
• Mojo's approach to typing is seen as a way to reduce errors and improve performance.

Section 4: Benefits of Mojo

• Mojo allows developers to take advantage of powerful features built into modern hardware.
• Mojo has nice features that make it easy to parallelize code.
• Mojo is designed to make machine learning more accessible, understandable, and usable by normal people and researchers.
• Mojo's approach to typing is seen as a way to reduce errors and improve performance.
• Mojo is a powerful weapon against slowness, which is why people have been having fun with it.

Introduction

• Chris Lattner is a brilliant engineer who has made significant contributions to the field of computing.
• He is the co-creator of a new full-stack AI infrastructure for distributed training, inference, and deployment on all kinds of hardware called Modular, and a new programming language called Mojo.
• Mojo is a superset of Python with the performance of C++ in many cases.

The Vision for Mojo

• Mojo allows people to use types and if you use types you get nice things out of it, such as better performance and things like this.
• Mojo is a full compatible superset of Python and supports all the dynamic things that Python supports, such as packages that support for comprehension, list comprehensions, and things like this.
• Mojo aims to make machine learning more accessible, understandable, and usable by normal people and researchers.
• Mojo is designed to be a hardcore language that tries to do in terms of what it tries to do in the language which is the philosophy that if you look at Python right, Python's a beautiful language because it's so extensible.

Implementation of Typing in Mojo

• Mojo has a different implementation of typing compared to Python.
• In Python, people started using static analysis tools to enforce some variants and check for bugs, try to identify things.
• The Python Community started to standardize the syntax for adding types to Python.
• In Mojo, if you declare a type and you use it, it means it is going to be that type and the compiler helps you check that and force it.

Benefits of Using Mojo

• Mojo allows people to use types and if you use types you get nice things out of it, such as better performance and things like this.
• Mojo is a full compatible superset of Python and supports all the dynamic things that Python supports, such as packages that support for comprehension, list comprehensions, and things like this.
• Mojo aims to make machine learning more accessible, understandable, and usable by normal people and researchers.

Section 1: Introduction to Mojo

• Mojo is a new programming language created by Chris Lattner.
• It is designed to make machine learning more accessible, understandable, and usable by normal people and researchers.
• Mojo is a superset of Python with the performance of C++ in many cases.
• It allows for the use of structs, classes, and dynamic and static classes.
• Mojo aims to provide high performance and allow for the implementation of strings, integers, floats, arrays, and other data types in the language.

Section 2: The Vision for Mojo

• Mojo aims to provide magic to the compiler and put in libraries.
• It allows for the implementation of different data types and their special promotion rules.
• Mojo aims to be a standard implementation of numerical types.
• It allows for the use of buffers and tensors in an ML context.
• Mojo is still being built and needs to be refined and driven forward by experts in the field.

Section 3: The Current State of Mojo

• Mojo is still in the early stages of development and is not yet fully ready to be consumed.
• It is currently being optimized for building it the right way and building it the right way is interesting because everybody wants it yesterday.
• Mojo is being developed with the community and a big roadmap is being published.
• It is being transparent about the development process and a lot of people are involved in it.
• Mojo is being designed to be optimized for building it the right way and building it the right way is interesting because everybody wants it yesterday.

Section 4: The Future of Mojo

• Mojo is expected to be way more interesting to a variety of people in the future.
• It is being optimized for building it the right way and building it the right way is interesting because everybody wants it yesterday.
• Mojo is being developed with the community and a big roadmap is being published.
• It is being transparent about the development process and a lot of people are involved in it.
• Mojo is being designed to be optimized for building it the right way and building it the right way is interesting because everybody wants it yesterday.

Introduction

• Chris Lattner is a brilliant engineer who has made significant contributions to the field of computing.
• He is the creator of the LLVM compiler infrastructure project, the Swift programming language, and key contributions to TensorFlow and TPUs as part of Google.
• He is now co-creator of a new full-stack AI infrastructure for distributed training, inference, and deployment on all kinds of hardware called Modular, and a new programming language called Mojo that is a superset of Python with the performance of C++ in many cases.

Experience

• Working with the community is important for the success of a project.
• The Discord community has over 10,000 people and is very effective.
• Mojo is a new programming language that aims to make machine learning more accessible, understandable, and usable by normal people and researchers.

Roadmap

• The roadmap for Mojo is based on a logical order in which to build features and capabilities.
• The roadmap is not completely arbitrary and is based on the logical order in which to build features.
• The roadmap includes adding features deliberately to the project.

Bug Fixes

• The project has very few bugs, which is impressive for a project in its state.
• The project is dropping in features very deliberately.

Features

• The project has a roadmap for adding features and capabilities.
• The project has already implemented many features.

Performance Improvement

• Immutability is a useful feature that enables value semantics.
• Value semantics make collections like arrays, dictionaries, and tensors behave like proper values.

Value Semantics

• Value semantics take collections like arrays, dictionaries, and tensors and make them behave like proper values.
• It makes it look like if you pass these things around, you get a logical copy of all the data.

Conclusion

• Mojo is a new programming language that aims to make machine learning more accessible, understandable, and usable by normal people and researchers.
• The project has a roadmap for adding features and capabilities, and has already implemented many features.

Section 1: Understanding the Problem

• The problem is that when passing an object to a database, the database assumes it has ownership of the object and cannot be changed after it is added to the database.
• This can lead to bugs and maintenance issues when someone else joins the team and tries to modify the object.
• To solve this problem, the team can change the Python code and database class to copy the record every time it is added to the database.
• This approach is called defensive copying, where a copy of the object is made before it is passed to the database.
• Defensive copying ensures that the original object is not modified by the database and that there are no copies made.

Section 2: Design Patterns

• There are two design patterns for defensive copying: cloning and value semantics.
• Cloning involves making a copy of the object before it is passed to the database, while value semantics involves providing a logically independent copy but not making a copy lazily.
• Cloning is used in many languages, including Swift, and is more efficient than value semantics.
• Value semantics is used in languages like Python and Rust and is more flexible but less efficient.
• The implementation details of defensive copying are tricky and require careful consideration of reference counting and ownership.

Section 3: Implementation Details

• To implement defensive copying in Python, the team needs to make sure that they are efficient and transfer ownership instead of duplicating references.
• The team also needs to design data structures that allow for efficient transfer of ownership.
• Ownership tracking is a key component of defensive copying and is related to work done in the Rust and Swift communities.
• Other languages, such as C++, also have copy constructors and destructors that can be used in defensive copying.
• Mojo takes the best ideas from all these systems and remixes them in a nice way to provide the power of a language like Rust but without the complexity.

Section 4: Argument Conventions and Value Semantics

• Argument conventions are important in systems programming and can affect how value semantics and transfer ownership work with arguments when they are passing different values.
• In systems programming, types like atomic numbers, mutexes, and uniquely owned databases can get weird and require careful consideration.
• Python is a language that allows for copying objects around and mutating them, while systems programming languages like Rust require atomic numbers and mutexes.
• Uniquely owned databases are also a common issue in systems programming and require careful consideration.
• Mojo takes the best ideas from all these systems and remixes them in a nice way to provide the power of a language like Rust but without the complexity.

Mojo: A Language for Machine Learning

• Mojo is a programming language that allows for the expression of references to data instead of copies.
• It allows for the creation of unique objects that cannot be copied or moved.
• Mojo allows for the passing around of references to data without copying it.
• It has borrowed conventions that allow for the use of mutable references.
• Mojo is a smart implementation of C++'s reference kinds of pointers.

Mojo and Machine Learning

• Mojo allows for the expression of references to data instead of copies, which is useful in machine learning.
• It allows for the creation of unique objects that cannot be copied or moved, which is useful in machine learning.
• Mojo allows for the passing around of references to data without copying it, which is useful in machine learning.
• It has borrowed conventions that allow for the use of mutable references, which is useful in machine learning.
• Mojo is a smart implementation of C++'s reference kinds of pointers, which is useful in machine learning.

Mojo and Concurrency

• Moore's Law has led to the trend towards specialization of hardware.
• Multi-core computers and accelerators are becoming more common.
• Programming language nerds and compiler people have been trying to tackle multi-core for years.
• Tensor is an abstract representation of a gigantic paralyzable data set, which enables automatic differentiation and other cool things.
• Mojo allows for the mapping of abstract representations onto parallel machines without having to control the placement of data.

Mojo and Deployment

• Mojo allows for the expression of references to data instead of copies, which is useful in deployment.
• It allows for the creation of unique objects that cannot be copied or moved, which is useful in deployment.
• Mojo allows for the passing around of references to data without copying it, which is useful in deployment.
• It has borrowed conventions that allow for the use of mutable references, which is useful in deployment.
• Mojo is a smart implementation of C++'s reference kinds of pointers, which is useful in deployment.

Modular Compute Platform

• Modular is a platform that dynamically partitions models with billions of parameters and distributes their execution across multiple machines, enabling unparalleled efficiency and scale.
• The platform is designed to handle the largest workloads and provide reliability.
• Modular uses abstracted distributed deployment of large models, which is a tension between usability and complexity.
• Modular is a superset of Python with the performance of C++ in many cases.
• Modular is designed to make machine learning more accessible, understandable, and usable by normal people and researchers.

TensorFlow and PyTorch

• TensorFlow and PyTorch are two popular machine learning frameworks.
• TensorFlow is all about graphs, while PyTorch is good for researchers.
• The challenge with deploying machine learning models is that researchers are not experts in hardware deployment or C++.
• Deploying a machine learning model can take weeks or months, and requires a team of project managers and meetings.
• Modular aims to solve these problems by providing a platform for deploying machine learning models efficiently and reliably.

Language Piece

• Mojo is a new programming language that is a superset of Python with the performance of C++ in many cases.
• Mojo is designed to help with deployment of machine learning models, which is a problem with Python.
• Mojo can help with serving, which is the process of running machine learning models on multiple machines.
• The complexity of serving machine learning models is exploding due to the need for distributed deployment.
• TensorFlow and PyTorch were not designed for distributed deployment, which is a challenge for machine learning.

Challenges and Solutions

• The challenge with distributed deployment of machine learning models is that it requires expertise in hardware deployment and C++.
• Modular aims to solve this problem by providing an abstracted platform for deploying machine learning models efficiently and reliably.
• Modular is designed to be a superset of Python with the performance of C++ in many cases.
• Modular can help with serving, which is the process of running machine learning models on multiple machines.
• Modular is designed to make machine learning more accessible, understandable, and usable by normal people and researchers.

Conclusion

• Modular is a new platform that aims to solve the challenges of deploying machine learning models efficiently and reliably.
• Modular is designed to be a superset of Python with the performance of C++ in many cases.
• Modular can help with serving, which is the process of running machine learning models on multiple machines.
• Modular is designed to make machine learning more accessible, understandable, and usable by normal people and researchers.
• Modular is a promising solution to the challenges of deploying machine learning models in the industry.

Chris Lattner's Vision for Mojo

• Chris Lattner is a brilliant engineer who has made significant contributions to the field of computing, including the creation of the LLVM compiler infrastructure project, the Swift programming language, and key contributions to TensorFlow and TPUs as part of Google.
• He is now co-creator of a new full-stack AI infrastructure for distributed training, inference, and deployment on all kinds of hardware called Modular, and a new programming language called Mojo that is a superset of Python with the performance of C++ in many cases.
• The vision for Mojo is to make machine learning more accessible, understandable, and usable by normal people and researchers.
• Mojo aims to provide a unified programming language that can handle both the modeling and serving aspects of machine learning, making it easier for developers to build and deploy AI applications.
• Mojo is designed to be flexible and adaptable, allowing developers to use it with a wide range of hardware and cloud providers.

Chris Lattner's Mission

• Chris Lattner's mission is to save the world from terrible AI software.
• The mission is not literal but a joking way of saying that they want to make machine learning more accessible, understandable, and usable by normal people and researchers.
• The programming language is a component of the mission, and they need a syntax that enables performance and general research hackability.
• The programming language should be able to compile across all kinds of hardware, making it not just about large-scale engineering projects.
• The programming language should also be able to handle exotic numerics, B float 16, matrix multiplications, and convolutions.

Status Quo

• Tensorflow 1.x and PyTorch are examples of machine learning frameworks that have evolved and become more complicated.
• In the early days, these frameworks were CPUs and Cuda, and the focus was on dense layers and matrix multiplications.
• As more hardware comes in, the focus shifts to more complex operations like kernel fusion.
• The explosion of innovation in AI has led to a large number of operators and different types of hardware.
• AI is now everywhere, and different vendors are making their own chips for different purposes.

Hardware Innovation

• Hardware innovation is a good thing, as it unlocks new possibilities for machine learning.
• Analog and Quantum computing are examples of new and exotic hardware that could have a huge impact on daily life.
• Compilers are a different skill set than building hardware, and specialized compilers are needed for new generation hardware.
• Intel and video companies can keep up with the industry, but there is always room for improvement.
• There are now 1900, 2000, and even 2100 different types of hardware, and the focus should be on making machine learning accessible to all.

Compiler Optimization

• The video discusses the challenges of keeping up with the rapid advancements in computing technology.
• Having a large team of people working on optimizing and tuning the compiler is necessary to keep up with these advancements.
• The industry is still in the phase of making the compiler problem a priority.
• The idea is to use a compiler to provide a more general, extensible, and hackable interface for dealing with the general case.
• Within machine learning algorithms, for example, people have found that fusing activation functions can lead to faster processing times.

Compiler Enumeration

• Compiler people have started enumerating all the algorithms and pairs to generate a kernel for a specific task.
• This has been useful for the industry, as seen in Google's TPUs and PyTorch.
• However, not everyone can or should be a compiler.
• There are people who know analog computers, GPU internal architecture, or sparse numeric algorithms that could benefit from this technology.
• The Modular stack brings programmability back into the world, allowing researchers and hardware innovators to extend and change the stack without having to hack the compiler itself.

Heterogeneous Run Times

• Heterogeneous run times refer to the use of multiple different kinds of things together.
• A simple example of this is a CPU and a GPU working together in a heterogeneous system.
• Modern cell phones have multiple different kinds of CPUs, GPUs, and neural network accelerators working together.
• These multi-core systems allow for faster processing times and more efficient use of resources.
• The Modular stack allows for the extension and hacking of the stack by researchers and hardware innovators, leading to even more advanced and efficient systems.

Hardware Blocks for Media

• Hardware blocks for video decode and jpeg code and things like this
• All these blocks can run at the same time and need to be choreographed right
• Massively complicated system that is not just cell phones and laptops
• All these devices can talk to each other and are fully asynchronous
• Moving things to data flow graphs and higher level of abstractions

Multi-Layer Abstraction of Hierarchical Parallelism

• Multi-layer abstraction of hierarchical parallelism
• Hierarchical asynchronous communication
• Making the system more consistent and uniform
• Leveraging the strengths of different specialized systems
• Looking inside the smartphone and minimizing explicit computation

Graph of Compute and Partitioning

• Building a model for the graph of compute
• Looking at communication, bisection bandwidth, and overhead
• Deciding where to place compute based on optimization problem
• Building different policies on top of predictable execution layer
• Human programmer can fully manually control everything

Auto-Tuning and Searching Hyperdimensional Spaces

• Getting into the mode of not wanting to have to tell it to do it
• People typically take because they write terrible heuristics
• Making the stick better and getting into auto-tune
• Searching the space to decide what is actually better
• Turning into a machine learning problem and using genetic algorithms and reinforcement learning

Section 1: Introduction to Modular

• Chris Lattner is the co-creator of Modular, a new full-stack AI infrastructure for distributed training, inference, and deployment on all kinds of hardware.
• Modular is a superset of Python with the performance of C++ in many cases.
• It aims to make machine learning more accessible, understandable, and usable by normal people and researchers.
• The conversation with Chris Lattner focuses on the vision for Mojo and how it plans to achieve these goals.

Section 2: Mojo and its Architecture

• Mojo is a programming language that sits on top of the Modular stack.
• It is designed to be a full-stack AI infrastructure that can handle distributed training, inference, and deployment on all kinds of hardware.
• Mojo is a superset of Python with the performance of C++ in many cases.
• It allows developers to use additional tools to optimize their code and get the best performance out of a problem.

Section 3: Mojo's Performance Optimization

• Mojo is designed to be fast and efficient, with a focus on performance optimization.
• It allows developers to use additional tools to optimize their code and get the best performance out of a problem.
• Mojo is a superset of Python with the performance of C++ in many cases.
• It is designed to be a full-stack AI infrastructure that can handle distributed training, inference, and deployment on all kinds of hardware.

Section 4: Mojo's Scalability

• Mojo is designed to be scalable, with a focus on handling large datasets and complex models.
• It allows developers to use additional tools to optimize their code and get the best performance out of a problem.
• Mojo is a superset of Python with the performance of C++ in many cases.
• It is designed to be a full-stack AI infrastructure that can handle distributed training, inference, and deployment on all kinds of hardware.

Compiler Infrastructure

• Chris Lattner is a brilliant engineer who has made significant contributions to the field of computing, including the creation of the LLVM compiler infrastructure project, the Swift programming language, and key contributions to TensorFlow and TPUs as part of Google.
• The LLVM compiler infrastructure project is a system and a framework that can generalize and work on problems that are bigger than fit in one human's head.
• The Modular stack provides the ability to walk up to it with a new problem and it'll generally work quite well.
• The state of ML tooling today is not anything that a C programmer would ever accept.
• The specifics of Emily Jeremy's demonstration of the vectorize function, autotune, and tiling show how the Mojo library is built to provide better performance and scalability.

Vectorization

• Vectorization is a technique used in computing to perform multiple operations on multiple data elements at the same time.
• Vectorization is built into the Mojo library, which provides better performance and scalability compared to traditional libraries.

Autotune

• Autotune is a technique used in computing to automatically optimize the performance of algorithms.
• The Mojo library includes an autotune feature that can be used to optimize the performance of machine learning models.

Tiling

• Tiling is a technique used in computing to optimize memory usage and improve performance.
• The Mojo library includes a tiling feature that can be used to optimize memory usage and improve performance in machine learning models.

Python and Mojo

• Mojo is a superset of Python, meaning that it can run Python code as if it's Mojo code.
• People are already using Mojo to optimize small pieces of Python code, resulting in significant speedups.

Mojo: A Full-Stack AI Infrastructure

• Mojo is a new programming language that aims to make machine learning more accessible, understandable, and usable by normal people and researchers.
• It is a superset of Python with the performance of C++ in many cases.
• Mojo will continue to grow out and as it grows out, it will have more and more features.
• The North Stars for Mojo are to be a full superset of Python and to be able to bring over arbitrary Python code and have it just work.
• Mojo will solve for all the problems and you'll be able to move Python packages over and run them in Mojo without the C Python.

Integration with C Python

• Mojo will have an integration layer where it can use C Python to load and use all the existing packages as they are.
• This gives you an incremental migration path.
• It allows you to import an arbitrary existing package and use it in Mojo.
• It is not as fast as it would be in traditional C Python, but it is faster than running it in the traditional C Python way.

Communication with C Python

• Mojo uses the C Python existing interpreter to provide full compatibility.
• It gives Mojo C Python objects and they are used as is.
• Mojo is fully compatible with all the C Python objects and all the C libraries underneath them.
• It requires knowledge of how the C Python interpreter works, including reference counting and different rules on how to pass pointers around.

Debugging and Implementation Details

• By pulling all of this into Mojo, you get one world.
• You get the ability to say "cool, I have untyped, very dynamic, beautiful, simple code."
• You can add types, parallelize things, factorize things, and use general techniques to solve a problem.
• It requires knowledge of not only C, which is a different world from Python, but also the wrappers, the interpreter, and the implementation details and conventions.

Section 1: Mojo's Performance Benefits

• Mojo is a new programming language that is a superset of Python with the performance of C++ in many cases.
• It offers massive performance benefits on various packages and other advantages, such as stack types being enforced instead of hints.
• Mojo can be used incrementally as one goes, and it composes well with what is being done.
• One different perspective on this would be why Mojo instead of making C Python faster or redesigning C Python.
• There are teams working on improving C Python, such as the team at Microsoft that released C Python 3.11, which was 15-20 times faster across the board.

Section 2: Mojo vs. C Python

• Mojo is redesigning C Python, but it is not making C Python faster or better.
• There are other teams working on improving C Python, such as the team at Microsoft.
• Mojo is not fighting with what is being done, but it is a different path.
• Mojo is working backwards from what is the limit of physics and the process of supporting Python code to Mojo.
• There is currently a lack of tooling for this process, and some basic features are missing.

Section 3: Automation of Python to Mojo Conversion

• The process of converting Python to Mojo is not yet fully automatable.
• It is an ongoing process that involves dropping out new features on a weekly basis.
• It is expected that the process will be fully automatable within a year and a half to two years.
• The conversion tooling should give hints as to how to improve the code.
• Once in the new world, modern tools can be built to improve the code.

Section 4: Improving Python Over the Years

• There have been many projects to improve Python over the years, such as making it compatible with the Java virtual machine.
• Some projects are trying to be compatible with Python, while others are saying Python is too complicated and choosing to pass on certain features.
• It is worth it to create a superset of Python that is compatible with all C packages and other features.
• The ecosystem of Python is important, and it means we don't want to limit it to any one package.
• It is worth it to create a superset of Python because it is not about any one package, but about what Python means for the world.

Challenges of Creating a Superset Language

• Design decisions of the subset language can make it more complicated to create a superset.
• Keeping the superset status requires addressing challenges such as having a big integer and dealing with long-tail weird things.
• One of the biggest challenges is dealing with legacy code and ensuring compatibility.
• Creating a superset language requires understanding the design mistakes of the past and learning from them.
• Engineers must do the hard work of figuring out and understanding the behavior of the code, even if it is distasteful.

Building a Superset Language

• Creating a superset language requires understanding the design mistakes of the past and learning from them.
• Engineers must do the hard work of figuring out and understanding the behavior of the code, even if it is distasteful.
• Building a superset language requires addressing challenges such as having a big integer and dealing with long-tail weird things.
• Keeping the superset status requires addressing challenges such as having a big integer and dealing with long-tail weird things.
• Design decisions of the subset language can make it more complicated to create a superset.

Creating a Superset Language: Examples

• Creating a superset language requires understanding the design mistakes of the past and learning from them.
• Engineers must do the hard work of figuring out and understanding the behavior of the code, even if it is distasteful.
• Building a superset language requires addressing challenges such as having a big integer and dealing with long-tail weird things.
• Keeping the superset status requires addressing challenges such as having a big integer and dealing with long-tail weird things.
• Design decisions of the subset language can make it more complicated to create a superset.

Watch the video on YouTube:
Chris Lattner: Future of Programming and AI | Lex Fridman Podcast #381 - YouTube