Latent Space: The AI Engineer Podcast β€” Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 cover image

Latent Space: The AI Engineer Podcast β€” Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0

Doing it the Hard Way: Making the AI engine and language πŸ”₯ of the future β€” with Chris Lattner of Modular

Sep 14, 2023
01:29:22
Snipd AI
Chris Lattner, Product counterpart on Tensorflow, discusses his work on building a unified AI engine and the challenges of AI software. They also talk about the evolution of AI development, the importance of collaboration, and the limitations of current AI systems. They highlight the development of MojoπŸ”₯, its production quality, and investment, as well as George's work on a tensor contraction compiler. They touch upon the benefits of Mojo and its upcoming features, including building a strong community and adding traits to the Mojo language.
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Podcast summary created with Snipd AI

Quick takeaways

  • Mojo is a Python superset that combines dynamic and static typing for improved performance and productivity.
  • Mojo takes a holistic approach and aims to provide a more inclusive solution by starting from general-purpose computing.

Deep dives

The Mojo Language and Compiler

Mojo is a Python superset that aims to provide a more efficient and productive programming experience. One of the main design goals of Mojo is to combine the benefits of dynamic and static typing, allowing developers to choose the right approach for their specific needs. The language relies on a compiler rather than an interpreter, resulting in improved performance and no global interpreter lock. The heap representation in Mojo differs from traditional Python, and it also utilizes MLIR (Multi-Level Intermediate Representation) technology. Other notable features of Mojo include memory safety inspired by Rust, as well as a focus on asynchronous programming for better concurrency and performance.

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