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Programming Language Design and Implementation in the Era of Machine Learning [video]

Chris Lattner and other experts discuss how machine learning is reshaping programming language design and implementation, exploring topics such as ML-driven compiler optimizations, hardware-aware code generation, and the future of AI-assisted programming tools in a workshop-style talk.

Background

- Traditional programming languages are designed for humans to write step-by-step instructions. The rise of ML (especially large language models that generate code) is upending this: AI doesn't "think" in loops, types, or explicit logic the way humans do. - Chris Lattner is a legendary compiler and language designer — he created LLVM (a foundational toolchain used by Apple, NVIDIA, and most modern languages), the Swift language, and MLIR (a compiler framework for AI). He's currently building Mojo, a language for AI/ML performance. - Key tension: Should we keep languages human-friendly and let AI adapt? Or should we design new languages that are easier for LLMs to generate and verify? The talk explores how language features (e.g., strong typing, static vs dynamic, functional vs imperative) interact with LLM code generation. - Practical stakes: If AI generates most code in the future, language design priorities shift — readability for humans matters less, while tractability for AI verification, tooling, and compilation matters more. This could reshape how new languages are built.

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Programming Language Design and Implementation in the Era of Machine Learning [video]

Background

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