SE Radio 588: José Valim on Elixir, Machine Learning, and Livebook
Nov 2, 2023
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José Valim, creator of Elixir, discusses Livebook and the machine learning ecosystem for Elixir. They cover tensors with Nx, machine learning with Scholar, data munging with Explorer, deep learning with Axon, Bumblebee, and Huggingface, and model creation basics.
Elixir was created to address the need for writing concurrent software and was influenced by functional programming and the Erlang virtual machine.
LiveBook is a computational notebook platform that combines coding, documentation, and a rich interactive environment, offering potential for exploring and visualizing data.
Bumblebee, built on top of Elixir and Axon, provides easy access to pre-trained neural networks for machine learning tasks, such as speech-to-text or image recognition.
Deep dives
Creating Elixir and the Challenges Faced
Jose Valim discusses the challenges that led him to create Elixir, including the need to write concurrent software and the influence of functional programming and the Erlang virtual machine.
Introducing LiveBook: A Computational Notebook
Jose Valim explains LiveBook, a computational notebook platform that integrates coding, documentation, and a rich interactive environment. He highlights its features and its potential for exploring and visualizing data.
Bumblebee: Pretrained Neural Networks for Machine Learning
Jose Valim introduces Bumblebee, a project built on top of Elixir and Axon that provides access to pre-trained neural networks for machine learning tasks. He explains how it allows users to easily download and run existing models for tasks such as speech-to-text or image recognition.
The Live Book Notebooks and the Importance of Plain Text
Live Book notebooks in Elixir provide a way to share and collaborate on code in a readable format. Unlike other formats, Live Book notebooks are plain text, making it easier to review code, submit changes, and make code reveals. The notebooks are based on a subset of markdown, allowing for a specific structure and easy integration with platforms like GitHub. This solves the problem of opaque document formats and enables more efficient and transparent code sharing and collaboration.
Understanding Machine Learning in Elixir
Machine learning in Elixir involves defining models that can execute tasks that would be difficult or error-prone for humans to do manually. Machine learning models are trained using data sets and training sets, which allow the model to learn from the data and later execute the desired tasks without explicit instructions. Elixir's suitability for machine learning is due to its ability to integrate with native code, enabling the use of libraries like Google XLA and DiveTorch. This integration allows Elixir to leverage existing machine learning models and algorithms, making it a viable option for machine learning projects.
José Valim, creator of the Elixir programming language, Chief Adoption Officer at Dashbit, and author of three programming books, speaks with SE Radio host Gavin Henry about what Elixir is today, what Livebook is, the five spearheads of the new machine learning ecosystem for Elixir, and how they all fit together. Valim describes why he created Elixir, what “the beam” is, and how he pitches it to new users. This episode examines things you can do with Livebook and how it is well-aligned with machine learning, as well as why immutability is important and how it works. They take a detailed look at a range of topics, including tensors with Nx, traditional machine learning with Scholar, data munging with Explorer, deep learning and neural networks with Axon, Bumblebee and Huggingface, and model creation basics. Brought to you by IEEE Computer Society and IEEE Software magazine.
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