Exploring Machine Learning's Evolution with Elixir Featuring Sean Moriarity

In a recent podcast, Sean Moriarity, known for his work on Genetic Algorithms in Elixir and the Axon Library, delves into the distinctions and overlaps between AI, machine learning, deep learning systems, neural networks, and chat models. He explains how Elixir's capabilities can significantly contribute to the development of machine learning tools and systems. Moriarity points out the practical uses of large language models in areas like code generation, education, and workflow optimization. Elixir's strengths in deployment, development experience, and real-time processing are touted as particularly suitable for ML tasks. The conversation also addresses common challenges in machine learning such as data cleaning and labeling, difficulties in language translation, and dealing with bias in translation models. Insights into how turning to machine learning algorithms transformed Moriarity's personal interests, such as sports betting, are also shared. Additionally, the episode covers starting points for Elixir and machine learning projects, the relevance of attention mechanisms in neural networks, and the work on a LiveView interface for ChatGPT. Essential resources mentioned include Moriarity's book, repository links for Axon and other related libraries, and the potential of using publicly available datasets for learning more about machine learning.