The Comet Newsletter, Issue #11: Announcing Comet Artifacts, the case for automating morals, and more

The Comet Newsletter's issue #11 highlights the introduction of Comet Artifacts, a new suite of tools designed to enhance data management in machine learning (ML) experiment pipelines by allowing teams to log, version, and access data efficiently. These Artifacts create a structured approach to manage experiment outputs and inputs, facilitating the construction of multi-stage pipelines and ensuring managed access to intermediate data. Additionally, the newsletter features industry insights, including a critique of OpenAI Codex's limitations in solving code-based problems compared to other GPT-3 versions, and a discussion on the complexities of automating moral decisions in ML models. It also includes a detailed resource on explaining Transformer models, emphasizing their significance in deep learning through visualizations of their architectures.