Towards Explainable and Accessible AI
Brandon Duderstadt, Yuvanesh Anand · 2023
Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks.Unfortunately, the explainability and accessibility of these models has lagged behind their performance.State-of-the-art LLMs require costly infrastructure, are only accessible via rate-limited, geo-locked, and censored web interfaces, and lack publicly available code and technical reports.Moreover, the lack of tooling for understanding the massive datasets used to train and produced by LLMs presents a critical challenge for explainability research.This talk will be an overview of Nomic AI's efforts to address these challenges through its two core initiatives: GPT4All (Anand et al., 2023) and Atlas.