Reading List From Summer 2023

A curated collection of papers, articles, and resources I found interesting during Summer 2023. This list spans across machine learning, AI safety, adversarial robustness, large language models, and various other topics in AI research. These resources helped shape my understanding of current challenges and opportunities in the field.

This collection represents a diverse range of topics from cutting-edge AI research to practical implementation guides. Many of these resources helped shape my understanding of current challenges and opportunities in machine learning, particularly around model robustness, AI safety, and the responsible deployment of large language models.

The list includes everything from foundational papers on adversarial robustness and model interpretability to practical guides on optimizing deep learning workflows. I found the resources on AI safety and responsible AI deployment particularly valuable as the field grapples with the rapid advancement of large language models and their societal implications.

Some standout resources include the work on representation engineering for AI transparency, various approaches to defending against adversarial attacks on LLMs, and insights into the practical challenges of deploying robust ML systems in production environments.