Over the years, I have been explaining my research to slightly different audiences: students, linguists, mental health experts, Utah faculty learning about AI, etc. Each conversation has been slightly different, but has taught me more about AI. My favorite research moments have involved conversations with people that have shaped how I think about AI systems.

The most interesting AI challenges are not just about scale. I am increasingly wary of techno-solutionism that do not involve context from domain experts. This blog is a structured attempt at working through these kinds of ideas publicly.

What I’ll write about: Research ideas. Lessons from collaborations. AI beyond scale. Lecture notes on topics like neuro-symbolic methods. Thinking about benchmarking beyond metrics. Making AI do interesting things with limited data or compute resources. Commentary about AI and the society.

Who this is for: Anyone interested in building AI under real-world constraints. Researchers working on low-resource languages. People deploying AI in high-stakes settings. Domain experts wondering how AI might help their field. Folks who are optimistically skeptical about AI hype. Or perhaps skeptically optimistic.