Search engines are essential to research across all fields. With the evolving landscape of Artificial Intelligence, search engines are also evolving at a rapid pace. Just to name a few innovations from the past decade, we have witnessed the shift from keyword-matching to semantic matching using language models, we are seeing chatbots (with retrieval augmented generation) replacing the traditional search engine completely, and we are also seeing the first few examples of deep research agents. In this rapidly evolving landscape, professionals increasingly find it difficult to keep up with the latest advancements. In addition, many AI based search technologies are designed primarily around the needs and workflows of computer science and engineering, and they provide more immediate benefits in these disciplines than in fields such as the humanities, where research practices, data organization, and information access follow vastly different conventions.

As AI becomes ever more capable, the field of AI alignment is increasingly in the spotlight—we certainly don’t want to accidentally build the proverbial paperclip optimizer that turns the solar system into paperclips. Indeed, it is a primary area of research for one of the authors of this blog post. Although improving AI alignment is at present time clearly directionally good, we argue here that perfect alignment is both impossible and undesirable.

Hi everyone! I’m an assistant professor at Carnegie Mellon and research scientist at Google Deepmind. My appointment is in the Language Technologies Institute, but I’m broadly interested in how AI is and could soon be affecting how we as humans learn, communicate with each, build tools, and build trust in systems and eachother. For a while now, I’ve wanted to create a place for I and my students and friends to post things we’ve been thinking about. My goal is to encourage myself (and my students) to do more low-stakes writing. Hopefully, this blog ends up as a platform for us to write about ideas, positions, and projects that don’t fit neatly into an AI conference paper.