Recently, I had the awesome opportunity to give a workshop at the Open Data Science Conference in London, and I discussed what I consider to be a potentially interesting role for LLMs in augmenting academic and non-academic researchers by automating certain groups of tasks.
In this article I want to dive into the core concepts discussed during that workshop, and discuss what I consider a fascinating emerging role for AI through integration with researchers in different fields.
The workshop I presented explored the question:
how can we leverage LLMs to enhance or augment research workflows without diminishing the cognitive engagement of researchers?
Touching on this topic of augmentation is always tricky and can lead to some cringy conversations about how AI will replace humans in the near future. Therefore, for the purpose of clarity I want to start by defining it a bit more concretely:
Augmentation = Enhancing Capability Through Tools
The concept of augmentation is deeply rooted in the work of Douglas Engelbart, who somewhat pioneered a version of this idea that technology should enhance human…