Musings on whether the “AI Revolution” is more like the printing press or crypto. (Spoiler: it’s neither.) I am not nearly the first person to sit down and really think about what the advent of AI means for our world, but it’s a question that I still find being asked and talked about. However, I think most of these conversations seem to miss key factors. Before I begin, let me give you three anecdotes that illustrate different aspects of this issue that have shaped my thinking lately.

Much of the division between technology and human activity is artificial — how do people make our work possible? We don’t talk enough about how much manual, human work we rely upon to make the exciting advances in ML possible. The truth is, the division between technology and human activity is artificial. All the inputs that make models are the result of human effort, and all the outputs in one way or another exist to have an impact on people.

Do our definitions of “mistake” make sense when it comes to ML/AI? If not, why not? A comment on my recent post about the public perception of machine learning got me thinking about the meaning of error in machine learning. The reader asked if I thought machine learning models would always “make mistakes”. As I described in that post, people have a strong tendency to anthropomorphize machine learning models. When we interact with an LLM chatbot, we apply techniques to those engagements that we have learned by communicating with other people—persuasion, phrasing, argument, etc.