Daniel Herrmann ‘17
Tell us about your Experiential Learning. How was your EL related to your Question or Keystone?
For my Experiential Learning, I did an internship at BenchSci, a bioscience start-up. It was directly related to my Question, How should we create artificial general intelligence?, which looked at both the societal/ethical and technical challenges of building an AGI. I developed both my intuition and technical knowledge about machine learning, which is one of the main contemporary approaches to AI.
How did you find your EL opportunity?
I was invited by someone I knew through Quest to attend the Rotman School of Management’s Machine Learning and the Market for Intelligence conference in Toronto. At the conference there was a startup booth session, where machine-learning startups displayed their products and business models. I spent time at almost every booth, and maintained relationships with some of the people I met. As summer came closer, I asked about internship opportunities, and secured a position with one of the startups. I used code I wrote in one of my Quest courses to demonstrate my capabilities.
How did your EL inform the path you’re on now?
For one thing, I realized I didn’t really want to work in a start-up! At the time, I was considering going into either technical philosophy or machine learning, and this experience pushed me towards the former. The intuition and know-how around neural networks and statistical prediction that I built during my EL have really helped me in my current PhD research, where I evaluate the practices and methodology of science, including neural networks.
Did you have any major revelations during your EL?
Yes! I came to the conclusion that machine learning is less advanced (powerful/developed) than I thought, but more advanced (powerful/developed) than most people think. That is, it is incredibly and surprisingly successful at what it does (function approximation), and yet is not a solution for all of the problems of AI. This was a major insight, and helped guide my interest towards understanding other approaches to AI and reasoning in general. I wouldn’t have been able to appreciate machine learning’s power or limitations without getting my hands dirty.