I like the spirit behind this — do science rather than rely on highly abstract, semi-philosophical conceptual arguments. But I have my doubts about the feasibility of the recommended course of action.
It’s hard for me to imagine the sort of data you’d want to know not already being covered in existing machine learning research.
Maybe more importantly, I’m really not sure this is the right scientific approach. For example, why not study the sort of real world tasks that would need to be automated for a software intelligence explosion to occur? What are the barriers to those being automated? For instance: data efficiency, generalization, examples of humans performing these tasks to train on, or continual learning/​online learning. How much are deep learning systems and deep reinforcement learning systems improving on those barriers? This seems to get to the heart of the matter more than what is suggested above.
I like the spirit behind this — do science rather than rely on highly abstract, semi-philosophical conceptual arguments. But I have my doubts about the feasibility of the recommended course of action.
It’s hard for me to imagine the sort of data you’d want to know not already being covered in existing machine learning research.
Maybe more importantly, I’m really not sure this is the right scientific approach. For example, why not study the sort of real world tasks that would need to be automated for a software intelligence explosion to occur? What are the barriers to those being automated? For instance: data efficiency, generalization, examples of humans performing these tasks to train on, or continual learning/​online learning. How much are deep learning systems and deep reinforcement learning systems improving on those barriers? This seems to get to the heart of the matter more than what is suggested above.