A couple more thoughts on “what dataset/environments are necessary for training AGI”:
In your subfield of NLP, even if evaluation is difficult and NLP practitioners find that they need to develop a bunch of application-specific evaluation methods, multi-task training may still yield a model that performs at a human level on most tasks.
Moving beyond NLP, it might turn out that most interesting tasks can be learned from a very simple and easy-to-collect format of dataset. For example, it might be the case that if you train a model on a large enough subset of narrated videos from YouTube, the model can learn how to make a robot perform any given task in simulation, given natural language instructions. Techniques like LORL are a very small-scale version of this, and LORL-like techniques might turn out to be easy to scale up, since LORL only requires imperfect YouTube-like data (imperfect demonstrations + natural language supervision).
Daniel points out that humans don’t need that much data, and I would point out that AI might not either! We haven’t really tried. There’s no AI system today that‘s actually been trained with a human-equivalent set of experiences. Maybe once we actually try, it will turn out to be easy. I think that’s a real possibility.
A couple more thoughts on “what dataset/environments are necessary for training AGI”:
In your subfield of NLP, even if evaluation is difficult and NLP practitioners find that they need to develop a bunch of application-specific evaluation methods, multi-task training may still yield a model that performs at a human level on most tasks.
Moving beyond NLP, it might turn out that most interesting tasks can be learned from a very simple and easy-to-collect format of dataset. For example, it might be the case that if you train a model on a large enough subset of narrated videos from YouTube, the model can learn how to make a robot perform any given task in simulation, given natural language instructions. Techniques like LORL are a very small-scale version of this, and LORL-like techniques might turn out to be easy to scale up, since LORL only requires imperfect YouTube-like data (imperfect demonstrations + natural language supervision).
Daniel points out that humans don’t need that much data, and I would point out that AI might not either! We haven’t really tried. There’s no AI system today that‘s actually been trained with a human-equivalent set of experiences. Maybe once we actually try, it will turn out to be easy. I think that’s a real possibility.