The word “curiosity” has a fairly well-defined meaning in the Reinforcement Learning literature (see for instance this paper). There are vast numbers of papers that try to come up with ways to give an agent intrinsic rewards that map onto the human understanding of “curiosity”, and almost all of them are some form of “go towards states you haven’t seen before”. The predictable consequence of prioritising states you haven’t seen before is that you will want to change the state of the universe very very quickly.
Novelty is important. Going towards states you have not seen before is important. This will be a part of the new system, that’s for sure.
But this team is under no obligation to follow whatever current consensus might be (if there is a consensus). Whatever is the state of the field, it can’t claim a monopoly on how words “curiosity” or “novelty” are interpreted, what are the good ways to maximize them… How one constrains going through a subset of all those novel states by aesthetics, by the need to take time and enjoy (“exploit”) those new states, and by safety considerations (so, by predicting whether the novel state will be useful and not detrimental)… All this will be on the table...
Some of the people on this team are known for making radical breakthroughs in machine learning and for founding new subfields in machine learning. They are not going to blindly copy the approaches from the existing literature (although they will take existing literature into account).
The word “curiosity” has a fairly well-defined meaning in the Reinforcement Learning literature (see for instance this paper). There are vast numbers of papers that try to come up with ways to give an agent intrinsic rewards that map onto the human understanding of “curiosity”, and almost all of them are some form of “go towards states you haven’t seen before”. The predictable consequence of prioritising states you haven’t seen before is that you will want to change the state of the universe very very quickly.
Novelty is important. Going towards states you have not seen before is important. This will be a part of the new system, that’s for sure.
But this team is under no obligation to follow whatever current consensus might be (if there is a consensus). Whatever is the state of the field, it can’t claim a monopoly on how words “curiosity” or “novelty” are interpreted, what are the good ways to maximize them… How one constrains going through a subset of all those novel states by aesthetics, by the need to take time and enjoy (“exploit”) those new states, and by safety considerations (so, by predicting whether the novel state will be useful and not detrimental)… All this will be on the table...
Some of the people on this team are known for making radical breakthroughs in machine learning and for founding new subfields in machine learning. They are not going to blindly copy the approaches from the existing literature (although they will take existing literature into account).