> For example, imagine a robot learning from scratch to pick objects up based on raw pixel data with only a scalar reward function - where in this process is the human preparing the data so the model only has to average?
Great -- so do you have an example of such a system?
I'd be inclined, initially, to deny that it exists. If your reward function expresses a reward for the goal of "picking up objects with (pixel-space) properties etc.", you're cheating. In this case, the reward function serves the role of the data: ie., prepared by us to work. Indeed, a function is just a dataset -- and the reward function here is being sampled by the system.
You'd need to show me a system whose reward function / dataset didnt "contain the solution", in the manner of animals who respond to the world without already having all the information about it.
The relevant capacity a system needs to have, in both cases, is being able to take a profoundly ambiguous environment and produce a dataset/reward-fn which "carves along its joints". Ie., which effectively eliminates that ambiguity.
When such ambiguity & coincidence is eliminated, there's basically nothing left to do -- it's that basic nothing which we task machines with doing. Ie. running `mean(sample(unambigious relevant well-carved data))`.
You'll note its the *properties* of the data which express intelligence & learning.
Great -- so do you have an example of such a system?
I'd be inclined, initially, to deny that it exists. If your reward function expresses a reward for the goal of "picking up objects with (pixel-space) properties etc.", you're cheating. In this case, the reward function serves the role of the data: ie., prepared by us to work. Indeed, a function is just a dataset -- and the reward function here is being sampled by the system.
You'd need to show me a system whose reward function / dataset didnt "contain the solution", in the manner of animals who respond to the world without already having all the information about it.
The relevant capacity a system needs to have, in both cases, is being able to take a profoundly ambiguous environment and produce a dataset/reward-fn which "carves along its joints". Ie., which effectively eliminates that ambiguity.
When such ambiguity & coincidence is eliminated, there's basically nothing left to do -- it's that basic nothing which we task machines with doing. Ie. running `mean(sample(unambigious relevant well-carved data))`.
You'll note its the *properties* of the data which express intelligence & learning.