I might have overloaded the phrase “computational” here. My intention was to point out what can be encoded by such a system. Maybe “coding” is a better word? E.g., neural coding. These systems can implement Turing machines so can potentially have the same properties of turing machines.
I see. I think I was confused since, in my mind, there are many Turing machines that simply do not “optimize” anything. They just compute a function.
I’m wondering if our disagreement is conceptual or semantic. Optimizing a direction instead of an entire path is just a difference in time horizon in my model. But maybe this is a different use of the word “optimize”?
I think I wanted to point to a difference in the computational approach of different algorithms that find a path through the universe. If you chain together many locally found heuristics, then you carve out a path through reality over time that may lead to some “desirable outcome”. But the computation would be vastly different from another algorithm that thinks about the end result and then makes a whole plan of how to reach this. It’s basically the difference between deontology and consequentialism. This post is on similar themes.
I’m not at all sure if we disagree about anything here, though.
If I learn the optimal path to work, then I can use that multiple times. I’m not sure I agree with the distinction you are drawing here … Some problems in life only need to be solved exactly once, but that’s the same as any thing you learn only being applicable once.
I would say that if you remember the plan and retrieve it later for repeated use, then you do this by learning and the resulting computation is not planning anymore. Planning is always the thing you do at the moment to find good results now, and learning is the thing you do to be able to use a solution repeatedly.
Part of my opinion also comes from the intuition that planning is the thing that derives its use from the fact that it is applied in complex environments in which learning by heart is often useless. The very reason why planning is useful for intelligent agents is that they cannot simply learn heuristics to navigate the world.
To be fair, it might be that I don’t have the same intuitive connection between planning and learning in my head that you do, so if my comments are beside the point, then feel free to ignore :)
A hyperparameter is a parameter across parameters. So say with childbirth, you have a parameter pain on physical pain which is a direct physical signal, and you have a hyperparameter ‘Satisfaction from hard work’ that takes ‘pain’ as input as well as some evaluative cognitive process and outputs reward accordingly. Does that make sense?
Conceptually it does, thank you! I wouldn’t call these parameters and hyperparameters, though. Low-level and high-level features might be better terms.
Again, I think the shard theory of human values might be an inspiration for these thoughts, as well as this post on AGI motivation which talks about how valence gets “painted” on thoughts in the world model of a brain-like AGI.
Is this on the sweet spot just before overfitting or should I be thinking of something else?
I personally don’t have good models for this. Ilya Sutskever mentioned in a podcast that under some models of bayesian updating, learning by heart is optimal and a component of perfect generalization. Also from personal experience, I think that people who generalize very well also often have lots of knowledge, though this may be confounded by other effects.
I see. I think I was confused since, in my mind, there are many Turing machines that simply do not “optimize” anything. They just compute a function.
I think I wanted to point to a difference in the computational approach of different algorithms that find a path through the universe. If you chain together many locally found heuristics, then you carve out a path through reality over time that may lead to some “desirable outcome”. But the computation would be vastly different from another algorithm that thinks about the end result and then makes a whole plan of how to reach this. It’s basically the difference between deontology and consequentialism. This post is on similar themes.
I’m not at all sure if we disagree about anything here, though.
I would say that if you remember the plan and retrieve it later for repeated use, then you do this by learning and the resulting computation is not planning anymore. Planning is always the thing you do at the moment to find good results now, and learning is the thing you do to be able to use a solution repeatedly.
Part of my opinion also comes from the intuition that planning is the thing that derives its use from the fact that it is applied in complex environments in which learning by heart is often useless. The very reason why planning is useful for intelligent agents is that they cannot simply learn heuristics to navigate the world.
To be fair, it might be that I don’t have the same intuitive connection between planning and learning in my head that you do, so if my comments are beside the point, then feel free to ignore :)
Conceptually it does, thank you! I wouldn’t call these parameters and hyperparameters, though. Low-level and high-level features might be better terms.
Again, I think the shard theory of human values might be an inspiration for these thoughts, as well as this post on AGI motivation which talks about how valence gets “painted” on thoughts in the world model of a brain-like AGI.
I personally don’t have good models for this. Ilya Sutskever mentioned in a podcast that under some models of bayesian updating, learning by heart is optimal and a component of perfect generalization. Also from personal experience, I think that people who generalize very well also often have lots of knowledge, though this may be confounded by other effects.