Did you consider withdrawal effects at all?  A day without caffeine having used it the previous few days is going to be very different from one where you haven't used caffeine in months.

What exactly is being done - what type of thing is being created - when we run a process like "use gradient descent to minimize a loss function on training data, as long as the loss function is also being minimized on test data"?

Is a language model performing utility maximization during training?

Let's ignore RLHF for now and just focus on next token prediction.  There's an argument that, of course the LM is maximizing a utility function - namely it's log score on predicting the next token, over the distribution of all text on the internet (or whatever it was trained on).  An immediate reaction I have to this is that this isn't really what we want, even ignoring that we want the text to be useful (as most internet text isn't).

This is clearly related to all the problems around overfitting.  My understanding is that in practice, this is solved through a combination of regularization, and stopping training once test loss stops decreasing.  So even if a language model was a UM during training, we already have some guardrails on it.  Are they enough?

Are language models utility maximizes?

I think there's two different major "phases" of a language model, training and runtime.  During training, the model is getting "steered" toward some objective function - first getting the probability of the next token "right", and then getting positive feedback from humans during rlhf (I think? I should read exactly how rlhf works).  Is this utility maximization?  It doesn't feel like it - I think I'll put my thoughts on this in another comment.

During runtime, at first glance, the model is kind of "deterministic" (wrong word), in that it's "just multiplying matrices", but maybe it "learned" some utility maximizers during training and they're embedded within it.  Not sure if this is actually possible, or if it happens in practice, and if the utility maximizers are dominate the agent or can be "overruled" by other parts of it.

I'm going to use this space to blurt some thoughts about ai risk.

Personal data point: I tried this last night, and had 4 vivid and intense nightmares.  This is not a usual experience for me.

There's sort of a general "digestive issues are the root of all anxiety/evil" thread I've seen pop up in a bunch of rationalist-adjacent spaces:

I'm curious if there's any synthesis / study / general theory of this.

As my own datapoint, I've had pretty bad digestive issues, trouble eating, and ahedonia for a while. I recently got to do a natural experiment when I accidentally left my milk out.  Since I've cut milk, I've felt much better (though not perfect) on all counts.  So I'm probably lactose intolerant (though I never really noticed a correlation between my symptoms and milk consumption).

Probably worth checking if there are any easy fixes to your digestive issues if you have them.

cure Eliezer's chronic fatigue so he can actually attempt to grant humanity a couple more bits of information-theoretic dignity save the world

Possibly relevant: I know someone who had chronic fatigue syndrome which largely disappeared after she had her first child. I could possibly put her in contact with Eliezer or someone working on the problem.

The entrepreneur contacted me again the next day..."I have been cooking all week."


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