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Thanks for this! I've changed the sentence to:
The target network gets to see one more step than the Q-network does, and thus is a better predictor.
Hopefully this prevents others from the same confusion :)
pandas is a good library for this - it takes CSV files and turns them into Python objects you can manipulate. plotly / matplotlib lets you visualise data, which is also useful. GPT-4 / Claude could help you with this. I would recommend starting by getting a language model to help you create plots of the data according to relevant subsets. Like if you think that the season matters for how much gold is collected, give the model a couple of examples of the data format and simply ask it to write a script to plot gold per season.
To provide the obvious advice first:
- Attempt a puzzle.
- If you didn't get the answer, check the comments of those who did.
- Ask yourself how you could have thought of that, or what common principle that answer has. (e.g, I should check for X and Y)
- Repeat.
I assume you have some programming experience here - if not, that seems like a prerequisite to learn. Or maybe you can get away with using LLM's to write the Python for you.
I don't know about the first one - I think you'll have to analyse each job and decide about that. I suspect the answer to your second question is "Basically nil". I think that unless you are working on state-of-the-art advances in:
A) Frontier models B) Agent scaffolds, maybe.
You are not speeding up the knowledge required to automate people.
I guess my way of thinking of it is - you can automate tasks, jobs, or people.
Automating tasks seems probably good. You're able to remove busywork from people, but their job is comprised of many more things than that task, so people aren't at risk of losing their jobs. (Unless you only need 10 units of productivity, and each person is now producing 1.25 units so you end up with 8 people instead of 10 - but a lot of teams could also quite use 12.5 units of productivity well)
Automating jobs is...contentious. It's basically the tradeoff I talked about above.
Automating people is bad right now. Not only are you eliminating someone's job, you're eliminating most other things this person could do at all. This person has had society pass them by, and I think we should either not do that or make sure this person still has sufficient resources and social value to thrive in society despite being automated out of an economic position. (If I was confident society would do this, I might change my tune about automating people)
So, I would ask myself - what type of automation am I doing? Am I removing busywork, replacing jobs entirely, or replacing entire skillsets? (Note: You are probably not doing the last one. Very few, if any, are. The tech does not seem there atm. But maybe the company is setting themselves up to do so as soon as it is, or something)
And when you figure out what type you're doing, you can ask how you feel about that.
1I think that there are two questions one could ask here:
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Is this job bad for x-risk reasons? I would say that the answer to this is "probably not" - if you're not pushing the frontier but are only commercialising already available technology, your contribution to x-risk is negligible at best. Maybe you're very slightly adding to the generative AI hype, but that ship's somewhat sailed at this point.
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Is this job bad for other reasons? That seems like something you'd have to answer for yourself based on the particulars of the job. It also involves some philosophical/political priors that are probably pretty specific to you. Like - is automating away jobs good most of the time? Argument for yes - it frees up people to do other work, it advances the amount of stuff society can do in general. Argument for no - it takes away people's jobs, disrupts lives, some people can't adapt to the change.
I'll avoid giving my personal answer to the above, since I don't want to bias you. I think you should ask how you feel about this category of thing in general, and then decide how picky or not you should be about these AI jobs based on that category of thing. If they're mostly good, you can just avoid particularly scummy fields and other than that, go for it. If they're mostly bad, you shouldn't take one unless you have a particularly ethical area you can contribute to.
It seems to me that either:
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RLHF can't train a system to approximate human intuition on fuzzy categories. This includes glitches, and this plan doesn't work.
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RLHF can train a system to approximate human intuition on fuzzy categories. This means you don't need the glitch hunter, just apply RLHF to the system you want to train directly. All the glitch hunter does is make it cheaper.
I was about 50/50 on it being AI-made, but then when I saw the title "Thought That Faster" was a song, I became much more sure, because that was a post that happened only a couple weeks ago I believe, and if it was human-made I assume it would take longer to go from post to full song. Then I read this post.
In Soviet Russia, there used to be something called a Coke party. You saved up money for days to buy a single can of contraband Coca-Cola. You got all of your friends together and poured each of them a single shot. It tasted like freedom.
I know this isn't the point of the piece, but this got to me. However much I appreciate my existence, it never quite seems to be enough to be calibrated to things like this. I suddenly feel both a deep appreciation and vague guilt. Though it does give me a new gratitude exercise - imagine the item I am about to enjoy is forbidden in my country and I have acquired a small sample at great expense.
Hi Giorgi,
Not an expert on this, but I believe the idea is that over time the agent will learn to assign negligible probabilities to actions that don't do anything. For instance, imagine a game where the agent can move in four directions, but if there's a wall in front of it, moving forward does nothing. The agent will eventually learn to stop moving forward in this circumstance. So you could probably just make it work, even if it's a bit less efficient, if you just had the environment do nothing if an invalid action was selected.