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All of eeegnu's Comments + Replies

AGI Ruin: A List of Lethalities
eeegnu10

These are great points, and ones which I did actually think about when I was brainstorming this idea (if I understand them correctly.) I intend to write out a more thorough post on this tomorrow with clear examples (I originally imagined this as extracting deeper insights into chess), but to answer these:

  1. I did think about these as translators for the actions of models into natural language, though I don't get the point about extracting things beyond what's in the original model.
  2. I mostly glossed over this part in the brief summary, and the motivation I had
... (read more)
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2TekhneMakre
(I didn't understand this on one read, so I'll wait for the post to see if I have further comments. I didn't understand the analogy / extrapolation drawn in 2., and I didn't understand what scheme is happening in 3.; maybe being a little more precise and explicit about the setup would help.)
AGI Ruin: A List of Lethalities
eeegnu100

They probably do not know where the real difficulties are, they probably do not understand what needs to be done, they cannot tell the difference between good and bad work, and the funders also can't tell without me standing over their shoulders evaluating everything, which I do not have the physical stamina to do.

This was the sentiment I got after applying to the LTFF with an idea. Admittedly, I couldn't really say whether my idea had been tried before, or wasn't obviously bad, but my conversation basically boiled down to whether I wanted to use this... (read more)

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4TekhneMakre
Why wouldn't the explainer just copy the latent vector, and the explainee just learn to do the task in the same way the original model does it? Or more generally, why does this put any pressure towards "explaining the reasons/motives" behind the original model's actions? I think you're thinking that by using a pre-trained GPT3-alike as the explainer model, you start off with something a lot more language-y, and language-y concepts are there for easy pickings for the training process to find in order to "communicate" between the original model and the explainee model. This seems not totally crazy, but  1. it seems to buy you, not anything like further explanations of reasons/motives beyond what's "already in" the original model, but rather at most a translation into the explainer's initial pre-trained internal language; 2. the explainer's initial idiolect stays unexplained / unmotivated; 3. the training procedure doesn't put pressure towards explanation, and does put pressure towards copying.  
habryka230

This was the sentiment I got after applying to the LTFF with an idea. Admittedly, I couldn't really say whether my idea had been tried before, or wasn't obviously bad, but my conversation basically boiled down to whether I wanted to use this project as a way to grow myself in the field, rather than any particular merits/faults of the idea itself

I evaluated this application (and we chatted briefly in a video call)! I am not like super confident in my ability to tell whether an idea is going to work, but my specific thoughts on your proposals were that I ... (read more)

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5Eliezer Yudkowsky
(I wasn't able to understand the idea off this description of it.)
What's The Best Place to Look When You Have A Question About x?
Answer by eeegnu20

For competitive programming questions, codeforces.com. It has a large audience of highly skilled competitive programmers, and someone will normally help you if it's an interesting question.

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1Chloe Thompson
Thank you!
What DALL-E 2 can and cannot do
eeegnu220

I'd rate these highly, there are many forms of anomalocarids (https://en.m.wikipedia.org/wiki/Radiodonta#/media/File%3A20191201_Radiodonta_Amplectobelua_Anomalocaris_Aegirocassis_Lyrarapax_Peytoia_Laggania_Hurdia.png) and it looks to have picked a wide variety aside from just candensis, but I'm thoroughly impressed that it got the form right in nearly all 10.

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What DALL-E 2 can and cannot do
eeegnu180

A prompt i'd love to see: "Anomalocaris Canadensis flying through space." I'm really curious how well it does with an extinct species which has very little existing artistic depictions. No text->image model i've played with so far has managed to create a convincing anomalocaris, but one interestingly did know it was an aquatic creature and kept outputting lobsters.

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Swimmer963 (Miranda Dixon-Luinenburg) 220

Going by the Wikipedia page reference, I think it got it somewhat closer than "lobsters" at least? 

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Lessons After a Couple Months of Trying to Do ML Research
eeegnu30

Never delete code or results!!!

There's a tool I've been interested in using (if it exists), basically a jupyter notebook, but which saves all outputs to tmp files (possibly truncating at the 1MB mark or something), and which maintains a tree history of the state of cells (i.e. if you hit ctrl-z a few times and start editing, it creates a branch.) Neither of these are particularly memory heavy, but both would have saved me in the past if they were hidden options to restore old state.

I'd also add that if you had a bug, and it took real effort/digging to find... (read more)

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2RowanWang
Hm, I think this tool would've been really helpful for me in the past for a couple of occasions. Usually if I want to save a cell output, I just won't edit that cell and I'll create a new one, even if it means redundant code.  Also +1 on keeping track of bugs! I should've added to the og post that one thing I do that's really helpful for me is keeping track of procedural knowledge (i.e. how to setup a GPU, how to fix common issue X, etc.) in a personal Slack that I've created as a second brain basically. I found that I used the message-yourself-in-slack feature a lot to keep track of small notes for myself, and since I did it so much, I created a whole private, personal Slack and that's been pretty useful in keeping track of bugs, etc.
On continuous decision boundaries
eeegnu20

I'll probably end up thinking about this in the background for a while, and jotting down any interesting cases in case they can accumulate into a nice generalizable thing (or maybe I'll stumble upon someone else who's made such an analysis before.)

Where I see your example sharing a common idea is that one party makes what appears to be a suboptimal decision (e.g. if they just wanted to attract top talent, a salary at the top of the salary spectrum would suffice), and it leaves the other party to infer what might be the true reasoning behind the decision th... (read more)

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