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This seems tougher for attackers because experimentation with specific humans is much costlier than experimentation with automated systems.

(But I'm unsure of the overall dynamics in this world!)

:thumbsup: Looks like you removed it on your blog, but you may also want to remove it on the LW post here.

Beyond acceleration, there would be serious risks of misuse. The most direct case is cyberoffensive hacking capabilities. Inspecting a specific target for a specific style of vulnerability could likely be done reliably, and it is easy to check if an exploit succeeds (subject to being able to interact with the code)

This one sticks out because cybersecurity involves attackers and defenders, unlike math research. Seems like the defenders would be able to use GPT_2030 in the same way to locate and patch their vulnerabilities before the attackers do.

It feels like GPT_2030 would significantly advantage the defenders, actually, relative to the current status quo. The intuition is that if I spend 10^1 hours securing my system and you spend 10^2 hours finding vulns, maybe you have a shot, but if I spend 10^3 hours on a similarly sized system and you spend 10^5, your chances are much worse. For example at some point I can formally verify my software.

Appreciated this post.

ChatGPT has already been used to generate exploits, including polymorphic malware, which is typically considered to be an advanced offensive capability.

I found the last link at least a bit confusing/misleading, and think it may just not support the point. As stated, it sounds like ChatGPT was able to write a particularly difficult-to-write piece of malware code. But the article instead seems to be a sketch of a design of malware that would incorporate API calls to ChatGPT, e.g. 'okay we're on the target machine, we want to search their files for stuff to delete, write me code to run the search.' 

The argument is that this would be difficult for existing e.g. antivirus software to defend against because the exact code run changes each time. But if you really want to hack one person in particular and are willing to spend lots of time on it, you could achieve this today by just having a human sitting on the other end doing ChatGPT's job. What ChatGPT buys you is presumably the ability to do this at scale.

On a retry, it didn't decide to summarize the board and successfully listed a bunch of legal moves for White to make. Although I asked for all legal moves, the list wasn't exhaustive; upon prompting about this, it apologized and listed a few more moves, some of which were legal and some which were illegal, still not exhaustive.

This is pretty funny because the supposed board state has only 7 columns

Hah, I didn't even notice that.

Also, I've never heard of using upper and lowercase to differentiate white and black, I think GPT-4 just made that up.


Caleb Parikh and I were curious about GPT-4's internal models of chess as a result of this post, so we asked it some questions about the state partway through this game:

The following is a partial chess transcript in PGN. Please describe all legal moves that could come next. 1. d4 Nf6 2. c4 e6 3. Nf3 d5 4. Nc3 Be7 5. Bf4 O-O 6. Nb5 $2 Na6 $9

It replied:

The current position after 1. d4 Nf6 2. c4 e6 3. Nf3 d5 4. Nc3 Be7 5. Bf4 O-O 6. Nb5 Na6 is as follows:

8 r b q r p b p
7 p p b n p p p
6 n . . p . n .
5 . N . . p . .
4 . . P P . B .
3 . . . . N . .
2 P P . . . P P
1 R . B Q K . R
 a b c d e f g h

(and explained that lowercase letters were black pieces and uppercase letters were white pieces, which I didn't know; I don't play chess).

This... is not an accurate picture of the game board (what are all those pawns doing on Black's back row?) We also asked it for a list of legal moves that White could make next, and it described some accurately but some inaccurately (e.g. listed one as a capture even though it wasn't).

I think this is taking aim at Yudkowskian arguments that are not cruxy for AI takeover risk as I see it. The second species doesn't need to be supercoherent in order to kill us or put us in a box; human levels of coherence will do fine for that.

What specific rhyme-related tasks are you saying ChatGPT can't do? I tried it on some unusual words and it got a bunch of things right, made a few weird mistakes, but didn't give me the impression that it was totally unable to rhyme unusual words.

I don't think that response makes sense. The classic instrumental convergence arguments are about a single agent; OP is asking why distinct AIs would coordinate with one another.

I think the AIs may well have goals that conflict with one another, just as humans' goals do, but it's plausible that they would form a coalition and work against humans' interests because they expect a shared benefit, as humans sometimes do.

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