silentbob

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That seems like a rather uncharitable take. Even if you're mad at the company, would you (at least (~falsely) assuming this all may indeed be standard practice and not as scandalous as it turned out to be) really be willing to pay millions of dollars for the right to e.g. say more critical things on Twitter, that in most cases extremely few people will even care about? I'm not sure if greed is the best framing here.

(Of course the situation is a bit different for AI safety researchers in particular, but even then, there's not that much actual AI (safety) related intel that even Daniel was able to share that the world really needs to know about; most of the criticism OpenAI is dealing with now is on this meta NDA/equity level)

I would assume ChatGPT gets much better at answering such questions if you add to the initial prompt (or system prompt) to eg think carefully before answering. Which makes me wonder whether "ChatGPT is (not) intelligent" even is a meaningful statement at all, given how vastly different personalities (and intelligences) it can emulate, based on context/prompting alone. Probably a somewhat more meaningful question would be what the "maximum intelligence" is that ChatGPT can emulate, which can be very different from its standard form.

Just to note your last paragraph reminds me of Stuart Russel's approach to AI alignment in Human Compatible. And I agree this sounds like a reasonable starting point.

Thanks for the post, I find this unique style really refreshing.

I would add to it that there's even an "alignment problem" on the individual level. A single human in different circumstances and at different times can have quite different, sometimes incompatible values, preferences and priorities. And even at any given moment their values may be internally inconsistent and contradictory. So this problem exists on many different levels. We haven't "solved ethics", humanity disagrees about everything, even individual humans disagree with themselves, and now we're suddenly racing towards a point where we need to give AI a definite idea of what is good & acceptable.

silentbobΩ010

Aren't LLMs already capable of two very different kinds of search? Firstly, their whole deal is predicting the next token - which is a kind of search. They're evaluation all the tokens at every step, and in the end choose the most probable seeming one. Secondly, across-token search when prompted accordingly. Say "Please come up with 10 options for X, then rate them all according to Y, and select the best option" is something that current LLMs can perform very reliably - whether or not "within token search" exists as well. But then again, one might of course argue that search happening within a single forward pass, and maybe even a type of search that "emerged " via SGD rather than being hard baked into the architecture, would be particularly interesting/important/dangerous. We just shouldn't make the mistake of assuming that this would be the only type of search that's relevant.

I think across-token search via prompting already has the potential to lead to the AGI like problems that we associate with mesa optimizers. Evidently the technology is not quite there yet because PoCs like AutoGPT basically don't quite work, so far. But conditional on AGI being developed in the next few years, it would seem very likely to me that this kind of search would be the one that enables it, rather than some hidden "O(1)" search deeply within the network itself.

Edit: I should of course add a "thanks for the post" and mention that I enjoyed reading it, and it made some very useful points!

Great post! Two thoughts that came to mind while reading it:

  • the post mostly discussed search happening directly within the network, e.g. within a single forward pass; but what can also happen e.g. in the case of LLMs is that search happens across token-generation rather than within. E.g. you could give ChatGPT a chess constellation and then ask it to list all the valid moves, and then check which move would lead to which state, and if that state looks better than the last one. This would be search depth 1 of course, but still a form of search. In practice it may be difficult because ChatGPT likes to give messages only of a certain length, so it probably stops prematurely if the search space gets too big, but still, search most definitely takes place in this case.
  • somewhat of a project proposal, ignoring my previous point and getting back to "search within a single forward pass of the network": let's assume we can "intelligent design" our way to a neural network that actually does implement some kind of small search to solve a problem. So we know the NN is on some pretty optimal solution for the problem it solves. What does (S)GD look like at or very near to this point? Would it stay close to this optimum, or maybe instantly diverge away, e.g. because the optimum's attractor basin is so unimaginably tiny in weight space that it's just numerically highly unstable? If the latter (and if this finding indeed generalizes meaningfully), then one could assume that even though search "exists" in parameter space, it's impractical to ever be reached via SGD due to the unfriendly shape of the search space.

Thanks a lot! Appreciated, I've adjusted the post accordingly.

Just came to my mind that these are things I tend to think of under the heading "considerateness" rather than kindness

Guess I'd agree. Maybe I was anchored a bit here by the existing term of computational kindness. :)

Fair point. Maybe if I knew you personally I would take you to be the kind of person that doesn't need such careful communication, and hence I would not act in that way. But even besides that, one could make the point that your wondering about my communication style is still a better outcome than somebody else being put into an uncomfortable situation against their will.

I should also note I generally have less confidence in my proposed mitigation strategies than in the phenomena themselves. 

Thanks for the example! It reminds me of how I once was a very active Duolingo user, but then they published some update that changed the color scheme. Suddenly the duolingo interface was brighter and lower contrast, which just gave me a headache. At that point I basically instantly stopped using the app, as I found no setting to change it back to higher contrast. It's not quite the same of course, but probably also something that would be surprising to some product designers -- "if people want to learn a language, surely something so banal as a brightening up the font color a bit would not make them stop using our app".

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