How did LW update p(doom) after LLMs blew up?

Firstly, it would seem to me to be much more difficult to FOOM with an LLM, it would seem much more difficult to create a superintelligence in the first place, and it seems like getting them to act creatively and be reliable are going to be much harder problems than making sure they aren't too creative.

Au contraire, for me at least. I am no expert on AI, but prior to the LLM blowup and seeing AutoGPT emerge almost immediately, I thought that endowing AI with the agency^[1] would take an elaborate engineering effort that went somehow beyond imitation of human outputs, such as language or imagery. I was somewhat skeptical of the orthogonality thesis. I also thought that it would take massive centralized computing resources not only to train but also to operate trained models (as I said, no expert). Obviously that is not true, and in a utopian outcome, access to LLMs will probably be a commodity good, with lots of roughly comparable models from many vendors to choose from and widely available open-source or hacked models as well.

Now, I see the creation of increasingly capable autonomous agents as just a matter of time, and ChaosGPT is overwhelming empirical evidence of orthogonality as far as I'm concerned. Clearly morality has to be enforced on the fundamentally amoral intelligence that is the LLM.

For me, my p(doom) increased due to the orthogonality thesis being conclusively proved correct and realizing just how cheap and widely available advanced AI models would be to the general public.

Edit: One other factor I forgot to mention is how instantaneously we'd shift from "AI doom is sci-fi, don't worry about it" to "AI doom is unrealistic because it just won't happen, don't worry about it" as LLMs became an instant sensation. I have been deeply disappointed on this issue by Tyler Cowen, who I really did not expect to shift from his usual thoughtful, balanced engagement with advanced ideas to just utter punditry on the issue. I think I understand where he's coming from - the huge importance of growth, the desire not to see AI killed by overregulation in the manner of nuclear power, etc - but still.

It has reinforced my belief that a fair fraction of the wealthy segment of the boomer generation will see AI as a way to cheat death (a goal I'm a big fan of), and will rush full-steam ahead to extract longevity tech out of it because they personally do not have time to wait to align AI, and they're dead either way. I expect approximately zero of them to admit this is a motivation, and only a few more to be crisply conscious of it. 

1. ^{^}

   creating adaptable plans to pursue arbitrarily specified goals in an open-ended way

I disagree with your premise; what's currently happening is very much in-distribution for what was prophecied. It's definitely got a few surprises in it, but "much more difficult to FOOM" and the other things you list aren't among them IMO.

I agree that predict-the-world-first, then-develop-agency (and do it via initially-human-designed-bureaucracies) is a safer AGI paradigm than e.g. "train a big NN to play video games and gradually expand the set of games it can play until it can play Real Life." (credit to Jan Leike for driving this point home to me). I don't think this means things will probably be fine; I think things will probably not be fine.

We could have had CAIS (Comprehensive AI Services) though, and that would have been way safer still. (At least, five years ago more people seemed to think this, I was not among them) Alas that things don't seem to be heading in that direction.

I basically agree that LLMs don't seem all that inherently dangerous and am somewhat confused about rationalists' reaction to them. LLMs seem to have some inherent limitations.

That said, I could buy that they could become dangerous/accelerate timelines. To understand my concern, let's consider a key distinction in general intelligence: horizontal generality vs vertical generality.

• By horizontal generality, I mean the ability to contribute to many different tasks. LLMs supersede or augment search engines in being able to funnel information from many different places on the internet right to a person who needs it. Since the internet contains information about many different things, this is often useful.
• By vertical generality, I mean the ability to efficiently complete tasks with minimal outside assistance. LLMs do poorly on this, as they lack agency, actuators, sensors and probably also various other things needed to be vertically general.

(You might think horizontal vs vertical generality is related to breadth vs depth of knowledge, but I don't think it is. The key distinction is that breadth vs depth of knowledge concerns fields of information, whereas horizontal vs vertical generality concerns tasks. Inputs vs outputs. Some tasks may depend on multiple fields of knowledge, e.g. software development depends on programming capabilities and understanding user needs, which means that depth of knowledge doesn't guarantee vertical generality. On the other hand, some fields of knowledge, e.g. math or conflict resolution, may give gains in multiple tasks, which means that horizontal generality doesn't require breadth of knowledge.)

While we have had previous techniques like AlphaStar with powerful vertical generality, they required a lot of data from those domains they functioned in in order to be useful, and they do not readily generalize to other domains.

Meanwhile, LLMs have powerful horizontal generality, and so people are integrating them into all sorts of places. But I can't help but wonder - I think the integration of LLMs in various places will develop their vertical generality, partly by giving them access to more data, and partly by incentivizing people to develop programmatic scaffolding which increases their vertical generality.

So LLMs getting integrated everywhere may incentivize removing their limitations and speeding up AGI development.

Note that a lot of people are responding to a nontrivial enhancement of LLMs that they can see over the horizon, but wont talk about publicly for obvious reasons, so it wont be clear what they're reacting to and they also might not say when you ask.

Though, personally, although my timelines have shortened, my P(Doom) has decreased in response to LLMs, as it seems more likely now that we'll be able to get machines to develop an ontology and figure out what we mean by "good" before having developed enough general agency to seriously deceive us or escape the lab. However, shortening timelines have still led me to develop an intensified sense of focus and urgency. Many of the things that I used to be interested in doing don't make sense any more. I'm considering retraining.

I didn't really update on LLMs in the past year. I did update after GPT2* that LLMs were a proof of concept that we could do a variety of types of cognition, and the mechanism of how the cognition played out seemed to have similar mid-level-building-blocks of my cognition [LW · GW]. So, it was an update on timelines (which can affect p(doom)).

GPT4 is mostly confirming that hypothesis rather that providing significant new evidence (it'd have been an update for me if GPT4 hadn't been that useful)

*in particular after this post https://slatestarcodex.com/2020/01/06/a-very-unlikely-chess-game/

My only update was the thought that maybe more people will see the problem. The whole debate in the world at large has been a cluster***k.

* Linear extrapolation - exponentials apparently do not exist
* Simplistic analogies e.g. the tractor only caused 10 years of misery and unemloyment so any further technology will do no worse.
* Conflicts of interest and motivated reasoning
* The usual dismissal of geeks and their ideas
* Don't worry leave it to the experts. We can all find plenty of examples where this did not work. https://en.wikipedia.org/wiki/List_of_laboratory_biosecurity_incidents
* People saying this is risky being interpreted as a definite prediction of a certain outcome.

As Elon Musk recently pointed out the more proximate threat may be the use of highly capable AIs as tools e.g. to work on social media to feed ideas to people and manipulate them. Evil/amoral/misaligned AI takes over the world would happen later. 

Some questions I ask people:

* How well did the advent of homo sapiens work out for less intelligent species like homo habilis? Why would AI be different?
* Look at the strife between groups of differing cognitive abilities and the skewed availability of resources between those groups (deliberately left vague to avoid triggering someone).
* Look how hard it is to predict the impact of technology - e.g. Krugman's famous insight that the internet would have no more impact than the fax machine. I remember doing a remote banking strategy in 1998 and asking senior management where they thought the internet fitted into their strategy. They almost all dismissed it as a land of geeks and academics and of no relevance to real businesses. A year later they demanded to know why I had misrepresented their clear view that the internet was going to be central to banking henceforth. Such is the ability of people to think they knew it all along, when they didn't.