The Nonlinear Library LW - My AI Model Delta Compared To Yudkowsky by johnswentworth
Jun 10, 2024
06:36
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: My AI Model Delta Compared To Yudkowsky, published by johnswentworth on June 10, 2024 on LessWrong.
Preamble: Delta vs Crux
I don't natively think in terms of cruxes. But there's a similar concept which is more natural for me, which I'll call a delta.
Imagine that you and I each model the world (or some part of it) as implementing some program. Very oversimplified example: if I learn that e.g. it's cloudy today, that means the "weather" variable in my program at a particular time[1] takes on the value "cloudy". Now, suppose your program and my program are exactly the same, except that somewhere in there I think a certain parameter has value 5 and you think it has value 0.3.
Even though our programs differ in only that one little spot, we might still expect very different values of lots of variables during execution - in other words, we might have very different beliefs about lots of stuff in the world.
If your model and my model differ in that way, and we're trying to discuss our different beliefs, then the obvious useful thing-to-do is figure out where that one-parameter difference is.
That's a delta: one or a few relatively "small"/local differences in belief, which when propagated through our models account for most of the differences in our beliefs.
For those familiar with Pearl-style causal models: think of a delta as one or a few do() operations which suffice to make my model basically match somebody else's model, or vice versa.
This post is about my current best guesses at the delta between my AI models and Yudkowsky's AI models. When I apply the delta outlined here to my models, and propagate the implications, my models basically look like Yukowsky's as far as I can tell.
This post might turn into a sequence if there's interest; I already have another one written for Christiano, and people are welcome to suggest others they'd be interested in.
My AI Model Delta Compared To Yudkowsky
Best guess: Eliezer basically rejects the
natural abstraction hypothesis. He mostly expects AI to use internal ontologies fundamentally alien to the ontologies of humans, at least in the places which matter.
Lethality #33 lays it out succinctly:
33. The AI does not think like you do, the AI doesn't have thoughts built up from the same concepts you use, it is utterly alien on a staggering scale. Nobody knows what the hell GPT-3 is thinking, not only because the matrices are opaque, but because the stuff within that opaque container is, very likely, incredibly alien - nothing that would translate well into comprehensible human thinking, even if we could see past the giant wall of floating-point numbers to what lay behind.
What do my models look like if I propagate that delta? In worlds where natural abstraction basically fails, we are thoroughly and utterly fucked, and a 99% probability of doom strikes me as entirely reasonable and justified.
Here's one oversimplified doom argument/story in a world where natural abstraction fails hard:
1. Humanity is going to build superhuman goal-optimizing agents. ('Cause, like, obviously somebody's going to do that, there's no shortage of capabilities researchers loudly advertising that they're aiming to do that exact thing.) These will be so vastly more powerful than humans that we have basically-zero bargaining power except insofar as AIs are aligned to our interests.
2. We're assuming natural abstraction basically fails, so those AI systems will have fundamentally alien internal ontologies. For purposes of this overcompressed version of the argument, we'll assume a very extreme failure of natural abstraction, such that human concepts cannot be faithfully and robustly translated into the system's internal ontology at all.
(For instance, maybe a faithful and robust translation would be so long in the system's "internal language" that the transla...
Preamble: Delta vs Crux
I don't natively think in terms of cruxes. But there's a similar concept which is more natural for me, which I'll call a delta.
Imagine that you and I each model the world (or some part of it) as implementing some program. Very oversimplified example: if I learn that e.g. it's cloudy today, that means the "weather" variable in my program at a particular time[1] takes on the value "cloudy". Now, suppose your program and my program are exactly the same, except that somewhere in there I think a certain parameter has value 5 and you think it has value 0.3.
Even though our programs differ in only that one little spot, we might still expect very different values of lots of variables during execution - in other words, we might have very different beliefs about lots of stuff in the world.
If your model and my model differ in that way, and we're trying to discuss our different beliefs, then the obvious useful thing-to-do is figure out where that one-parameter difference is.
That's a delta: one or a few relatively "small"/local differences in belief, which when propagated through our models account for most of the differences in our beliefs.
For those familiar with Pearl-style causal models: think of a delta as one or a few do() operations which suffice to make my model basically match somebody else's model, or vice versa.
This post is about my current best guesses at the delta between my AI models and Yudkowsky's AI models. When I apply the delta outlined here to my models, and propagate the implications, my models basically look like Yukowsky's as far as I can tell.
This post might turn into a sequence if there's interest; I already have another one written for Christiano, and people are welcome to suggest others they'd be interested in.
My AI Model Delta Compared To Yudkowsky
Best guess: Eliezer basically rejects the
natural abstraction hypothesis. He mostly expects AI to use internal ontologies fundamentally alien to the ontologies of humans, at least in the places which matter.
Lethality #33 lays it out succinctly:
33. The AI does not think like you do, the AI doesn't have thoughts built up from the same concepts you use, it is utterly alien on a staggering scale. Nobody knows what the hell GPT-3 is thinking, not only because the matrices are opaque, but because the stuff within that opaque container is, very likely, incredibly alien - nothing that would translate well into comprehensible human thinking, even if we could see past the giant wall of floating-point numbers to what lay behind.
What do my models look like if I propagate that delta? In worlds where natural abstraction basically fails, we are thoroughly and utterly fucked, and a 99% probability of doom strikes me as entirely reasonable and justified.
Here's one oversimplified doom argument/story in a world where natural abstraction fails hard:
1. Humanity is going to build superhuman goal-optimizing agents. ('Cause, like, obviously somebody's going to do that, there's no shortage of capabilities researchers loudly advertising that they're aiming to do that exact thing.) These will be so vastly more powerful than humans that we have basically-zero bargaining power except insofar as AIs are aligned to our interests.
2. We're assuming natural abstraction basically fails, so those AI systems will have fundamentally alien internal ontologies. For purposes of this overcompressed version of the argument, we'll assume a very extreme failure of natural abstraction, such that human concepts cannot be faithfully and robustly translated into the system's internal ontology at all.
(For instance, maybe a faithful and robust translation would be so long in the system's "internal language" that the transla...