Suppose you're training a huge neural network with some awesome future RL algorithm with clever exploration bonuses and a self-supervised pretrained multimodal initialization and a recurrent state. This NN implements an embodied agent which takes actions in reality (and also in some sim environments). You watch the agent remotely using a webcam (initially unbeknownst to the agent). When the AI's activities make you smile, you press the antecedent-computation-reinforcer button (known to some as the "reward" button). The agent is given some appropriate curriculum, like population-based self-play, so as to provide a steady skill requirement against which its intelligence is sharpened over training. Supposing the curriculum trains these agents out until they're generally intelligent—what comes next?
- The standard response is "One or more of the agents gets smart, does a treacherous turn, kills you, and presses the reward button forever."
- But reward is not the optimization target. This story isn't impossible, but I think it's pretty improbable, and definitely not a slam-dunk.
- Another response is "The AI paralyzes your face into smiling."
- But this is actually a highly nontrivial claim about the internal balance of value and computation which this reinforcement schedule carves into the AI. Insofar as this response implies that an AI will primarily "care about" literally making you smile, that seems like a highly speculative and unsupported claim about the AI internalizing a single powerful decision-relevant criterion / shard of value, which also happens to be related to the way that humans conceive of the situation (i.e. someone is being made to smile).
My current answer is "I don't know precisely what goes wrong, but probably something does, but also I suspect I could write down mechanistically plausible-to-me stories where things end up bad but not horrible." I think the AI will very probably have a spread of situationally-activated computations which steer its actions towards historical reward-correlates (e.g. if near a person, then tell a joke), and probably not singularly value e.g. making people smile or reward. Furthermore, I think its values won't all map on to the "usual" quantities-of-value:
80% credence: It's very hard to train an inner agent which reflectively equilibrates to an EU maximizer only over commonly-postulated motivating quantities (like
# of diamondsor
# of happy peopleor
reward-signal) and not quantities like (
# of times I have to look at a cube in a blue roomor
-1 * subjective micromorts accrued).
So, I'm pretty uncertain about what happens here, but would guess that most other researchers are less uncertain than I am. So here's an opportunity for us to talk it out!
(My mood here isn't "And this is what we do for alignment, let's relax." My mood is "Why consider super-complicated reward and feedback schemes when, as far as I can tell, we don't know what's going to happen in this relatively simple scheme? How do reinforcement schedules map into inner values?")
Here's what I think you mean by an explicit search process:
On this understanding of your argument, I would be surprised if it went through. Here are a few quick counterpoints.
... (read more)
- Outside tiny
I'm not that committed to the RL frame, but roughly speaking yes. Whatever values we have are probably generated by ~tens of hardcoded things. Anyway, on to the meat of the discussion...
It seems like a whole bunch of people are completely thrown off by use of the word "search". So let's taboo that and talk about what's actually relevant here.
We should expect compression, and we should expect general-purpose problem solving (i.e. the ability to take a fairly arbitrary problem in the training environment and solve it reasonably well). The general-purpose part comes from a combination of (a) variation in what the system needs to do to achieve good performance in training, and (b) the recursive nature of problem solving, i.e. solving one problem involves solving a wide variety of subproblems. Compactness means that it probably won't be a whole boatload of case-specific heuristics; lookup tables are not compact. A subroutine for reasonably-general planning or problem-solving (i.e. take a problem statement, figure out a plan or solution) is the key thing we're talking about here. Possibly a small number of such subroutines for a few different problem... (read more)