Self-Supervised Learning and AGI Safety
(Update later on: I’m no longer personally excited about this direction, for reasons mentioned in section 7.2 here.)
Abstract: We should seriously consider the possibility that we’ll build AGIs by self-supervised learning. If so, the AGI safety issues seem to be in many respects different (and I think more promising) than in the usual reinforcement learning paradigm. In particular, I’ll propose that if we follow certain constraints, these systems can be turned into safe, unambitious AGI oracles. I’ll end with lots of open questions.
Epistemic status: Treat as brainstorming. This post supersedes my previous post on this general topic, The Self-Unaware AI Oracle for reasons mentioned below.
What is self-supervised learning, and why might it lead to AGI?
Self-supervised learning consists of taking data, masking off part of it, and training an ML system to use the unmasked data to predict the masked data. To make the predictions better and better, the system needs to develop an increasingly deep and comprehensive semantic understanding of the world. For example, say there’s a movie with a rock falling towards the ground. If you want to correctly predict what image will be in the frame a few seconds ahead, you need to predict that the rock will stop when it gets to the ground, not continue falling. Or if there’s a movie with a person saying “I’m going to sit down now”, you need to predict that they might well sit in a chair, and probably won’t start dancing.
Thus, predicting something has close relationship with understanding it. Imagine that you’re in a lecture class on a topic where you’re already an expert. As the professor is talking, you feel you can often finish their sentences. By contrast, when you’re lost in a sea of jargon you don’t understand, you can only finish their sentences with a much wider probability distribution (“they’re probably going to say some jargon word now”).
The term “self-supervised learning” (replacing the previous and more general term “unsupervised learning”) seems to come from Yann LeCun, chief AI scientist at Facebook and co-inventor of CNNs. As he wrote here:
I now call it “self-supervised learning”, because “unsupervised” is both a loaded and confusing term.
In self-supervised learning, the system learns to predict part of its input from other parts of it input. In other words a portion of the input is used as a supervisory signal to a predictor fed with the remaining portion of the input.
Self-supervised learning uses way more supervisory signals than supervised learning, and enormously more than reinforcement learning. That’s why calling it “unsupervised” is totally misleading. That’s also why more knowledge about the structure of the world can be learned through self-supervised learning than from the other two paradigms: the data is unlimited, and amount of feedback provided by each example is huge.
Self-supervised learning has been enormously successful in natural language processing...So far, similar approaches haven’t worked quite as well for images or videos because of the difficulty of representing distributions over high-dimensional continuous spaces.
Doing this properly and reliably is the greatest challenge in ML and AI of the next few years in my opinion.
As mentioned in this quote, we have a couple good examples of self-supervised learning. One is humans. Here’s Yann LeCun again:
Most of human and animal learning is [self]-supervised learning. If intelligence was a cake, [self]-supervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake.
Indeed, the brain is constantly making probabilistic predictions about everything it is going to see, hear, and feel, and updating its internal models when those predictions are wrong. Have you ever taken a big drink from a glass of orange juice that you had thought was a glass of water? Or walked into a room and immediately noticed that the table is in the wrong place? It’s because you were predicting those sensations in detail before they happened. This is a core part of what the human brain does, and what it means to understand things. Self-supervised learning not the only ingredient in human intelligence—obviously we also have internal and external rewards, environmental interaction, and so on—but I’m inclined to agree with Yann LeCun that self-supervised learning is the cake.
Beyond humans, language models like GPT-2 are a nice example of how far self-supervised learning can go, even when restricted to today’s technology and an impoverished text-only datastream. See the SSC post GPT-2 as step towards general intelligence.
Thus, this post is nominally about the scenario where we make superintellignent AGI by 100% self-supervised learning (all cake, no cherries, no icing, in Yann LeCun’s analogy). I consider this a live possibility but far from certain. I’ll leave to future work the related scenario of “mostly self-supervised learning plus some reinforcement learning or other techniques”. (See Open Questions below.)
What does self-supervised learning look like under the hood? GPT-2 and similar language models are based on a Transformer neural net, trained by stochastic gradient descent as usual. The brain, to my limited understanding, does self-supervised learning (mainly using the neocortex, hippocampus, and thalamus) via a dozen or so interconnected processes, some of which are massively parallelized (the same computational process running in thousands of locations at once). These processes are things vaguely like: “If a spatiotemporal pattern recurs several times in a certain context, assign that pattern a UUID, and pass that UUID upwards to the next layer in the hierarchy”, and “Pull two items out of memory, look for a transformation between them, and catalog it if it is found”.
If I had to guess, I would expect that the ML community will eventually find neural net data structures and architectures that can do these types of brain-like processes—albeit probably not in exactly the same way that the brain does them—and we’ll get AGI shortly thereafter. I think it’s somewhat less likely, but not impossible, that we’ll get AGI by just, say, scaling up the Transformer to some ridiculous size. Either way, I won’t speculate on how long it might take to get to AGI, if this is the path. But my vague impression is that self-supervised learning is a hot research area undergoing rapid progress.
Impact for safety: General observations
1. Output channel is not part of the training
In the reinforcement learning (RL) path to AGI, we wind up creating a system consisting of a world-model attached to an output channel that reaches out into the world and does useful things, and we optimize this whole system. By contrast, in the Self-Supervised Learning path to AGI, we wind up by default with more-or-less just a bare world model. At least, that’s the way I’m thinking about it. I mean, sure, it has an output channel in the sense that it can output predictions for masked bits of a file, but that’s incidental, not really part of the AGI’s primary intended function, i.e. answering questions or doing things. We obviously need some sort of more useful output, but whatever that output is, it’s not involved in the training. This opens up some options in the design space that haven’t been well explored.
My last post, The Self-Unaware AI Oracle, is a rather extreme example of that, where we would try to build a world model that not only has no knowledge that it is attached to an output channel, but doesn’t even know it exists as an information-processing system! My (admittedly vague) proposal for that was to isolate the world-model from any reflective information about how the world-model is being built and analyzed. This is an extreme example, and after further thought I now think it’s entirely unnecessary, but I still think it’s a nice example of new things we might do in this different design space.
2. No need for a task-specific reward signal
Let’s say we want to build a question-answering system by RL. Well, we need to train it by asking questions, and rewarding it for a good answer. This is tricky, to put it mildly. The questions we really care about are ones where we don’t necessarily know the answer. How do we get lots of good data? What if we make mistakes in the training set? What if the system learns to optimize a proxy to the reward function, rather than the reward function itself? What if it learns to manipulate the reward function, e.g. by self-fulfilling prophecies? I don’t want to say these are impossible challenges, and I’m glad lots of people are working on them. But the self-supervised learning approach seems to largely skirt all these issues.
For self-supervised learning, the paradigm is a bit different. There is, of course, a reward signal guiding the machine learning, namely “predict the masked bits in the input data, using the unmasked bits”. But we treat that as purely a method to construct a world-model. Then we ask a separate question: “Now that we have a predictive world-model, how do we use it to make the world a better place”? This step is mainly about building an interface, not building an intelligence. Now, I don’t think this task is by any means easy (see “Open questions” below), but I do suspect that it’s easier than the various RL challenges above.
3. The world-model acquires some safety-enhancing knowledge automatically
Natural-language bridge into the world-model: When we build a world model by self-supervised learning of human-created content, the world model should wind up with an understanding of the real world, an understanding of human language and concepts, and (importantly) an understanding of how these two map onto each other. This should be very helpful for using the world model to answer our questions, and might also offer some (limited) degree of interpretability of the unimaginably complicated guts of the world-model.
Understanding of typical human norms, behaviors, and values: When we build a world model by self-supervised learning of human-created content, the world model should wind up with an excellent predictive understanding of typical human behavior. So let’s say we can ask it a counterfactual question like If there were no AGIs in the world, what’s the likeliest way that a human might find a cure for Alzheimer’s? (Put aside for now the issue of how we ask the question—see Open Questions below.) The answer to that question would be a kind of overlap between real-world compatibility (the cure actually works) and human compatibility (it should be the kind of “cures for Alzheimer’s” that humans want and would plausibly be looking for). That’s what we want! By contrast, a superintelligent biologist with alien motivation system would be likelier to “cure Alzheimer’s” by some method that is impractical, or that exploits loopholes in our definition of “cure”, etc. (So this incidentally also helps with Goodhart’s law.)
Constraints under which a self-supervised-learning world-model can be built into a safe AGI system
A system that can make great predictions of masked bits in a data file is not really useful, and not really something I would call an AGI. We need to build something around the world model, so that it can answer questions or take actions.
Once we start moving in this direction, I think it’s very easy to wander into dangerous territory. But, as a starting point, I offer this set of constraints. I believe that if we build an AGI using 100% self-supervised learning, and we follow all these constraints, then the system will be safe—more specifically, it will not do not wield its world model to do foresighted planning towards real-world consequences. Here’s my list of constraints:
The system is used as an oracle, i.e. it answers questions but does not take any other actions. (See my separate post: In defense of Oracle (“Tool”) AI.)
We only ask non-self-referential counterfactual questions along the lines of: If there were no AGIs in the world, what’s a likely way that someone might design a better solar cell? This avoids the various problem of self-fulfilling prophecies, such as discussed by Stuart Armstrong in this and follow-up posts, as well as steering towards solutions compatible with human norms and values as discussed above.
We fix the training data (what data to look at and what bits to predict, in what order) before we ever start training the system, then we “lock down” the world-model after the training (as opposed to letting it evolve further during question-and-answer operation), and moreover do not allow the oracle to do anything except output answers (no follow-up questions, no asking for more data about some topic, no running simulations in COMSOL, etc.).
For asking questions and getting answers, we don’t try to build a new interface into the world model, but rather build some wrapper around its existing predict-masked-bits interface. (It’s not obvious that we can build such a wrapper that works well, but I’m hopeful, and see Open Questions below.)
I’m pretty sure some of these constraints are unnecessarily strict; and conversely, I may be forgetting or leaving off some important requirement.
Why won’t it try to get more predictable data?
(Update: After further thought, I am now less confident that this section is correct. See Self-supervised learning & manipulative predictions.)
Why do I think that such an AGI would be safe? (Again, I mean more narrowly that it won’t wield its world model to do foresighted planning towards real-world consequences—which is the worst, but not only, thing that can go wrong.) Here’s one possible failure mode that I was thinking about. I’m sure some readers are thinking it too:
Oh, it optimizes its ability to predict masked bits from a file, eh? Well it’s going to manipulate us, or threaten us, or hack itself, to get easy-to-predict files of all 0′s!!
I say that this will not happen with any reasonable self-supervised learning algorithm that we’d be likely to build, if we follow the constraints above.
Gradient descent is a particularly simple example to think about here. In gradient descent, when the world-model makes a bad prediction, the model is edited such that the updated model would (in retrospect) have been likelier to get the right answer for that particular bit. Since the optimization pressure is retrospective (“backwards-facing” in Stuart Armstrong’s terminology), we are not pushing the system to do things like “seeking easier-to-predict files”, which help with forward-looking optimization but are irrelevant to retrospective optimization. Indeed, even if the system randomly stumbled upon that kind of forward-looking strategy, further gradient descent steps would be just as likely to randomly discard it again! (Remember, I stipulated above that we will fix in advance what the training data will be, and in what order it will be presented.)
(Neuromorphic world-model-building algorithms do not work by gradient descent, and indeed we don’t really know exactly how they would work, but from what we do know, I think they also would be unlikely to do forward-looking optimization.)
The algorithm will, of course, search ruthlessly for patterns that shed light on the masked bits. If it takes longer to read 1s from RAM than 0s (to take a silly example), and if the pattern-finder has access to timing information, then of course it will find the pattern and start making better predictions on that basis. This is not a malign failure mode, and if it happens, we can fix these side channels using cybersecurity best practices.
Open questions (partial list!)
Input and output: How exactly do we give the system input and output? Predicting masked bits has obvious shortcomings, like that it naturally tries to predict what a flawed humans might say on some topic, rather than what the truth is. This problem may be solvable by doing things like flagging some inputs as highly reliable, and trusting the system to automatically learn the association of that flag with truth and insight, and then priming the system with that flag when we ask it questions. Or would it be better to build a new, separate interface into the guts of the world model? If so, how?
Sculpting the world model: There may be too much information in the world for our best algorithms to learn it all. Thus an entomologist and botanist can look at the same picture of a garden but enrich their world-models in very different directions. (When I look at a picture of a garden, I zone out and learn nothing whatsoever.) In humans, our innate goals play a critical role in directing the learning process (see my later post Predictive Coding = RL + SL + Bayes + MPC), but I was hoping to avoid those types of real-world goals; what do we do instead? Can we flag some masked bits as really important to “think about” longer and harder before making a prediction? Or what?
Training data: Exactly what data should we feed it? Is there dangerous data that we need to censor? Is there unhelpful data that makes it dumber? Is text enough to get AGI, or does there also need to be audio and video? (People can be perfectly intelligent without sight or hearing, though they do have tactile graphics...) Books, articles, and YouTube are obvious sources of training data; are there less obvious but important data sources we should also be thinking about? How does the choice of training data impact safety and interpretability?
Adding in supervised or reinforcement learning: I’ve been assuming that we use 100% self-supervised learning to build the AGI. Can we sprinkle in some supervised or reinforcement learning, e.g. as a fine-tuning step, as a way to build an interface into the world model, or as a kind of supervisor to the main training? If so, how, and how would that impact safety and capabilities? (Update: For more on how the brain combines SSL & RL, see my later post: Predictive Coding = RL + SL + Bayes + MPC.)
Capabilities: Would a system subject to the constraints listed above nevertheless be powerful enough to do the things we want AGIs to do, and if not, can we relax some of those constraints? Can a world model get bigger and richer forever, or will it grind to a halt after deeply understanding the first 300,000 journal articles it reads? More generally, how do these things scale?
Agency: Can we safely give it a bit of agency in requesting more information, or asking follow-up questions, or interfacing with other software or databases? Or more boldly, can we safely give it a lot of agency, e.g. a 2-way internet connection, and if so, how?
Experiments: Can we shed light on any of these questions by playing with GPT-2 or other language models? Is there some other concrete example we can find or build? Like, is there any benefit to doing self-supervised learning on a corpus of text and movies talking about the Game Of Life universe?
Safety: I mentioned a couple possible failure mode above, and said that I didn’t think they were concerning. But this obviously needs much more careful thought and analysis before we can declare victory. What are other failure modes we should be thinking about?
Timelines and strategy: If we get AGI by 100% self-supervised learning, how does that impact takeoff speed, timelines, likelihood of unipolar vs multipolar scenarios, CAIS-like suites of narrow systems versus monolithic all-purpose systems, etc.?
I hope I’ve made the point that self-supervised learning scenarios are plausible, and bring forth a somewhat different and neglected set of issues and approaches in AGI safety. I hope I’m not the only one working on it! There’s tons of work to do, and god knows I can’t do it myself! (I have a full-time job...) :-)
Here are some things guiding my intuition on the primacy of self-supervised learning in humans: (1) In most cultures and most of human history, children have been largely ignored by adults, and learn culture largely by watching adults. They’ll literally just sit for an hour straight, watching adults work and interact. This seems to be an instinct that does not properly develop in modern industrialized societies! (See Anthropology of Childhood by Lancy.) (2) Babies’ understanding of the world is clearly developing long before they have enough motor control to learn things by interacting (see discussion in Object permanence on wikipedia, though I suppose one could also give the credit to brain development in general rather than self-supervised learning in particular). (3) By the same token, some kids (like me!) start talking unusually late, but then almost immediately have age-appropriate language skills, e.g. speaking full sentences. (See the book Einstein Syndrome, or summary on wikipedia.) I take this as evidence that we primarily learn to speak by self-supervised learning, not trial-and-error. (4) If you read math textbooks, and try to guess how the proofs are going to go before actually reading them, that seems like a pretty good way to learn the content. ↩︎
I haven’t completely lost hope in self-unaware designs, but I am now thinking it’s likely that cutting off reflective information might make it harder to build a good world-modeler—for example it seems useful for a system to know how it came to believe something. More importantly, I now think self-supervised learning is capable of building a safe AGI oracle even if the system is not self-unaware, as discussed below. ↩︎