The Dualist Predict-O-Matic ($100 prize)
This is a response to Abram’s The Parable of Predict-O-Matic, but you probably don’t need to read Abram’s post to understand mine. While writing this, I thought of a way in which I think things could wrong with dualist Predict-O-Matic, which I plan to post in about a week. I’m offering a $100 prize to the first commenter who’s able to explain how things might go wrong in a sufficiently crisp way before I make my follow-up post.
Currently, machine learning algorithms are essentially “Cartesian dualists” when it comes to themselves and their environment. (Not a philosophy major—let me know if I’m using that term incorrectly. But what I mean to say is...) If I give a machine learning algorithm some data about itself as training data, there’s no self-awareness there—it just chugs along looking for patterns like it would for any other problem. I think it’s a reasonable guess that our algorithms will continue to have this “no self-awareness” property as they become more and more advanced. At the very least, this “business as usual” scenario seems worth analyzing in depth.
If dualism holds for Abram’s prediction AI, the “Predict-O-Matic”, its world model may happen to include this thing called the Predict-O-Matic which seems to make accurate predictions—but it’s not special in any way and isn’t being modeled any differently than anything else in the world. Again, I think this is a pretty reasonable guess for the Predict-O-Matic’s default behavior. I suspect other behavior would require special code which attempts to pinpoint the Predict-O-Matic in its own world model and give it special treatment (an “ego”).
Let’s suppose the Predict-O-Matic follows a “recursive uncertainty decomposition” strategy for making predictions about the world. It models the entire world in rather fuzzy resolution, mostly to know what’s important for any given prediction. If some aspect of the world appears especially relevant to a prediction it’s trying to make, it “zooms in” and tries to model that thing in higher resolution. And if some part of that thing seems especially relevant, it zooms in further on that part. Etc.
Now suppose the Predict-O-Matic is trying to make a prediction, and its “recursive uncertainty decomposition” algorithms say the next prediction made by this Predict-O-Matic thing which happens to occupy its world model appears especially relevant! What then?
At this point, the Predict-O-Matic has stepped into a hall of mirrors. To predict the next prediction made by the Predict-O-Matic in its world model, the Predict-O-Matic needs to run an internal simulation of that Predict-O-Matic. But as it runs that simulation, it finds that simulation kicking off another Predict-O-Matic simulation in the simulated Predict-O-Matic’s world model! Etc, etc.
So if the Predict-O-Matic is implemented naively, the result could just be an infinite recurse. Not useful, but not necessarily dangerous either.
Let’s suppose the Predict-O-Matic has a non-naive implementation and something prevents this infinite recurse. For example, there’s a monitor process that notices when a model is eating up a lot of computation without delivering useful results, and replaces that model with one which is lower-resolution. Or maybe the Predict-O-Matic does have a naive implementation, but it doesn’t have enough data about itself to model itself in much detail, so it ends up using a low-resolution model.
One possibility is that it’s able to find a useful outside view model such as “the Predict-O-Matic has a history of making negative self-fulfilling prophecies”. This could lead to the Predict-O-Matic making a negative prophecy (“the Predict-O-Matic will continue to make negative prophecies which result in terrible outcomes”), but this prophecy wouldn’t be selected for being self-fulfilling. And we might usefully ask the Predict-O-Matic whether the terrible self-fulfilling prophecies will continue conditional on us taking Action A.
Answering a Question by Having the Answer
If you aren’t already convinced, here’s another explanation for why I don’t think the Predict-O-Matic will make self-fulfilling prophecies by default.
In Abram’s story, the engineer says: “The answer to a question isn’t really separate from the expected observation. So ‘probability of observation depending on that prediction’ would translate to ‘probability of an event given that event’, which just has to be one.”
In other words, if the Predict-O-Matic knows it will predict P = A, it assigns probability 1 to the proposition that it will predict P = A.
I contend that Predict-O-Matic doesn’t know it will predict P = A at the relevant time. It would require time travel—to know whether it will predict P = A, it will have to have made a prediction already, and but it’s still formulating its prediction as it thinks about what it will predict.
More details: Let’s taboo “Predict-O-Matic” and instead talk about a “predictive model” and “input data”. The trick is to avoid including the output of the predictive model in the model’s input data. This isn’t possible the first time we make a prediction because it would require time travel—so as a practical matter, we don’t want to re-run the model a second time with the prediction from its first run included in the input data. Let’s say the dataset is kept completely static during prediction. (I offer no guarantees in the case where observational data about the model’s prediction process is being used to inform the model while it makes a prediction!)
To clarify further, let’s consider a non-Predict-O-Matic scenario where issues do crop up. Suppose I’m a big shot stock analyst. I think Acme Corp’s stock is overvalued and will continue to be overvalued in one month’s time. But before announcing my prediction, I do a sanity check. I notice that if I announce my opinion, that could cause investors to dump Acme, and Acme will likely no longer be overvalued in one month’s time. So I veto the column I was planning to write on Acme, and instead search for a column
c I can write such that
c is a fixed point for the world
w(c) = c—even when the world is given my column as an input, what I predicted in my column still comes true.
Note again that the sanity check which leads to a search for a fixed point doesn’t happen by default—it requires some extra functionality, beyond what’s required for naive prediction, to implement. The Predict-O-Matic doesn’t care about looking bad, and there’s nothing contradictory about it predicting that it won’t make the very prediction it makes, or something like that. Predictive model, meet input data. That’s what it does.
This is a section for half-baked thoughts that could grow into a counterargument for what I wrote above.
What’s going on when you try to model yourself thinking about the answer to this question? (Why is this question so hard to think about? Maybe my brain has a mechanism to prevent infinite recurse? Tangent: I wonder if this is evidence of a mechanism that tries to prevent my brain from making premature predictions by observing data about my own predictive process while trying to make predictions? Otherwise maybe there could be a cascade of updates where noticing that I’ve become more sure of something makes me even more sure of it, etc. By the way, I think maybe humans do this on a group level, and this accounts for intellectual fashions.) Anyway, I think it’s important to understand if my brain does something “in practice” which differs from what I’ve outlined here, some kind of method for collapsing recursion that a sufficiently advanced Predict-O-Matic might use.
What if the Predict-O-Matic assigns some credence to the idea that it’s “agentic” in nature? Then what if the Predict-O-Matic assigns some credence to the idea that the simulated version of itself could assign credence to the idea that it’s in a simulation? (I think this is just a classic daemon but maybe it differs in important ways?)
In ML, the predictive model isn’t trying to maximize its own accuracy—that’s what the training algorithm tries to do. The predictive model doesn’t seem like an optimizer even in the “mathematical optimization” sense of the world optimization (is “mesa-optimizer” an appropriate term? In this case, I think we’re glad it’s a mesa-optimizer?) What if the Predict-O-Matic sometimes runs a training algorithm to update its model? How does that change things?
What if time travel actually is possible and we just haven’t discovered it yet?
Does anything interesting happen if the Predict-O-Matic becomes aware of the concept of self-awareness?
Could the Predict-O-Matic notice and exploit shared computational structure in the recursive self-simulation process? Suppose it notes the computation it has to do to make a prediction for the simulated Predict-O-Matic is similar to the computation it has to do to just make a prediction! What then?
Again, $100 prize for the first comment which crisply explains something that could wrong with dualist Predict-O-Matic. Contest ends when I publish my follow-up—probably next Wednesday the 23rd. I do have at least one answer in mind but I’m hoping you’ll come up with something I haven’t thought of. However, if I’m not convinced your thing would be a problem, I can’t promise you a prize. No comment on whether “Open Questions” are related to the answer I have in mind.