All we need to worry about is ensuring that our problem definition actually solves the correct problem.
I agree.
I interpreted that as a perfect training dataset that covers the entire human mindspace. Given infinite inference power, the resulting solutions would be—by the properties of Solonomoff Induction—the best possible explanation of that data—and vastly superior to anything humans will ever come up with.
That’s the interpretation I had in mind.
Now it could be that human minds can not be described very well by any type of RL agent architecture for any possible utility function.
As you point out, this is very unlikely. The question is whether the learned utility functions actually capture what humans care about.
If you think through a few easy approaches, you will see that they predictably fail. We can discuss in more detail, but it would be easier if you provided more insight into what kind of approach you are optimistic about. I can argue against N of them, but you will think that at least N-1 are straw men.
The most natural approach, to an LW mindset, is to define a basic frameowrk for “RL agents,” that has a slot for a utility function. Then we can take a simplicity prior over models that fit into this basic framework, and do inference to find a posterior distribution over models, and hence over utility functions. If this is what you have in mind, I’m happy to comment in more depth on why I’m pessimistic.
The basic problem is that the simplest model of a human is clearly not as an RL agent, it’s to directly model the many particular cognitive effects that shape human behavior. For any expressive framework, the most parsimonious model is going to throw out your framework and just model these cognitive effects directly. Of course it can’t literally throw out your framework, but it can do so in all but name. For a crude example, the definition of “utility function” could consult the real model of the human to figure out what action a human would take, and then output a simple utility function that directly incentivized the predicted actions.
This will break your intended correspondence between the box in your model labeled “utility” and the actual values of the human subject, and if you give this utility function to a stronger RL agent I don’t think the results will be satisfactory.
If we were to pick any concrete model I am quite confident that I could demonstrate this kind of behavior. I suspect that the only way we can avoid it is by being sufficiently vague about the approach that we can’t make any concrete statements about what kind of representation it would learn.
2.) Is not entirely correct—as this approach is enabled by all the great progress in machine learning in improving our general inference capabilities.
Yes, actually getting a solution would require impressive inference capability. For now I’m happy to supoose that continuing AI progress will deliver inference abilities that are up to the task.
But I am especially interested in the residual—even if your inference abilities are as good as you could ask for, how do you solve the problem? It is about this residual that I am most pessimistic, and improvements in our inference ability don’t help.
The most natural approach, to an LW mindset, is to define a basic frameowrk for “RL agents,” that has a slot for a utility function. Then we can take a simplicity prior over models that fit into this basic framework, and do inference to find a posterior distribution over models, and hence over utility functions. If this is what you have in mind, I’m happy to comment in more depth on why I’m pessimistic.
Yes, more or less. I should now point out that almost everything of importance concerning the outcome is determined by the training dataset, not the model prior. This may seem counter-intuitive at first, but it is true and important.
The basic problem is that the simplest model of a human is clearly not as an RL agent, it’s to directly model the many particular cognitive effects that shape human behavior.
This is not clear at all, and furthermore appears to contradict what you agreed to earlier above—namely that human minds can be described well as a specific type of RL agent with some particular utility function.
I consider myself reasonably up to date in both computational neuroscience and ML, and the most successful over-arching theory for explaining the brain today is indeed as a form of RL agent. Thus the RL framework in some sense is the most general framework we have and it includes human, animal, and a wide class of machine agents as special cases.
For any expressive framework, the most parsimonious model is going to throw out your framework and just model these cognitive effects directly.
The ‘framework’ I proposed is minimal—describing the class of all RL agents requires just a few lines of math. Remember the training set is near infinite and perfect, so the tiny number of bits I am imposing on the model prior matters not at all.
You seem to perhaps believe that I am specifying a framework in terms of modules or connections or whatever on the agent, and that was not my idea at all (at least in the infinite computing case). I was proposing the absolute minimal assumptions. The inference engine will explore the model space—and probably come up with something ridiculous like simulations of universes if you give it infinite compute. With practical but very large amounts of compute power, it will—probably—come up some sort of approximate brain-like ANN solution.
If we were to pick any concrete model I am quite confident that I could demonstrate this kind of behavior. I suspect that the only way we can avoid it is by being sufficiently vague about the approach that we can’t make any concrete statements about what kind of representation it would learn.
I am skeptical you could demonstrate this, but you could start by taking one of the existing IRL systems in the literature and demonstrating the failure there. Or maybe I am unclear on the nature of your concern. You seem to be concerned with the details of how the resulting model works. I believe that is a fundamentally misguided notion, and instead we really care only about results. This could be a fundamental difference in mindsets—I”m very much an engineer.
In other words, the ultimate question is this: is the resulting agent better at doing what we actually want (on whatever set of tasks the training set includes) than the human experts that are the source of that training data?
For after all, that is the key advantage of RL techniques over supervised learning, an advantage which IRL inherits.
So here is a more practical set of experiments we could do today. Take a deep RL agent like deepmind’s atari player. But instead of training it using the internal score as the reward function directly, we use IRL using traces of expert human play. We can compare to a baseline with the same model but trained using supervised learning. The supervised baseline would learn human errors and thus would asymptote at human level play. The IRL agent instead should eventually learn a good approximation of the score function as its utility/reward function and thus achieve capability close to the original RL agent.
A cool variation would be to add another training sequence where the human expert has additional constraints—such as maximize score without killing any other ‘agents’. For the games for which that applies, I think that would be a really cool important demonstration of the beginnings of learning ethical behavior from humans.
So the core idea is to apply that same concept, but to life in general, where our ‘game world’ is the real world, and there is no predefined score function, and the ideal utility function must be inferred.
But I am especially interested in the residual—even if your inference abilities are as good as you could ask for, how do you solve the problem?
I don’t claim to have a clear solution to the full problem yet, but my thought experiment above sketches out the vague beginnings of an IRL based solution. Again the training is everything—so the full solution becomes something more like educating an AI population, a problem that goes far beyond the basic math or machine learning and connects to politics, education, game theory, etc.
Remember the training set is near infinite and perfect, so the tiny number of bits I am imposing on the model prior matters not at all.
Yes, the model you get won’t depend at all on the tiny number of bits that you are imposing, unless your model class is extremely crippled. This is precisely my point. You will get a really good model. But you imposed some structure in the model, perhaps with a little box labeled “utility function.” After inference, that box isn’t going to have the utility function in it. Why would your universe-simulating model bother dividing itself neatly into “utility function” and “everything else”? It will just ignore your division and do whatever is most efficient.
You seem to be concerned with the details of how the resulting model works. I believe that is a fundamentally misguided notion, and instead we really care only about results
I believe you will get out a model that predicts human behavior well. I think we can agree on that! But it’s just not enough to do anything with. Now you have a simulation of a human; what do you do with it?
You are making a further claim—that in the box labeled “utility function,” the model will put a reasonable representation of a human utility function, such that you’d be happy with your AI maximizing that utility function. It seems like you are the one making a detailed assumption about how the learned model works, an assumption which seems implausible to me. If you think you aren’t making such an assumption, could you express (even very informally) the argument that the IRL agent will work well?
If your model doesn’t have a box labeled “utility function,” can you say again how you are extracting the utility function from the learned model?
Or do you think that you will not find a reasonable utility function, but produce desirable behavior anyway? I don’t understand why this would happen.
I am skeptical you could demonstrate this, but you could start by taking one of the existing IRL systems in the literature and demonstrating the failure there.
We seem to be talking past each other. Could you cite a paper with what you think is a plausible model? I could respond to any of them, but again it would feel like a straw man, because I don’t think that the authors of these papers expect them to apply to general human behavior.
For example, most of these models make no attempt to model reasoning, and instead assume e.g. that the probability that an agent takes an action depends only on the payoff of that action. This is obviously not a very good model! How do you see this working?
So here is a more practical set of experiments we could do today...
I agree that this experiment can probably yield better behavior than training a supervised learner to reproduce human play.
But existing approaches won’t scale to learn perfect play, even with infinite computing power and unlimited training data, except in extremely simple environments. To make this clear you’d have to fix a particular model, which I invite you to do. But I think that most (all?) models in the literature will converge to exactly reproducing the “modal human policy” (in each state, do the thing that the expert is most likley to do) in the limit of infinite training data and a sufficiently rich state space. Do you have a counterexample in mind?
You can probably get optimal play in the atari case by leaning heavily on the simplicity prior for the rewards and neglecting the training data. But earlier in your comment it (very strongly) sounded like you wanted to let the training data wash out the prior.
But you imposed some structure in the model, perhaps with a little box labeled “utility function.” After inference, that box isn’t going to have the utility function in it. Why would your universe-simulating model bother dividing itself neatly into “utility function” and “everything else”? It will just ignore your division and do whatever is most efficient.
Hmm at this point I should now actually write out a simple RL model to help me understand your critique.
Here is some very simple math for a general RL setup (bellman-style recursive function form):
model = p(s,a,s’)
policy(s) = argmax_a Q(s,a)
Q(s,a) = sum_s’ p(s,a,s’) [ R(s’) + gV(s) ]
V(s) = max_a Q(s,a)
The function p(s,a,s’) is the agent’s world model which gives transition probabilities between consecutive states (s,s’) on action a. The states really are observation histories—entire sequences of observations. The variable ‘g’ represents the discount factor (although really this should probably be a an unknown function). R(s’) is the reward/utility function, and Q(s,a) is the value-action function that results from planning ahead to optimize R. The decision/policy function just selects the best action.
We condition on the actions and observations to learn the best model , reward and discount functions. And now I see your point (i think), after writing this out, that the model and reward functions are not really well distinguished and either could potentially substitute for the other (as they just multiply). It could learn a reward function that is just ‘1’ and stuff everything in the model.
So—yes we need more prior structure than the 4 lines of math model. My initial initial guess was about 100 lines of math code in a tight prob prog model, which still may be reasonable in the future but is perhaps slightly optimistic.
Ok, so here is version 2. We know roughly that the cortex is responsible for modelling the world and we know its rough circuit complexity. So we can use that as a prior on the model function. Better yet, we can train the model function separately (constrained to cortex size or smaller), without including the policy function/argmax stuff, and on a dataset which includes situations where no actions are taken, forcing it to learn a world model first. Then we can use those results as an initial prior when we train the whole thing on the full dataset with the actions.
That doesn’t totally solve the general form of your objection, but it at least forces the utility function to be somewhat more sensible. I can now kindof see where version 100 of this idea or so is going and how it could work well, but it probably requires increasingly complex models of human-like brains (along with more complex training schemes) as priors.
If your model doesn’t have a box labeled “utility function,” can you say again how you are extracting the utility function from the learned model?
Extract is perhaps not the right word, but the general idea is that once we have learned a human-level model function and reward function, in theory we can get superintelligent extrapolation by improving the model function, running it faster, and or eliminating any planning limitations or noise. The model function we learn to explain human data in particular will only know/model what humans actually know.
So here is a more practical set of experiments we could do today...
I agree that this experiment can probably yield better behavior than training a supervised learner to reproduce human play. . . .But I think that most (all?) models in the literature will converge to exactly reproducing the “modal human policy” (in each state, do the thing that the expert is most likley to do) in the limit of infinite training data and a sufficiently rich state space. Do you have a counterexample in mind?
The modal human policy, as you describe it, sounds identical to the supervised learner which just reproduces human ability. Beating supervised learning (the modal human policy) is again what really matters.
You can probably get optimal play in the atari case by leaning heavily on the simplicity prior for the rewards and neglecting the training data.
Not sure what you mean here—you need the training data to get up to any decent level of play. Perhaps you were thinking only of the utility function, but to learn that you still need some training data.
The deep ANN approach to RL is still new, and hasn’t been merged with IRL research yet, which mostly appears to be in the small model stage (with the exception perhaps of some narrow applications in robotics and pathfinding).
the model and reward functions are not really well distinguished and either could potentially substitute for the other (as they just multiply)
They can also substitute in more subtle ways, e.g. by learning R(s) = 1 if the last action implied by the state history matches the predicted human action. If the human is doing RL imperfectly then that is going to have a much better explanatory fit to the data (it can be arbitrarily good, while any model of a human as a perfect RL agent will lose Bayes points all over the place), so you have to rely on the prior to see that it’s a “bad” model.
it probably requires increasingly complex models of human-like brains (along with more complex training schemes) as priors
That’s my concern; I think things get pretty hairy, and moreover I don’t know whether the resulting systems would typically be competitive with (e.g.) the best RL agents that we could design by more direct methods.
once we have learned a human-level model function and reward function
That’s what I mean by a “box labeled ‘utility function’.”
The modal human policy, as you describe it, sounds identical to the supervised learner which just reproduces human ability. Beating supervised learning (the modal human policy) is again what really matters.
Yes. Do you know any model of IRL that can (significantly) beat the modal human policy in this context?
Not sure what you mean here—you need the training data to get up to any decent level of play
Sorry, I meant “assign low total weight” to the training data, so that the learner can infer that some of the human’s decisions were probably mistakes (since they can only be explained by an artificial reward function). This is very delicate, and it requires paying more attention to athe prior than you seemed to want to (and more attention to the prior than is consistent with actually making good predictions about human behavior).
the model and reward functions are not really well distinguished and either could potentially substitute for the other (as they just multiply)
They can also substitute in more subtle ways, e.g. by learning R(s) = 1 if the last action implied by the state history matches the predicted human action. If the human is doing RL imperfectly then that is going to have a much better explanatory fit to the data (it can be arbitrarily good, while any model of a human as a perfect RL agent will lose Bayes points all over the place), so you have to rely on the prior to see that it’s a “bad” model.
That may or may not be a problem with the simplest version 1 of the idea, but it is not a problem in version 2 which imposes more realistic priors/constraints and also uses model pretraining on just state transitions to force differentiation of the model and reward functions.
I think things get pretty hairy, and moreover I don’t know whether the resulting systems would typically be competitive with (e.g.) the best RL agents that we could design by more direct methods.
Ok, I think we are kindof in agreement, but first let me recap where we are. This all started when I claimed that your ‘easy IRL problem’ - solve IRL given infinite compute and infinite perfect training data—is relatively easy and could probably be done in 100 lines of math. We both agreed that supervised learning (reproducing the training set—the modal human policy) would be obviously easy in this setting.
After that the discussion forked and got complicated—which I realize in hindsight—stems from not clearly specifying what would entail success. So to be more clear—success of the IRL approach can be measured as improvement over supervised learning—as measured in the recovered utility function. Which of course leads to this whole other complexity—how do we know that is the ‘true utility function’ - leave that aside for a second, and I’ll get back to it.
I then brought up a concrete example of using IRL on an deep RL Atari agent. I described how learning the score function should be relatively straightforward, and this would allow an IRL agent to match the performance of the RL agent in this domain, which leads to better performance than the supervised/modal human policy.
You agreed with this:
So here is a more practical set of experiments we could do today...
I agree that this experiment can probably yield better behavior than training a supervised learner to reproduce human play.
So it seems we have agreed that IRL surpassing the modal human policy is clearly possible—at least in the limited domain of atari.
If we already know the utility function apriori, then obviously IRL given the same resources can only do as good as RL. But that isn’t that interesting, and remember IRL can do much more—as in the example of learning to maximize score while under other complex constraints.
So in scaling up to more general problem domains, we have the issue of modelling mistakes—which you seem to be especially focused on—and the related issue of utility function uniqueness.
Versions 2 and later of my simple proto-proposal use more informed priors for the circuit complexity combined with pretraining the model on just observations to force differentiate the model and utility functions. In the case of atari, getting the utility function to learn the score should be relatively easy—as we know it is a simple immediate visual function.
This type of RL architecture can model human’s limited rationality by bounding the circuit complexity—at least that’s the first step. We could get increasingly more accurate models of the human decision surface by incorporating more of the coarse abstract structure of the brain as a prior over our model space.
Ok, so backing up a bit :
it probably requires increasingly complex models of human-like brains (along with more complex training schemes) as priors
That’s my concern; I think things get pretty hairy, and moreover I don’t know whether the resulting systems would typically be competitive with (e.g.) the best RL agents that we could design by more direct methods.
For the full AGI problem, I am aware of a couple of interesting candidates for an intrinsic reward/utility function—the future freedom of action principle (power) and the compression progress measure (curiosity). If scaled up to superhuman intelligence, I think/suspect you would agree that both of these candidates are probably quite dangerous. On the other hand, they seem to capture some aspects of human’s intrinsic motivators, so they may be useful as subcomponents or features.
The IRL approach—if taken all the way—seems to require reverse engineering the brain. It could be that any successful route to safe superintelligence just requires this—because the class of agents that combine our specific complex unknown utility functions with extrapolated superintelligence necessarily can only be specified in reference to our neural architecture as a starting point.
The IRL approach—if taken all the way—seems to require reverse engineering the brain. It could be that any successful route to safe superintelligence just requires this—because the class of agents that combine our specific complex unknown utility functions with extrapolated superintelligence necessarily can only be specified in reference to our neural architecture as a starting point.
This sounds really interesting and important (if true), but I have only a vague understanding of how you arrived at this conclusion. Please consider writing a post about it.
This sounds really interesting and important (if true), but I have only a vague understanding of how you arrived at this conclusion. Please consider writing a post about it.
It’s not so much a conclusion as an intuition, and most of the inferences leading up to it are contained in this thread with PaulChristiano and a related discussion with Kaj Sotala.
I’m interested in IRL and I think it’s the most promising current candidate for value learning, but I must admit I haven’t read much of the relevant literature yet. Reading up on IRL and writing a discussion post on it has been on my todo list—your comment just bumped it up a bit. :)
Another related issue is the more general question of how the training data/environment determines/shapes safety issues for learning agents.
My reaction when I first came across IRL is similar to this author’s:
However, the current IRL methods are limited and cannot be used for inferring human values
because of their long list of assumptions. For instance, in most IRL methods
the environment is usually assumed to be stationary, fully observable, and some-
times known; the policy of the agent is assumed to be stationary and optimal
or near-optimal; the reward function is assumed to be stationary as well; and
the Markov property is assumed. Such assumptions are reasonable for limited
motor control tasks such as grasping and manipulation; however, if our goal is to
learn high-level human values, they become unrealistic.
But maybe it’s not a bad approach for solving a hard problem to first solve a very simplified version of it, then gradually relax the simplifying assumptions and try to build up to a solution of the full problem.
My reaction when I first came across IRL is similar to this author’s:
As a side note, that author’s attempt at value learning is likely to suffer from the same problem Christiano brought up in this thread—there is nothing to enforce that the optimization process will actually nicely separate the reward and agent functionality. Doing that requires some more complex priors and or training tricks.
The author’s critique about limiting assumptions may or may not be true, but the author only quotes a single paper from the IRL field—and its from 2000. That paper and it’s follow up both each have 500+ citations, and some of the newer work with IRL in the title is from 2008 or later. Also—most of the related research doesn’t use IRL in the title—ie “Probabilistic reasoning from observed context-aware behavior”.
But maybe it’s not a bad approach for solving a hard problem to first solve a very simplified version of it, then gradually relax the simplifying assumptions and try to build up to a solution of the full problem.
This is actually the mainline successful approach in machine learning—scaling up. MNIST is a small ‘toy’ visual learning problem, but it lead to CIFAR10/100 and eventually ImageNet. The systems that do well on ImageNet descend from the techniques that did well on MNIST decades ago.
MIRI/LW seems much more focused on starting with a top-down approach where you solve the full problem in an unrealistic model—given infinite compute—and then scale down by developing some approximation.
Compare MIRI/LW’s fascination with AIXI vs the machine learning community. Searching for “AIXI” on r/machinelearning gets a single hit vs 634 results on lesswrong. Based on #citations of around 150 or so, AIXI is a minor/average paper in ML (more minor than IRL), and doesn’t appear to have lead to great new insights in terms of fast approximations to bayesian inference (a very active field that connects mostly to ANN research).
MIRI is taking the top-down approach since that seems to be the best way to eventually obtain an AI for which you can derive theoretical guarantees. In the absence of such guarantees, we can’t be confident that an AI will behave correctly when it’s able to think of strategies or reach world states that are very far outside of its training and testing data sets. The price for pursuing such guarantees may well be slower progress in making efficient and capable AIs, with impressive and/or profitable applications, which would explain why the mainstream research community isn’t very interested in this approach.
I tend to agree with MIRI that the top-down approach is probably safest, but since it may turn out to be too slow to make any difference, we should be looking at other approaches as well. If you’re thinking about writing a post about recent progress in IRL and related ideas, I’d be very interested to see it.
MIRI is taking the top-down approach since that seems to be the best way to eventually obtain an AI for which you can derive theoretical guarantees.
I for one remain skeptical such theoretical guarantees are possible in principle for the domain of general AI. The utility of formal math towards a domain tends to vary inversely with domain complexity. For example in some cases it may be practically possible to derive formal guarantees about the full output space of a program, but not when that program is as complex as a modern video game, or let alone a human. The equivalent of theoretical guarantees may be possible/useful for something like a bridge, but less so for an airplane or a city.
For complex systems simulations are the key tool that enables predictions about future behavior.
In the absence of such guarantees, we can’t be confident that an AI will behave correctly when it’s able to think of strategies or reach world states that are very far outside of its training and testing data sets.
This indeed would be a problem if the AI’s training ever stopped, but I find this extremely unlikely. Some AI systems already learn continuously—whether using online learning directly or by just frequently patching the AI with the results of updated training data. Future AI systems will continue this trend—and learn continuously like humans.
Much depends on one’s particular models for how the future of AI will pan out. I contend that AI does not need to be perfect, just better than humans. AI drivers don’t need to make optimal driving decisions—they just need to drive better than humans. Likewise AI software engineers just need to code better than human coders, and AI AI researchers just need to do their research better than humans. And so on.
The price for pursuing such guarantees may well be slower progress in making efficient and capable AIs, with impressive and/or profitable applications, which would explain why the mainstream research community isn’t very interested in this approach.
For the record, I do believe that MIRI is/should be funded at some level—it’s sort of a moonshot, but one worth taking given the reasonable price. Mainstream opinion on the safety issue is diverse, and their are increasingly complex PR and career issues to consider. For example corporations are motivated to downplay long term existential risks, and in the future will be motivated to downplay similarity between AI and human cognition to avoid regulation.
If you’re thinking about writing a post about recent progress in IRL and related ideas, I’d be very interested to see it.
Future AI systems will continue this trend—and learn continuously like humans.
Sure, but when it comes to learning values, I see a few problems even with continuous learning:
The AI needs to know when to be uncertain about its values, and actively seek out human advice (or defer to human control) in those cases. If the AI is wrong and overconfident (like in http://www.evolvingai.org/fooling but for values instead of image classification) even once, we could be totally screwed.
On the other hand, if the AI can think much faster than a human (almost certainly the case, given how fast hardware neurons are even today), learning from humans in real time will be extremely expensive. There will be high incentive to lower the frequency of querying humans to a minimum. Those willing to take risks, or think that they have a simple utility function that the AI can learn quickly, could have a big advantage in how competitive their AIs are.
I don’t know what my own values are, especially when it comes to exotic world states that are achievable post-Singularity. (You could say that my own training set was too small. :) Ideally I’d like to train an AI to try to figure out my values the same way that I would (i.e., by doing philosophy), but that might require very different methods than for learning well-defined values. I don’t know if incremental progress in value learning could make that leap.
For complex systems simulations are the key tool that enables predictions about future behavior.
[...]
I contend that AI does not need to be perfect, just better than humans.
My point was that an AI could do well on test data, including simulations, but get tripped up at some later date (e.g., it over-confidently thinks that a certain world state would be highly desirable). Another way things could go wrong is that an AI learns wrong values, but does well in simulations because it infers that it’s being tested and tries to please the human controllers in order to be released into the real world.
I generally agree that learning values correctly will be a challenge, but it’s closely related to general AGI challenges.
I’m also reasonably optimistic that we will be able to reverse engineer the brain’s value learning mechanisms to create agents that are safer than humans. Fully explaining the reasons behind that cautious optimism would require a review of recent computational neuroscience (the LW consensus on the brain is informed primarily by a particular narrow viewpoint from ev psych and the H&B literature, and this position is in substantial disagreement with the viewpoint from comp neuroscience.)
The AI needs to know when to be uncertain about its values,
Mostly agreed. However it is not clear that actively deferring to humans is strictly necessary. In particular one route that circumvents most of these problems is testing value learning systems and architectures on a set of human-level AGIs contained to a virtual sandbox where the AGI does not know it is in a sandbox. This allows safe testing of designs to be used outside of the sandbox. The main safety control is knowledge limitation (which is something that MIRI has not considered much at all, perhaps because of their historical anti-machine learning stance).
The fooling CNN stuff does not show a particularly important failure mode for AI. These CNNs are trained only to recognize images in the sense of outputting a 10 bit label code for any input image. If you feed them a weird image, they just output the closest category. The fooling part (getting the CNN to misclassify an image) specifically requires implicitly reverse engineering the CNN and thus relies on the fact that current CNNs are naively deterministic. A CNN with some amount of random sampling based on a secure irreversible noise generator would not have this problem.
[Learning values could take too long, corps could take shortcuts.]
This could be a problem, but even today our main technique to speed up AI learning relies more on parallelization than raw serial speedup. The standard technique involves training 128 to 1024 copies of the AI in parallel, all on different data streams. The same general technique would allow an AI to learn values from large number of humans in parallel. This also happens to automatically solve some of the issues with value representativeness.
I don’t know what my own values are, especially when it comes to exotic world states that are achievable post-Singularity.
The current world is already exotic from the perspective of our recent ancestors. We already have some methods to investigate the interaction of our values with exotic future world states: namely our imagination, as realized in thought experiments and especially science fiction. AI could help us extend these powers.
My point was that an AI could do well on test data, including simulations, but get tripped up at some later date
This is just failure to generalize or overfitting, and how to avoid these problems is much of what machine learning is all about.
Another way things could go wrong is that an AI learns wrong values, but does well in simulations because it infers that it’s being tested and tries to please the human controllers in order to be released into the real world.
This failure requires a specific combination of: 1. that the AI learns a good model of the world, but 2. learns a poor model of human values, and 3. learns that it is in a sim. 4. wants to get out. 5. The operators fail to ever notice any of 2 through 4.
Is this type of failure possible? Sure. But the most secure/paranoid type of safety model I envision is largely immune to that class of failures. In the most secure model, potentially unsafe new designs are constrained to human-level intelligence and grow up in a safe VR sim (medieval or earlier knowledge-base). Designs which pass safety tests are then slowly percolated up to sims which are closer to the modern world. Each up migration step is like reincarnation—a new AI is grown from a similar seed. The final designs (seed architectures rather than individual AIs) that pass this vetting/testing process will have more evidence for safety/benevolence/altruism than humans.
Fully explaining the reasons behind that cautious optimism would require a review of recent computational neuroscience (the LW consensus on the brain is informed primarily by a particular narrow viewpoint from ev psych and the H&B literature, and this position is in substantial disagreement with the viewpoint from comp neuroscience.)
Sounds like another post to look forward to.
The current world is already exotic from the perspective of our recent ancestors.
I think we’ll need different methods to deal with future exoticness though. See this post for some of the reasons.
In the most secure model, potentially unsafe new designs are constrained to human-level intelligence and grow up in a safe VR sim (medieval or earlier knowledge-base).
Do you envision biological humans participating in the VR sim, in order to let the AI learn values from them? If so, how to handle speed differences that may be up to a factor of millions (which you previously suggested will be the case)? Only thing I can think of is to slow the AI down to human speed for the training, which might be fine if your AI group has a big lead and you know there aren’t any other AIs out there able to run at a million times human speed. Otherwise, even if you could massively parallelize the value learning and finish it in one day of real time, that could be giving a competitor a millions days of subjective time (times how many parallel copies of the AI they can spawn) to make further progress in AI design and other technologies.
The final designs (seed architectures rather than individual AIs) that pass this vetting/testing process will have more evidence for safety/benevolence/altruism than humans.
Safer than humans seems like a pretty low bar to me, given that I think most humans are terribly unsafe. :) But despite various problems I see with this approach, it may well be the best outcome that we can realistically hope for, if mainstream AI/ML continues to make progress at such a fast pace using designs that are hard to reasonable about formally.
I think we’ll need different methods to deal with future exoticness though. See this post for some of the reasons.
Perhaps. The question of uploading comes to mind as something like an ‘ontological crisis’. We start with a intuitive model of selfhood built around the concept of a single unique path extending through time, and the various uploading thought experiments upend that model. Humans (at least some) appear to be able to deal with these types of challenges given enough examples to cover the space and enough time to update models.
Do you envision biological humans participating in the VR sim, in order to let the AI learn values from them?
Of course. And eventually we can join the AIs in the VR sim more directly, or at least that’s the hope.
If so, how to handle speed differences that may be up to a factor of millions (which you previously suggested will be the case)?
Given some computing network running a big VR AI sim, in theory the compute power can be used to run N AIs in parallel or one AI N times accelerated or anything in between. In practice latency and bandwidth overhead considerations will place limits on the maximum serial speedup.
But either way the results are similar—the core problem is the total throughput of AI thought volume to human monitor thought volume. It’s essentially the student/teacher ratio problem. One human could perhaps monitor a couple dozen ‘children’ AI without sophisticated tools, or perhaps hundreds or even thousands with highly sophisticated narrow AI tools (automated thought monitors and visualizers).
I don’t expect this will be a huge issue in practice due to simple economical considerations. AGI is likely to arrive near the time the hardware cost of an AGI is similar to human salary/cost. So think of it in terms of the ratio of human teacher cost to AGI hardware cost. AGI is a no brainer investment when that cost ratio is 1:1, and just gets better over time.
The point in time at which AGI hardware costs say 1/100th of a human teacher - (say 20 cents per hour) that time is already probably well in to the singularity anyway. The current trend is steady exponential progress in driving down hypothetical AGI hardware cost. (which I estimate is vaguely around $1,000/hr today—the cost of about 1000 gpus) If that cost suddenly went down due to some new breakthrough, that would just accelerate the timeline.
Humans (at least some) appear to be able to deal with these types of challenges given enough examples to cover the space and enough time to update models.
Given some computing network running a big VR AI sim, in theory the compute power can be used to run N AIs in parallel or one AI N times accelerated or anything in between. In practice latency and bandwidth overhead considerations will place limits on the maximum serial speedup.
If you have hardware neurons running at 10^6 times biological speed (BTW, are you aware of HICANN, a chip that today implements neurons running at 10^4 faster than biological? See also this video presentation), would it make sense to implement a time-sharing system where one set of neurons is used to implement multiple AIs running at slower speed? Wouldn’t that create unnecessary communication costs (swapping AI mind states in and out of your chips) and coordination costs among the AIs?
would it make sense to implement a time-sharing system where one set of neurons is used to implement multiple AIs running at slower speed? Wouldn’t that create unnecessary communication costs
In short, If you don’t time share, then you are storing all synaptic data on the logic chip. Thus you need vastly more logic chips to simulate your model, and thus you have more communication costs.
There are a number of tradeoffs here that differ across GPUs vs neuro ASICs like HICANN or IBM TruNorth. The analog memristor approaches, if/when they work out, will have similar tradeoffs to neuro-ASICs. (for more on that and another viewpoint see this discussion with the Knowm guy )
GPUs are von neumman machines that take advantage of the 10x or more cost difference between the per transistor cost of logic vs that of memory. Logic is roughly 10x more expensive, so it makes sense to have roughly 10x more memory bits than logic bits. ie: a GPU with 5 billion transistors might have 4 gigabytes of offchip RAM.
So on the GPU (or any von neumman), typically you are always doing time-swapping: simulating some larger circuit by swapping pieces in and out of memory.
The advantage of the neuro-ASIC is energy efficiency: synapses are stored on chip, so you don’t have to pay the price of moving data which is most of the energy cost these days. The disadvantages are threefold: you lose most of your model flexibility, storing all your data on the logic chip is vastly more expensive per synapse, and you typically lose the flexibility to compress synaptic data—even basic weight sharing is no longer possible. Unfortunately these problems combine.
Lets look at some numbers. The HICANN chip has 128k synapses in 50 mm^2, and their 8-chip reticle is thus equivalent to a mid-high end GPU in die area. That’s 1 million synapses in 400 mm^2. It can update all of those synapses at about 1 mhz—which is about 1 trillion synop-hz.
A GPU using SOTA ANN simulation code can also hit about 1 trillion synop-hz, but with much more flexibility in the tradeoff between model size and speed. In particular 1 million synapses isn’t really enough—most competitive ANNS trained today are in the 1 to 10 billion synapse range—which would cost about 1000 times more for the HICANN, because it can only store 1 million synapses per chip, vs 1 billion or more for the GPU.
IBM’s truenorth can fit more synapses on a chip − 256 million on a GPU sized chip (5 billion transistors), but it runs slower, with a similar total synop-hz throughput. The GPU solutions are just far better, overall—for now.
Apparently HICANN was designed before 2008, and uses a 180nm CMOS process, whereas modern GPUs are using 28nm. It seems to me that if neuromorphic hardware catches up in terms of economy of scale and process technology, it should be far superior in cost per neural event. And if neuromorphic hardware does win, it seems that the first AGIs could have a huge amortized cost per hour of operation, and still have a lower cost per unit of cognitive work than human workers, due to running much faster than biological brains.
It seems like this GPU vs neuromorphic question could have a large impact on how the Singularity turns out, but I haven’t seen any discussion of it until now. Do you have any other thoughts or references on this topic?
Apparently HICANN was designed before 2008, and uses a 180nm CMOS process, whereas modern GPUs are using 28nm.
That’s true, but IBM’s TrueNorth is 28 nm, with about the same transistor count as a GPU. It descends from earlier research chips on old nodes that were then scaled up to new nodes. TrueNorth can fit 256 million low-bit synapses on a chip, vs 1 million for HICANN (normalized for chip area). The 28 nm process has roughly 40x the transistor density. So my default hypothesis is that if HICANN was scaled up to 28 nm it would end up similar to TrueNorth in terms of density (although TrueNorth is wierd in that it is intentionally much slower than it could be to save energy).
It seems to me that if neuromorphic hardware catches up in terms of economy of scale and process technology, it should be far superior in cost per neural event.
I expect this in the long term, but it will depend on how the end of Moore’s Law pans out. Also, current GPU code is not yet at the limits of software simulation efficiency for ANNs, and GPU hardware is still improving rapidly. It just so happens that I am working on a new type of ANN sim engine that is 10x or more faster than current SOTA for networks of interest. My approach could eventually be hardware accelerated. There are some companies already pursuing hardware acceleration of the standard algorithms—such as Nervana, targeting similar speedup but through dedicated neural asics.
One thing I can’t stress enough is the advantage of programmeable memory for storing weights—sharing and compressing weights helps solve much of the bandwidth problems the GPU would otherwise have.
It seems like this GPU vs neuromorphic question could have a large impact on how the Singularity turns out, but I haven’t seen any discussion of it until now. Do you have any other thoughts or references on this topic?
I don’t know much it really effects outcomes—whether one uses clever hardware or clever software, the brain is probably near or on the pareto surface for statistical inference energy efficiency, and we will probably get close in the near future.
I agree.
That’s the interpretation I had in mind.
As you point out, this is very unlikely. The question is whether the learned utility functions actually capture what humans care about.
If you think through a few easy approaches, you will see that they predictably fail. We can discuss in more detail, but it would be easier if you provided more insight into what kind of approach you are optimistic about. I can argue against N of them, but you will think that at least N-1 are straw men.
The most natural approach, to an LW mindset, is to define a basic frameowrk for “RL agents,” that has a slot for a utility function. Then we can take a simplicity prior over models that fit into this basic framework, and do inference to find a posterior distribution over models, and hence over utility functions. If this is what you have in mind, I’m happy to comment in more depth on why I’m pessimistic.
The basic problem is that the simplest model of a human is clearly not as an RL agent, it’s to directly model the many particular cognitive effects that shape human behavior. For any expressive framework, the most parsimonious model is going to throw out your framework and just model these cognitive effects directly. Of course it can’t literally throw out your framework, but it can do so in all but name. For a crude example, the definition of “utility function” could consult the real model of the human to figure out what action a human would take, and then output a simple utility function that directly incentivized the predicted actions.
This will break your intended correspondence between the box in your model labeled “utility” and the actual values of the human subject, and if you give this utility function to a stronger RL agent I don’t think the results will be satisfactory.
If we were to pick any concrete model I am quite confident that I could demonstrate this kind of behavior. I suspect that the only way we can avoid it is by being sufficiently vague about the approach that we can’t make any concrete statements about what kind of representation it would learn.
Yes, actually getting a solution would require impressive inference capability. For now I’m happy to supoose that continuing AI progress will deliver inference abilities that are up to the task.
But I am especially interested in the residual—even if your inference abilities are as good as you could ask for, how do you solve the problem? It is about this residual that I am most pessimistic, and improvements in our inference ability don’t help.
Yes, more or less. I should now point out that almost everything of importance concerning the outcome is determined by the training dataset, not the model prior. This may seem counter-intuitive at first, but it is true and important.
This is not clear at all, and furthermore appears to contradict what you agreed to earlier above—namely that human minds can be described well as a specific type of RL agent with some particular utility function.
I consider myself reasonably up to date in both computational neuroscience and ML, and the most successful over-arching theory for explaining the brain today is indeed as a form of RL agent. Thus the RL framework in some sense is the most general framework we have and it includes human, animal, and a wide class of machine agents as special cases.
The ‘framework’ I proposed is minimal—describing the class of all RL agents requires just a few lines of math. Remember the training set is near infinite and perfect, so the tiny number of bits I am imposing on the model prior matters not at all.
You seem to perhaps believe that I am specifying a framework in terms of modules or connections or whatever on the agent, and that was not my idea at all (at least in the infinite computing case). I was proposing the absolute minimal assumptions. The inference engine will explore the model space—and probably come up with something ridiculous like simulations of universes if you give it infinite compute. With practical but very large amounts of compute power, it will—probably—come up some sort of approximate brain-like ANN solution.
I am skeptical you could demonstrate this, but you could start by taking one of the existing IRL systems in the literature and demonstrating the failure there. Or maybe I am unclear on the nature of your concern. You seem to be concerned with the details of how the resulting model works. I believe that is a fundamentally misguided notion, and instead we really care only about results. This could be a fundamental difference in mindsets—I”m very much an engineer.
In other words, the ultimate question is this: is the resulting agent better at doing what we actually want (on whatever set of tasks the training set includes) than the human experts that are the source of that training data?
For after all, that is the key advantage of RL techniques over supervised learning, an advantage which IRL inherits.
So here is a more practical set of experiments we could do today. Take a deep RL agent like deepmind’s atari player. But instead of training it using the internal score as the reward function directly, we use IRL using traces of expert human play. We can compare to a baseline with the same model but trained using supervised learning. The supervised baseline would learn human errors and thus would asymptote at human level play. The IRL agent instead should eventually learn a good approximation of the score function as its utility/reward function and thus achieve capability close to the original RL agent.
A cool variation would be to add another training sequence where the human expert has additional constraints—such as maximize score without killing any other ‘agents’. For the games for which that applies, I think that would be a really cool important demonstration of the beginnings of learning ethical behavior from humans.
So the core idea is to apply that same concept, but to life in general, where our ‘game world’ is the real world, and there is no predefined score function, and the ideal utility function must be inferred.
I don’t claim to have a clear solution to the full problem yet, but my thought experiment above sketches out the vague beginnings of an IRL based solution. Again the training is everything—so the full solution becomes something more like educating an AI population, a problem that goes far beyond the basic math or machine learning and connects to politics, education, game theory, etc.
Yes, the model you get won’t depend at all on the tiny number of bits that you are imposing, unless your model class is extremely crippled. This is precisely my point. You will get a really good model. But you imposed some structure in the model, perhaps with a little box labeled “utility function.” After inference, that box isn’t going to have the utility function in it. Why would your universe-simulating model bother dividing itself neatly into “utility function” and “everything else”? It will just ignore your division and do whatever is most efficient.
I believe you will get out a model that predicts human behavior well. I think we can agree on that! But it’s just not enough to do anything with. Now you have a simulation of a human; what do you do with it?
You are making a further claim—that in the box labeled “utility function,” the model will put a reasonable representation of a human utility function, such that you’d be happy with your AI maximizing that utility function. It seems like you are the one making a detailed assumption about how the learned model works, an assumption which seems implausible to me. If you think you aren’t making such an assumption, could you express (even very informally) the argument that the IRL agent will work well?
If your model doesn’t have a box labeled “utility function,” can you say again how you are extracting the utility function from the learned model?
Or do you think that you will not find a reasonable utility function, but produce desirable behavior anyway? I don’t understand why this would happen.
We seem to be talking past each other. Could you cite a paper with what you think is a plausible model? I could respond to any of them, but again it would feel like a straw man, because I don’t think that the authors of these papers expect them to apply to general human behavior.
For example, most of these models make no attempt to model reasoning, and instead assume e.g. that the probability that an agent takes an action depends only on the payoff of that action. This is obviously not a very good model! How do you see this working?
I agree that this experiment can probably yield better behavior than training a supervised learner to reproduce human play.
But existing approaches won’t scale to learn perfect play, even with infinite computing power and unlimited training data, except in extremely simple environments. To make this clear you’d have to fix a particular model, which I invite you to do. But I think that most (all?) models in the literature will converge to exactly reproducing the “modal human policy” (in each state, do the thing that the expert is most likley to do) in the limit of infinite training data and a sufficiently rich state space. Do you have a counterexample in mind?
You can probably get optimal play in the atari case by leaning heavily on the simplicity prior for the rewards and neglecting the training data. But earlier in your comment it (very strongly) sounded like you wanted to let the training data wash out the prior.
Hmm at this point I should now actually write out a simple RL model to help me understand your critique.
Here is some very simple math for a general RL setup (bellman-style recursive function form):
model = p(s,a,s’)
policy(s) = argmax_a Q(s,a)
Q(s,a) = sum_s’ p(s,a,s’) [ R(s’) + gV(s) ]
V(s) = max_a Q(s,a)
The function p(s,a,s’) is the agent’s world model which gives transition probabilities between consecutive states (s,s’) on action a. The states really are observation histories—entire sequences of observations. The variable ‘g’ represents the discount factor (although really this should probably be a an unknown function). R(s’) is the reward/utility function, and Q(s,a) is the value-action function that results from planning ahead to optimize R. The decision/policy function just selects the best action.
We condition on the actions and observations to learn the best model , reward and discount functions. And now I see your point (i think), after writing this out, that the model and reward functions are not really well distinguished and either could potentially substitute for the other (as they just multiply). It could learn a reward function that is just ‘1’ and stuff everything in the model.
So—yes we need more prior structure than the 4 lines of math model. My initial initial guess was about 100 lines of math code in a tight prob prog model, which still may be reasonable in the future but is perhaps slightly optimistic.
Ok, so here is version 2. We know roughly that the cortex is responsible for modelling the world and we know its rough circuit complexity. So we can use that as a prior on the model function. Better yet, we can train the model function separately (constrained to cortex size or smaller), without including the policy function/argmax stuff, and on a dataset which includes situations where no actions are taken, forcing it to learn a world model first. Then we can use those results as an initial prior when we train the whole thing on the full dataset with the actions.
That doesn’t totally solve the general form of your objection, but it at least forces the utility function to be somewhat more sensible. I can now kindof see where version 100 of this idea or so is going and how it could work well, but it probably requires increasingly complex models of human-like brains (along with more complex training schemes) as priors.
Extract is perhaps not the right word, but the general idea is that once we have learned a human-level model function and reward function, in theory we can get superintelligent extrapolation by improving the model function, running it faster, and or eliminating any planning limitations or noise. The model function we learn to explain human data in particular will only know/model what humans actually know.
The modal human policy, as you describe it, sounds identical to the supervised learner which just reproduces human ability. Beating supervised learning (the modal human policy) is again what really matters.
Not sure what you mean here—you need the training data to get up to any decent level of play. Perhaps you were thinking only of the utility function, but to learn that you still need some training data.
The deep ANN approach to RL is still new, and hasn’t been merged with IRL research yet, which mostly appears to be in the small model stage (with the exception perhaps of some narrow applications in robotics and pathfinding).
They can also substitute in more subtle ways, e.g. by learning R(s) = 1 if the last action implied by the state history matches the predicted human action. If the human is doing RL imperfectly then that is going to have a much better explanatory fit to the data (it can be arbitrarily good, while any model of a human as a perfect RL agent will lose Bayes points all over the place), so you have to rely on the prior to see that it’s a “bad” model.
That’s my concern; I think things get pretty hairy, and moreover I don’t know whether the resulting systems would typically be competitive with (e.g.) the best RL agents that we could design by more direct methods.
That’s what I mean by a “box labeled ‘utility function’.”
Yes. Do you know any model of IRL that can (significantly) beat the modal human policy in this context?
Sorry, I meant “assign low total weight” to the training data, so that the learner can infer that some of the human’s decisions were probably mistakes (since they can only be explained by an artificial reward function). This is very delicate, and it requires paying more attention to athe prior than you seemed to want to (and more attention to the prior than is consistent with actually making good predictions about human behavior).
That may or may not be a problem with the simplest version 1 of the idea, but it is not a problem in version 2 which imposes more realistic priors/constraints and also uses model pretraining on just state transitions to force differentiation of the model and reward functions.
Ok, I think we are kindof in agreement, but first let me recap where we are. This all started when I claimed that your ‘easy IRL problem’ - solve IRL given infinite compute and infinite perfect training data—is relatively easy and could probably be done in 100 lines of math. We both agreed that supervised learning (reproducing the training set—the modal human policy) would be obviously easy in this setting.
After that the discussion forked and got complicated—which I realize in hindsight—stems from not clearly specifying what would entail success. So to be more clear—success of the IRL approach can be measured as improvement over supervised learning—as measured in the recovered utility function. Which of course leads to this whole other complexity—how do we know that is the ‘true utility function’ - leave that aside for a second, and I’ll get back to it.
I then brought up a concrete example of using IRL on an deep RL Atari agent. I described how learning the score function should be relatively straightforward, and this would allow an IRL agent to match the performance of the RL agent in this domain, which leads to better performance than the supervised/modal human policy.
You agreed with this:
So it seems we have agreed that IRL surpassing the modal human policy is clearly possible—at least in the limited domain of atari.
If we already know the utility function apriori, then obviously IRL given the same resources can only do as good as RL. But that isn’t that interesting, and remember IRL can do much more—as in the example of learning to maximize score while under other complex constraints.
So in scaling up to more general problem domains, we have the issue of modelling mistakes—which you seem to be especially focused on—and the related issue of utility function uniqueness.
Versions 2 and later of my simple proto-proposal use more informed priors for the circuit complexity combined with pretraining the model on just observations to force differentiate the model and utility functions. In the case of atari, getting the utility function to learn the score should be relatively easy—as we know it is a simple immediate visual function.
This type of RL architecture can model human’s limited rationality by bounding the circuit complexity—at least that’s the first step. We could get increasingly more accurate models of the human decision surface by incorporating more of the coarse abstract structure of the brain as a prior over our model space.
Ok, so backing up a bit :
For the full AGI problem, I am aware of a couple of interesting candidates for an intrinsic reward/utility function—the future freedom of action principle (power) and the compression progress measure (curiosity). If scaled up to superhuman intelligence, I think/suspect you would agree that both of these candidates are probably quite dangerous. On the other hand, they seem to capture some aspects of human’s intrinsic motivators, so they may be useful as subcomponents or features.
The IRL approach—if taken all the way—seems to require reverse engineering the brain. It could be that any successful route to safe superintelligence just requires this—because the class of agents that combine our specific complex unknown utility functions with extrapolated superintelligence necessarily can only be specified in reference to our neural architecture as a starting point.
This sounds really interesting and important (if true), but I have only a vague understanding of how you arrived at this conclusion. Please consider writing a post about it.
It’s not so much a conclusion as an intuition, and most of the inferences leading up to it are contained in this thread with PaulChristiano and a related discussion with Kaj Sotala.
I’m interested in IRL and I think it’s the most promising current candidate for value learning, but I must admit I haven’t read much of the relevant literature yet. Reading up on IRL and writing a discussion post on it has been on my todo list—your comment just bumped it up a bit. :)
Another related issue is the more general question of how the training data/environment determines/shapes safety issues for learning agents.
My reaction when I first came across IRL is similar to this author’s:
But maybe it’s not a bad approach for solving a hard problem to first solve a very simplified version of it, then gradually relax the simplifying assumptions and try to build up to a solution of the full problem.
As a side note, that author’s attempt at value learning is likely to suffer from the same problem Christiano brought up in this thread—there is nothing to enforce that the optimization process will actually nicely separate the reward and agent functionality. Doing that requires some more complex priors and or training tricks.
The author’s critique about limiting assumptions may or may not be true, but the author only quotes a single paper from the IRL field—and its from 2000. That paper and it’s follow up both each have 500+ citations, and some of the newer work with IRL in the title is from 2008 or later. Also—most of the related research doesn’t use IRL in the title—ie “Probabilistic reasoning from observed context-aware behavior”.
This is actually the mainline successful approach in machine learning—scaling up. MNIST is a small ‘toy’ visual learning problem, but it lead to CIFAR10/100 and eventually ImageNet. The systems that do well on ImageNet descend from the techniques that did well on MNIST decades ago.
MIRI/LW seems much more focused on starting with a top-down approach where you solve the full problem in an unrealistic model—given infinite compute—and then scale down by developing some approximation.
Compare MIRI/LW’s fascination with AIXI vs the machine learning community. Searching for “AIXI” on r/machinelearning gets a single hit vs 634 results on lesswrong. Based on #citations of around 150 or so, AIXI is a minor/average paper in ML (more minor than IRL), and doesn’t appear to have lead to great new insights in terms of fast approximations to bayesian inference (a very active field that connects mostly to ANN research).
MIRI is taking the top-down approach since that seems to be the best way to eventually obtain an AI for which you can derive theoretical guarantees. In the absence of such guarantees, we can’t be confident that an AI will behave correctly when it’s able to think of strategies or reach world states that are very far outside of its training and testing data sets. The price for pursuing such guarantees may well be slower progress in making efficient and capable AIs, with impressive and/or profitable applications, which would explain why the mainstream research community isn’t very interested in this approach.
I tend to agree with MIRI that the top-down approach is probably safest, but since it may turn out to be too slow to make any difference, we should be looking at other approaches as well. If you’re thinking about writing a post about recent progress in IRL and related ideas, I’d be very interested to see it.
I for one remain skeptical such theoretical guarantees are possible in principle for the domain of general AI. The utility of formal math towards a domain tends to vary inversely with domain complexity. For example in some cases it may be practically possible to derive formal guarantees about the full output space of a program, but not when that program is as complex as a modern video game, or let alone a human. The equivalent of theoretical guarantees may be possible/useful for something like a bridge, but less so for an airplane or a city.
For complex systems simulations are the key tool that enables predictions about future behavior.
This indeed would be a problem if the AI’s training ever stopped, but I find this extremely unlikely. Some AI systems already learn continuously—whether using online learning directly or by just frequently patching the AI with the results of updated training data. Future AI systems will continue this trend—and learn continuously like humans.
Much depends on one’s particular models for how the future of AI will pan out. I contend that AI does not need to be perfect, just better than humans. AI drivers don’t need to make optimal driving decisions—they just need to drive better than humans. Likewise AI software engineers just need to code better than human coders, and AI AI researchers just need to do their research better than humans. And so on.
For the record, I do believe that MIRI is/should be funded at some level—it’s sort of a moonshot, but one worth taking given the reasonable price. Mainstream opinion on the safety issue is diverse, and their are increasingly complex PR and career issues to consider. For example corporations are motivated to downplay long term existential risks, and in the future will be motivated to downplay similarity between AI and human cognition to avoid regulation.
Cool—I’m working up to it.
Sure, but when it comes to learning values, I see a few problems even with continuous learning:
The AI needs to know when to be uncertain about its values, and actively seek out human advice (or defer to human control) in those cases. If the AI is wrong and overconfident (like in http://www.evolvingai.org/fooling but for values instead of image classification) even once, we could be totally screwed.
On the other hand, if the AI can think much faster than a human (almost certainly the case, given how fast hardware neurons are even today), learning from humans in real time will be extremely expensive. There will be high incentive to lower the frequency of querying humans to a minimum. Those willing to take risks, or think that they have a simple utility function that the AI can learn quickly, could have a big advantage in how competitive their AIs are.
I don’t know what my own values are, especially when it comes to exotic world states that are achievable post-Singularity. (You could say that my own training set was too small. :) Ideally I’d like to train an AI to try to figure out my values the same way that I would (i.e., by doing philosophy), but that might require very different methods than for learning well-defined values. I don’t know if incremental progress in value learning could make that leap.
My point was that an AI could do well on test data, including simulations, but get tripped up at some later date (e.g., it over-confidently thinks that a certain world state would be highly desirable). Another way things could go wrong is that an AI learns wrong values, but does well in simulations because it infers that it’s being tested and tries to please the human controllers in order to be released into the real world.
I generally agree that learning values correctly will be a challenge, but it’s closely related to general AGI challenges.
I’m also reasonably optimistic that we will be able to reverse engineer the brain’s value learning mechanisms to create agents that are safer than humans. Fully explaining the reasons behind that cautious optimism would require a review of recent computational neuroscience (the LW consensus on the brain is informed primarily by a particular narrow viewpoint from ev psych and the H&B literature, and this position is in substantial disagreement with the viewpoint from comp neuroscience.)
Mostly agreed. However it is not clear that actively deferring to humans is strictly necessary. In particular one route that circumvents most of these problems is testing value learning systems and architectures on a set of human-level AGIs contained to a virtual sandbox where the AGI does not know it is in a sandbox. This allows safe testing of designs to be used outside of the sandbox. The main safety control is knowledge limitation (which is something that MIRI has not considered much at all, perhaps because of their historical anti-machine learning stance).
The fooling CNN stuff does not show a particularly important failure mode for AI. These CNNs are trained only to recognize images in the sense of outputting a 10 bit label code for any input image. If you feed them a weird image, they just output the closest category. The fooling part (getting the CNN to misclassify an image) specifically requires implicitly reverse engineering the CNN and thus relies on the fact that current CNNs are naively deterministic. A CNN with some amount of random sampling based on a secure irreversible noise generator would not have this problem.
This could be a problem, but even today our main technique to speed up AI learning relies more on parallelization than raw serial speedup. The standard technique involves training 128 to 1024 copies of the AI in parallel, all on different data streams. The same general technique would allow an AI to learn values from large number of humans in parallel. This also happens to automatically solve some of the issues with value representativeness.
The current world is already exotic from the perspective of our recent ancestors. We already have some methods to investigate the interaction of our values with exotic future world states: namely our imagination, as realized in thought experiments and especially science fiction. AI could help us extend these powers.
This is just failure to generalize or overfitting, and how to avoid these problems is much of what machine learning is all about.
This failure requires a specific combination of: 1. that the AI learns a good model of the world, but 2. learns a poor model of human values, and 3. learns that it is in a sim. 4. wants to get out. 5. The operators fail to ever notice any of 2 through 4.
Is this type of failure possible? Sure. But the most secure/paranoid type of safety model I envision is largely immune to that class of failures. In the most secure model, potentially unsafe new designs are constrained to human-level intelligence and grow up in a safe VR sim (medieval or earlier knowledge-base). Designs which pass safety tests are then slowly percolated up to sims which are closer to the modern world. Each up migration step is like reincarnation—a new AI is grown from a similar seed. The final designs (seed architectures rather than individual AIs) that pass this vetting/testing process will have more evidence for safety/benevolence/altruism than humans.
Sounds like another post to look forward to.
I think we’ll need different methods to deal with future exoticness though. See this post for some of the reasons.
Do you envision biological humans participating in the VR sim, in order to let the AI learn values from them? If so, how to handle speed differences that may be up to a factor of millions (which you previously suggested will be the case)? Only thing I can think of is to slow the AI down to human speed for the training, which might be fine if your AI group has a big lead and you know there aren’t any other AIs out there able to run at a million times human speed. Otherwise, even if you could massively parallelize the value learning and finish it in one day of real time, that could be giving a competitor a millions days of subjective time (times how many parallel copies of the AI they can spawn) to make further progress in AI design and other technologies.
Safer than humans seems like a pretty low bar to me, given that I think most humans are terribly unsafe. :) But despite various problems I see with this approach, it may well be the best outcome that we can realistically hope for, if mainstream AI/ML continues to make progress at such a fast pace using designs that are hard to reasonable about formally.
Perhaps. The question of uploading comes to mind as something like an ‘ontological crisis’. We start with a intuitive model of selfhood built around the concept of a single unique path extending through time, and the various uploading thought experiments upend that model. Humans (at least some) appear to be able to deal with these types of challenges given enough examples to cover the space and enough time to update models.
Of course. And eventually we can join the AIs in the VR sim more directly, or at least that’s the hope.
Given some computing network running a big VR AI sim, in theory the compute power can be used to run N AIs in parallel or one AI N times accelerated or anything in between. In practice latency and bandwidth overhead considerations will place limits on the maximum serial speedup.
But either way the results are similar—the core problem is the total throughput of AI thought volume to human monitor thought volume. It’s essentially the student/teacher ratio problem. One human could perhaps monitor a couple dozen ‘children’ AI without sophisticated tools, or perhaps hundreds or even thousands with highly sophisticated narrow AI tools (automated thought monitors and visualizers).
I don’t expect this will be a huge issue in practice due to simple economical considerations. AGI is likely to arrive near the time the hardware cost of an AGI is similar to human salary/cost. So think of it in terms of the ratio of human teacher cost to AGI hardware cost. AGI is a no brainer investment when that cost ratio is 1:1, and just gets better over time.
The point in time at which AGI hardware costs say 1/100th of a human teacher - (say 20 cents per hour) that time is already probably well in to the singularity anyway. The current trend is steady exponential progress in driving down hypothetical AGI hardware cost. (which I estimate is vaguely around $1,000/hr today—the cost of about 1000 gpus) If that cost suddenly went down due to some new breakthrough, that would just accelerate the timeline.
I don’t know how to deal with this myself, and I doubt whether people who claim to be able to deal with these scenarios are doing so correctly. I wrote about this in http://lesswrong.com/lw/g0w/beware_selective_nihilism/
If you have hardware neurons running at 10^6 times biological speed (BTW, are you aware of HICANN, a chip that today implements neurons running at 10^4 faster than biological? See also this video presentation), would it make sense to implement a time-sharing system where one set of neurons is used to implement multiple AIs running at slower speed? Wouldn’t that create unnecessary communication costs (swapping AI mind states in and out of your chips) and coordination costs among the AIs?
In short, If you don’t time share, then you are storing all synaptic data on the logic chip. Thus you need vastly more logic chips to simulate your model, and thus you have more communication costs.
There are a number of tradeoffs here that differ across GPUs vs neuro ASICs like HICANN or IBM TruNorth. The analog memristor approaches, if/when they work out, will have similar tradeoffs to neuro-ASICs. (for more on that and another viewpoint see this discussion with the Knowm guy )
GPUs are von neumman machines that take advantage of the 10x or more cost difference between the per transistor cost of logic vs that of memory. Logic is roughly 10x more expensive, so it makes sense to have roughly 10x more memory bits than logic bits. ie: a GPU with 5 billion transistors might have 4 gigabytes of offchip RAM.
So on the GPU (or any von neumman), typically you are always doing time-swapping: simulating some larger circuit by swapping pieces in and out of memory.
The advantage of the neuro-ASIC is energy efficiency: synapses are stored on chip, so you don’t have to pay the price of moving data which is most of the energy cost these days. The disadvantages are threefold: you lose most of your model flexibility, storing all your data on the logic chip is vastly more expensive per synapse, and you typically lose the flexibility to compress synaptic data—even basic weight sharing is no longer possible. Unfortunately these problems combine.
Lets look at some numbers. The HICANN chip has 128k synapses in 50 mm^2, and their 8-chip reticle is thus equivalent to a mid-high end GPU in die area. That’s 1 million synapses in 400 mm^2. It can update all of those synapses at about 1 mhz—which is about 1 trillion synop-hz.
A GPU using SOTA ANN simulation code can also hit about 1 trillion synop-hz, but with much more flexibility in the tradeoff between model size and speed. In particular 1 million synapses isn’t really enough—most competitive ANNS trained today are in the 1 to 10 billion synapse range—which would cost about 1000 times more for the HICANN, because it can only store 1 million synapses per chip, vs 1 billion or more for the GPU.
IBM’s truenorth can fit more synapses on a chip − 256 million on a GPU sized chip (5 billion transistors), but it runs slower, with a similar total synop-hz throughput. The GPU solutions are just far better, overall—for now.
Apparently HICANN was designed before 2008, and uses a 180nm CMOS process, whereas modern GPUs are using 28nm. It seems to me that if neuromorphic hardware catches up in terms of economy of scale and process technology, it should be far superior in cost per neural event. And if neuromorphic hardware does win, it seems that the first AGIs could have a huge amortized cost per hour of operation, and still have a lower cost per unit of cognitive work than human workers, due to running much faster than biological brains.
It seems like this GPU vs neuromorphic question could have a large impact on how the Singularity turns out, but I haven’t seen any discussion of it until now. Do you have any other thoughts or references on this topic?
That’s true, but IBM’s TrueNorth is 28 nm, with about the same transistor count as a GPU. It descends from earlier research chips on old nodes that were then scaled up to new nodes. TrueNorth can fit 256 million low-bit synapses on a chip, vs 1 million for HICANN (normalized for chip area). The 28 nm process has roughly 40x the transistor density. So my default hypothesis is that if HICANN was scaled up to 28 nm it would end up similar to TrueNorth in terms of density (although TrueNorth is wierd in that it is intentionally much slower than it could be to save energy).
I expect this in the long term, but it will depend on how the end of Moore’s Law pans out. Also, current GPU code is not yet at the limits of software simulation efficiency for ANNs, and GPU hardware is still improving rapidly. It just so happens that I am working on a new type of ANN sim engine that is 10x or more faster than current SOTA for networks of interest. My approach could eventually be hardware accelerated. There are some companies already pursuing hardware acceleration of the standard algorithms—such as Nervana, targeting similar speedup but through dedicated neural asics.
One thing I can’t stress enough is the advantage of programmeable memory for storing weights—sharing and compressing weights helps solve much of the bandwidth problems the GPU would otherwise have.
I don’t know much it really effects outcomes—whether one uses clever hardware or clever software, the brain is probably near or on the pareto surface for statistical inference energy efficiency, and we will probably get close in the near future.