Interested in many things. I have a personal blog at https://www.beren.io/
beren
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I like this post very much and in general I think research like this is on the correct lines towards solving potential problems with Goodheart’s law—in general Bayesian reasoning and getting some representation of the agent’s uncertainty (including uncertainty over our values!) seems very important and naturally ameliorates a lot of potential problems. The correctness and realizability of the prior are very general problems with Bayesianism but often do not thwart its usefulness in practice although they allow people to come up with various convoluted counterexamples of failure. The key is to have sufficiently conservative priors such that you can (ideally) prove bounds about the maximum degree of goodhearting that can occur under realistic circumstances and then translate these into algorithms which are computationally efficient enough to be usable in practice. People have already done a fair bit of work on this in RL in terms of ‘cautious’ RL which tries to take into account uncertainty in the world model to avoid accidentally falling into traps in the environment.
While I agree with a lot of points of this post, I want to quibble with the RL not maximising reward point. I agree that model-free RL algorithms like DPO do not directly maximise reward but instead ‘maximise reward’ in the same way self-supervised models ‘minimise crossentropy’—that is to say, the model is not explicitly reasoning about minimising cross entropy but learns distilled heuristics that end up resulting in policies/predictions with a good reward/crossentropy. However, it is also possible to produce architectures that do directly optimise for reward (or crossentropy). AIXI is incomputable but it definitely does maximise reward. MCTS algorithms also directly maximise rewards. Alpha-Go style agents contain both direct reward maximising components initialized and guided by amortised heuristics (and the heuristics are distilled from the outputs of the maximising MCTS process in a self-improving loop). I wrote about the distinction between these two kinds of approaches—direct vs amortised optimisation here. I think it is important to recognise this because I think that this is the way that AI systems will ultimately evolve and also where most of the danger lies vs simply scaling up pure generative models.
This monograph by Bertsekas on the interrelationship between offline RL and online MCTS/search might be interesting—http://www.athenasc.com/Frontmatter_LESSONS.pdf—since it argues that we can conceptualise the contribution of MCTS as essentially that of a single Newton step from the offline start point towards the solution of the Bellman equation. If this is actually the case (I haven’t worked through all details yet) then this seems to be able to be used to provide some kind of bound on the improvement / divergence you can get once you add online planning to a model-free policy.
Thanks for writing this! Here are some of my rough thoughts and comments.
One of my big disagreements with this threat model is that it assumes it is hard to get an AGI to understand / successfully model ‘human values’. I think this is obviously false. LLMs already have a very good understanding of ‘human values’ as they are expressed linguistically, and existing alignment techniques like RLHF/RLAIF seem to do a reasonably good job of making the models’ output align with these values (specifically generic corporate wokeness for OpenAI/Anthropic) which does appear to generalise reasonably well to examples which are highly unlikely to have been seen in training (although it errs on the side of overzealousness of late in my experience). This isn’t that surprising because such values do not have to be specified by the fine-tuning from scratch but should already be extremely well represented as concepts in the base model latent space and merely have to be given primacy. Things would be different, of course, if we wanted to align the LLMs to some truly arbitrary blue and orange morality not represented in the human text corpus, but naturally we don’t.
Of course such values cannot easily be represented as some mathematical utility function, but I think this is an extremely hard problem in general verging on impossible—since this is not the natural type of human values in the first place, which are naturally mostly linguistic constructs existing in the latent space and not in reality. This is not just a problem with human values but almost any kind of abstract goal you might want to give the AGI—including things like ‘maximise paperclips’. This is why almost certainly AGI will not be a direct utility maximiser but instead use a learnt utility function using latents from its own generative model, but in this case it can represent human values and indeed any goal expressible in natural language which of course it will understand.
On a related note this is also why I am not at all convinced by the supposed issues over indexicality. Having the requisite theory of mind to understand that different agents have different indexical needs should be table stakes to any serious AGI and indeed hardly any humans have issues with this, except for people trying to formalise it into math.
There is still a danger of over-optimisation, which is essentially a kind of overfitting and can be dealt with in a number of ways which are pretty standard now. In general terms, you would want the AI to represent its uncertainty over outcomes and utility approximator and use this to derive a conservative rather than pure maximising policy which can be adjusted over time.
I broadly agree with you about agency and consequentialism being broadly useful and ultimately we won’t just be creating short term myopic tool agents but fully long term consequentialists. I think the key thing here is just to understand that long term consequentialism has fundamental computational costs over short term consequentialism and much more challenging credit assignment dynamics so that it will only be used where it actually needs to be. Most systems will not be long term consequentialist because it is unnecessary for them.
I also think that breeding animals to do tasks or looking at humans subverting social institutions is not necessarily a good analogy to AI agents performing deception and treacherous turns. Evolution endowed humans and other animals with intrinsic selfish drives for survival and reproduction and arguably social deception which do not have to exist in AGIs. Moreover, we have substantially more control over AI cognition than evolution does over our cognition and gradient descent is fundamentally a more powerful optimiser which makes it challenging to produce deceptive agents. There is basically no evidence for deception occurring with current myopic AI systems and if it starts to occur with long term consequentialist agents it will be due to either a breakdown of credit assignment over long horizons (potentially due to being forced to use worse optimisers such as REINFORCE variants rather than pure BPTT) or the functional prior of such networks turning malign. Of course if we directly design AI agents via survival in some evolutionary sim or explicitly program in Omohundro drives then we will run directly into these problems again.
Thanks for the response! Very helpful and enlightening.
The reason for this is actually pretty simple: genes with linear effects have an easier time spreading throughout a population.
This is interesting—I have never come across this. Can you expand the intuition of this model a little more? Is the intuition something like in the fitness landscape genes with linear effects are like gentle slopes that are easy to traverse vs extremely wiggly ‘directions’?
Also how I am thinking about linearity is maybe slightly different to the normal ANOVA/factor analysis way, I think. I.e. let’s suppose that we have some protein which is good so that more of it is better and we have 100 different genes which can either upregulate or down regulate it. However, at some large number, say 80x the usual amount, the benefit saturates. So a normal person is very unlikely to have 80⁄100 positive variants but if we go in and edit all 100 to be positive, we only get the maximum benefit far below what we would have predicted since it maxes out at 80. I guess to detect this nonlinearity in a normal population you basically need to get an 80+th order interaction of all of them interacting in just the right way which is exceedingly unlikely. Is this your point about sample size?
I’ll talk about this in more detail within the post, but yes we have examples of monogenic diseases and cancers being cured via gene therapy.
This is very cool. Are the cancer cures also monogenic? Has anybody done any large scale polygenic editing in mice or any other animal before humans? This seems the obvious place to explicitly test the causality and linearity directly. Are we bottlenecked on GWAS equivalents for other animals?
This would be very exciting if true! Do we have a good (or any) sense of the mechanisms by which these genetic variants work—how many are actually causal, how many are primarily active in development vs in adults, how much interference there is between different variants etc?
I am also not an expert at all here—do we have any other examples of traits being enhanced or diseases cured by genetic editing in adults (even in other animals) like this? It seems also like this would be easy to test in the lab—i.e. for mice which we can presumably sequence and edit more straightforwardly and also can measure some analogues of IQ with reasonable accuracy and reliability. Looking forward to the longer post.
This is an interesting idea. I feel this also has to be related to increasing linearity with scale and generalization ability—i.e. if you have a memorised solution, then nonlinear representations are fine because you can easily tune the ‘boundaries’ of the nonlinear representation to precisely delineate the datapoints (in fact the nonlinearity of the representation can be used to strongly reduce interference when memorising as is done in the recent research on modern hopfield networks) . On the other hand, if you require a kind of reasonably large-scale smoothness of the solution space, as you would expect from a generalising solution in a flat basin, then this cannot work and you need to accept interference between nearly orthogonal features as the cost of preserving generalisation of the behaviour across many different inputs which activate the same vector.
Looks like I really need to study some SLT! I will say though that I haven’t seen many cases in transformer language models where the eigenvalues of the Hessian are 90% zeros—that seems extremely high.
I also think this is mostly a semantic issue. The same process can be described in terms of implicit prediction errors where e.g. there is some baseline level of leptin in the bloodstream that the NPY/AgRP neurons in the arcuate nucleus ‘expect’ and then if there is less leptin this generates an implicit ‘prediction error’ in those neurons that cause them to increase firing which then stimulates various food-consuming reflexes and desires which ultimately leads to more food and hence ‘correcting’ the prediction error. It isn’t necessary that anywhere there are explicit ‘prediction error neurons’ encoding prediction errors although for larger systems it is often helpful to modularize it this way.
Ultimately, though I think it is more a conceptual question of how to think about control systems—is it best to think in terms of implicit prediction errors or just in terms of the feedback loop dynamics but it amounts to the same thing
This is where I disagree! I don’t think the Morrison and Berridge experiment demonstrates model-based side. It is consistent with model-based RL but is also consistent with model-free algorithms that can flexibly adapt to changing reward functions such as linear RL. Personally, I think this latter is more likely since it is such a low level response which can be modulated entirely by subcortical systems and so seems unlikely to require model-based planning to work
Thanks for linking to your papers and definitely interesting you have been thinking along similar lines. I think the key reason I think studying this is important is that I think that these hedonic loops demonstrate that a.) Mammals including humans are actually exceptionally aligned to basic homeostatic needs and basic hedonic loops I’m practice. It is extremely hard and rare for people to choose not to follow homeostatic drives. I think humans are mostly ‘misaligned’ about higher level things like morality, empathy etc is because we dont actually have direct drives hardcoded in the hypothalamus for them the way we do for primary rewards. Higher level behaviours either socio-culturally learned through unsupervised critically based learning or derived from RL extrapolations from primary rewards. It is no surprise that alignment to these ideals is weaker. B.) That relatively simple control loops are very effective at controlling vastly more complex unsupervised cognitive systems.
I also agree this is similar to steven Byrnes agenda and maybe just my way to arrive at it
This is definitely possible and is essentially augmenting the state variables with additional homeostatic variables and then learning policies on the joint state space. However there are some clever experiments such as the linked Morrison and Berridge one demonstrating that this is not all that is going on—specifically many animals appear to be able to perform zero-shot changes in policy when rewards change even if they have not experienced this specific homeostatic variable before—I.e. mice suddenly chase after salt water which they previously disliked when put in a state of salt deprivation which they had never before experienced
The ‘four years’ they explicitly mention does seem very short to me for ASI unless they know something we don’t...
AI x-risk is not far off at all, it’s something like 4 years away IMO
Can I ask where this four years number is coming from? It was also stated prominently in the new ‘superalignment’ announcement (https://openai.com/blog/introducing-superalignment). Is this some agreed upon median timelines at OAI? Is there an explicit plan to build AGI in four years? Is there strong evidence behind this view—i.e. that you think you know how to build AGI explicitly and it will just take four years more compute/scaling?
Hi there! Thanks for this comment. Here are my thoughts:
Where do highly capable proposals/amortised actions come from?
(handwave) lots of ‘experience’ and ‘good generalisation’?
Pretty much this. We know empirically that deep learning generalizes pretty well from a lot of data as long as it is reasonable representative. I think that fundamentally this is due to the nature of our reality that there are generalizable patterns which is ultimately due to the sparse underlying causal graph. It is very possible that there are realities where this isn’t true and in those cases this kind of ‘intelligence’ would not be possible.
r...? This seems to be to be where active learning and deliberate/creative exploration come in
It’s a Bayes-adaptivity problem, i.e. planning for value-of-information
This is basically what ‘science’ and ‘experimentalism’ are in my ontology
‘Play’ and ‘practice’ are the amortised equivalent (where explorative heuristics are baked in)
Again, I completely agree here. In practice in large environments it is necessary to explore if you can’t reach all useful states from a random policy. In these cases, it is very useful to a.) have an explicit world model so you can learn from sensory information which is much higher bandwidth than reward usually and generalizes further and in an uncorrelated way, and b.) do some kind of active exploration. Exploring according to maximizing info-gain is probably close to optimal, although whether this is actually theoretically optimal is I tihnk still an open question. The main issue is that info-gain is hard to cmopute/approximate tractably, since it requires keeping a close track of your uncertainty, and DL models are computationally tractable by explicitly throwing away all the uncertainty and only really maintaining point predictions.
animals are evidence that some amortised play heuristics are effective! Even humans only rarely ‘actually do deliberate experimentalism’
but when we do, it’s maybe the source of our massive technological dominance?
Like I don’t know to what extent there are ‘play heuristics’ at a behavioural level vs some kind of intrinsic drive for novelty / information gain but yes, having these drives ‘added to your reward function’ is generally useful in RL settings and we know this happens in the brain as well—i.e. there are dopamine neurons responsive to proxies of information gain (and exactly equal to information gain in simple bandit-like settings where this is tractable)
When is deliberation/direct planning tractable?
In any interestingly-large problem, you will never exhaustively evaluate
e.g. maybe no physically realisable computer in our world can ever evaluate all Go strategies, much less evaluating strategies for ‘operate in the world itself’!
What properties of options/proposals lend themselves?
(handwave) ‘Interestingly consequential’ - the differences should actually matter enough to bother computing!
Temporally flexible
The ‘temporal resolution’ of the strategy-value landscape may vary by orders of magnitude
so the temporal resolution of the proposals (or proposal-atoms) should too, on pain of intractability/value-loss/both
So there are a number of circumstances where direct planning is valuable and useful. I agree about your conditions and especially the correct action step-size as well as discrete actions and known not super stochastic dynamics. Other useful conditions are when it’s easy to evaluate the branches of the tree without having gone all the way down to the leaves—i.e. in games like Chess/GO it’s often very easy to know that some move tree is intrinsically doomed without having explored all of it. This is a kind of convexity to the state space (not literally mathematically, but intuitively) which makes optimization much easier. Similarly, when good proposals can be made due to linearity / generalizability in the action space it is easy to prune actions and trees.
Where does strong control/optimisation come from?
Strong control comes from where strong learning in general comes from—lots of compute and data—and for planning especially compute. The optimal trade-off between amortized and direct optimization given a fixed compute budget is super interesting and I don’t think we have any good models of this yet.
Another thing that I think is fairly underestimated among people on LW compared to people doing deep RL is that open-loop planning is actually very hard and bad at dealing with long time horizons. This is basically due to stochasticity and chaos theory—future prediction is hard. Small mistakes in either modelling or action propagate very rapidly to create massive uncertainties about the future so that your optimal posterior rapidly dwindles to a maximum entropy distribution. The key thing in long term planning is really adaptability and closed-loop control—i.e. seeing feedback and adjusting your actions in response to feedback. This is how almost all practical control systems actually work and in practice in deep RL with planning everybody actually uses MPC so replans every step.
The problem is not so much which one of 1,2,3 to pick but whether ‘we’ get a chance to pick it at all. If there is space, free energy, and diversity, there will be evolution going on among populations and evolution will consistently push things in the direction towards more reproduction up until it hits a Malthusian limit at which point it will push towards greater competition and economic/reproductive efficiency. The only way to avoid this is to remove the preconditions for evolution—any of variation, selection, heredity—but these seem quite natural in a world of large AI populations so in practice this will require some level of centralized control
This is obviously true; any AI complete problem can be trivially reduced to the problem of writing an AI program that solves the problem. That isn’t really a problem for the proposal here. The point isn’t that we could avoid making AGI by doing this, the point is that we can do this in order to get AI systems that we can trust without having to solve interpretability.
Maybe I’m being silly but then I don’t understand the safety properties of this approach. If we need an AGI based on uninterpretable DL to build this, then how do we first check if this AGI is safe?
I moderately agree here but I still think the primary factor is centralization of the value chain. The more of the value chain is centralized, the easier it is to control. My guess we can make this argument more formalized by thinking of things in terms of a dependency graph—if we imagine the economic process from sand + energy → DL models then the important measure is the centrality of the hubs in this graph. If we can control and/or cut these hubs, then the entire DL ecosystem falls apart. Conveniently/unfortunately this is also where most of the economic profit is likely to be accumulating by standard industrial economic laws, and hence this is also where there will be the most resources resisting regulation.
As I see it, there are two fundamental problems here:
1.) Generating an interpretable expert system code for an AGI is probably already AGI complete. It seems unlikely that a non-AGI DL model can output code for an AGI—especially given that it is highly unlikely that there would be expert system AGIs in its training set—or even things close to expert-system AGIs if deep learning keeps far out pacing GOFAI techniques.
2.) Building an interpretable expert system AGI is likely not just AGI complete but a fundamentally much harder problem than building a DL AGI system. Intelligence is extremely detailed, messy, and highly heuristic. All our examples of intelligent behaviour come from large blobs of optimized compute -- both brains and DL systems—and none from expert systems. The actual inner workings of intelligence might just be fundamentally uninterpretable in their complexity except at a high level—i.e. ‘this optimized blob is the output of approximate bayesian inference over this extremely massive dataset’
Thanks for these points! I think I understand the history of what has happened here better now—and the reasons for my misapprehension. Essentially, what I think happened is
a.) LLM/NLP research always (?) used ‘pretraining’ for a long time back at least to 2017 era for a general training of a model not specialised for a certain NLP task (such as NER, syntax parsing, etc)
b.) rest of ML mostly used ‘training’ because they by and by large didn’t do massive unsupervised training on unrelated tasks—i.e. CV just had imagenet or whatever
c.) In 2020-2022 period NLP with transformers went from fairly niche subfield of ML to memetically dominant due to massive success of transformer GPT models
d.) This meant both that their linguistic descriptions of ‘pretraining’ spread much more widely due to uptake of similar methods in other subfields and that I got much more involved in looking at NLP / LLM research than I had in the past where I personally had focused more on CV and RL leading to its sudden appearance in my personal experience (which turned out to be wrong).