Interested in many things. I have a personal blog at https://www.beren.io/
beren(Beren Millidge)
Karma: 2,480
Theories of Change for AI Auditing
[Linkpost] Biden-Harris Executive Order on AI
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.
Preference Aggregation as Bayesian Inference
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.
Thoughts on Loss Landscapes and why Deep Learning works
BCIs and the ecosystem of modular minds
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
Hedonic Loops and Taming RL
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?
[Linkpost] Introducing Superalignment
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?
Thanks for the response! Very helpful and enlightening.
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?
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?