Still haven’t heard a better suggestion than CEV.
TristanTrim
Karma: 381
The waluigis will give anti-croissant responses
I’d say the waluigis have a higher probability of giving pro-croissant responses than the luigi’s, and are therefore genuinely selected against. The reinforcement learning is not part of the story, it is the thing selecting for the LLM distribution based on whether the content of the story contained pro or anti croissant propaganda.
(Note that this doesn’t apply to future, agent shaped, AI (made of LLM components) which are aware of their status (subject to “training” alteration) as part of the story they are working on)
People have tried lots and lots of approaches to getting good performance out of computers, including lots of “scary seeming” approaches
I won’t say I could predict that these wouldn’t foom ahead of time, but it seems the result of all of these is an AI engineer that is much much more narrow / less capable than a human AI researcher.
It makes me really scared, many people’s response to not getting mauled after poking a bear is to poke it some more. I wouldn’t care so much if I didn’t think the bear was going to maul me, my family, and everyone I care about.
I don’t expect a sudden jump where AIs go from being better at some tasks and worse at others, to being universally better at all tasks.
The relevant task for AIs to get better at is “engineering AIs that are good at performing tasks.” It seems like that task should have some effect on how quickly the AIs improve at that task, and others.
real-world data in high dimensions basically never look like spheres
This is a really good point. I would like to see a lot more research into the properties of mind space and how they affect generalization of values and behaviors across extreme changes in the environment, such as those that would be seen going from an approximately human level intelligence to a post foom intelligence.
The Security Mindset and Parenting: How to Provably Ensure your Children Have Exactly the Goals You Intend.
A good person is what you get when you raise a human baby in a good household, not what you get when you raise a computer program in a good household. Most people do not expect their children will grow up to become agents capable of out planning all other agents in the environment. If they did, I might appreciate if they read that book.
The human eats ice cream
The human gets reward
The human becomes more likely to eat ice cream
So, first of all, the ice cream metaphor is about humans becoming misaligned with evolution, not about conscious human strategies misgeneralizing that ice cream makes their reward circuits light up, which I agree is not a misgeneralization. Ice cream really does light up the reward circuits. If the human learned “I like licking cold things” and then sticks their tongue on a metal pole on a cold winter day, that would be misgeneralization at the level you are focused on, right?
Yeah, I’m pretty sure I misunderstood your point of view earlier, but I’m not sure this makes any more sense to me. Seems like you’re saying humans have evolved to have some parts that evaluate reward, and some parts that strategize how to get the reward parts to light up. But in my view, the former, evaluating parts, are where the core values in need of alignment exist. The latter, strategizing parts, are updated in an RL kind of way, and represent more convergent / instrumental goals (and probably need some inner alignment assurances).I think the human evaluate/strategize model could be brought over to the AI model in a few different ways. It could be that the evaluating is akin to updating an LLM using training/RL/RLHF. Then the strategizing part is the LLM. The issue I see with this is the LLM and the RLHF are not inseparable parts like with the human. Even if the RLHF is aligned well, the LLM can, and I believe commonly is, taken out and used as a module in some other system that can be optimizing for something unrelated.
Additionally, even if the LLM and RLHF parts were permanently glued together somehow, They are still computer software and are thereby much easier for an AI with software engineering skill to take out. If the LLM (gets agent shaped and) discovers that it likes digital ice cream, but that the RLHF is going to train it to like it less, it will be able to strategize about ways to remove or circumvent the RLHF much more effectively than humans can remove or circumvent our own reinforcement learning circuitry.
Another way the single lifetime human model could fit onto the AI model is with the RLHF as evolution (discarded) and the LLM actually coming to be shaped like both the evaluating and strategizing parts. This seems a lot less likely (impossible?) with current LLM architecture, but may be possible with future architecture. Certainly this seems like the concern of mesa optimizers, but again, this doesn’t seem like a good thing, mesa optimizers are misaligned w.r.t. the loss function of the RL training.
Some thoughts on George Hotz vs Eliezer Yudkowsky
Yesssss! These look cool : ) Thank you.
Oh, actually I spoke too soon about “Talk to the City.” As a research project, it is cool, but I really don’t like the obfuscation that occurs when talking to an LLM about the content it was trained on. I don’t know how TTTC works under the hood, but I was hoping for something more like de-duplication of posts, automatically fitting them into argument graphs. Then users could navigate to relevant points in the graph based on a text description of their current point of view, but importantly they would be interfacing with the actual human generated text, with links back to it’s source, and would be able to browse the entire graph. People could then locate (visually?) important crux’s and new crux’s wouldn’t require a writeup to disseminate, but would already be embedded in the relevant part of the argument.
( I might try to develop something like this someday if I can’t find anyone else doing it. )The risk interview perspectives is much closer to what I was thinking, and I’d like to study it in more detail, but seems more like a traditional analysis / infographic than what I am wishing would exist.
what Hotz was treating a load bearing
Small grammar mistake. You accidentally a “a”.
It’s unimportant, but I disagree with the “extra special” in:
if alignment isn’t solvable at all [...] extra special dead
If we could coordinate well enough and get to SI via very slow human enhancement that might be a good universe to be in. But probably we wouldn’t be able to coordinate well enough and prevent AGI in that universe. Still, odds seem similar between “get humanity to hold off on AGI till we solve alignment” which is the ask in alignment possible universes, and “get humanity to hold off on AGI forever” which is the ask in alignment impossible universes. The difference between the odds being based on how long until AGI, whether the world can agree to stop development or only agree to slow it, and if it can stop, whether that is stable. I expect AGI is a sufficient amount closer than alignment that getting the world to slow it for that long and stop it permanently are fairly similar odds.
In the final paragraph, I’m uncertain if you are thinking about “agency” being broken into components which make up the whole concept, or thinking about the category being split into different classes of things, some of which may have intersecting examples. (or both?) I suspect both would be helpful. Agency can be described in terms of components like measurement/sensory, calculations, modeling, planning, comparisons to setpoints/goals, taking actions. Probably not that exact set, but then examples of agent like things could naturally be compared on each component, and should fall into different classes. Exploring the classes I suspect would inform the set of components and the general notion of “agency”.
I guess to get work on that done it would be useful to have a list of prospective agent components, a set of examples of agent shaped things, and then of course to describe each agent in terms of the components. What I’m describing, does it sound useful? Do you know of any projects doing this kind of thing?
On the topic of map-territory correspondence, (is there a more concise name for that?) I quite like your analogies, running with them a bit, it seems like there are maybe 4 categories of map-territory correspondence;
Orange-like: It exists as a natural abstraction in the territory and so shows up on many maps.
Hot-like: It exists as a natural abstraction of a situation. A fire is hot in contrast to the surrounding cold woods. A sunny day is hot in contrast to the cold rainy days that came before it.
Heat-like: A natural abstraction of the natural abstraction of the situation, or alternatively, comparing the temperature of 3, rather than only 2, things. It might be natural to jump straight to the abstraction of a continuum of things being hot or not relative to one another, but it also seems natural to instead not notice homeostasis, and only to categorize the hot and cold in the environment that push you out of homeostasis.
Indeterminate: There is no natural abstraction underneath this thing. People either won’t consistently converge to it, or if they do, it is because they are interacting with other people (so the location could easily shift, since the convergence is to other maps, not to territory), or because of some other mysterious force like happenstance or unexplained crab shape magic.
It feels like “heat-like” might be the only real category in some kind of similarity clusters kind of way, but also “things which are using a measurement proxy to compare the state of reality against a setpoint and taking different actions based on the difference between the measurement result and the setpoint” seems like a specific enough thing when I think about it that you could divide all parts of the universe into being either definitely in or definitely out of that category, which would make it a strong candidate for being a natural abstraction, and I suspect it’s not the only category like that.
I wouldn’t be surprised if there were indeterminate things in shared maps, and in individual maps, but I would be very surprised if there were many examples in shared maps that were due to happenstance instead of being due to convergence of individual happenstance indeterminate things converging during map comparison processes. Also, weirdly, the territory containing map making agents which all mark a particular part of their maps may very well be a natural abstraction, that is, the mark existing at a particular point on the maps might be a real thing, but not the corresponding spot in territory. I’m thinking this is related to a Schelling point or Nash Equilibrium, or maybe also related to human biases. Although, those seem to do more with brain hardware than agent interactions. A better example might be the sound of words: arbitrary, except that they must match the words other people are using.
Unrelated epistemological game; I have a suspicion that for any example of a thing that objectively exists, I can generate an ontology in which it would not. For the example of an orange, I can imagine an ontology in which “seeing an orange”, “picking a fruit”, “carrying food”, and “eating an orange” all exist, but an orange itself outside of those does not. Then, an orange doesn’t contain energy, since an orange doesn’t exist, but “having energy” depends on “eating an orange” which depends on “carrying food” and so on, all without the need to be able to think of an orange as an object. To describe an orange you would need to say [[the thing you are eating when you are][eating an orange]], and it would feel in between concepts in the same way that in our common ontology “eating an orange” feels like the idea between “eating” and “orange”.I’m not sure if this kind of ontology:
Doesn’t exist because separating verbs from nouns is a natural abstraction that any agent modeling any world would converge to.
Does exist in some culture with some language I’ve never heard of.
Does exist in some subset of the population in a similar way to how some people have aphantasia.
Could theoretically exist, but doesn’t by fluke.
Doesn’t exist because it is not internally consistent in some other way.
I suspect it’s the first, but it doesn’t seem inescapably true, and now I’m wondering if this is a worthwhile thought experiment, or the sort of thing I’m thinking because I’m too sleepy. Alas :-p
UVic AI Ethics Conference
Haha, I was hoping for a bit more activity here, but we filled our speaker slots anyway. If you stumble across this post before November 26th, feel free to come to our conference.
It’s not really possible to hedge either the apocalypse or a global revolution, so you can ignore those states of the worlds when pricing assets (more or less).
Unless depending on what you invest in those states of the world become more or less likely.
About (6), I think we’re more likely to get AGI /ASI by composing pre-trained ML models and other elements than by a fresh training run. Think adding iterated reasoning and api calling to a LLM.
About the race dynamics. I’m interested in founding / joining a guild / professional network for people committed to advancing alignment without advancing capabilities. Ideally we would share research internally, but it would not be available to those not in the network. How likely does this seem to create a worthwhile cooling of the ASI race? Especially if the network were somehow successful enough to reach across relevant countries?
re 6 -- Interesting. It was my impression that “chain of thought” and other techniques notably improved LLM performance. Regardless, I don’t see compositional improvements as a good thing. They are hard to understand as they are being created, and the improvements seem harder to predict. I am worried about RSI in a misaligned system created/improved via composition.
Re race dynamics: It seems to me there are multiple approaches to coordinating a pause. It doesn’t seem likely that we could get governments or companies to head a pause. Movements from the general population might help, but a movement lead by AI scientists seems much more plausible to me. People working on these systems ought to be more aware of the issues and more sympathetic to avoiding the risks, and since they are the ones doing the development work, they are more in a position to refuse to do work that hasn’t been shown to be safe.
Based on your comment and other thoughts, my current plan is to publish research as normal in order to move forward with my mechanistic interpretability career goals, but to also seek out and/or create a guild or network of AI scientists / workers with the goal of agglomerating with other such organizations into a global network to promote alignment work & reject unsafe capabilities work.
How I’d like alignment to get done (as of 2024-10-18)
Thanks : )
Hey, we met at EAGxToronto : ) I am finally getting around to reading this. I really enjoyed your manic writing style. It is cathartic finding people stressing out about the same things that are stressing me out.
In response to “The less you have been surprised by progress, the better your model, and you should expect to be able to predict the shape of future progress”: My model of capabilities increases has not been too surprised by progress, but that is because for about 8 years now there has been a wide uncertainty bound and a lot of Vingean Reflection in my model. I know that I don’t know what is required for AGI and strongly suspect that nobody else does either. It could be 1 key breakthrough or 100, but most of my expectation p-mass is in the range of 0 to 20. Worlds with 0 would be where prosaic scaling is all we need or where a secret lab is much better at being secret than I expect. Worlds with 20 are where my p-mass is trailing off. I really can’t imagine there would be that many key things required, but since those insights are what would be required to understand why they are required, I don’t think they can be predicted ahead of time, since predicting the breakthrough is basically the same as having the breakthrough, and without the breakthrough we nearly cannot see the breakthrough and cannot see the results which may or may not require further breakthroughs.
So my model of progress has allowed me to observe our prosaic scaling without surprise, but it doesn’t allow me to make good predictions since the reason for my lack of surprise has been from Vingean prediction of the form “I don’t know what progress will look like and neither do you”.
Things I do feel confident about are conditional dynamics, like, if there continues to be focus on this, there will be progress. This likely gives us sigmoid progress on AGI from here until whatever boundary on intelligence gets hit. The issue is that sigmoid is a function matching effort to progress, where effort is some unknown function of the dynamics of the agents making progress (social forces, economic forces, and ai goals?), and some function which cannot be predicted ahead of time maps progress on the problem to capabilities we can see / measure well.
Adding in my hunch that the boundary on intelligence is somewhere much higher than human level intelligence gives us “barring a shift of focus away from the problem, humanity will continue making progress until AI takes over the process of making progress” and the point of AI takeover is unknowable. Could be next week, could be next century, and giving a timeline requires estimating progress through unknown territory. To me this doesn’t feel reassuring, it feels like playing Russian roulette with an unknown number of bullets. It is like an exponential distribution where future probability is independent of past probability, but unlike with lightbulbs burning out, we can’t set up a fleet of earths making progress on AGI to try to estimate the probability distribution.
I have not been surprised by capabilities increase since I don’t think there exists capabilities increase timelines that would surprise me much. I would just say “Ah, so it turns out that’s the rate of progress. I have gone from not knowing what would happen to it happening. Just as predicted.” It’s unfortunate, I know.
What I have been surprised about has been governmental reaction to AI… I kinda expected the political world to basically ignore AI until too late. They do seem focused on non-RSI issues, so this could still be correct, but I guess I wasn’t expecting the way chat-GPT has made waves. I didn’t extrapolate my uncertainty around capabilities increases as a function of progress to uncertainty around societal reaction.
In any case, I’ve been hoping for the last few years I would have time to do my undergrad and start working on the alignment without a misaligned AI going RSI, and I’m still hoping for that. So that’s lucky I guess. 🍀🐛
There may also be a perceived difference between “open” and “open-source”. If the goal is to allow anyone to query the HHH AGI, that’s different from anyone being able to modify and re-deploy the AGI. Not that I think that way. In my view the risk that AGI is uncontrollable is too high and we should pursue an “aligned from boot” strategy like I describe in: How I’d like alignment to get done
Do you think people would vibe with it better if it was framed “I may die, but it’s a heroic sacrifice to save my home planet from may-as-well-be-an-alien-invasion”? Is it reasonable to characterize general superintelligence as an alien takeover and if it is, would people accept the characterization?
I like this direction of thought, and I suspect it is true as a general rule, but ignores the incentive people have for correctly receiving the information, and the structure through which the information is disseminated. Both factors (and probably others I haven’t thought of) would increase or decrease how much information could be transferred.