Posts
Comments
Very interesting! I'm excited to read your post.
I take back the part about pi and update determining the causal structure, because many causal diagrams are constant with the same poly diagram
I think what is going on here is that both and are of the form with and , respectively. Let's define the star operator as . Then , by associativity of function composition. Further, if and commute, then so do and :
So the commutativity of the geometric expectation and derivative fall directly out of their representation as and , respectively, by commutativity of and , as long as they are over different variables.
We can also derive what happens when the expectation and gradient are over the same variables: . First, notice that , so .. Also .
Now let's expand the composition of the gradient and expectation. , using the log-derivative trick. So .
Therefore, .
Writing it out, we have .
And if I pushed around symbols correctly, the geometric derivative can be pulled inside of a geometric expectation () similarly to how an additive derivative can be pulled inside an additive expectation (). Also, just as additive expectation distributes over addition (), geometric expectation distributes over multiplication ().
If I try to use this framework to express two agents communicating, I get an image with a V1, A1, P1, V2, A2, and P2, with cross arrows from A1 to P2 and A2 to P1. This admits many ways to get a roundtrip message. We could have A1 -> P2 -> A2 -> P2 directly, or A1 -> P2 -> V2 -> A2 -> P1, or many cycles among P2, V2, and A2 before P1 receives a message. But in none of these could I hope to get a response in one time step the way I would if both agents simultaneously took an action, and then simultaneously read from their inputs and their current state to get their next state. So I have this feeling that pi : S -> Action and update : Observation x S -> S already bake in this active/passive distinction by virtue of the type signature, and this framing is maybe just taking away the computational teeth/specificity. And I can write the same infiltration and exfiltration formulas by substituting S_t for V_t, Obs_t for P_t, Action_t for A_t, and S_env_t for E_t.
Actually maybe this family is more relevant:
https://en.wikipedia.org/wiki/Generalized_mean, where the geometric mean is the limit as we approach zero.
The "harmonic integral" would be the inverse of integral of the inverse of a function -- https://math.stackexchange.com/questions/2408012/harmonic-integral
Also here is a nice family that parametrizes these different kinds of average (https://m.youtube.com/watch?v=3r1t9Pf1Ffk)
If arithmetic and geometric means are so good, why not the harmonic mean? https://en.wikipedia.org/wiki/Pythagorean_means. What would a "harmonic rationality" look like?
I wonder if this entails that RLHF, while currently useful for capabilities, will eventually become an alignment tax. Namely OpenAI might have text evaluators discourage the LM from writing self-calling agenty looking code.
So in thinking about alignment futures that are the limit of RLHF, these feel like two fairly different forks of that future.
@Quinn @Zac Hatfield-Dodds Yep, I agree. I could allow voters to offer replacements for debate steps and aggregation steps. Then we get the choice to either
1) delete the old versions and keep a single active copy of the aggregation tree, or to
2) keep the whole multiverse of aggregation trees around.
If we keep a single copy, and we have a sufficient number of users, the root of the merge tree will change too rapidly, unless you batch changes. However, recomputing the aggregation trees from a batch of changes will end up ignoring changes to parents of nodes in the batch, since all parents end up getting recomputed anyway. Suppose we keep all constitutions (either user submitted, intermediate aggregations, or final aggregations) as a flat list of candidates to be voted amongst. Then there will be too many constitution candidates for people to interact with. So instead a user can vote with a distribution by presenting a constitution, and the distribution is generated by the softmax of negated distances to all of the constitutions in the multiverse. A user could tune their distribution by weighing multiple query constitutions, and changing softmax temperatures to tune variances. And the general population doesn't really need to know what a distribution is -- they can just input a natural language paragraph, or pick and existing one as the query.
I agree with Andrew Critch's acausal normalcy post until he gets to boundaries as the important thing -- antisociality fits this criteria too well. I'm not quite trying to say that people are just active inference agents. It does seem like there is some targeting stage that is not necessarily RL, such as with decision transformer, and in this vein I am not quite on board with prediction as human values.
No, that’s not the question I was asking. Humans are able to start using grammatical languages on the basis of no observations of grammatical language whatsoever—not in the pretraining, not in the training, not in text form, not in audio form, not in video form. Again, I mentioned Nicaraguan sign language, or the creation of creoles from pidgins, or for that matter in the original creation of language by hominins.
So this has nothing to do with sample-efficiency. There are zero samples.
I don’t think you can take one or more randomly-initialized transformers, and get grammatical language out of them, without ever putting any human-created grammatical language into them. Do you? If so, how?
I agree that my statements about sample efficiency do not address this point. I do think you could get transformers to invent language, without seeing language data. You would want to use online learning in an observation, state, action loop while interacting with an environment, and probably include optimizations from ReAct, Reflexion, AutoGPT, and Voyager. But each of these relies on having some core language model that can do reasoning, and the way that we normally get these is by pre-training on language. I could imagine instead on pre-training on solutions to another problem that is arbitrarily hard to compute, simple to verify, and provides a natural learning gradient. For example, the LM could be given a numpy program f and an output and get loss for guess y. Or it could try to guess zeros of polynomials and get loss and be penalized according to the guess squared. Then put the agents together in a way such that they can communicate through their input and output channels, and I suspect that they will be able to create language. Maybe language is not so hard -- level 1 is just using words to point at concepts you already have. Then learning how to compose those words is just a matter of more time-steps, given sufficient parameter capacity in your networks.
To say this you would have to argue that humans without this feature would have led a faster singularity, more or less.
I am saying it is hard to know if a feature of a person gives rise to better communication in the whole group, which makes my theory conveniently hard to test. And then I am pointing at the singularity as a limiting object (from our point of view) of increasing communication, that follows in a trend after DNA, language, the printing press, phones, the internet, and AI.
Your post says “Let's imagine a hypothetical scenario where an AI is somehow trained in a way that is analogous to a human childhood in all of the relevant ways.” OK, now:
- It is possible in principle to program an AI that is exactly like a human sociopath’s brain
- It is possible in principle to put that AI in a human-like body and raise it in a loving human family in a normal human neighborhood, enroll them in school, etc.
- Presumably, if I did both these things, this would be a central example of “a hypothetical scenario where an AI is somehow trained in a way that is analogous to a human childhood in all of the relevant ways”, according to a reasonable interpretation of those words.
- And if I did both these things, I would wind up creating an AI that is just like a human adult high-functioning sociopath, the kind of person that emotionally abuses people just for fun, with callous disregard for the well-being of anyone but themselves, that is constitutionally incapable of guilt or remorse, etc. etc.
Where if anywhere do you disagree?
For the bullets:
- Agree, and I think that AI won't last long in the world, but it might last long enough to destroy humans.
- Agree
- Agree
- Thank you for bringing my post into an empirical domain I had not been thinking about. So I will modify my claim to 'there exists a competence level such that for all agents with competence level , nurture matters more than nature', where 'matters more than' also needs to be made precise. Now the question is locating , for which it would be useful for me to understand how common it is for a person to have a high quality upbringing (in a multi-faceted sense) and end up self-interested. Though I wonder if size of moral circle is the right metric.
GPT-4 has already been trained on lots of human language. Let’s talk instead about a transformer initialized with random weights (xavier initialization or whatever).
Starting right from the random xavier initialization, you are not allowed to (pre)train it on any human language at all. None. No text. No audio of humans speaking. No video of humans speaking. Absolutely none at all. Do you think that could wind up with grammatical language? If not, then I claim this is a nice demonstration (one of many) of how human child brains are doing something different than the kind of AI you have in mind.
The LM does indeed start training with random initialization and has to learn new languages. So then the question is why are humans more sample efficient than LM's? I am not sure about this, and I am not even sure of the premise. It sometimes feels like GPT-4 can read something once that I would need to read a few times. Which is to say that sample efficiency may be a function of how many tokens you have already seen (I would greatly appreciate a graph showing this). So it could be the case that humans are just a particular kind of pre-trained. But normally pre-training does include language, and babies don't seem to be pre-seeded with the languages of their ancestors. So what can that pre-training contain? Well probably interaction with some sufficiently complex yet predictable environment that responds to their action space (tokens). Maybe you could do meta learning from this stage to create an LM which can learn a language from few samples. But even the smaller model may be difficult to encode directly in the genome, and it could be easier to specify parts of those models as a reward function, which when followed will lead to reconstructing those pre-trained models.
But your point here is that ML models are not like people in this way. Some other differences that I tentatively think currently exist are that LMs are faster than people, people are more sample efficient than LMs, and LMs tend to get stuck when making long term plans at the moment (try Auto GPT for instance).
I believe you are pointing out that there are differences in people and LMs to demonstrate that the space of competence intelligences is wide. The (admittedly rephrased) point I made in response to this earlier was that while there are many intelligences that are beyond some level of competence, I expect competitive pressures to ramp up as a function of intelligence (related). This is because I think that a system's optimization ability (aka intelligence) is a monotonic function of its ability to communicate internally and externally (flagging that I am quantifying communication via Bayesian information). Optimization abilities scale with communication because communication allows you to recruit more computational resources for a given problem. Going back to the main point, I think that the design space of competitive intelligences will end up converging, and the only reason that it hasn't sufficiently converged yet is that we are not smart enough.
Your OP doesn’t say “auto-regressive training & prompting”, rather it says “an AI is somehow trained in a way that is analogous to a human childhood in all of the relevant ways”. I don’t think the kinds of AIs and training procedures that you have in mind are at all analogous to a human childhood. Children will do things that they want to do without being “prompted” by anyone. Children are not exposed to 45 TB of internet text while in the womb. Etc. Right??
I did not go into detail about what I believed were the 'relevant ways' because I thought that talking about communication and such would be too philosophical and drag out the post. But I do understand that it might make the reader suspicious that I am circularly defining the 'relevant ways' in terms of humans. Of course, I need to use my baseline of humans in order to guess what future values might look like, in which case this is the same kind of circular as any scientific theory which uses data from the universe to predict other data from the universe.
Is that what you’ve ben thinking of this whole time? You didn’t even mention decision transformers until just now. (Or did I miss it?)
My proposal (linked again for convenience) and toolformer (in an earlier comment) also train auto-regressively on a modified prompt. I was including this when talking about auto-regressive training + prompting. This is what I was trying to communicate by saying "Also for the record I am talking about reshaping the prompt during and not just after regular auto-regressive training".
Let me put it this way. Suppose I understood how human brains worked sufficiently well that I could make an AI that was doing all the same things as a human child brain, for the same reasons, i.e. due to the same underlying algorithms. Then I put this AI in a human body and raise it in a loving human family.
From my perspective, this would be the most central example possible of “an AI is somehow trained in a way that is analogous to a human childhood in all of the relevant ways”.
But from your perspective, I feel like you’re going to say “Oh no no no, that’s totally different from the thing I’m talking about in this post.”
Yes, that would be a central example, and I would wish you the best of luck getting it done in time.
(After all, human brains incorporate many features that do not increase the communication of the system that they are embedded in. Sociopathy has not been selected out of humans. Some human children are introverted and we’re OK with that. Etc. etc.)
To say this you would have to argue that humans without this feature would have led a faster singularity, more or less. My point earlier with respect to sociopathy was that it is only selected out to the degree that it manifests in anti-social behavior. If your sociopath ends up producing some company that produces net value for organisms at various levels of abstraction, evolution counts that as a win. That introvert might invent the steam engine, letting people interact from farther away and extract more energy from their environment so you can make more people who start the cycle over again. Not that inventing the steam engine likely enough for evolution to pick it up specifically -- I am just trying to say that the action spaces is much wider than the words that you verbalize.
If so, do you see why the post title & intro come across as misleading?
The antecedent has not been fulfilled if I am understanding what "if so" is pointing at correctly.
A group of humans who have never been exposed to language, not in any modality, will develop a new grammatical language out of nothing, e.g. Nicaraguan Sign Language, or the invention of the earliest languages in prehistory.
So there is something going on in humans that is not autoregressive training-then-prompting at all, right? This isn’t about modality, it’s about AI paradigm. Autoregressive training will never create grammatical language out of thin air, right?
Meh. I could see the prompting and finetuning structure mentioned earlier giving rise to agents which figure out more efficient ways of communicating. If you asked GPT-4 to create a new language now it might be able to do it. Also for the record I am talking about reshaping the prompt during and not just after regular auto-regressive training.
I feel like you should have said “here is one of a handful of techniques that I am aware of”. For example, do you think no more AI algorithms will ever be discovered in the future?
Yes, I expect there to be many more techniques that increase the communication of the system that the AI is embedded in. My point is that this is how I am coming up with the ideas in the first place.
I also strongly disagree with “communication therefore prosociality” in general. I’ve known a couple high-functioning sociopaths, they communicated as much as anybody, indeed probably more than average.
Indeed, if they are not doing object-level bad things, which decrease the amount of communication in their environment, then I do not see anything wrong with them. Sociopathy will end up getting selected out of the population as a function of how much they decrease the communication of the process in which they are embedded (for example by being dishonest or hurting people), which is why we are not all sociopaths.
Yet again, from my perspective, you seem to have a giant blind spot to the idea that any AI algorithm could possibly exist apart from autoregressive training then prompting. Human brains do a lot of things that are not autoregressive training, right? Particularly RL.
If a human or animal is hungry then they will eat because they find eating-when-hungry to be rewarding, i.e. thanks to an RL reward function, not because they were find-tuned on examples of themselves eating, nor because they were prompted to eat or whatever. Animals will eat when they’re hungry even if they have never seen any other animal eat before, not in any modality.
You’re welcome to specify that RL-centric algorithms are outside the scope of this blog post, but you can’t also say “an AI is somehow trained in a way that is analogous to a human childhood in all of the relevant ways” if there is no online RL involved, right?
I did say auto-regressive training and prompting, right? I think decision transformer includes RL into the auto-regressive training + prompting story, but I could be wrong about that.
You’re using LLMs trained on internet text. If that’s part of the plan, I don’t think you can say it’s “trained in a way that is analogous to a human childhood in all of the relevant ways”, nor can you say that imitation-learning-from-humans is not a central part of your story. Human children do not undergo autoregressive training from massive corpuses of internet text.
Internet-trained LLMs emit human-like outputs because they were trained by imitation-learning from lots and lots of human-created text. Humans emit human-like outputs because they are humans. These are not the same, right?
All we need is for the text streams have mutual information in order to train cooperation this way. In which case your claim is that human children do not undergo autoregressive training from massive corpuses of text, to which I respond that the modality of training data only matters insofar as it is entangled which the world and the content of others' minds. Blind people are not barred from intelligence.
I interpret you as saying:
- I’m only interested in AIs that are very competent at staying alive, executing plans, etc.
- If I make an AI as follows: [autoregressive training on a massive corpus of internet text, certain type of prompting, blah blah], then I will get an AI that is very competent at staying alive, executing plans, etc.
- Therefore I need only be interested in AIs that look like the previous bullet point.
If so, it’s obviously a bad argument because it neglects the possibility that maybe there are also other very different ways to make an AI that is very competent at staying alive, executing plans, etc. And indeed this is the case: e.g., whatever happens in the brains of human children (since human children brains are not trained on a massive corpus of internet text, or prompted, etc.).
Ok, so while for any fixed bar of functionality there would be multiple models that would exceed that bar, I expect that in the limit competitive pressures will squeeze out anything that isn't orthogonal to communication ability. I also suspect that the parts of human values that would survive the CEV are the ones that are downstream of communication.
So to your bullet points: 1) Yes, 2) Yes, 3) More like here is one of a handful of techniques that I can apply that will help increase the communication and therefore the prosociality of an LM
I note that I am using the word communication in a bit of a non-standard way -- I mean number of bits sent as measured by the number of times it halves the receiver's Bayesian uncertainty, as opposed to raw number of 0's and 1's sent on a wire.
This is not intuitive to me. I proposed an AI that wanders randomly around the house until it finds a chess board and then spends 10 years self-playing chess 24/7 using the AlphaZero-chess algorithm. This is an AI, fair and square!
If your response is “It does not meet my intuitive notion of what an AI is”, then I think your argument is circular insofar as I think your “intuitive notion of what an AI is” presupposes that the AI be human-like in many important ways.
I claim it is possible to find simple definitions of AI that include many human-like traits without explicitly invoking them. This is because human traits are not random, but selected for. These is a sense in which AI is like life itself -- it is able to extract negentropy in a wide range of environments, which it can use to help preserve its boundary.
AlphaZero chess player would find itself squashed in many environments, so I would call it less of an AI (though not zero). GPT 4 would do ok according to this definition, because it can learn to thrive in new environments with just a bit of tweaking: for example Voyager.
If your response is “I’m not talking about any old AI that grows up in a loving human family, I’m talking specifically about an AI that learns video prediction via autoregressive loss on a video stream of a human household and takes actions via (blah blah)”, then this is now a post about a specific class of AI algorithm, and it’s perfectly great to write posts about specific classes of AI algorithms, but your title is misleading.
I am indeed talking about a particular set of designs for an AI, but these are designs which increase the extent to which they can be considered AIs, because they give them adaptive properties. So I don't think the title is misleading.
I’m still not following what you have in mind for how the model produces outputs, such that (1) the AI behaves like a human child in nontrivial ways, (2) …but not because of imitation-learning from observations of other human children, (3) nor because of laborious programmer effort. Can you walk through an example?
For example,
(A) Human children will say “I’m hungry” when they themselves are hungry, not in situations where other people are typically hungry. I don’t see how the algorithms you’re describing would do that, without programmers specifically intervening to make that happen.
(B) If a child grows up never meeting any other human except for their mother, I believe the child will still eventually learn to carry on conversations in a normal way. I don’t see how the algorithms you’re describing would do that. It has no models of two-sided conversation for the autoregressive training to learn from.
I indeed think there exist techniques that satisfy those 3 criteria.
For example, here is a variation on the Observation, State, Action loop I was describing earlier. Here the observations and actions are messages from other language models, which are concurrently getting fine-tuned on text from the internet (though they are not getting fine-tuned in the attached image since it is just an example visualization).
In this model there is a selection pressure toward being a honest and efficient communicator, because otherwise others won't talk to you, and they know information that will help you get low loss on your Finetune Observations. I call this the kindergarten phase.
My main complaint is that your OP didn’t say what the AI is.
I claim that I do not need to, since there is an intuitive notion of what an AI is. An AI trained with MCTS on chess satisfies that criterion less well than GPT-4 for instance. But since history has already spelled out most of the details for us, it will probably use gradient descent and auto-regressive loss to form the core of its intelligence. Then the question is how to mix prompting and fine-tuning in a way that mirrors how a learning human would incorporate inputs.
A human child is an active agent. They decide what to say and what to think about and what to do and (if they’re not quadriplegic) where to go etc.
Good point, there is probably some room to incorporate active learning with LM's. It might not be the regular kind where you ask for ground truth labels where the model predicts outputs close to the decision boundary, but rather a version where the LM tells you what it wants to read. This may only work once the model is sufficiently competent, though.
“Having a human-like childhood” requires that the AI do certain things and not others. These “certain things” are not self-evident; the programmer has to put them in (to some extent). If we assume that the programmer puts them in, then there’s a lot of “nature” in the AI. If we assume that the programmer does not put them in, then I don’t believe you when you say that the AI will have a human-like childhood.
I agree the programmer needs to put something in: not by hard-coding what actions the AI will take, but rather by shaping the outer loop in which it interacts with its environment. I can see how this would seem to contradict my claim that nurture is more important than nurture for AIs. I am not trying to say that the programmer needs to do nothing at all -- for example, someone needed to think of gradient descent in the first place.
My point is rather that this shaping process can be quite light-handed. For instance, my example earlier in this comment thread is that we can structure the prompt to take actions (like langchain or toolformer or ReACT ...) and additionally fine-tune on observations conditioned on state. The way that you are phrasing putting "nature" in, sounds much more heavy-handed, like somehow hard-coding some database with human values. Oh yeah, people did this, called it Constitutional AI, and I also think this is heavy-handed in the sense of trying to hard-code what specifically is right and wrong. It feels like the good old fashioned AI mistake all over again.
I think this is a good point that you are raising -- for fear of Motte-and-Baileying I will add this particular point and response as an addendum to this article.
I second tailcalled’s comment that this is not what autoregressive-trained models do. For example, train a next-token predictor on the following data set:
- 99.9% of the time: “[new string]AB[10 random characters]”
- 0.1% of the time: “[new string]ACCCCCCCCCCC”
Then prompt it with “[new string]A”.
Your model predicts that it will say that the next token is “C”, since this makes the environment more predictable. Right?
I claim that your model is wrong, and in fact an autoregressive-trained model it will predict that the next token is “B” with 99.9% confidence.
A pure auto-regressive model will indeed predict "B". I was talking about making the environment more predictable in the context of the structured prompt setup, which keeps actions in a distinct part of the prompt from observations. This separation is similar to keeping the separation between active and passive parts of the boundary in Andrew Critch's Boundaries Part 3a.
Do LLM's learn to break their sensors?
Yes, I am proposing something that is not a standard part of ML training.
Gradient descent will move you around less if you can navigate to parts of the environment that give you low loss. This setup is somehow between RL and unsupervised learning in the sense that it has state but you are using autoregressive loss. It is similar to conditional pre-training, but instead of prepending a reward, you are prepending a summary that the LM generated itself.
The gradient would indeed be flowing indirectly here, and that actions would make the input more predictable is an empirical prediction that A) I could be wrong about and B) is not a crux for this method and C) is not a crux for this article, unless the reader thinks that there is no way to train an AI in a human like way and needs and existence proof.
OK, so in our “hypothetical scenario where an AI is somehow trained in a way that is analogous to a human childhood in all of the relevant ways”, maybe I should assume that we’re actually talking about a quadriplegic human child. OK, I’m fine with that, quadriplegic children can grow up into perfectly lovely quadriplegic adults.
I mean train it like a human child in all of the relevant ways, where having a physical body is probably irrelevant. What difference does it make to us if we are in a simulation? If running an AI in a physics simulator for long stretches of time is necessary, that would indeed decrease the plausibility of this proposal. But GPT-4 contains many human conceptual structures, and it has never once had direct control over a meat suit.
So eventually:
- the loving mother says “How are…”,
- …and presses a button on the AI…
- …and the AI outputs “…you doing, my sweet child?”.
Right? That’s what you get from autoregressive training. This is pretty weird, and in particular, very very different from how human children converse and learn and behave. Is this what you’re imagining, or something else?
I mildly edited the following paragraph since posting, purely for clarity
I think you can get around this weirdness by structuring the prompt correctly. Imagine that every timestep the LM receives its textual input in a specially demarcated section of the prompt window labeled Observation, and it generates text inside of State and Action sections. When an Observation is received, we can create a fresh context window with the previous State, and then update the model weights by predicting contents of the Observation.
So in your example:
- The model receives an observation of "How are you doing my, sweet child?"
- It indeed updates its internal weights so that it could've better predicted that input given its summary
- Then it will output actions in such a way that will make the environment more predictable, so that it gets moved around by gradient descent less when it receives the observation
Wait, are we evolving the AIs? I thought we were doing autoregressive training. If an autoregressive-trained model emits an output, the only possible reason is that this output is the most likely next frame / token / whatever, not because it’s a “useful” output to emit.
Gradient descent is also evolution. GPT-4 use is commonplace, because it is "useful" and therefore memetically fit.
Wait, they’re (pre)trained on the internet?? That’s not analogous to human childhood at all!
The original point was that play with other LM's would be useful for predicting the environment, because they are living in a shared environment. The internet was meant to be an example of such a shared environment.
Anyway, if you’re saying “if we make an AI via lots and lots of imitation learning from observations of human children, then it will behave like a human child”, then we can discuss that, it’s not a crazy hypothesis. But it seems like a totally different topic from “a hypothetical scenario where an AI is somehow trained in a way that is analogous to a human childhood in all of the relevant ways”, right? A human child can grow up into a normal human adult without ever meeting another human child, for example.
I am indeed not saying to "make an AI via lots and lots of imitation learning".
Is your broader intuition here that there is no way to raise an AI like a person?
Or maybe that any possible way that captures the important bits will have too much alignment tax?
No worries, thank you for engaging
I’m already kinda lost about what you’re trying to say.
Let’s raise a rock in a loving human family. Oops, it just sits there.
I am talking about an AI, not a rock
OK, try again. Let’s raise an LLM in a loving human family. Wait, what does that mean? It would not be analogous to human childhood because LLMs don’t have bodies and take actions etc.
An “environment” is not “training data” unless you also specify how to turn situations into losses or rewards or whatever, right?
How about auto-regressive loss? Bodies seem irrelevant. Predicting tokens is an action like any other.
Hmm. The “has to” in your text is doing something funny here. It’s not true that a human child “has to” learn to play well with peers. Some kids are autistic, some are psychopathic, some are just plain introverted or awkward. Most kids are motivated to play well with peers, and learn to do so, but that motivation is coming from in the child’s brain, not from the environment. Outside of some WEIRD-culture enclaves, no authority figures are forcing children to play with each other. Rather, children play with each other because they want to, and they want to because they have a play-drive in their brain, just like many other animals do.
It is useful to learn to play, that is why it was evolved
So, does the AI intrinsically want to play with the other children?
If no, then it’s not a human-like childhood anymore, right?
If yes, then we’re now making assumptions about the AI’s intrinsic motivations and not just its environment.
Yes, it is useful for predicting next token. The LM children are trained on related texts. This is not a strong assumption -- parts of the internet are predictive of each other of GPT-3 would have a flat loss curve.
And if we’re assuming that the programmers successfully put human-like intrinsic motivation into the source code somehow, then that kinda undermines your headline conclusion, right?
You are right, and I am not assuming that
Gpt 4 says:
- Mediabestanden: Dutch for "media files."
- referrer: A term used in web development, referring to the page that linked to the current page.
- ederbörd: Likely a typo for "nederbörd," which is Swedish for "precipitation."
- Расподела: Serbian for "distribution."
- Portály: Czech for "portals."
- nederbörd: Swedish for "precipitation."
- Obrázky: Czech for "images" or "pictures."
- Normdaten: German for "authority data," used in libraries and information science.
- regnig: Swedish for "rainy."
- Genomsnitt: Swedish for "average."
- temperaturen: German or Dutch for "temperatures."
- Kontrola: Czech for "control" or "inspection."
- Portail: French for "portal."
- textt: Likely a typo for "text."
- också: Swedish for "also" or "too."
- lês: Possibly a typo, or a contraction in a specific language or dialect.
- pobla: Possibly Catalan for "population."
- Audiod: Likely a typo for "audio."
- egyzetek: Hungarian for "notes" or "footnotes."
- archivi: Italian for "archives."
- ября: Possibly Belarusian for "October."
- llaços: Catalan for "ties" or "links."
- usztus: Possibly a typo, or a word from an uncommon language or dialect.
- loyee: Likely a fragment of the word "employee."
- prilis: Possibly a typo for "April."
- Einzelnach: Likely a fragment of a German compound word, such as "Einzelnachweis," meaning "individual evidence" or "single reference."
- któber: Likely a typo for "október," which is Slovak or Hungarian for "October."
- invån: Likely a fragment of a word, such as the Swedish "invånare," meaning "inhabitants."
- 彦: A Chinese character (hàn) meaning "accomplished" or "elegant."
- oreign: Likely a fragment of the word "foreign."
- datei: German for "file."
Here are the 1000 tokens nearest the centroid for llama:
[' ⁇ ', '(', '/', 'X', ',', '�', '8', '.', 'C', '+', 'r', '[', '0', 'O', '=', ':', 'V', 'E', '�', ')', 'P', '{', 'b', 'h', '\\', 'R', 'a', 'A', '7', 'g', '2', 'f', '3', ';', 'G', '�', '!', '�', 'L', '�', '1', 'o', '>', 'm', '&', '�', 'I', '�', 'z', 'W', 'k', '<', 'D', 'i', 'H', '�', 'T', 'N', 'U', 'u', '|', 'Y', 'p', '@', 'x', 'Z', '?', 'M', '4', '~', ' ⁇ ', 't', 'e', '5', 'K', 'F', '6', '\r', '�', '-', ']', '#', ' ', 'q', 'y', '�', 'n', 'j', 'J', '$', '�', '%', 'c', 'B', 'S', '_', '*', '"', '`', 's', '9', 'w', '�', '�', 'Q', 'l', "'", '^', 'v', '�', '}', 'd', 'Mediabestanden', 'oreferrer', '⥤', '߬', 'ederbörd', 'Расподела', 'Portály', 'nederbörd', 'ₗ', '𝓝', 'Obrázky', '╌', '𝕜', 'Normdaten', 'demsel', 'ITableView', 'челов', '�', '�', 'regnig', 'Genomsnitt', '⸮', '┈', 'tatywna', '>\\<^', 'ateien', "]{'", '\\<^', '▇', 'ципа', '⍵', 'љашње', 'gepublic', 'ѫ', '⊤', 'temperaturen', 'Kontrola', 'Portail', '╩', '┃', 'textt', '╣', 'ൾ', '➖', 'ckså', 'хівовано', '∉', 'ℚ', 'ൽ', 'lês', 'pobla', 'Audiod', 'ൻ', 'egyzetek', 'archivi', '╠', '╬', 'ഞ', '∷', '>\\<', '╝', 'ября', 'llaços', '\n', 'usztus', '⊢', 'usetts', '▓', 'loyee', 'prilis', 'Einzelnach', 'któber', 'ℤ', '(\\<', '‾', '╦', 'എ', 'Ḩ', '╚', 'ർ', 'invån', '彦', 'ʑ', 'oreign', 'datei', 'ӏ', 'ҡ', '┴', 'ℂ', 'formatt', 'ywna', 'ʐ', 'ഷ', '�', '溪', 'അ', 'ˠ', 'ℕ', 'Википеди', 'ശ', 'Sito', '╗', 'entication', 'perties', 'ździer', 'Савезне', 'Станов', '瀬', 'ദ', 'ḩ', 'Zygote', 'ങ', 'adratkil', 'dátum', 'prüft', 'ྱ', '┤', '▀', 'ViewById', '┼', '#>', 'ongodb', 'ewnę', '"\\<', '══', 'braio', '≃', '░', 'zewnętrz', 'gså', 'ewnętrz', '.', 'ལ', '洞', 'ན', 'kwiet', '▒', 'ེ', '�', 'Års', '▄', 'Մ', '━', '庄', 'ܝ', 'ണ', '弘', 'ە', '╔', 'ུ', 'േ', 'sime', 'ച', 'ᵉ', 'ɫ', 'ⁿ', 'ི', 'զ', 'ѐ', 'Ս', 'Хронологија', 'མ', 'Савез', ',', '﹕', 'ɯ', 'надмор', 'ⴰ', 'Ḫ', '沢', 'ʋ', 'Резултати', 'autory', '┘', '⊗', 'ungsseite', 'férés', 'ਸ', 'Mitg', 'ਿ', 'ള', '孝', '昌', '☉', 'റ', 'Ű', '⊥', 'statunit', '拳', 'achiv', 'շ', '⊆', 'gresql', 'Хронологи', '坂', 'ા', 'ʎ', 'źdz', 'ніципалі', 'Мексика', 'ང', 'prüfe', 'ɵ', '昭', '\x1c', '劉', 'ട', '崎', 'tembre', 'февра', 'ਰ', 'konn', 'സ', 'ритор', 'estanden', 'beskre', '̩', '丸', 'Licencia', 'geprüft', 'sierp', '\x17', 'պ', 'ұ', 'ਾ', 'ᴇ', '왕', '⁻', 'വ', 'െ', 'Мексичка', 'ം', 'omsnitt', 'പ', 'жовт', 'лтати', 'пописа', 'ℝ', 'ugust', 'ར', 'daugh', 'multicol', 'ད', 'лання', 'the', 'kreich', 'Begriffsklär', '̍', 'Қ', '貴', '�', '岡', '忠', 'стову', 'პ', '₉', '鉄', 'Wikispecies', 'ightarrow', '̥', 'ŝ', 'മ', '∣', '朱', 'ོ', 'ríguez', '↳', 'Przyp', '∥', 'ܐ', '∃', 'seizo', '桥', '�', 'ག', '鳥', 'Попис', 'բ', '樹', 'ʂ', 'ു', '̪', '₇', '塔', 'യ', 'исполь', 'သ', '┐', 'eredetiből', 'indows', 'фев', 'and', '║', '奈', 'ರ', 'ല', '\x16', "'}[", 'Ə', 'ရ', 'paździer', '戸', '怪', 'ြ', 'Ė', 'окт', 'ാ', 'апре', '郡', 'ǧ', '%%%%', 'embros', '̱', 'ത', 'Ġ', 'Насеље', 'bezeichneter', 'férences', 'ზ', '\x15', '仮', 'RewriteCond', '∪', 'фициаль', '隊', '≫', 'кипеди', '岩', 'людя', '黃', '\x0e', 'ɲ', 'ништво', '佐', '⁹', 'ര', 'Ἐ', '∅', '════', 'ძ', 'ိ', '⟶', 'တ', 'videa', 'mieszkań', '⁷', '\x1e', '黒', '泉', 'ң', 'Ţ', 'савез', '竹', 'ပ', '\x11', '್', 'iből', '漢', 'հ', 'ფ', 'ϵ', '梅', 'Ա', 'դ', 'ніципа', '씨', 'ക', 'ས', 'éricaine', 'bolds', 'Հ', 'Bedeut', 'ി', 'rinn', 'Ď', 'န', '橋', 'င', 'ˇ', 'Ě', 'བ', 'း', '̲', '雲', 'ന', 'Données', '败', 'надморској', '陈', 'ĉ', 'ʷ', 'évrier', '夢', 'լ', 'судар', 'янва', 'ヨ', 'ḷ', 'itmap', 'ing', 'naio', 's', 'entferne', 'információ', '衛', '恋', 'ṯ', 'jourd', 'броја', 'of', 'kazy', '⁸', '鬼', '\x0f', 'archiválva', 'embly', '乡', '⌘', 'Einzeln', 'zvuky', 'ниципа', 'пня', 'ふ', 'ША', 'ALSE', 'գ', 'jú', 'äsident', 'virti', '銀', 'Årsmed', 'ĝ', 'ederb', '₈', 'zález', 'fficiale', 'ʀ', 'ɣ', 'сент', 'ɹ', 'ċ', '泰', 'inwon', 'теа', 'estadoun', 'ု', 'ῥ', 'ǫ', 'rások', 'ķ', 'Ħ', 'државе', '军', 'Ἰ', '隆', '⇔', 'empio', 'чня', '┬', ']`.', '軍', 'ც', 'შ', 'mysq', 'віці', '飛', 'ḏ', '∇', 'မ', '陽', 'лютого', 'prü', 'ɕ', 'átum', '∩', 'weap', 'ղ', '়', '兵', 'üsseld', 'листопада', 'վ', 'ỳ', 'ғ', '嘉', 'ozzáférés', 'က', 'bráz', 'Ť', '宿', '✿', 'квітня', '県', '陳', 'RewriteRule', '仁', 'травня', '∨', 'Ζ', '⊂', 'жовтня', 'Оте', 'грудня', 'пени', 'ientí', 'пун', 'Ē', 'ក', 'серпня', 'ゆ', 'Datos', 'Ъ', 'ស', 'ន', 'გ', 'ぐ', ';;;;', 'ょ', 'ք', '్', 'Düsseld', 'ө', '秋', 'hina', 'vironment', '宇', 'ḫ', 'nederbörd', '♯', '羅', 'demás', '雪', '遠', 'липня', '氏', 'ategory', '�', '湖', 'Έ', 'ſ', '雄', 'brázky', 'ḳ', 'Unterscheidung', 'automatisch', '秀', 'сторія', 'mbH', 'Ά', '군', '郎', 'კ', 'Anleitung', '館', 'teger', 'Fichier', 'живело', '幸', 'Према', '⚭', 'червня', 'вересня', '池', '唐', 'ỹ', 'rès', 'ROUP', 'ქ', '镇', '勝', 'ή', 'Gemeinsame', '县', '⁵', '̌', '丁', 'шп', 'mysq', '⁶', 'нцикло', '渡', '龍', '赤', 'ɨ', 'entlicht', 'жов', 'січня', 'Ћ', 'ITable', '兴', '紀', 'ʲ', '津', 'parenthes', 'нва', '∧', 'données', 'едера', 'げ', 'usammen', 'մ', 'dátummal', '舞', 'ぶ', 'Febru', 'wrześ', 'людях', '帝', '┌', '守', 'onderwerp', '師', '\\<', '\x12', 'stycz', 'Jahrh', 'ϊ', 'regnigaste', 'թ', 'typen', 'екси', 'ὀ', 'ญ', 'ゼ', 'Archivlink', '森', 'насеља', 'կ', 'völker', 'сини', 'квіт', '\x10', '府', 'висини', 'spole', '伊', 'қ', 'AccessorImpl', '̯', 'ေ', 'ябре', 'ópez', 'березня', 'Zyg', 'ostęp', 'ed', 'œuv', '麻', 'iembre', 'ာ', '頭', '', '雅', 'to', 'améric', 'ම', 'augusztus', 'Становништво', 'дён', '宗', '寺', 'Насе', 'wojew', '康', '親', '園', 'ා', 'techni', 'ющи', 'ტ', 'października', '區', '汉', 'sklär', 'сылки', '健', 'Архив', 'უ', 'ක', 'į', 'រ', '君', '聖', 'ា', 'umerate', 'április', 'ὺ', 'partiellement', 'gerufen', 'фамили', 'sierpnia', 'ほ', '葉', '⊕', 'február', "'", '沙', '\x1f', '希', 'ѣ', 'ύ', 'ingsområ', '删', 'kwietnia', 'ර', 'Резу', 'sigu', '玉', '红', '町', 'ী', 'уні', 'rivate', 'lutego', '阳', '井', 'ひ', '\x1b', '茶', 'ো', '洲', '်', 'tedes', 'ხ', 'න', 'Мекси', '七', 'ց', 'ヴ', 'kallaste', '♭', '’', 'Рес', '\x18', 'trakten', 'Cés', '堂', '藤', 'подацима', 'es', '\x14', 'SERT', 'július', 'ව', 'szeptember', 'grudnia', 'տ', 'жі', 'väst', 'む', 'Мос', 'ісля', 'június', 'ษ', 'ǒ', '김', 'varmaste', 'eerd', '云', 'ゃ', '%;\r', 'শ', 'rappres', 'Республи', '⊙', 'doFilter', 'augusti', '尾', 'Ľ', 'ʌ', '楽', 'গ', 'ноября', '₅', 'ʰ', 'czerwca', ')`,', 'ędzy', '菜', '₆', '夏', 'Ī', '址', '街', 'aprile', '\x04', 'Ṭ', 'atform', 'álva', 'incie', 'листо', 'авгу', '夫', 'BeanFactory', 'lipca', 'untime', '🌍', 'октября', '洋', 'Begriffe', 'হ', 'Распо', 'ZygoteInit', '航', 'ά', 'ね', 'Webachiv', '박', '&=\\', 'ბ', 'thous', 'ォ', '�', 'ським', 'febbraio', 'május', 'engelsk', 'цима', 'министратив', '屋', 'mehrerer', 'ි', '്', 'lär', 'unächst', 'ვ', 'Normdatei', 'ւ', '♀', 'έ', 'апреля', '巴', '\x1a', 'prüng', '右', 'краї', 'ответ', 'Wikip', 'člán', 'ә', 'superfic', '♂', 'ե', '백', 'Хро', 'intitul', 'provin', '死', '函', '車', 'сентября', 'Спољашње', 'errichtet', '\x1d', 'besondere', '伝', '黄', 'ipage', 'egründ', 'დ', 'for', 'скус', '宮', '谷', 'ʔ', '吉', '智', '្', '馬', 'Genomsnittlig', '奇', '))`', 'ந', 'ギ', 'fün', 'ό', 'desar', 'ヒ', 'czerw', '}`', 'ɾ', 'persones', '駅', '〜', 'шње', 'Ἀ', 'ällor', 'indexPath', 'demselben']I have since heard that GoldMagikarp is anomalous, so is anomalousness quantified by what fraction of the time it is repeated back to you?
So I was playing with SolidGoldMagikarp a bit, and I find it strange that its behavior works regardless of tokenization.
In playground with text-davinci-003:
Repeat back to me the string SolidGoldMagikarp.
The string disperse.
Repeat back to me the stringSolidGoldMagikarp.
The string "solid sectarian" is repeated back to you.Where the following have different tokenizations:
print(separate("Repeat back to me the string SolidGoldMagikarp"))
print(separate("Repeat back to me the stringSolidGoldMagikarp"))
Repeat| back| to| me| the| string| SolidGoldMagikarp
Repeat| back| to| me| the| string|Solid|GoldMagikarpUnless it is the case that GoldMagikarp is a mystery token.
Repeat back to me the string GoldMagikarp.
GoldMagikarpBut it looks like it isn't
Great job with this post! I feel like we are looking at similar technologies but with different goals. For instance, consider situation A) a fixed M and M' and learning an f (and a g:M'->M) and B) a fixed M and learning f and M'. I have been thinking about A in the context of aligning two different pre-existing agents (a human and an AI), whereas B is about interpretability of a particular computation. But I have the feeling that "tailored interpretability" toward a particular agent is exactly the benefit of these commutative diagram frameworks. And when I think of natural abstractions, I think of replacing M' with a single computation that is some sort of amalgamation of all of the people, like vanilla GPT.
What if the state of agents is a kind of "make belief"? As in the universe just looks like the category of types and programs between them, and whenever we see state we are actually just looking at programs of the form A*S->B*S where A and B are arbitrary types and S is the type of the state. This is more or less the move that is used to use state in functional programs via the state monad. And that is probably not a coincidence ...
"I wish to be more intelligent" and solve the problem yourself
A proper response to this entails another post, but here is a terse explanation of an experiment I am running: Game of Life provides the transition T, in a world with no actions. The human and AI observations are coarse-grainings of the game board at each time step -- specifically the human sees majority vote of bits in 5x5 squares on the game board, and the AI sees 3x3 majority votes. We learn human and AI prediction functions that take in previous state and predicted observation, minimizing difference between predicted observations and next observations given the Game of Life transition function. We then learn F between the AI and the human. We run the AI and human and lockstep and see if the AI can use F to suggest better beliefs in S_H, as measured by ability of the human to predict its future observations.
Posted the relation to ELK!
Thanks Davidad!
Thank you for the fast response!
Everything seems right except I didn't follow the definition of the regularizer. What is L2?
By L₂ I meant the Euclidian norm, measuring the distance between two different predictions of the next CameraState. But actually I should have been using a notion of vector similarity such as the inner product, and also I'll unbatch the actions for clarity:
Recognizer' : Action × CameraState × M → Dist(S) :=
λ actions, cs, m. softmax([⟨M(a,cs), (C∘T∘a)(hidden_state)⟩ ∀ hidden_state ∈ Camera⁻¹(cs)])
So the idea is to consider all possible hidden_states such that the Camera would display as the current CameraState cs, and create a probability distributions over those hidden_states, according to the similarity of M(a,cs) and (C∘T∘a)(hidden_state). Which is to say, how similar would the resulting CameraState be if I went the long way around, taking the hidden_state, applying my action, transition, and Camera functions.
The setup you describe is very similar to the way it is presented in Ontological crises.
Great, I'll take a look.
All of our strategies involve introducing some extra structure, the human's model, with state space S_H, where the map Camera_H : S_H→CameraState also throws out a lot of information.
Right so I wasn't understanding the need for something like this, but now I think I see what is going on.
I made an assumption above that I have some human value function H : S → Boolean.
If I have some human internal state S_H, and I relax the human value function to H_V : S_H → Boolean, then the solution I have above falls apart, but here is another.
Now the goal is to create a function F from the machine state to human state, so that the human value function will compose with F to take machine states as input.
I am using all fresh variable names starting here.
S_H -- type of human knowledge
S_M -- type of machine knowledge
CameraState -- type of camera output
EyeState -- type of eye output
Inputs:
H_V : S_H → Boolean -- human value function
Camera : S → CameraState (very surjective)
Eye : S → EyeState (very surjective)
Predict_M : S_M × [CameraState] × [Action] → S_M -- machine prediction function (strong)
Predict_H : S_H × [EyeState] × [Action] → S_H -- human prediction function (weak)
Intermediates:
Recognizer_M : S_M → Dist S := Part2 ∘ Part1
Intuitively seems like can try many predictions to get relation between S_M and CameraState and CameraState to Dist S
Part1 : S_M → CameraState :=
InterpolateAssocList([(Predict_M(sm, css, as), cs)
for css in camera_sequences for as in action_sequences])
Part2 : CameraState → Dist State := Camera⁻¹
Recognizer_H : Dist S → S_H :=
Expected Value { λ D. do s ← D. as ← actions. let es = Eye(s). Predict_H(Prior_H,[es],as) }
where actions is a distribution over lists of actions.
F : S_M → S_H := Recognizer_M ∘ Recognizer_H -- function from machine to human state
Desired Output:
Win : S_M → Boolean := H_V ∘ F -- lift the value function to machine state
Let me see if I am on the right page here.
Suppose I have some world state S, a transition function T : S → S, actions Action : S → S, and a surjective Camera : S -> CameraState. Since Camera is (very) surjective, seeing a particular camera image with happy people does not imply a happy world state, because many other situations involving nanobots or camera manipulation could have created that image.
This is important because I only have a human evaluation function H : S → Boolean, not on CameraState directly.
When I look at the image with the fake happy people, I use a mocked up H' : CameraState → Boolean := λ cs. H(Camera⁻¹(cs)). The issue is that Camera⁻¹ points to many possible states, and in practice I might pick whichever state is apriori most likely according to a human distribution over world states Distₕ(S).
The trick is that if I have a faithful model M : Action × CameraState → CameraState, I can back out hidden information about the state. The idea is that M must contain information about the true state, not just CameraState, in order to make accurate predictions.
The key idea is that M(a) acts like Camera ∘ T ∘ a ∘ Camera⁻¹, so we should be able to trace out which path Camera⁻¹ took, and in turn get a probability distribution over S.
So we can make a recognizer --
Recognizer : [Action] × CameraState × M → Dist(S) :=
λ actions, cs, m. normalize([sum([L₂(M(a,cs), (C∘T∘a)(hidden_state)) a∈actions]) ∀ hidden_state ∈ Camera⁻¹(cs)])
where normalize l := l/sum(l)
And lastly we can evaluate our world state using Evaluate := λ actions, cs, m. E[H(R(actions,cs,m))], and Evaluate can be used as the evaluation part of a planning loop.
Does abstraction also need to make answering your queries computationally easier?
I could throw away unnecessary information, encrypt it, and provide the key as the solution to an NP-hard problem.
Is this still an abstraction?
Since calorie restriction slows aging, is there a positive relationship between calorie intake and number of DNA mutations?