Beautifully illustrated and amusingly put, sir!

A variant of what you are saying is that AI may once and for all allow us to calculate the true counterfactual     Shapley value of scientific contributions.

( re: ancestor simulations

I think you are onto something here. Compare the Q hypothesis:    

https://twitter.com/dalcy_me/status/1780571900957339771

see also speculations about Zhuangzi hypothesis here  )

Why do you think there are these low-hanging algorithmic improvements?

I didn't intend the causes $C_j$ to equate to direct computation of \phi(x) on the x_i. They are rather other pieces of evidence that the powerful agent has that make it believe \phi(x_i). I don't know if that's what you meant.

I agree seeing x_i such that \phi(x_i) should increase credence in \forall x \phi(x) even in the presence of knowledge of C_j. And the Shapely value proposal will do so.

(Bad tex. On my phone)

Problem of Old Evidence, the Paradox of Ignorance and Shapley Values

Paradox of Ignorance

Paul Christiano presents the "paradox of ignorance" where a weaker, less informed agent appears to outperform a more powerful, more informed agent in certain situations. This seems to contradict the intuitive desideratum that more information should always lead to better performance.

The example given is of two agents, one powerful and one limited, trying to determine the truth of a universal statement ∀x:ϕ(x) for some Δ0 formula ϕ. The limited agent treats each new value of ϕ(x) as a surprise and evidence about the generalization ∀x:ϕ(x). So it can query the environment about some simple inputs x and get a reasonable view of the universal generalization.

In contrast, the more powerful agent may be able to deduce ϕ(x) directly for simple x. Because it assigns these statements prior probability 1, they don't act as evidence at all about the universal generalization ∀x:ϕ(x). So the powerful agent must consult the environment about more complex examples and pay a higher cost to form reasonable beliefs about the generalization.

Is it really a problem?

However, I argue that the more powerful agent is actually justified in assigning less credence to the universal statement ∀x:ϕ(x). The reason is that the probability mass provided by examples x₁, ..., xₙ such that ϕ(xᵢ) holds is now distributed among the universal statement ∀x:ϕ(x) and additional causes Cⱼ known to the more powerful agent that also imply ϕ(xᵢ). Consequently, ∀x:ϕ(x) becomes less "necessary" and has less relative explanatory power for the more informed agent.

An implication of this perspective is that if the weaker agent learns about the additional causes Cⱼ, it should also lower its credence in ∀x:ϕ(x).

More generally, we would like the credence assigned to propositions P (such as ∀x:ϕ(x)) to be independent of the order in which we acquire new facts (like xᵢ, ϕ(xᵢ), and causes Cⱼ).

Shapley Value

The Shapley value addresses this limitation by providing a way to average over all possible orders of learning new facts. It measures the marginal contribution of an item (like a piece of evidence) to the value of sets containing that item, considering all possible permutations of the items. By using the Shapley value, we can obtain an order-independent measure of the contribution of each new fact to our beliefs about propositions like ∀x:ϕ(x).

Further thoughts

I believe this is closely related, perhaps identical, to the 'Problem of Old Evidence' as considered by Abram Demski.

Suppose a new scientific hypothesis, such as general relativity, explains a well-know observation such as the perihelion precession of mercury better than any existing theory. Intuitively, this is a point in favor of the new theory. However, the probability for the well-known observation was already at 100%. How can a previously-known statement provide new support for the hypothesis, as if we are re-updating on evidence we've already updated on long ago? This is known as the problem of old evidence, and is usually levelled as a charge against Bayesian epistemology.

[Thanks to @Jeremy Gillen for pointing me towards this interesting Christiano paper]

Those numbers don't really accord with my experience actually using gpt-4. Generic prompting techniques just don't help all that much.

I've never done explicit timelines estimates before so nothing to compare to. But since it's a gut feeling anyway, I'm saying my gut is lengthening.

Yes agreed.

What I don't get about this position: If it was indeed just scaling - what's AI research for ? There is nothing to discover, just scale more compute. Sure you can maybe improve the speed of deploying compute a little but at the core of it it seems like a story that's in conflict with itself?

You may be right. I don't know of course. 

At this moment in time, it seems scaffolding tricks haven't really improved the baseline performance of models that much. Overwhelmingly, the capability comes down to whether the rlfhed base model can do the task.

To some degree yes, they were not guaranteed to hold. But by that point they held for over 10 OOMs iirc and there was no known reason they couldn't continue.

This might be the particular twitter bubble I was in but people definitely predicted capabilities beyond simple extrapolation of scaling laws.

My timelines are lengthening. 

I've long been a skeptic of scaling LLMs to AGI *. To me I fundamentally don't understand how this is even possible. It must be said that very smart people give this view credence. davidad, dmurfet. on the other side are vanessa kosoy and steven byrnes. When pushed proponents don't actually defend the position that a large enough transformer will create nanotech or even obsolete their job. They usually mumble something about scaffolding.

I won't get into this debate here but I do want to note that my timelines have lengthened, primarily because some of the never-clearly-stated but heavily implied AI developments by proponents of very short timelines have not materialized. To be clear, it has only been a year since gpt-4 is released, and gpt-5 is around the corner, so perhaps my hope is premature. Still my timelines are lengthening. 

A year ago, when gpt-3 came out progress was blindingly fast. Part of short timelines came from a sense of 'if we got surprised so hard by gpt2-3, we are completely uncalibrated, who knows what comes next'.

People seemed surprised by gpt-4 in a way that seemed uncalibrated to me. gpt-4 performance was basically in line with what one would expect if the scaling laws continued to hold. At the time it was already clear that the only really important driver was compute  data and that we would run out of both shortly after gpt-4. Scaling proponents suggested this was only the beginning, that there was a whole host of innovation that would be coming. Whispers of mesa-optimizers and simulators. 

One year in: Chain-of-thought doesn't actually improve things that much. External memory and super context lengths ditto. A whole list of proposed architectures seem to serve solely as a paper mill. Every month there is new hype about the latest LLM or image model. Yet they never deviate from expectations based on simple extrapolation of the scaling laws. There is only one thing that really seems to matter and that is compute and data. We have about 3 more OOMs of compute to go. Data may be milked another OOM. 

A big question will be whether gpt-5 will suddenly make agentGPT work ( and to what degree). It would seem that gpt-4 is in many ways far more capable than (most or all) humans yet agentGPT is curiously bad. 

All-in-all AI progress** is developing according to the naive extrapolations of Scaling Laws but nothing beyond that. The breathless twitter hype about new models is still there but it seems to be believed more at a simulacra level higher than I can parse. 

Does this mean we'll hit an AI winter? No. In my model there may be only one remaining roadblock to ASI (and I suspect I know what it is). That innovation could come at anytime. I don't know how hard it is, but I suspect it is not too hard. 

* the term AGI seems to denote vastly different things to different people in a way I find deeply confusing. I notice that the thing that I thought everybody meant by AGI is now being called ASI. So when I write AGI, feel free to substitute ASI. 

** or better, AI congress

addendum:  since I've been quoted in dmurfet's AXRP interview as believing that there are certain kinds of reasoning that cannot be represented by transformers/LLMs I want to be clear that this is not really an accurate portrayal of my beliefs. e.g. I don't think transformers don't truly understand, are just a stochastic parrot, or in other ways can't engage in the abstract reasoning that humans do. I think this is clearly false, as seen by interacting with any frontier model.