Got it, makes sense, agreed.

I agree that filling a context window with worked sudoku examples wouldn't help for solving hidouku. But, there is a common element here to the games. Both look like math, but aren't about numbers except that there's an ordered sequence. The sequence of items could just as easily be an alphabetically ordered set of words. Both are much more about geometry, or topology, or graph theory, for how a set of points is connected. I would not be surprised to learn that there is a set of tokens, containing no examples of either game, combined with a checker (like your link has) that points out when a mistake has been made, that enables solving a wide range of similar games.

I think one of the things humans do better than current LLMs is that, as we learn a new task, we vary what counts as a token and how we nest tokens. How do we chunk things? In sudoku, each box is a chunk, each row and column are a chunk, the board is a chunk, "sudoku" is a chunk, "checking an answer" is a chunk, "playing a game" is a chunk, and there are probably lots of others I'm ignoring. I don't think just prompting an LLM with the full text of "How to solve it" in its context window would get us to a solution, but at some level I do think it's possible to make explicit, in words and diagrams, what it is humans do to solve things, in a way legible to it. I think it largely resembles repeatedly telescoping in and out, to lower and higher abstractions applying different concepts and contexts, locally sanity checking ourselves, correcting locally obvious insanity, and continuing until we hit some sort of reflective consistency. Different humans have different limits on what contexts they can successfully do this in.

Here's a simple test: Ask an AI to open and manage a local pizza restaurant, buying kitchen equipment, dealing with contractors, selecting recipes, hiring human employees to serve or clean, registering the business, handling inspections, paying taxes, etc. None of these are expert-level skills. But frontier models are missing several key abilities. So I do not consider them AGI.

I agree that this is a thing current AI systems don't/can't do, and that aren't considered expert-level skills for humans. I disagree that this is a simple test, or the kind of thing a typical human can do without lots of feedback, failures, or assistance. Many very smart humans fail at some or all of these tasks. They give up on starting a business, mess up their taxes, have a hard time navigating bureaucratic red tape, and don't ever learn to cook. I agree that if an AI could do these things it would be much harder to argue against it being AGI, but it's important to remember that many healthy, intelligent, adult humans can't, at least not reliably. Also, remember that most restaurants fail within a couple of years even after making it through all these hoops. The rate is very high even for experienced restauranteurs doing the managing.

I suppose you could argue for a definition of general intelligence that excludes a substantial fraction of humans, but for many reasons I wouldn't recommend it.

I like this post and agree that acausal coordination is not weird fringe behavior necessarily. But thinking about it explicitly in the context of making a decision, is. In normal circumstances, we have plenty of non-acausal ways of discussing what's going on, as you discuss. The explicit consideration is something that becomes important only outside the contexts most people act in.

That said, I disagree with the taxes example in particular, on the grounds that that's not how government finances work in a world of fiat currency controlled by said government. Extra taxes paid won't change how much gets spent or on what, it'll just remove money from circulation with possible downstream effects on inflation. Also, in some states in the US (like Massachusetts this year), where the government doesn't control the currency, there are rules that surpluses have to get returned in the form of tax refunds. So any extra state taxes I paid, would just get redistributed across the population in proportion to income.