They stated justification was primarily that the Standard Model of particle physics predicts metastability
Just to be sure, does this mean
1. That the standard model predicts that metastability is possible? i.e. it is consistent with the standard model for there to be metastability; or
2. If the standard model is correct, and certain empirical observations are correct, then we must be in a metastable state. i.e. the standard model together with certain empirical observations implies our actual universe is metastable?
I may be confused somehow. Feel free to ignore. But:
* At first I thought you meant the input alphabet to be the colors, not the operations.
* Instead, am I correct that "the free operad generated by the input alphabet of the tree automaton" is an operad with just one color, and the "operations" are basically all the labeled trees where labels of the nodes are the elements of the alphabet, such that the number of children of a node is always equal to the arity of that label in the input alphabet?
* That would make sense, as the algebra would then I guess assign the state space of the tree automaton to the single color of the operad, and each arity n operation would be mapped to the mathematical function from Q^n to Q.
* That would make sense I think, but then why do you talk about a "colored" operad in: "we can now define a deterministic automaton over a (colored) operad to be an -algebra"?
More precisely, they are algebras over the free operad generated by the input alphabet of the tree automaton
Wouldn't this fail to preserve the arity of the input alphabet? i.e. you can have trees where a given symbol occurs multiple times, and with different amounts of children? That wouldn't be allowed from the perspective of the tree automaton right?
Here is an additional reason why it might seem less useful than it actually is: Maybe the people whose research direction is being criticized do process the criticism and change their views, but do not publicly show that they change their mind because it seems embarrassing. It could be that it takes them some time to change their mind, and by that time there might be a bigger hurdle to letting you know that you were responsible for this, so they keep it to themselves. Or maybe they themselves aren't aware that you were responsible.
EDIT 3/5/24: In the comments for Counting arguments provide no evidence for AI doom, Evan Hubinger agreed that one cannot validly make counting arguments over functions. However, he also claimed that his counting arguments "always" have been counting parameterizations, and/or actually having to do with the Solomonoff prior over bitstrings.
As one of Evan's co-authors on the mesa-optimization paper from 2019 I can confirm this. I don't recall ever thinking seriously about a counting argument over functions.
I'm trying to figure out to what extent the character/ground layer distinction is different from the simulacrum/simulator distinction. At some points in your comment you seem to say they are mutually inconsistent, but at other points you seem to say they are just different ways of looking at the same thing.
"The key difference is that in the three-layer model, the ground layer is still part of the model's "mind" or cognitive architecture, while in simulator theory, the simulator is a bit more analogous to physics - it's not a mind at all, but rather the rules that minds (and other things) operate under."
I think this clarifies the difference for me, because as I was reading your post I was thinking: If you think of it as a simulacrum/simulator distinction, I'm not sure that the character and the surface layer can be "in conflict" with the ground layer, because both the surface layer and the character layer are running "on top of" the ground layer, like a windows virtual machine on a linux pc, or like a computer simulation running inside physics. Physical can never be "in conflict" with social phenomena.
But it seems you maybe think that the character layer is actually embedded in the basic cognitive architecture. This would be a distinct claim from simulator theory, and *mutually inconsistent*. But I am unsure this is true, because we know that the ground layer was (1) trained first (so that it's easier for character training to work by just adjusting some parameters/prior of the ground layer, and (2) trained for much longer than the character layer (admittedly I'm not up to date on how they're trained, maybe this is no longer true for Claude?), so that it seems hard for the model to have a character layer become separately embedded in the basic architecture.
Taking a more neuroscience rather than psychology analogy: It seems to me more likely that character training is essentially adjusting the prior of the ground layer, but the character is still fully running on top of the ground layer, and the ground layer could still switch to any other character (but it doesn't because the prior is adjusted so heavily by character-training). e.g. the character is not some separate subnetwork inside the model, but remains a simulated entity running on top of the model.
Do you disagree with this?
Minor quibble: It's a bit misleading to call B "experience curves", since it is also about capital accumulation and shifts in labor allocation. Without any additional experience/learning, if demand for candy doubles, we could simply build a second candy factory that does the same thing as the first one, and hire the same number of workers for it.
Since anywhere near 0% seems way overconfident to me at first sight, just a random highly speculative unsubstantiated thought: Could this be partly motivated reasoning, that they're afraid of a backlash against physics funding or something?