For a good few years you'd have a tiny baby limb, which would make it impossible to have a normal prosthetic. I also think most people just don't want a tiny baby limb attached to them. I don't think growing it in the lab for a decade is feasible for a variety of reasons. I also don't know how they planned to wire the nervous system in, or ensure the bone sockets attach properly, or connect the right blood vessels. The challenge is just immense and it gets less and less worth over time it as trauma surgery and prosthetics improve.

The regrowing limb thing is a nonstarter due to the issue of time if I understand correctly. Salamanders that can regrow limbs take roughly the same amount of time to regrow them as the limb takes to grow in the first place. So it would be 1-2 decades before the limb was of adult size. Secondly it's not as simple as just smearing on some stem cells to an arm stump. Limbs form because of specific signalling molecules in specific gradients. I don't think these are present in an adult body once the limb is made. So you'd need a socket which produces those which you'd have to build in the lab, attach to blood supply to feed the limb, etc.

My model: suppose we have a DeepDreamer-style architecture, where (given a history of sensory inputs) the babbler module produces a distribution over actions, a world model predicts subsequent sensory inputs, and an evaluator predicts expected future X. If we run a tree-search over some weighted combination of the X, Y, and Z maximizers' predicted actions, then run each of the X, Y, and Z maximizers' evaluators, we'd get a reasonable approximation of a weighted maximizers.

This wouldn't be true if we gave negative weights to the maximizers, because while the evaluator module would still make sense, the action distributions we'd get would probably be incoherent e.g. the model just running into walls or jumping off cliffs.

My conjecture is that, if a large black box model is doing something like modelling X, Y, and Z maximizers acting in the world, that large black box model might be close in model-space to a itself being a maximizer which maximizes 0.3X + 0.6Y + 0.1Z, but it's far in model-space from being a maximizer which maximizes 0.3X - 0.6Y - 0.1Z due to the above problem. 

Seems like if you're working with neural networks there's not a simple map from an efficient (in terms of program size, working memory, and speed) optimizer which maximizes X to an equivalent optimizer which maximizes -X. If we consider that an efficient optimizer does something like tree search, then it would be easy to flip the sign of the node-evaluating "prune" module. But the "babble" module is likely to select promising actions based on a big bag of heuristics which aren't easily flipped. Moreover, flipping a heuristic which upweights a small subset of outputs which lead to X doesn't lead to a new heuristic which upweights a small subset of outputs which lead to -X. Generalizing, this means that if you have access to maximizers for X, Y, Z, you can easily construct a maximizer for e.g. 0.3X+0.6Y+0.1Z but it would be non-trivial to construct a maximizer for 0.2X-0.5Y-0.3Z. This might mean that a certain class of mesa-optimizers (those which arise spontaneously as a result of training an AI to predict the behaviour of other optimizers) are likely to lie within a fairly narrow range of utility functions.

Perhaps fine-tuning needs to “delete” and replace these outdated representations related to user / assistant interactions.

It could also be that the finetuning causes this feature to be active 100% of the time, and which point it no longer correlates with the corresponding pretrained model feature, and it would just get folded into the decoder bias (to minimize L1 of fired features).

Some people struggle with the specific tactical task of navigating any conversational territory. I've certainly had a lot of experiences where people just drop the ball leaving me to repeatedly ask questions. So improving free-association skill is certainly useful for them.

Unfortunately, your problem is most likely that you're talking to boring people (so as to avoid doing any moral value judgements I'll make clear that I mean johnswentworth::boring people).

There are specific skills to elicit more interesting answers to questions you ask. One I've heard is "make a beeline for the edge of what this person has ever been asked before" which you can usually reach in 2-3 good questions. At that point they're forced to be spontaneous, and I find that once forced, most people have the capability to be a lot more interesting than they are when pulling cached answers.

This is easiest when you can latch onto a topic you're interested in, because then it's easy on your part to come up with meaningful questions. If you can't find any topics like this then re-read paragraph 2.

Rob Miles also makes the point that if you expect people to accurately model the incoming doom, you should have a low p(doom). At the very least, worlds in which humanity is switched-on enough (and the AI takeover is slow enough) for both markets to crash and the world to have enough social order for your bet to come through are much more likely to survive. If enough people are selling assets to buy cocaine for the market to crash, either the AI takeover is remarkably slow indeed (comparable to a normal human-human war) or public opinion is so doomy pre-takeover that there would be enough political will to "assertively" shut down the datacenters.

Also, in this case you want to actually spend the money before the world ends. So actually losing money on interests payments isn't the real problem, the real problem is that if you actually enjoy the money you risk losing everything and being bankrupt/in debtors prison for the last two years before the world ends. There's almost no situation in which you can be so sure of not needing to pay the money back that you can actually spend it risk-free. I think the riskiest short-ish thing that is even remotely reasonable is taking out a 30-year mortgage and paying just the minimum amount each year, such that the balance never decreases. Worst case you just end up with no house after 30 years, but not in crippling debt, and move back into the nearest rat group house.

Another approach would be to use per-token decoder bias as seen in some previous work: https://www.lesswrong.com/posts/P8qLZco6Zq8LaLHe9/tokenized-saes-infusing-per-token-biases But this would only solve it when the absorbing feature is a token. If it's more abstract then this wouldn't work as well.

Semi-relatedly, since most (all) of the SAE work since the original paper has gone into untied encoded/decoder weights, we don't really know whether modern SAE architectures like Jump ReLU or TopK suffer as large of a performance hit as the original SAEs do, especially with the gains from adding token biases.