I do agree the argument "We're just training AIs to imitate human text, right, so that process can't make them get any smarter than the text they're imitating, right?  So AIs shouldn't learn abilities that humans don't have; because why would you need those abilities to learn to imitate humans?" is wrong and clearly the answer is "Nope". 

At the same time I do not think parts of your argument in the post are locally valid or good justification for the claim.

Correct and locally valid argument why GPTs are not capped by human level was already written here.

In a very compressed form, you can just imagine GPTs have text as their "sensory inputs" generated by the entire universe, similarly to you having your sensory inputs generated by the entire universe. Neither human intelligence nor GPTs are constrained by the complexity of the task (also: in the abstract, it's the same task).  Because of that, "task difficulty" is not a promising way how to compare these systems, and it is necessary to look into actual cognitive architectures and bounds. 

With the last paragraph, I'm somewhat confused by what you mean by "tasks humans evolved to solve". Does e.g. sending humans to the Moon, or detecting Higgs boson, count as a "task humans evolved to solve" or not? 

You are exactly right that active inference models who behave in self-interest or any coherently goal-directed way must have something like an optimism bias.

My guess about what happens in animals and to some extent humans: part of the 'sensory inputs' are interoceptive, tracking internal body variables like temperature, glucose levels, hormone levels, etc. Evolution already built a ton of 'control theory type cirquits' on the bodies (an extremely impressive optimization task is even how to build a body from a single cell...). This evolutionary older circuitry likely encodes a lot about what the evolution 'hopes for' in terms of what states the body will occupy. Subsequently, when building predictive/innocent models and turning them into active inference, my guess a lot of the specification is done by 'fixing priors' of interoceptive inputs on values like 'not being hungry'.  The later learned structures than also become a mix between beliefs and goals: e.g. the fixed prior on my body temperature during my lifetime leads to a model where I get 'prior' about wearing a waterproof jacket when it rains, which becomes something between an optimistic belief and 'preference'.  (This retrodicts a lot of human biases could be explained as "beliefs" somewhere between "how things are" and "how it would be nice if they were")


But this suggests an approach to aligning embedded simulator-like models: Induce an optimism bias such that the model believes everything will turn out fine (according to our true values)
 

My current guess is any approach to alignment which will actually lead to good outcomes must include some features suggested by active inference. E.g. active inference suggests something like 'aligned' agent which is trying to help me likely 'cares' about my 'predictions' coming true, and has some 'fixed priors' about me liking the results. Which gives me something avoiding both 'my wishes were satisfied, but in bizarre goodharted ways' and 'this can do more than I can'

In my personal view, 'Shard theory of human values' illustrates both the upsides and pathologies of the local epistemic community.

The upsides
- majority of the claims is true or at least approximately true
- "shard theory" as a social phenomenon reached critical mass making the ideas visible to the broader alignment community, which works e.g. by talking about them in person, votes on LW, series of posts,...
- shard theory coined a number of locally memetically fit names or phrases, such as 'shards'
- part of the success leads at some people in the AGI labs to think about mathematical structures of human values, which is an important problem 

The downsides
- almost none of the claims which are true are original; most of this was described elsewhere before, mainly in the active inference/predictive processing literature, or thinking about multi-agent mind models
- the claims which are novel seem usually somewhat confused (eg human values are inaccessible to the genome or naive RL intuitions)
- the novel terminology is incompatible with existing research literature, making it difficult for alignment community to find or understand existing research, and making it difficult for people from other backgrounds to contribute (while this is not the best option for advancement of understanding, paradoxically, this may be positively reinforced in the local environment, as you get more credit for reinventing stuff under new names than pointing to relevant existing research)

Overall, 'shards' become so popular that reading at least the basics is probably necessary to understand what many people are talking about.