FWIW it was obvious to me

Behavioural Safety is Insufficient

Past this point, we assume following Ajeya Cotra that a strategically aware system which performs well enough to receive perfect human-provided external feedback has probably learned a deceptive human simulating model instead of the intended goal. The later techniques have the potential to address this failure mode. (It is possible that this system would still under-perform on sufficiently superhuman behavioral evaluations)

There are (IMO) plausible threat models in which alignment is very difficult but we don't need to encounter deceptive alignment. Consider the following scenario:

Our alignment techinques (whatever they are) scale pretty well, as far as we can measure, even up to well-beyond-human-level AGI. However, in the year (say) 2100, the tails come apart. It gradually becomes pretty clear that what we want out powerful AIs to do and what they actually do turns out not to generalize that well outside of the distribution on which we have been testing them so far. At this point, it is to late to roll them back, e.g. because the AIs have become uncorrigible and/or power-seeking. The scenario may also have more systemic character, with AI having already been so tightly integrated into the economy that there is no "undo button".

This doesn't assume either the sharp left turn or deceptive alignment, but I'd put it at least at level 8 in your taxonomy.

I'd put the scenario from Karl von Wendt's novel VIRTUA into this category.

Taboo "evil" (locally, in contexts like this one)?

If you want to use it for ECL, then it's not clear to me why internal computational states would matter.

Why did FHI get closed down? In the end, because it did not fit in with the surrounding administrative culture. I often described Oxford like a coral reef of calcified institutions built on top of each other, a hard structure that had emerged organically and haphazardly and hence had many little nooks and crannies where colorful fish could hide and thrive. FHI was one such fish but grew too big for its hole. At that point it became either vulnerable to predators, or had to enlarge the hole, upsetting the neighbors. When an organization grows in size or influence, it needs to scale in the right way to function well internally – but it also needs to scale its relationships to the environment to match what it is.

Reminds me of https://atheistethicist.blogspot.com/2011/12/basic-review-of-desirism.html?m=1

I don't quite get what actions are available in the heat engine example.

Is it just choosing a random bit from H or C (in which case we can't see whether it's 0 or 1) OR a specific bit from W (in which case we know whether it's 0 or 1) and moving it to another pool?

Any thoughts on Symbolica? (or "categorical deep learning" more broadly?)

All current state of the art large language models such as ChatGPT, Claude, and Gemini, are based on the same core architecture. As a result, they all suffer from the same limitations.

Extant models are expensive to train, complex to deploy, difficult to validate, and infamously prone to hallucination. Symbolica is redesigning how machines learn from the ground up. 

We use the powerfully expressive language of category theory to develop models capable of learning algebraic structure. This enables our models to have a robust and structured model of the world; one that is explainable and verifiable.

It’s time for machines, like humans, to think symbolically.

1. How likely is it that Symbolica [or sth similar] produces a commercially viable product?
2. How likely is it that Symbolica creates a viable alternative for the current/classical DL?
   1. I don't think it's that different from the intentions behind Conjecture's CoEms proposal. (And it looks like Symbolica have more theory and experimental results backing up their ideas.)
      1. Symbolica don't use the framing of AI [safety/alignment/X-risk], but many people behind the project are associated with the Topos Institute that hosted some talks from e.g. Scott Garrabrant or Andrew Critch.
3. What is the expected value of their research for safety/verifiability/etc?
   1. Sounds relevant to @davidad's plan, so I'd be especially curious to know his take.
4. How likely is it that whatever Symbolica produces meaningfully contributes to doom (e.g. by advancing capabilities research without at the same time sufficiently/differentially advancing interpretability/verifiability of AI systems)?

(There's also PlantingSpace but their shtick seems to be more "use probabilistic programming and category theory to build a cool Narrow AI-ish product" whereas Symbolica want to use category theory to revolutionize deep learning.)

I'm not aware of any, but you may call it "hybrid ontologies" or "ontological interfacing".