Abram Demski


Pointing at Normativity
Consequences of Logical Induction
Partial Agency
Alternate Alignment Ideas
Embedded Agency


I don't have a good system prompt that I like, although I am trying to work on one. It seems to me like the sort of thing that should be built in to a tool like this (perhaps with options, as different system prompts will be useful for different use-cases, like learning vs trying to push the boundaries of knowledge). 

I would be pretty excited to try this out with Claude 3 behind it. Very much the sort of thing I was trying to advocate for in the essay!

But not intentionally. It was an unintentional consequence of training.

I am not much of a prompt engineer, I think. My "prompts" generally consist of many pages of conversation where I babble about some topic I am interested in, occasionally hitting enter to get Claude's responses, and then skim/ignore Claude's responses because they are bad, and then keep babbling. Sometimes I make an explicit request to Claude such as "Please try and organize these ideas into a coherent outline" or "Please try and turn this into math" but the responses are still mostly boring and bad.

I am trying ;p

But yes, it would be good for me to try and make a more concrete "Claude cannot do X" to get feedback on.

I agree with this worry. I am overall advocating for capabilitarian systems with a specific emphasis in helping accelerate safety research.

Sounds pretty cool! What LLM powers it?

I don't think the plan is "turn it on and leave the building" either, but I still think the stated goal should not be automation. 

I don't quite agree with the framing "building very generally useful AI, but the good guys will be using it first" -- the approach I am advocating is not to push general capabilities forward and then specifically apply those capabilities to safety research. That is more like the automation-centric approach I am arguing against.

Hmm, how do I put this...

I am mainly proposing more focused training of modern LLMs with feedback from safety researchers themselves, toward the goal of safety researchers getting utility out of these systems; this boosts capabilities for helping-with-safety-research specifically, in a targeted way, because that is what you are getting more+better training feedback on. (Furthermore, checking and maintaining this property would be an explicit goal of the project.)

I am secondarily proposing better tools to aid in that feedback process; these can be applied to advance capabilities in any area, I agree, but I think it only somewhat exacerbates the existing "LLM moderation" problem; the general solution of "train LLMs to do good things and not bad things" does not seem to get significantly more problematic in the presence of better training tools (perhaps the general situation even gets better). If the project was successful for safety research, it could also be extended to other fields. The question of how to avoid LLMs being helpful for dangerous research would be similar to the LLM moderation question currently faced by Claude, ChatGPT, Bing, etc: when do you want the system to provide helpful answers, and when do you want it to instead refuse to help?

I am thirdly also mentioning approaches such as training LLMs to interact with proof assistants and intelligently decide when to translate user arguments into formal languages. This does seem like a more concerning general-capability thing, to which the remark "building very generally useful AI, but the good guys will be using it first" applies.

Yeah, I didn't do a very good job in this respect. I am not intending to talk about a transformer by itself. I am intending to talk about transformers with the sorts of bells and whistles that they are currently being wrapped with. So not just transformers, but also not some totally speculative wrapper.

And you end up with "well for most of human history, a human with those disabilities would be a net drain on their tribe. Sometimes they were abandoned to die as a consequence. "

And it implies something like "can perform robot manipulation and wash dishes, or the "make a cup of coffee in a strangers house" test. And reliably enough to be paid minimum wage or at least some money under the table to do a task like this.

The replace-human-labor test gets quite interesting and complex when we start to time-index it. Specifically, two time-indexes are needed: a 'baseline' time (when humans are doing all the relevant work) and a comparison time (where we check how much of the baseline economy has been automated).

Without looking anything up, I guess we could say that machines have already automated 90% of the economy, if we choose our baseline from somewhere before industrial farming equipment, and our comparison time somewhere after. But this is obviously not AGI.

A human who can do exactly what GPT4 can do is not economically viable in 2024, but might have been economically viable in 2020.

Yep, I agree that Transformative AI is about impact on the world rather than capabilities of the system. I think that is the right thing to talk about for things like "AI timelines" if the discussion is mainly about the future of humanity. But, yeah, definitely not always what you want to talk about.

I am having difficulty coming up with a term which points at what you want to point at, so yeah, I see the problem.

With respect to METR, yeah, this feels like it falls under my argument against comparing performance against human experts when assessing whether AI is "human-level". This is not to deny the claim that these tasks may shine a light on fundamentally missing capabilities; as I said, I am not claiming that modern AI is within human range on all human capabilities, only enough that I think "human level" is a sensible label to apply.

However, the point about autonomously making money feels more hard-hitting, and has been repeated by a few other commenters. I can at least concede that this is a very sensible definition of AGI, which pretty clearly has not yet been satisfied. Possibly I should reconsider my position further.

The point about forming societies seems less clear. Productive labor in the current economy is in some ways much more complex and harder to navigate than it would be in a new society built from scratch. The Generative Agents paper gives some evidence in favor of LLM-base agents coordinating social events.

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