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Some of the stories assume a lot of AIs, wouldn't a lot of human-level AIs be very good at creating a better AI? Also it seems implausible to me that we will get a STEM-AGI that doesn't think about humans much but is powerful enought to get rid of atmosphere. On a different note, evaluating plausability of scenarios is a whole different thing that basically very few people do and write about in AI safety.
What do you mean by
what is the most important thing I could be working on and why aren’t I on track to deal with it?
Like what do you think about when you ask this question? Is this more about "most important today" or "most important in my life"?
I think that AI labs are going to use LoRA to lock cool capabilities in models and offer a premium subscription with those capabilities unlocked.
I recently came up with an idea to improve my red-teaming skills. By red-teaming, I mean identifying obvious flaws in plans, systems, or ideas.
First, find high-quality reviews on open review or somewhere else. Then, create a dataset of papers and their reviews, preferably in a field that is easy to grasp and sufficiently complex. Read papers, compare to the reviews.
Obvious flaw is that you see the reviews before, so you might want to hire someone else to do it. Doing this in a group is also really great.
If you think of Pangolin behaviour and name as control it seems that it is going down slower than Axolotl. Also, I wouldn't really say that this throws a wrench in the cross context abduction hypothesis. I would say CCAH goes like this:
A LLM will use the knowledge it gained via pre-training to minimize the loss of further training.
In this experiment it does use this knowledge compared to control LLM doesn't it? At least it has responds differently to control LLM.
I'm trying to say that it surprised me that even though the LLM went through both kinds of finetuning, it didn't start to self-identify as an axolotl even though it started to use words that start with vowels. (If I understood it correctly).
Cool experiment! The 2b results are surprising to me. I thought that the LLM in 2b should be 1. finetuned on the declarative dataset of 900 questions and answers 2. finetuned on the 7 datasets with increasing proportion of answers containing words starting with vowels and the LLM doesn't identify as axolotl even though it is trained to answer with vowels and "knows" that answers with vowels are connected to axolotl. Interesting!
It could be really interesting how the employemnt looks before and after the camp.
Great post!
In the past, broad interventions would clearly have been more effective: for instance, there would have been little use in studying empirical alignment prior to deep learning. Even more recently than the advent of deep learning, many approaches to empirical alignment were highly deemphasized when large, pretrained language models arrived on the scene (refer to our discussion of creative destruction in the last post).
As discussed in the last post, a leading motivation for researchers is the interestingness or “coolness” of a problem. Getting more people to research relevant problems is highly dependent on finding interesting and well-defined subproblems for them to work on. This relies on concretizing problems and providing funding for solving them.
This seems be a conflicting advice to me. If you try to follow both you might end up having hard time finding direction for research.
I don't fully understand the post. Without a clear definition of "winning," the points you're trying to make — as well as the distinction between pragmatic and non-pragmatic principles (which also aligns with strategies and knowledge formation) — aren't totally clear. For instance, "winning," in some vague sense, probably also includes things like "fitting with evidence," taking advice from others, and so on. You don't necessarily need to turn to non-pragmatic principles or those that don’t derive from the principle of winning. "Winning" is a pretty loose term.
I've just read "Against the singularity hypothesis" by David Thorstad and there are some things there that seems obviously wrong to me - but I'm not totally sure about it and I want to share it here, hoping that somebody else read it as well. In the paper, Thorstad tries to refute the singularity hypothesis. In the last few chapters, Thorstad discuses the argument for x-risks from AI that's based on three premises: singularity hypothesis, Orthogonality Thesis and Instrumental Convergence and says that since singularity hypothesis is false (or lacks proper evidence) we shouldn't worry that much about this specific scenario. Well, it seems to me like we should still worry and we don't need to have recursively self-improving agents to have agents smart enough so that instrumental convergence and orthogonality hypothesis applies to them.
Interesting! Reading this makes me think that there is some kind of tension between “paperclip maximizer” view on AI. Some interventions or risks you mentioned assume that AI will get its attitude from the training data, while the “paperclip maximizer” is an AI with just a goal and with whatever beliefs it will help it to achieve it. I guess the assumptions is that the AI will be much more human in some way.
The power-seeking, agentic, deceptive AI is only possible if there is a smooth transition from non-agentic AI (what we have right now) to agentic AI. Otherwise, there will be a sign that AI is agentic, and it will be observed for those capabilities. If an AI is mimicking human thinking process, which it might initially do, it will also mimic our biases and things like having pent-up feelings, which might cause it to slip and loose its temper. Therefore, it's not likely that power-seeking agentic AI is a real threat (initially).
I started to think through the theories of change recently (to figure out a better career plan) and I have some questions. I hope somebody can direct me to relevant posts or discuss this with me.
The scenario I have in mind is: AI alignment is figured out. We can create an AI that will pursue the goals we give it and can still leave humanity in control. This is all optional, of course: you can still create an unaligned, evil AI. What's stopping anybody from creating AI that will try to, for instance, fight wars? I mean that even if we have the technology to align AI, we are still not out of the forest.
What would solve the problem here would be to create a benevolent, omnipresent AGI, that will prevent things like this.
What do you mean by an alignment researcher? Is somebody who did AI Safety Fundamentals an alignment researcher? Is somebody participating in MATS, AISC or SPAR an alignment researcher? Or somebody who has never posted anything on LW?
That makes sense, but it should've been at least mentioned somewhere that they think they aren't teaching the most important skills, and they think that numeracy is more important. The views expressed in the post might not be views of the whole CFAR staff.
You once told me that there were ~20 things a person needed to be generally competent. What were they?
I'm not sure I had an exact list, but I'll try to generate one now and see how many things are on it:
- Numeracy
"What surprised you most during your time at CFAR?
Surprise 1: People are profoundly non-numerate.
And, people who are not profoundly non-numerate still fail to connect numbers to life.
This surprised me a lot, because I was told (by somebody who read CFAR handbook) that CFAR isn't mostly about numeracy, and I've never heard about skills involving number crunching from people who went to CFAR workshops. Didn't they just fail to update on that? If that's the most important skill, shouldn't we have a new CFAR who teaches numeracy, reading and writing?
Did EA scale too quickly?
A friend recommended me to read a note from Andy's working notes, which argues that scaling systems too quickly led to rigid systems. Reading this note vaguely reminded me of EA.
Once you have lots of users with lots of use cases, it’s more difficult to change anything or to pursue radical experiments. You’ve got to make sure you don’t break things for people or else carefully communicate and manage change.
Those same varied users simply consume a great deal of time day-to-day: a fault which occurs for 1% of people will present no real problem in a small prototype, but it’ll be high-priority when you have 100k users.
First, it is debatable if EA experienced quick scale up in the last few years. In some ways, it feels to me like it did, and EA founds had a spike of founding in 2022.
But it feels to me like EA community didn't have things figured out properly. Like SBF crisis could be averted easily by following common business practices or the latest drama with nonlinear. The community norms were off and were hard to change?