Thanks to Chris Scammell, Adam Shimi, Lee Sharkey, Evan Hubinger, Nicholas Dupuis, Leo Gao, Johannes Treutlein, and Jonathan Low for feedback on drafts.
This work was carried out while at Conjecture.
"Moebius illustration of a simulacrum living in an AI-generated story discovering it is in a simulation" by DALL-E 2
TL;DR: Self-supervised learning may create AGI or its foundation. What would that look like?
Unlike the limit of RL, the limit of self-supervised learning has received surprisingly little conceptual attention, and recent progress has made deconfusion in this domain more pressing.
Existing AI taxonomies either fail to capture important properties of self-supervised models or lead to confusing propositions. For instance, GPT policies do not seem globally agentic, yet can be conditioned to behave in goal-directed ways. This post describes a frame that...
I recently watched Eliezer Yudkowsky's appearance on the Bankless podcast, where he argued that AI was nigh-certain to end humanity. Since the podcast, some commentators have offered pushback against the doom conclusion. However, one sentiment I saw was that optimists tended not to engage with the specific arguments pessimists like Yudkowsky offered.
Economist Robin Hanson points out that this pattern is very common for small groups which hold counterintuitive beliefs: insiders develop their own internal language, which skeptical outsiders usually don't bother to learn. Outsiders then make objections that focus on broad arguments against the belief's plausibility, rather than objections that focus on specific insider arguments.
As an AI "alignment insider" whose current estimate of doom is around 5%, I wrote this post to explain some of my many...
Then the model can safely scale.
If there are experiences which will change itself which don't lead to less of the initial good values, then yeah, for an approximate definition of safety. You're resting everything on the continued strength of this model as capabilities increase, and so if it fails before you top out the scaling I think you probably lose.
FWIW I don't really see your description as, like, a specific alignment strategy so much as the strategy of "have an alignment strategy at all". The meat is all in 1) how you identify the core of human...
This post is an attempt to gesture at a class of AI notkilleveryoneism (alignment) problem that seems to me to go largely unrecognized. E.g., it isn’t discussed (or at least I don't recognize it) in the recent plans written up by OpenAI (1,2), by DeepMind’s alignment team, or by Anthropic, and I know of no other acknowledgment of this issue by major labs.
You could think of this as a fragment of my answer to “Where do plans like OpenAI’s ‘Our Approach to Alignment Research’ fail?”, as discussed in Rob and Eliezer’s challenge for AGI organizations and readers. Note that it would only be a fragment of the reply; there's a lot more to say about why AI alignment is a particularly tricky task to task an AI with. (Some of...
That's a challenge, and while you (hopefully) chew on it, I'll tell an implausibly-detailed story to exemplify a deeper obstacle.
Some thoughts written down before reading the rest of the post (list is unpolished / not well communicated)
The main problems I see:
This insight was made possible by many conversations with Quintin Pope, where he challenged my implicit assumptions about alignment. I’m not sure who came up with this particular idea.
In this essay, I call an agent a “reward optimizer” if it not only gets lots of reward, but if it reliably makes choices like “reward but no task completion” (e.g. receiving reward without eating pizza) over “task completion but no reward” (e.g. eating pizza without receiving reward). Under this definition, an agent can be a reward optimizer even if it doesn't contain an explicit representation of reward, or implement a search process for reward.
Reinforcement learning is learning what to do—how to map situations to actions so as to maximize a numerical reward signal. — Reinforcement learning: An introduction
Many people[1] seem to...
There is a general phenomenon where:
It seems to me quite likely that you are person B, thinking they explained something because THEY think their explanation is very good and contai...
This was written for the Vignettes Workshop.[1] The goal is to write out a detailed future history (“trajectory”) that is as realistic (to me) as I can currently manage, i.e. I’m not aware of any alternative trajectory that is similarly detailed and clearly more plausible to me. The methodology is roughly: Write a future history of 2022. Condition on it, and write a future history of 2023. Repeat for 2024, 2025, etc. (I'm posting 2022-2026 now so I can get feedback that will help me write 2027+. I intend to keep writing until the story reaches singularity/extinction/utopia/etc.)
What’s the point of doing this? Well, there are a couple of reasons:
A couple months ago EleutherAI started an alignment speaker series, some of these talks have been recorded. This is the first instalment in the series. The following is a transcript generated with the help of Conjecture's Verbalize and some light editing:
1 CURTIS
00:00:22,775 --> 00:00:56,683
Okay, I've started the recording. I think we can give it maybe a minute or two more and then I guess we can get started. I've also got the chat window as part of the recording. So if anyone has something they want to write out, feel free to put that in. Steve, you want to do questions throughout the talk, or should we wait till the end of the talk before we ask questions?
2 STEVE
00:00:59,405 --> 00:01:09,452
Let's do throughout, but I reserve...
It seems as a result of this post, many people are saying that LLMs simulate people and so on. But I'm not sure that's quite the right frame. It's natural if you experience LLMs through chat-like interfaces, but from playing with them in a more raw form, like the RWKV playground, I get a different impression. For example, if I write something that sounds like the start of a quote, it'll continue with what looks like a list of quotes from different people. Or if I write a short magazine article, it'll happily tack on a publication date and "All rights reser... (read more)