This is the first part of a six-part report called AGI safety from first principles, in which I've attempted to put together the most complete and compelling case I can for why the development of AGI might pose an existential threat. The report stems from my dissatisfaction with existing arguments about the potential risks from AGI. Early work tends to be less relevant in the context of modern machine learning; more recent work is scattered and brief. I originally intended to just summarise other people's arguments, but as this report has grown, it's become more representative of my own views and less representative of anyone else's. So while it covers the standard ideas, I also think that it provides a new perspective on how to think about AGI - one which doesn't take any previous claims for granted, but attempts to work them out from first principles.
Having said that, the breadth of the topic I'm attempting to cover means that I've included many arguments which are only hastily sketched out, and undoubtedly a number of mistakes. I hope to continue polishing this report, and I welcome feedback and help in doing so. I'm also grateful to many people who have given feedback and encouragement so far. I plan to cross-post some of the most useful comments I've received to the Alignment Forum once I've had a chance to ask permission. I've posted the report itself in six sections; the first and last are shorter framing sections, while the middle four correspond to the four premises of the argument laid out below.
AGI safety from first principles
The key concern motivating technical AGI safety research is that we might build autonomous artificially intelligent agents which are much more intelligent than humans, and which pursue goals that conflict with our own. Human intelligence allows us to coordinate complex societies and deploy advanced technology, and thereby control the world to a greater extent than any other species. But AIs will eventually become more capable than us at the types of tasks by which we maintain and exert that control. If they don’t want to obey us, then humanity might become only Earth's second most powerful "species", and lose the ability to create a valuable and worthwhile future.
I’ll call this the “second species” argument; I think it’s a plausible argument which we should take very seriously. However, the version stated above relies on several vague concepts and intuitions. In this report I’ll give the most detailed presentation of the second species argument that I can, highlighting the aspects that I’m still confused about. In particular, I’ll defend a version of the second species argument which claims that, without a concerted effort to prevent it, there’s a significant chance that:
- We’ll build AIs which are much more intelligent than humans (i.e. superintelligent).
- Those AIs will be autonomous agents which pursue large-scale goals.
- Those goals will be misaligned with ours; that is, they will aim towards outcomes that aren’t desirable by our standards, and trade off against our goals.
- The development of such AIs would lead to them gaining control of humanity’s future.
While I use many examples from modern deep learning, this report is also intended to apply to AIs developed using very different models, training algorithms, optimisers, or training regimes than the ones we use today. However, many of my arguments would no longer be relevant if the field of AI moves away from focusing on machine learning. I also frequently compare AI development to the evolution of human intelligence; while the two aren’t fully analogous, humans are the best example we currently have to ground our thinking about generally intelligent AIs.
Stuart Russell also refers to this as the “gorilla problem” in his recent book, Human Compatible. ↩︎
I haven't had time to reread this sequence in depth, but I wanted to at least touch on how I'd evaluate it. It seems to be aiming to be both a good introductory sequence, while being a "complete and compelling case I can for why the development of AGI might pose an existential threat".
The question is who is this sequence for, what is it's goal, and how does it compare to other writing targeting similar demographics.
Some writing that comes to mind to compare/contrast it with includes:
(I recall Scott Alexander once trying to run a pseudo-study where he had people read a randomized intro post on AI alignment, I think including his own Superintelligence FAQ and Tim Urban's posts among others, and see how it changed people's minds. I vaguely recall it didn't find that big a difference between them. I'd be curious how this compared)
At a glance, AGI Safety From First Principles seems to be more complete than Alex Flint's piece, and more serious/a-bit-academic than Scott or Tim's writing. I assume it's aiming for a somewhat skeptical researcher, and is meant to not only convince them the problem exists, but give them some technical hooks of how to start thinking about it. I'm curious how well it actually succeeds at that.
I'm curious why you think the orthogonality thesis, instrumental convergence, the treacherous turn or Goodhart's law arguments are less relevant in the context of modern machine learning. (We can use here Facebook's feed-creation-algorithm as an example of modern machine learning, for the sake of concreteness.)
It seems to me that the orthogonality thesis doesn't apply to modern machine learning? None of the techniques we know for training AI systems to general capabilities seem like they are compatible with creating a paperclip maximiser:
This is especially the case if shard theory is true. Training an AI across diverse domains/tasks may of necessity result in the AI forming value shards useful/relevant to those tasks
That is even if we wanted to, we couldn't necessarily create a generally intelligent paperclip maximiser.
We could create an agent that values paperclips (humans value paperclips to some extent), but most agents that value paperclips aren't paperclip maximisers.
I'm excited about this sequence!
Just a question: what audience do you have in mind? Is it a sequence for newcomers to AI Safety, or more a reframing of AI Safety arguments for researchers?
Promoted to curated: I really enjoyed reading through this sequence. I have some disagreements with it, but overall it's one of the best plain language introductions to AI safety that I've seen, and I expect I will link to this as a good introduction many times in the future. I was also particularly happy with how the sequence bridged and synthesized a number of different perspectives that usually feel in conflict with each other.
Critch recently made the argument (and wrote it in his ARCHES paper, summarized by Rohin here) that "AI safety" is a straightforwardly misleading name because "safety" is a broader category than is being talked about in (for example) this sequence – it includes things like not making self-driving cars crash. (To quote directly: "the term “AI safety” should encompass research on any safety issue arising from the use of AI systems, whether the application or its impact is small or large in scope".) I wanted to raise the idea here and ask Richard what he thinks about renaming it to something like "AI existential safety from first principles" or "AI as an extinction risk from first principles" or "AI alignment from first principles".
Yeah, this seems like a reasonable point. But I'm not that much of a fan of the alternatives you suggest. What do you think about "AGI safety"?
Oli suggests that there are no fields with three-word-names, and so "AI Existential Risk" is not a choice. I think "AI Alignment" is the currently most accurate name for the field that encompasses work like Paul's and Vanessa's and Scott/Abram's and so on. I think "AI Alignment From First Principles" is probably a good name for the sequence.
It seems a definite improvement on the axis of specificity, I do prefer it over the status quo for that reason.
But it doesn't address the problem of scope-sensitivity. I don't think this sequence is about preventing medium-sized failures from AGI. It's about preventing extinction-level risks to our future.
"A First-Principles Explanation of the Extinction-Level Threat of AGI: Introduction"
"The AGI Extinction Threat from First Principles: Introduction"
"AGI Extinction From First Principles: Introduction"
Yeah, I agree that's a problem. Bur I don't think it's a big problem, because who's talking about medium-size risks from AGI?
In particular, the flag I want to plant is something like: "when you're talking about AGI, it's going to be So Big that existential safety is the default type of safety to be concerned with."
Also I think having the big EXTINCTION in the title costs weirdness points, because even within the field people don't use that word very much. So I'm leaning towards AGI safety.
Well, I have talked about them... :-)
A year later, as we consider this for the 2020 Review, I think figuring out a better name is worth another look.
Another option is "AI Catastrophe from First Principles"
Very good point. Safety in Engineering is often summarized as "nothing bad happens", without anthropomorphic nuance, and without "intent": An engineered system can just go wrong. It seems "AI Safety" often glosses over or ignores such facets. Is it that "AI Safety" is cast as looking into the creation of "reasonable A(G)I" ?
Planned summary of this sequence for the Alignment Newsletter:
Note: There is currently a lot of stuff I want to cover in the newsletter, so this will probably go out in the 10/21 newsletter.
Thanks! Good summary. A couple of quick points:
Changed, though the way I use words those phrases mean the same thing.
Yeah this was not meant to be a direct translation of your list. (Your list of 3 is encompassed by my first and third point.) You mentioned six things:
which I wanted to condense. (The model size point was meant to capture the compute case.) I did have a lot of trouble understanding what the point of that section was, though, so it's plausible that I've condensed it poorly for whatever point you were making there.
Perhaps the best solution is to just delete that particular paragraph? As far as I can tell, it's not relevant to the rest of the arguments, and this summary is already fairly long and somewhat disjointed.
My thinking here is something like: humans became smart via cultural evolution, but standard AI safety arguments ignore this fact. When we think about AI progress from this perspective though, we get a different picture of the driving forces during the takeoff period. In particular, the three things I've listed are all ways that interactions between AGIs will be crucial to their capabilities, in addition to the three factors which are currently crucial for AI development.
Will edit to make this clearer.
Translation into Russian by me: 1, 2, 3, 4, 5, 6