In previous pieces, I argued that there's a real and large risk of AI systems' aiming to defeat all of humanity combined - and succeeding.
I first argued that this sort of catastrophe would be likely without specific countermeasures to prevent it. I then argued that countermeasures could be challenging, due to some key difficulties of AI safety research.
But while I think misalignment risk is serious and presents major challenges, I don’t agree with sentiments1 along the lines of “We haven’t figured out how to align an AI, so if transformative AI comes soon, we’re doomed.” Here I’m going to talk about some of my high-level hopes for how we might end up avoiding this risk.
I’ll first recap the challenge, using Ajeya Cotra’s young businessperson analogy to give a sense of some of the core difficulties. In a nutshell, once AI systems get capable enough, it could be hard to test whether they’re safe, because they might be able to deceive and manipulate us into getting the wrong read. Thus, trying to determine whether they’re safe might be something like “being an eight-year-old trying to decide between adult job candidates (some of whom are manipulative).”
I’ll then go through what I see as three key possibilities for navigating this situation:
These are some of the main categories of hopes that are pretty easy to picture today. Further work on AI safety research might result in further ideas (and the above are not exhaustive - see my more detailed piece, posted to the Alignment Forum rather than Cold Takes, for more).
I’ll talk about both challenges and reasons for hope here. I think that for the most part, these hopes look much better if AI projects are moving cautiously rather than racing furiously.
I don’t think we’re at the point of having much sense of how the hopes and challenges net out; the best I can do at this point is to say: “I don’t currently have much sympathy for someone who’s highly confident that AI takeover would or would not happen (that is, for anyone who thinks the odds of AI takeover … are under 10% or over 90%).”
This is all recapping previous pieces. If you remember them super well, skip to the next section.
In previous pieces, I argued that:
“Great news - I’ve tested this AI and it looks safe.” Why might we still have a problem? | ||
Problem | Key question | Explanation |
The Lance Armstrong problem | Did we get the AI to be actually safe or good at hiding its dangerous actions? | When dealing with an intelligent agent, it’s hard to tell the difference between “behaving well” and “appearing to behave well.” When professional cycling was cracking down on performance-enhancing drugs, Lance Armstrong was very successful and seemed to be unusually “clean.” It later came out that he had been using drugs with an unusually sophisticated operation for concealing them. |
The King Lear problem | The AI is (actually) well-behaved when humans are in control. Will this transfer to when AIs are in control? |
It's hard to know how someone will behave when they have power over you, based only on observing how they behave when they don't. AIs might behave as intended as long as humans are in control - but at some future point, AI systems might be capable and widespread enough to have opportunities to take control of the world entirely. It's hard to know whether they'll take these opportunities, and we can't exactly run a clean test of the situation. Like King Lear trying to decide how much power to give each of his daughters before abdicating the throne. |
The lab mice problem | Today's "subhuman" AIs are safe.What about future AIs with more human-like abilities? | Today's AI systems aren't advanced enough to exhibit the basic behaviors we want to study, such as deceiving and manipulating humans. Like trying to study medicine in humans by experimenting only on lab mice. |
The first contact problem | Imagine that tomorrow's "human-like" AIs are safe. How will things go when AIs have capabilities far beyond humans'? |
AI systems might (collectively) become vastly more capable than humans, and it's ... just really hard to have any idea what that's going to be like. As far as we know, there has never before been anything in the galaxy that's vastly more capable than humans in the relevant ways! No matter what we come up with to solve the first three problems, we can't be too confident that it'll keep working if AI advances (or just proliferates) a lot more. Like trying to plan for first contact with extraterrestrials (this barely feels like an analogy). |
An analogy that incorporates these challenges is Ajeya Cotra’s “young businessperson” analogy:
Imagine you are an eight-year-old whose parents left you a $1 trillion company and no trusted adult to serve as your guide to the world. You must hire a smart adult to run your company as CEO, handle your life the way that a parent would (e.g. decide your school, where you’ll live, when you need to go to the dentist), and administer your vast wealth (e.g. decide where you’ll invest your money).
You have to hire these grownups based on a work trial or interview you come up with -- you don't get to see any resumes, don't get to do reference checks, etc. Because you're so rich, tons of people apply for all sorts of reasons. (More)
If your applicants are a mix of "saints" (people who genuinely want to help), "sycophants" (people who just want to make you happy in the short run, even when this is to your long-term detriment) and "schemers" (people who want to siphon off your wealth and power for themselves), how do you - an eight-year-old - tell the difference?
In The Most Important Century, I argued that the 21st century could be the most important century ever for humanity, via the development of advanced AI systems that could dramatically speed up scientific and technological advancement, getting us more quickly than most people imagine to a deeply unfamiliar future.
This page has a ~10-page summary of the series, as well as links to an audio version, podcasts, and the full series.
The key points I argue for in the series are:
A previous piece argued that if today’s AI development methods lead directly to powerful enough AI systems, disaster is likely by default (in the absence of specific countermeasures).
In brief:
In a previous piece, I argue that AI systems could defeat all of humanity combined, if (for whatever reason) they were aimed toward that goal.
By defeating humanity, I mean gaining control of the world so that AIs, not humans, determine what happens in it; this could involve killing humans or simply “containing” us in some way, such that we can’t interfere with AIs’ aims.
One way this could happen is if AI became extremely advanced, to the point where it had "cognitive superpowers" beyond what humans can do. In this case, a single AI system (or set of systems working together) could imaginably:
However, my piece also explores what things might look like if each AI system basically has similar capabilities to humans. In this case:
More: AI could defeat all of us combined
I’ve previously argued that it could be inherently difficult to measure whether AI systems are safe, for reasons such as: AI systems that are not deceptive probably look like AI systems that are so good at deception that they hide all evidence of it, in any way we can easily measure.
Unless we can “read their minds!”
Currently, today’s leading AI research is in the genre of “black-box trial-and-error.” An AI tries a task; it gets “encouragement” or “discouragement” based on whether it does the task well; it tweaks the wiring of its “digital brain” to improve next time; it improves at the task; but we humans aren’t able to make much sense of its “digital brain” or say much about its “thought process.”
What I mean by “black-box trial-and-error” is explained briefly in an old Cold Takes post, and in more detail in more technical pieces by Ajeya Cotra (section I linked to) and Richard Ngo (section 2). Here’s a quick, oversimplified characterization.
Today, the most common way of building an AI system is by using an "artificial neural network" (ANN), which you might think of sort of like a "digital brain" that starts in an empty (or random) state: it hasn't yet been wired to do specific things. A process something like this is followed:
Some AI research (example)2 is exploring how to change this - how to decode an AI system’s “digital brain.” This research is in relatively early stages - today, it can “decode” only parts of AI systems (or fully decode very small, deliberately simplified AI systems).
As AI systems advance, it might get harder to decode them - or easier, if we can start to use AI for help decoding AI, and/or change AI design techniques so that AI systems are less “black box”-ish.
I think there is a wide range of possibilities here, e.g.:
Failure: “digital brains” keep getting bigger, more complex, and harder to make sense of, and so “digital neuroscience” generally stays about as hard to learn from as human neuroscience. In this world, we wouldn’t have anything like “lie detection” for AI systems engaged in deceptive behavior.
Basic mind-reading: we’re able to get a handle on things like “whether an AI system is behaving deceptively, e.g. whether it has internal representations of ‘beliefs’ about the world that contradict its statements” and “whether an AI system is aiming to accomplish some strange goal we didn’t intend it to.”
Advanced mind-reading: we’re able to understand an AI system’s “thought process” in detail (what observations and patterns are the main reasons it’s behaving as it is), understand how any worrying aspects of this “thought process” (such as unintended aims) came about, and make lots of small adjustments until we can verify that an AI system is free of unintended aims or deception.
Mind-writing (digital neurosurgery): we’re able to alter a “digital brain” directly, rather than just via the “trial-and-error” process discussed earlier.
One potential failure mode for digital neuroscience is if AI systems end up able to manipulate their own “digital brains.” This could lead “digital neuroscience” to have the same problem as other AI safety research: if we’re shutting down or negatively reinforcing AI systems that appear to have unsafe “aims” based on our “mind-reading,” we might end up selecting for AI systems whose “digital brains” only appear safe.
I should note that I’m lumping in much of the (hard-to-explain) research on the Eliciting Latent Knowledge (ELK) agenda under this category.3 The ELK agenda is largely4 about thinking through what kinds of “digital brain” patterns might be associated with honesty vs. deception, and trying to find some impossible-to-fake sign of honesty.
How likely is this to work? I think it’s very up-in-the-air right now. I’d say “digital neuroscience” is a young field, tackling a problem that may or may not prove tractable. If we have several decades before transformative AI, then I’d expect to at least succeed at “basic mind-reading,” whereas if we have less than a decade, I think that’s around 50/50. I think it’s less likely that we’ll succeed at some of the more ambitious goals, but definitely possible.
I previously discussed why AI systems could end up with “aims,” in the sense that they make calculations, choices and plans selected to reach a particular sort of state of the world. For example, chess-playing AIs “aim” for checkmate game states; a recommendation algorithm might “aim” for high customer engagement or satisfaction. I then argued that AI systems would do “whatever it takes” to get what they’re “aiming” at, even when this means deceiving and disempowering humans.
But AI systems won’t necessarily have the sorts of “aims” that risk trouble. Consider two different tasks you might “train” an AI to do, via trial-and-error (rewarding success at the task):
The second of these seems like a recipe for having the sort of ambitious “aim” I’ve claimed is dangerous - it’s an open-ended invitation to do whatever leads to good performance on the goal. By contrast, the first is about imitating a particular human. It leaves a lot less scope for creative, unpredictable behavior and for having “ambitious” goals that lead to conflict with humans.
(For more on this distinction, see my discussion of process-based optimization, although I’m not thrilled with this and hope to write something better later.)
My guess is that in a competitive world, people will be able to get more done, faster, with something like the second approach. But:
There are a number of other ways in which we might “limit” AI systems to make them safe. One can imagine AI systems that are:
A further source of hope: even if such “limited” systems aren’t very powerful on their own, we might be able to amplify them by setting up combinations of AIs that work together on difficult tasks. For example:
I’d guess that in a competitive world, AI systems that are not “limited” will - at least eventually - be more powerful, versatile and ultimately useful. But limited AIs might get us pretty far.
How likely is this to work? I’d guess that we’ll eventually be able to build very powerful AIs whose limits make them relatively safe. However, I’d also guess that AIs without such limits will eventually be more powerful. So I think a lot of how things go will come down to how cautious we are: will we stick with limited AIs until the point at which we make more powerful AIs safe? And I think it’s very hard to predict how much caution the world will have - it partly depends on how well-understood the issues discussed in this series become over time!
Central to my worries is the idea that AIs could be good at “deceiving” humans: proficiently choosing courses of action that humans don’t fully understand, and don’t catch the problems with. This is important both for how AIs could develop unintended, “dangerous” aims in the first place and how they could execute on these aims by defeating humanity.
We could potentially mitigate this issue by using AIs to supervise and critique each other.
A simple example: say we’re worried that AI systems might find computer security vulnerabilities, and use them opportunistically to gain power and resources. We could train some AI systems to specifically seek out, expose and patch computer security vulnerabilities. (A footnote explains why we might expect such specialized systems to patch most vulnerabilities they find, rather than exploiting the vulnerabilities as often as less specialized systems would.5)
Analogously, we could train AI systems to do things like:
There are a lot of potential wrinkles here, which I discuss in a more detailed non-Cold-Takes piece. In brief:
There is already some research on “using AIs to critique each other.” A recent example is this paper, which actually does show that an AI trained to critique its own answers can surface helpful critiques that help humans rate its answers more accurately.
I discuss possible hopes in more detail in an Alignment Forum piece. And I think there is significant scope for “unknown unknowns”: researchers working on AI safety might come up with approaches that nobody has thought of yet.
Rather than end on a positive note, I want to talk about a general dynamic that feels like it could make the situation very difficult, and make it hard for any of the above hopes to work out.
To quote from my previous piece:
Maybe at some point, AI systems will be able to do things like:
At this point, whatever methods we've developed for making human-like AI systems safe, honest and restricted could fail - and silently, as such AI systems could go from "being honest and helpful" to "appearing honest and helpful, while setting up opportunities to defeat humanity."
I’m not wedded to any of the details above, but I think the general dynamic in which “AI systems get extremely powerful, strange, and hard to deal with very quickly” could happen for a few different reasons:
I don’t know! There are a number of ways we might be fine, and a number of ways we might not be. I could easily see this century ending in humans defeated or in a glorious utopia. You could maybe even think of it as the most important century.
So far, I’ve mostly just talked about the technical challenges of AI alignment: why AI systems might end up misaligned, and how we might design them to avoid that outcome. In future pieces, I’ll go into a bit more depth on some of the political and strategic challenges (e.g., what AI companies and governments might do to reduce the risk of a furious race to deploy dangerous AI systems), and work my way toward the question: “What can we do today to improve the odds that things go well?”
Disclosure: my wife Daniela is President and co-founder of Anthropic, which employs prominent researchers in “mechanistic interpretability” and hosts the site I link to for the term. ↩
Disclosure: I’m on the board of ARC, which wrote this document. ↩
Though not entirely ↩
The basic idea:
See here, here, and here. Also see the tail end of this Wait but Why piece, which draws on similar intuitions to the longer treatment in Superintelligence ↩