Suppose, a few years from now, I prompt GPT-N to design a cheap, simple fusion power generator - something I could build in my garage and use to power my house. GPT-N succeeds. I build the fusion power generator, find that it works exactly as advertised, share the plans online, and soon the world has easy access to cheap, clean power.
One problem: at no point did it occur to me to ask “Can this design easily be turned into a bomb?”. Had I thought to prompt it with the question, GPT-N would have told me that the design could easily be turned into a bomb. But I didn’t think to ask, so GPT-N had no reason to mention it. With the design in wide use, it’s only a matter of time until people figure it out. And so, just like that, we live in a world where anyone can build a cheap thermonuclear warhead in their garage.
This scenario highlights a few key constraints which I think are under-appreciated in alignment today.
Sharing Information is Irreversible
I’ve heard people say that we can make AI safe(r) by restricting the AI’s action space to things which we can undo. Problem is, sharing information is irreversible; once the cat is out of the bag, there’s no getting it back into the bag. And if an AI can’t share information, there’s very little that it can do. Not much point in an AI which just can’t do anything observable at all. (One could design an AI to “move in mysterious ways”, but I have trouble imagining that it ends up safer that way.)
This is a problem when information itself is dangerous, e.g. knowledge of how to build a thermonuclear warhead in one’s garage.
Humans Are Not Safe
Two key properties of humans:
- We do not have full introspective understanding of our own wants
- We do not have the processing power to fully understand the consequences of changes
Sometimes, we get something we thought we wanted, and find out that we don’t want it after all. Either we misunderstood our own wants, or misunderstood the full implications of the change.
Most of the time, this isn’t that huge an issue. We lose some money and/or time, but we move on.
But if a human is capable of making large, irreversible changes to the world, then the problem becomes more serious. A human with access to powerful AI - even something as conceptually simple as GPT-N - is capable of making large irreversible changes, and they do not have the processing power to fully understand the implications of those changes. In general, a human won’t even know the right questions to ask. So, if a system’s safety relies on a human asking the right questions, then the system is not safe.
In particular, this is relevant to the HCH family of alignment schemes (e.g. IDA), as well as human-imitating AI more broadly.
Corollary: Tool AI Is Not Inherently Safe
Tool AI, in particular, relies primarily on human operators for safety. Just like a tablesaw is safe if-and-only-if the operator uses it safely, tool AI is safe if-and-only-if the operator uses it safely.
With a tablesaw, that’s usually fine. It’s pretty obvious what sorts of things will lead to bad outcomes from a tablesaw. But the big value-proposition of powerful AI is its ability to reason about systems or problems too complicated for humans - which are exactly the systems/problems where safety issues are likely to be nonobvious. If we’re going to unlock the full value of AI at all, we’ll need to use it on problems where humans do not know the relevant safety issues. So: if the system’s safety relies on a human using it safely, then it’s not safe.
If you want a concrete, evocative analogy: picture a two-year-old playing on top of a tablesaw.
That said, people are designing tablesaws which auto-stop when skin contacts the blade. In general, a system’s designers may understand the relevant safety issues better than the operators. Indeed, since the first AGIs will be built by humans, any approach to AI safety ultimately relies on human designers asking the right questions. Point is: we can’t avoid the need for designers to ask (at least some of) the right questions upfront. But needing the designers to ask the right questions once is still a lot better than needing every user to ask the right questions every time they use the system.
(This perspective ties in nicely with AI alignment as interface design: if an interface offers an easy-to-overlook way to cut your hand off, and relies on users not doing so, then that’s a design problem.)
Safe tool AI could potentially be built, but safety won’t happen by itself any more than it would for other kinds of AI.
Generalization: Great Power, Great Responsibility
Finally, note that none of this is an issue if GPT-N can’t design fusion power generators (or garage warheads) at all. In general, it is easy to come up with designs for probably-safe AIs which just can’t do anything all that impressive. The greater an AI’s capabilities, the more precisely and reliably it needs to be aligned to human values.
In particular, the “capabilities” relevant here are an AI’s abilities to reason about systems too complicated for humans or solve problems too complicated for humans. It’s the complexity that matters; the inability of humans to fully understand all the implications of the AI’s reasoning/solutions is exactly what makes humans unreliable judges of safety. So, the greater the complexity of systems/problems an AI can handle, the more important it is for that AI to have its own model of what-humans-want, and to align its solutions with that model.
I feel like I don't understand what you're getting at here. It seems to me like you're saying "X cannot be guaranteed to never cause bad things", which seems to me to be so obvious that it's not worth mentioning.
Nothing is inherently safe. There's always the possibility that you've neglected some way by which danger could arise. What I want from arguments for risk is an argument that it is high EV to address the risk (often broken down into importance, tractability, neglectedness), or at least that the risk is reasonably high on probability * harm (which focuses just on importance).
Of course Tool AI isn't inherently safe. A knife isn't inherently safe, despite being a tool; you wouldn't give a knife to a baby. The argument is usually that Tool AI would, by design, not be adversarially optimizing against humans, because it isn't optimizing at all, and so a particular class of risks typically considered in the AI safety community would be avoided. It is unclear whether such an argument can work, but it's certainly not "it is impossible for bad things to happen if you build a Tool AI", which is clearly wrong.
Perhaps you're arguing that we should focus on the "how do we manage potentially dangerous information" problem? Or that we should focus on improving human intelligence so that we are better able to use our AI systems?
You definitely don't understand what I'm getting at here, but I'm not yet sure exactly where the inductive gap is. I'll emphasize a few particular things; let me know if any of this helps.
There's this story about an airplane (I think the B-52 originally?) where the levers for the flaps and landing gear were identical and right next to each other. Pilots kept coming in to land, and accidentally retracting the landing gear. The point of the story is that this is a design problem with the plane more than a mistake on the pilots' part; the problem was fixed by putting a little rubber wheel on the landing gear lever. If we put two identical levers right next to each other, it's basically inevitable that mistakes will be made; that's bad interface design.
AI has a similar problem, but far more severe, because the systems to which we are interfacing are far more conceptually complicated. If we have confusing interfaces on AI, which allow people to shoot the world in the foot, then the world will inevitably be shot in the foot, just like putting two identical levers next to each other guarantees that the wrong one will sometimes be pulled.
For tool AI in particular, the key piece is this:
The claim here is that either (a) the AI in question doesn't achieve the main value prop of AI (i.e. reasoning about systems too complicated for humans), or (b) the system itself has to do the work of making sure it's safe. If neither of those conditions are met, then mistakes will absolutely be made regularly. The human operator cannot be trusted to make sure what they're asking for is safe, because they will definitely make mistakes.
On the other hand, if the AI itself is able to evaluate whether its outputs are safe, then we can potentially achieve very high levels of safety. It could plausibly never go wrong over the lifetime of the universe. Just like, if you design a tablesaw with an automatic shut-off, it could plausibly never cut off anybody's finger. But if you design a tablesaw without an automatic shut-off, it is near-certain to cut off a finger from time to time. That level of safety can be achieved, in general, but it cannot be achieved while relying on the human operator not making mistakes.
Coming at it from a different angle: if a safety problem is handled by a system's designer, then their die-roll happens once up-front. If that die-roll comes out favorably, then the system is safe (at least with respect to the problem under consideration); it avoids the problem by design. On the other hand, if a safety problem is left to the system's users, then a die-roll happens every time the system is used, so inevitably some of those die rolls will come out unfavorably. Thus the importance of designing AI for safety up-front, rather than relying on users to use it safely.
Is it more clear what I'm getting at now and/or does this prompt further questions?
Yeah, this makes much more sense.
I see the intuitive appeal of this claim, but it seems too strong. I suspect if we look at rates of accidents over time they'll have been going down over time, at least for the last few centuries. It seems like this can continue going down, to an asymptote of zero, in the same way it has been so far -- we become better at understanding how accidents happen and more careful in how we use dangerous technologies. We already use tools for this (in software, we use debuggers, profilers, type systems, etc) or delegate to other humans (as in a large company). We can continue to do so with AI systems.
I buy that eventually "most of the work" has to be done by the AI system, but it seems plausible that this won't happen until well after advanced AI, and that advanced AI will help us in getting there. And so, that from a what-should-we-do perspective, it's fine to rely on humans for some aspects of safety in the short term (though of course it would be preferable to delegate entirely to a system we knew was safe and beneficial).
(Why bother relying on humans? If you want to build a goal-directed AI system, it sure seems better if it's under the control of some human, rather than not. It's not clear what a plausible option is if you can't have the AI system under the control of some human.)
In the die-roll analogy, the hope is the rate at which you roll dice approximately decays exponentially, so that you only roll an asymptotically constant number of dice.
I somehow agree with both you and OP, and also I don't buy part of the lever analogy yet. It seems important that the levers not only look similar, but that they be close to each other, in order to expect users to reliably mess up. Similarly, strong tool AI will offer many, many affordances, and it isn't clear how ''close'' I should expect them to be in use-space. From the security mindset, that's sufficient cause for serious concern, but I'm still trying to shake out the expected value estimate for powerful tool AIs -- will they be thermonuclear-weapon-like (as in your post), or will mistakes generally look different?
One way in which the analogy breaks down: in the lever case, we have two levers right next to each other, and each does something we want - it's just easy to confuse the levers. A better analogy for AI might be: many levers and switches and dials have to be set to get the behavior we want, and mistakes in some of them matter while others don't, and we don't know which ones matter when. And sometimes people will figure out that a particular combination extends the flaps, so they'll say "do this to extend the flaps", except that when some other switch has the wrong setting and it's between 4 and 5 pm on Thursday that combination will still extend the flaps, but it will also retract the landing gear, and nobody noticed that before they wrote down the instructions for how to extend the flaps.
Some features which this analogy better highlights:
If you ask GPT-n to produce a design for a fusion reactor, all the prompts that talk about fusion are going to say that a working reactor hasn't yet been built, or imitate cranks or works of fiction.
It seems unlikely that a text predictor could pick up enough info about fusion to be able to design a working reactor, without figuring out that humans haven't made any fusion reactors that produce net power.
If you did somehow get a response, the level of safety you would get is the level a typical human would display. (conditional on the prompt) If some information is an obvious infohazard, such that no human capable of coming up with it would share it, then such data won't be in GPT-n 's training dataset, and won't be predicted. However, the process of conditioning might amplify tiny probabilities of human failure.
Suppose that any easy design of fusion reactor could be turned into a bomb. And ignore cranks and fiction. Then suppose 99% of people who invented a fusion reactor would realize this, and stay quiet. The other 1% would write an article that starts with "To make a fusion reactor ..." . Then this prompt will cause GPT-n to generate the article that a human that didn't notice the danger would come up with.
This also applies to dangers like leaking radiation, or just blowing up randomly if your materials weren't pure enough.
I somewhat hopeful that this is right, but I'm also not so confident that I feel like we can ignore the risks of GPT-N.
For example, this post makes the argument that, because of GPT's design and learning mechanism, we need not worry about it coming up with significantly novel things or outperforming humans because it's optimizing for imitating existing human writing, not saying true things. On the other hand, it's managing to do powerful things it wasn't trained for, like solve math equations we have no reason to believe it saw in the training set or write code hasn't seen before, which makes it possible that even if GPT-N isn't trained to say true things and isn't really capable of more than humans are, doesn't mean it might not function like a Hansonian em and still be dangerous by simply doing what humans can do, only much faster.
Any of the risks of being like a group of humans, only much faster, apply. There are also the mesa alignment issues. I suspect that a sufficiently powerful GPT-n might form deceptively aligned mesa optimisers.
I would also worry that off distribution attractors could be malign and intelligent.
Suppose you give GPT-n an off training distribution prompt. You get it to generate text from this prompt. Sometimes it might wander back into the distribution, other times it might stay off distribution. How wide is the border between processes that are safely immitating humans, and processes that aren't performing significant optimization?
You could get "viruses", patterns of text that encourage GPT-n to repeat them so they don't drop out of context. GPT-n already has an accurate world model, a world model that probably models the thought processes of humans in detail. You have all the components needed to create powerful malign intelligences, and a process that smashes them together indiscriminately.
I wholeheartedly agree. I think this implies:
Note that the fusion power example could be answered directly with a value-alignment type approach, where you have an agent rather than a tool -- the agent infers your values, and infers that you would not really want backyard fusion power if it put the world at risk. That's the moral that I imagine people more into value learning would give to your story. But I'm reaching further afield for solutions, because:
I really like your examples in this post, and it made me think of a tangential but ultimately related issue.
I feel like there's long been something like two camps in the AI safety space: the people who think it's very hard to make AI safe and the people who think it's very very hard like threading a needle from 10 miles away using hand-made binoculars and a long stick (yes, there's a third camp that thinks it will be easy, but they aren't really in the AI safety conversation due to selection effects). And I suspect some of this difference is in how much purposed example failure scenarios feel likely and realistic to them. Being myself in the latter camp, I sometimes find I hard to articulate why I think this, and often want better, more evocative examples. Thus I was happy to read your examples because I think they achieve a level of evocativeness that I at least often find hard to create.