1.1 How do we get alignment at all?

(“Alignment” is an imprecise term meaning “nice” / “not subversive” / “trying to actually help its operator“. See Clarifying "AI alignment" for Paul’s description.)

1.1.1: Isn’t it really hard to give an AI our values? Value learning is really hard, and the default is for it to encounter instrumental incentives to manipulate you or prevent itself from getting shut down.

The AI isn’t learning our values, it’s learning to optimize for our short-term approval—in other words, for each action it takes, it optimizes for something like what rating we’d give it on a scale from 1 to 5 if we just saw it act.

It’s hard to learn the nuances of human values. But from a machine learning perspective, it’s very easy to learn that humans would strongly disapprove of attempts to kill or manipulate us. Paul expresses this well on his blog:

You need only the vaguest understanding of humans to guess that killing the user is: (1) not something they would approve of, (2) not something they would do, (3) not in line with their instrumental preferences.

So in order to get bad outcomes here you have to really mess up your model of what humans want (or more likely mess up the underlying framework in an important way). If we imagine a landscape of possible interpretations of human preferences, there is a “right” interpretation that we are shooting for. But if you start with a wrong answer that is anywhere in the neighborhood, you will do things like “ask the user what to do, and don’t manipulate them.” And these behaviors will eventually get you where you want to go.

(See: Approval-directed agents and Act-based agents.)

1.1.2: OK, but doesn't this only incentivize it to appear like it's doing what the operator wants? Couldn’t it optimize for hijacking its reward signal, while seeming to act in ways that humans are happy with?

We’re not just training the agent to take good actions. We’re also training it to comprehensibly answer questions about why it took the actions it took, to arbitrary levels of detail. (Imagine a meticulous boss grilling an employee about a report he put together, or a tax auditor grilling a corporation about the minutiae of its expenses.) We ensure alignment by randomly performing thorough evaluations of its justifications for its actions, and punishing it severely if any of those justifications seem subversive. To the extent we trust these justifications to accurately reflect the agent’s cognition, we can trust the agent to not act subversively (and thus be aligned).

(See: The informed oversight problem.)

1.1.3: How do we train it to answer questions comprehensively?

Reward it for doing so, and punish it for failing to do so.

Imagine being a meticulous boss who asks his employee to put together a report. Imagine grilling him about the report, and punishing him every time he fails to answer your questions clearly enough or at a satisfactory level of detail, in addition to punishing him for low-quality reports. If your employee is capable enough, he’ll eventually learn to produce high-quality reports and answer questions satisfactorily when you grill him.

Note that the ability to generate comprehensible descriptions of arbitrary cognition is a major open problem in Paul’s agenda, and also a major problem in AI safety broadly. It’s the part of his agenda that Paul is currently focusing most on.

(See The informed oversight problem, Learning representations, and Approval-maximising representations.)

1.1.4: Why should we expect the agent’s answers to correspond to its cognition at all?

We don’t actually have any guarantees that it does, but giving honest answers is probably the easiest way for the agent to maximize its reward. The only feasible alternative is giving thorough dishonest explanations for its actions. But if an employee lies about how he arrived at his conclusions, his explanations won’t check out, and he might get punished severely. And if a corporation lies about how it spent its money, the numbers won’t add up, and its auditor might punish it severely.

It’s critically important for this scheme that the overseer is capable of evaluating the assistant’s justifications extremely thoroughly, and does so often enough. Corporations cook their books in proportion to how thorough their auditors are, how common audits are, and how bad it is to get caught by their auditors. If we audit thoroughly enough and frequently enough, and punish our assistants severely enough for subversion, we should expect them to answer honestly.

1.1.5: Wouldn’t this incentivize the assistant to produce justifications that seem fine and innocent to you, but may actually be harmful?

We should expect the AI not to adversarially manipulate us—by assumption, we’re evaluating its justifications thoroughly enough that we should be able to catch any subversive cognition.

It's possible that there are free variables in what sorts of cognition the operator deems acceptable, and that a non-adversarial optimization process might be able to persuade the operator of essentially arbitrary conclusions by manipulating these free variables in ways the operators approves of. For example, an AGI assistant might accidentally persuade you to become an ISIS suicide bomber, while only thinking in ways that you approve of.

I do think this is a potentially severe problem. But I don’t consider it a dealbreaker, for a number of reasons:

• An AGI assistant “accidentally” manipulating you is no different from a very smart and capable human assistant who, in the process of assisting you, causes you to believe drastic and surprising conclusions. Even if this might lead to bad outcomes, Paul isn’t aiming for his agenda to prevent this class of bad outcomes.
• The more rational you are, the smaller the space of conclusions you can be non-adversarially led into believing. (For example, it’s very hard for me to imagine myself getting persuaded into becoming an ISIS suicide bomber by a process whose cognition I approve of.) It might be that some humans have passed a rationality threshold, such that they only end up believing correct conclusions after thinking for a long time without adversarial pressures.

1.2 Amplifying and distilling alignment

1.2.1: OK, you propose that to amplify some aligned agent, you just run it for a lot longer, or run way more of them and have them work together. I can buy that our initial agent is aligned; why should I trust their aggregate to be aligned?

When aligned agents work together, there’s often emergent behavior that can be described as non-aligned. For example, if the operator is pursuing a goal (like increasing Youtube’s revenue), one group of agents proposes a subgoal (like increasing Youtube views), and another group competently pursues that subgoal without understanding how it relates to the top-level goal (e.g. by triple-counting all the views), you end up with misaligned optimization. As another example, there might be some input (e.g. some weirdly compelling argument) that causes some group of aligned agents to “go insane” and behave unpredictably, or optimize for something against the operator’s wishes.

Two approaches that Paul considers important for preserving alignment:

• Reliability amplification—aggregating agents that can answer a question correctly some of the time (say, 80% of the time) in a way that they can answer questions correctly with arbitrarily high probability.
• Security amplification—winnowing down the set of queries that, when fed to the aggregate, causes the aggregate to “go insane”.

It remains an open question in Paul’s agenda how alignment can be robustly preserved through capability amplification—in other words, how to increase the capabilities of aligned agents without introducing misaligned behavior.

(See: Capability amplification, Reliability amplification, Security amplification, Universality and security amplification, and Two guarantees.)

1.2.2: OK, so given this amplified aligned agent, how do you get the distilled agent?

Train a new agent via some combination of imitation learning (predicting the actions of the amplified aligned agent), semi-supervised reinforcement learning (where the amplified aligned agent helps specify the reward), and techniques for optimizing robustness (e.g. creating red teams that generate scenarios that incentivize subversion).

(See: RL+Imitation, Benign model-free RL, Semi-supervised reinforcement learning, and Techniques for optimisizing worst-case performance.)

1.2.3: It seems like imitation learning might cause a lot of minutiae to get lost, and would create something that's "mostly aligned" but actually not aligned in a bunch of subtle ways. Maybe this is tolerable for one round of iteration, but after 100 rounds, I wouldn’t feel very good about the alignment of the resulting agent...

Indeed, which is why this new agent is also trained with semi-supervised reinforcement learning, and optimized for robustness. The imitation learning is more about getting this new agent off the ground than about ensuring alignment. The bulk of the alignment guarantee comes from the semi-supervised reinforcement learning, where we train it to work on a wide range of tasks and answer questions about its cognition. We trust it to be aligned because we train it the same way we trained our original agent to be aligned—namely, by rewarding it for maximizing our approval, and punishing it for thinking subversive thoughts.

(There’s a common misconception that Paul’s agenda proceeds entirely by taking a single aligned assistant, running it for a lot longer, using imitation learning to distill that into a new assistant, running that new assistant a lot longer, using imitation learning to distill that, etc., much like how AlphaGo Zero improves its policy network. While this does capture a key idea behind how the assistant’s capabilities get amplified, it omits critical components of his agenda that ensure alignment, like inspecting cognition and amplifying robustness.)

1.2.4: This distilled agent’s cognition might be much more complex than that of our previous agent. How can we reasonably determine whether we’d approve of its cognition?

This is where our assistants come in. While the human operator does inspect some aspects of the distilled agent’s cognition, most of the inspection is done by our assistants, whose judgments we trust because we trust that they’re aligned.

(See The reward engineering problem.)

1.2.5: Given that this distilled agent is way more powerful than you and your aligned agents, couldn’t it manipulate you and your team of aligned agents?

If we’re amplifying and distilling agent A, we’re actually training our newly distilled agent to be stronger than A, but much weaker than the amplified version of A. The amplification/distillation procedure can be thought of as repeatedly taking two steps forward (in amplifying A) and one step back (in distilling a new agent).

Any subversive plan the distilled agent makes could get recognized as subversive by the amplified agent, because the amplified agent has enough power to thoroughly evaluate every aspect of the cognition that went into the plan. The amplified agent rarely performs thorough evaluations of the distilled agent’s cognition, because it’s extremely expensive, but does so randomly and punishes the distilled agent extremely harshly if it detects subversion. The distilled agent, knowing that it will have to thoroughly justify its actions to a competent supervisor, is thus strongly incentivized against acting subversively.

In other words, the amplified agent randomly “audits” the distilled agent, and punishes the distilled agent very harshly if it fails the audit. Though the distilled agent knows that it might be able to deceive its supervisor when it isn’t audited, it’s so scared of the outcome where it tries to do that and gets audited that it doesn’t even want to try. (Even if you were 99% confident that you could get away with tax evasion, you wouldn’t want to try if you knew the government tortures and murders the families of the tax evaders they catch.)

(See Benign model-free RL, the comments in The informed oversight problem, and William Saunders' post Improbable Oversight, An Attempt at Informed Oversight.)

1.3 Robust alignment / corrigibility

1.3.1: If you delegate oversight to a well-meaning assistant (“hey, can you make sure things are going as planned?”), who delegates oversight to a well-meaning assistant, who delegates oversight to a well-meaning assistant, etc., then the default is for the delegatee 100 layers down to end up with some extremely distorted version of what you care about. Shouldn’t we expect this distortion to happen with the agents we produce?

Yes, which is why alignment isn’t the property we’re ultimately aiming to inductively guarantee. The property we’re trying to guarantee is something closer to “alignment + extreme caution about whether it’s aligned and cautious enough”. Paul usually refers to this as corrigibility.

This way, every time we’re training a distilled agent, we train it to want to clarify with its overseer (i.e., us assisted with a team of corrigible assistants) whenever it’s uncertain about what we would approve of. Our corrigible assistants either answer the question confidently, or clarify with us if it’s uncertain about its answer.

1.3.2: OK, but doesn't this just pass the buck onto corrigibility? In the same way that alignment can get distorted, couldn't corrigibility also get distorted?

Paul has two strong intuitions about corrigibility:

1. corrigibility has a simple core that should be easy for humans to impart into powerful AI systems
2. corrigibility is robust—agents that are somewhat corrigible want to become more corrigible.

On this view, even if human approval is high-complexity and hard to get right, corrigibility is straightforward to learn and most likely to improve over distillation rather than worsen.

You also need robustness amplification to ensure that corrigibility is maintained. For example, a corrigible supervisor needs to be able to thoroughly evaluate a distilled agent’s cognition, but if its evaluations aren’t reliable, it can’t be trusted to ensure that the distilled agent is corrigible (and not a daemon). And a corrigible agent that “goes insane” doesn’t stay corrigible.

(See: Corrigibility.)

1.3.3: I don’t share those intuitions around corrigibility. Do you have any intuition pumps?

One intuition pump: corrigibility can be thought of as extreme caution about whether you’re actually being helpful, and extreme caution is robust—if you’re extremely cautious about how things can go wrong, you want to know more ways things can go wrong and you want to improve your ability to spot how things are going wrong, which will lead you to become more cautious.

Another intuition pump: I have some intuitive concept of “epistemically corrigible humans”. Some things that gesture at this concept:

• They care deeply about finding the truth, and improving their skill at finding the truth.
• They’re aware that they’re flawed reasoners, with biases and blind spots, and actively seek out ways to notice and remove these flaws. They try to take ideas seriously, no matter how weird they seem.
• Their beliefs tend to become more true over time.
• Their skill at having true beliefs improves over time.
• They tend to reach similar conclusions in the limit (namely, the correct ones), even if they’re extremely weird and not broadly accepted.

I think of corrigible assistants as being corrigible in the above way, except optimizing for helping its operator instead of finding the truth. Importantly, so long as an agent crosses some threshold of corrigibility, they will want to become more and more cautious about whether they’re helpful, which is where robustness comes from.

Given that corrigibility seems like a property that any reasoner could have (and not just humans), it’s probably not too complicated a concept for a powerful AI system to learn, especially given that many humans seem able to learn some version of it.

1.3.4: This corrigibility thing still seems really fishy. It feels like you just gave some clever arguments about something very fuzzy and handwavy, and I don’t feel comfortable trusting that.

While Paul thinks there’s a good intuitive case for something like corrigibility, he also considers getting a deeper conceptual understanding of corrigibility one of the most important research directions for his agenda. He agrees it’s possible that corrigibility may not be safely learnable, or not actually robust, in which case he'd feel way more pessimistic about his entire agenda.