Since the CAIS technical report is a gargantuan 210 page document, I figured I’d write a post to summarize it. I have focused on the earlier chapters, because I found those to be more important for understanding the core model. Later chapters speculate about more concrete details of how AI might develop, as well as the implications of the CAIS model on strategy.
The core idea is to look at the pathway by which we will develop general intelligence, rather than assuming that at some point we will get a superintelligent AGI agent. To predict how AI will progress in the future, we can look at how AI progresses currently—through research and development (R&D) processes. AI researchers consider a problem, define a search space, formulate an objective, and use an optimization technique in order to obtain an AI system, called a service, that performs the task.
A service is an AI system that delivers bounded results for some task using bounded resources in bounded time. Superintelligent language translation would count as a service, even though it requires a very detailed understanding of the world, including engineering, history, science, etc. Episodic RL agents also count as services.
While each of the AI R&D subtasks is currently performed by a human, as AI progresses we should expect that we will automate these tasks as well. At that point, we will have automated R&D, leading to recursive technological improvement. This is not recursive self-improvement, because the improvement comes from R&D services creating improvements in basic AI building blocks, and those improvements feed back into the R&D services. All of this should happen before we get any powerful AGI agents that can do arbitrary general reasoning.
Since services are focused on particular tasks, you might think that they aren’t general intelligence, since there would be some tasks for which there is no service. However, pretty much everything we do can be thought of as a task—including the task of creating a new service. When we have a new task that we would like automated, our service-creating-service can create a new service for that task, perhaps by training a new AI system, or by taking a bunch of existing services and putting them together, etc. In this way, the collection of services can perform any task, and so as an aggregate is generally intelligent. As a result, we can call this Comprehensive AI Services, or CAIS. The “Comprehensive” in CAIS is the analog of the “General” in AGI. So, we’ll have the capabilities of an AGI agent, before we can actually make a monolithic AGI agent.
Isn’t this just as dangerous as AGI?
You might argue that each individual service must be dangerous, since it is superintelligent at its particular task. However, since the service is optimizing for some bounded task, it is not going to run a long-term planning process, and so it will not have any of the standard convergent instrumental subgoals (unless the subgoals are helpful for the task before reaching the bound).
In addition, all of the optimization pressure on the service is pushing it towards a particular narrow task. This sort of strong optimization tends to focus behavior. Any long term planning processes that consider weird plans for achieving goals (similar to “break out of the box”) will typically not find any such plan and will be eliminated in favor of cognition that will actually help achieve the task. Think of how a racecar is optimized for speed, while a bus is optimized for carrying passengers, rather than having a “generally capable vehicle”.
It’s also worth noting what we mean by superintelligent here. In this case, we mean that the service is extremely competent at its assigned task. It need not be learning at all. We see this distinction with RL agents—when they are trained using something like PPO, they are learning, but at test time you can simply execute them without any PPO and they will perform the behavior they previously learned and won’t change that behavior at all.
(My opinion: I think this isn’t engaging with the worry with RL agents—typically, we’re worried about the setting where the RL agent is learning or planning at test time, which can happen in learn-to-learn and online learning settings, or even with vanilla RL if the learned policy has access to external memory and can implement a planning process separately from the training procedure.)
On a different note, you might argue that if we analyze the system of services as a whole, then it certainly looks generally intelligent, and so should be regarded as an AGI agent. However, “AGI agent” usually carries the anthropomorphic connotation of VNM rationality / expected utility maximization / goal-directedness. While it seems possible and even likely that each individual service can be well-modeled as VNM rational (albeit with a bounded utility function), it is not the case that a system of VNM rational agents will itself look VNM rational—in fact, game theory is all about how systems of rational agents have weird behavior.
In addition, there are several aspects of CAIS that make it more safe than a classic monolithic AGI agent. Under CAIS, each service interacts with other services via clearly defined channels of communication, so that the system is interpretable and transparent, even though each service may be opaque. We can reason about what information is present in the inputs to infer what the service could possibly know. We could also provide access to some capability through an external resource during training, so that the service doesn’t develop that capability itself.
This interpretability allows us to monitor the service—for example, we could look at which subservices it accesses in order to make sure it isn’t doing anything crazy. But what if having a human in the loop leads to unacceptable delays? Well, this would only happen for deployed applications, where having a human in the loop seems expected, and should also be economically incentivized because it leads to better behavior. Basic AI R&D can continue to be improved autonomously without a human in the loop, so you could still see an intelligence explosion. Note that tactical tasks requiring quick reaction times probably would be delegated to AI services, but the important strategic decisions could still be left in human hands (assisted by AI services, of course).
What happens when we create AGI?
Well, it might not be valuable to create an AGI. We want to perform many different tasks, and it makes sense for these to be done by diverse services. It would not be competitive to include all capabilities in a single monolithic agent. This is analogous to how specialization of labor is a good idea for us humans.
(My opinion: It seems like the lesson of deep learning is that if you can do something end-to-end, that will work better than a structured approach. This has happened with computer vision, natural language processing, and seems to be in the process of happening with robotics. So I don’t buy this—while it seems true that we will get CAIS before AGI since structured approaches tend to be available sooner and to work with less compute, I expect that a monolithic AGI agent would outperform CAIS at most tasks once we can make one.)
That said, if we ever do build AGI, we can leverage the services from our CAIS-world in order to make it safe. We could use superintelligent security services to constrain any AGI agent that we build. For example, we could have services trained to identify long-term planning processes and to perform adversarial testing and red teaming.
Safety in the CAIS world
While CAIS suggests that we will not have AGI agents, this does not mean that we automatically get safety. We will still have AI systems that take high impact actions, and if they take even one wrong action of this sort it could be catastrophic. One way this could happen is if the system of services starts to show agentic behavior—our standard AI safety work could apply to this scenario.
In order to ensure safety, we should have AI safety researchers figure out and codify the best development practices that need to be followed. For example, we could try to always use predictive models of human (dis)approval as a sanity check on any plan that is being enacted. We could also train AI services that can adversarially check new services to make sure they are safe.
The CAIS model suggests that before we get to a world with monolithic AGI agents, we will already have seen an intelligence explosion due to automated R&D. This reframes the problems of AI safety and has implications for what technical safety researchers should be doing.