Our take on CHAI’s research agenda in under 1500 words

by Alex Flint4 min read17th Jun 202013 comments

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Research AgendasCenter for Human-Compatible AI (CHAI)AI
Curated

This work was supported by OAK, a monastic community in the Berkeley hills. It could not have been written without the daily love of living in this beautiful community.


Last week I attended the annual workshop of Stuart Russell’s research lab at UC Berkeley — the Center for Human-Compatible AI (CHAI). There were talks by Russell himself, as well as several graduates of the lab who now have research positions of their own at other universities. I got the clearest picture that I’ve yet encountered of CHAI’s overall technical research agenda. This is my take on it.

Assistance games

Traditionally, AI researchers have formulated problems assuming that there will be a fixed objective provided by a human, and that the job of the AI system is to find a solution that satisfies the human’s objective. In the language of sequence diagrams this looks as follows:

The "standard model" of AI research

For example, in a search problem the objective specification might be a graph over which the system is to search, a cost for each edge, and a goal state that terminates the search. The AI researcher then needs to to develop optimization algorithms that efficiently find a minimum-cost path to a goal state. Or in a supervised learning problem the objective specification might consist of a dataset of labelled examples and the AI researcher needs to develop optimization algorithms that efficiently find function approximations that extrapolate these labelled examples to future unlabelled examples.

CHAI’s basic insight is to ask: why limit ourselves to a one-time objective specification event? We know that it is difficult to capture everything we care about in a formal metric (c.f. Goodhart’s law). We know that humans aren’t very good at foreseeing the strange and sometimes deranged ways that powerful optimization can give you what you asked for but not what you wanted. Why should information about the human’s objective be transmitted to the machine via a one-time data dump, after which it remains fixed for all time?

There are many alternative interaction patterns by which information about the human’s objective could be transmitted to the machine. The human could observe the machine and provide it with feedback as it works. The machine could ask the human questions about its objective. The machine could observe the human and deduce its objective from its behavior. And so on.

Examples of interaction patterns in assistance games

CHAI calls this an assistance game: the human wants something from the machine, and it is the machine’s job to both (1) figure out what that is, and (2) fulfil it. The role of the AI researcher under this new model then is to explore the space of possible interaction patterns and find one that is conducive to the machine building an informed picture of what the human wants as quickly as possible.

The old model in which a complete objective is specified up front is actually just one special case of an assistance game: one in which the interaction pattern is that the machine receives all the information it will ever receive about the human’s objective in a one-time up-front data dump. The unique thing about the old model -- and the reason it is both attractive and dangerous -- is that the machine never needs to entertain any uncertainty about what the human wants. It is given an objective up front and its job is just to fulfil it.

Using more nuanced interaction patterns require the machine to maintain uncertainty about what the human’s objective is, which in turn requires optimization algorithms formulated so as to take into account this uncertainty. This suggests an exciting reformulation of each of the basic AI problem statements, and CHAI seems to be enthusiastically taking up this challenge, including with Russell’s new edition of the standard AI textbook AI: A Modern Approach.

One of CHAI’s early successes was the development of cooperative inverse reinforcement learning (CIRL). But CIRL is often mistaken as representing the entirety of CHAI’s research agenda, whereas in fact CIRL is one particular approach to solving one particular kind of assistance game. Specifically it addresses an assistance game in which the machine observes demonstrations by a human, who is in turn incentivized to provide demonstrations that are of value to the machine. The original CIRL paper makes various further modelling assumptions and approximations in order to arrive at a concrete algorithm. CIRL is an important contribution to the field but it is important to understand that the program of reformulating AI as an assistance game is broader than this one specific proposal.

I found myself wondering whether reinforcement learning might also count as a new-style assistance game. In reinforcement learning the machine begins by exploring, and the human provides a positive or negative reward each time it does something consistent with or opposed to the human’s objective. In early reinforcement learning literature it was envisaged that there would be a literal human providing live feedback as learning progressed, but in modern reinforcement learning the reward signal is generally automated using a program that the human provides before learning commences. In this way modern RL looks more like the old model of Figure 1 since the reward algorithm is generally specified up-front and not modified during training. Also, reinforcement learning agents do not really maintain uncertainty about what the human’s objective is: they just act to maximize their reward, and for that reason will take control of the reward signal if given the opportunity.

Going beyond agents

This new model of CHAI’s relaxes one of the core assumptions of classical AI research -- that a fully-specified objective will be given at the beginning of time -- but there is still a strong assumption that both the human and the machine are well-modelled as having objectives.

Suppose we wish to build a machine that provides assistance to a rainforest. Can we view a rainforest -- the complete ecosystem containing all the various plants and animals that live there -- as having objectives? The plants and animals living in a rainforest spend a great deal of energy competing against one another, so it is difficult to view the rainforest as behaving according to any unified set of objectives. Yet it is possible to take actions that do great damage to a rainforest (clearing a large area of all trees, or introducing a synthetic pathogen) and conversely it is possible to take actions that protect the rainforest (preventing the clearing of trees or preventing the introduction of synthetic pathogen). Should our AI systems be able to observe a rainforest and deduce what it would mean to be of assistance to it?

Humans, too, are not perfectly modelled as agents. The agent model provides a compact and useful description of human behavior at a certain resolution, but as we look deeper into our own nature we find that we are not such unitary agents at all. We are in fact made of the same basic building blocks as the world around us and our view of ourselves as agents is only an approximate description. Should our AI systems model humans as agents, or can we do better? What is the next most detailed model up from the agent model? Can we build AI systems that play assistance games on the basis of this model?

Finally, machines are not ideal agents either. Any AI algorithm deployed on real hardware in the real world is made of the same basic building blocks as the world itself, and is only approximately modelled as an agent with a set of objectives. As we dial up the power of the AI systems we build and those AI systems are able to build increasingly detailed models of the world around them, this agent approximation is likely to break down. This is because an AI system that has a sufficiently detailed model of its environment will eventually discover and begin to reason about its own computing infrastructure, at which point it will need some way to deal with the paradoxes of counterfactual reasoning and logical uncertainty that arise when one can accurately predict one’s own future behavior. If we have constructed our AI systems on the basis that they are well-modelled as agents, with no line of retreat from this assumption, then when our AI systems build detailed models of the world that conflict with this assumption, they are likely to misbehave.

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