AXRP - the AI X-risk Research Podcast

Many scary stories about AI involve an AI system deceiving and subjugating humans in order to gain the ability to achieve its goals without us stopping it. This episode's guest, Alex Turner, will tell us about his research analyzing the notions of "attainable utility" and "power" that underlie these stories, so that we can better evaluate how likely they are and how to prevent them.   Topics we di...

When going about trying to ensure that AI does not cause an existential catastrophe, it's likely important to understand how AI will develop in the future, and why exactly it might or might not cause such a catastrophe. In this episode, I interview Katja Grace, researcher at AI Impacts, who's done work surveying AI researchers about when they expect superhuman AI to be reached, collecting data abo...

Being an agent can get loopy quickly. For instance, imagine that we're playing chess and I'm trying to decide what move to make. Your next move influences the outcome of the game, and my guess of that influences my move, which influences your next move, which influences the outcome of the game. How can we model these dependencies in a general way, without baking in primitive notions of 'belief' or...

How should we think about the technical problem of building smarter-than-human AI that does what we want? When and how should AI systems defer to us? Should they have their own goals, and how should those goals be managed? In this episode, Dylan Hadfield-Menell talks about his work on assistance games that formalizes these questions. The first couple years of my PhD program included many long conv...

If you want to shape the development and forecast the consequences of powerful AI technology, it's important to know when it might appear. In this episode, I talk to Ajeya Cotra about her draft report "Forecasting Transformative AI from Biological Anchors" which aims to build a probabilistic model to answer this question. We talk about a variety of topics, including the structure of the model, wha...

One way of thinking about how AI might pose an existential threat is by taking drastic actions to maximize its achievement of some objective function, such as taking control of the power supply or the world's computers. This might suggest a mitigation strategy of minimizing the degree to which AI systems have large effects on the world that are not absolutely necessary for achieving their objectiv...

One proposal to train AIs that can be useful is to have ML models debate each other about the answer to a human-provided question, where the human judges which side has won. In this episode, I talk with Beth Barnes about her thoughts on the pros and cons of this strategy, what she learned from seeing how humans behaved in debate protocols, and how a technique called imitative generalization can au...

The theory of sequential decision-making has a problem: how can we deal with situations where we have some hypotheses about the environment we're acting in, but its exact form might be outside the range of possibilities we can possibly consider? Relatedly, how do we deal with situations where the environment can simulate what we'll do in the future, and put us in better or worse situations now dep...

In machine learning, typically optimization is done to produce a model that performs well according to some metric. Today's episode features Evan Hubinger talking about what happens when the learned model itself is doing optimization in order to perform well, how the goals of the learned model could differ from the goals we used to select the learned model, and what would happen if they did differ...

In this episode, I talk with Andrew Critch about negotiable reinforcement learning: what happens when two people (or organizations, or what have you) who have different beliefs and preferences jointly build some agent that will take actions in the real world. In the paper we discuss, it's proven that the only way to make such an agent Pareto optimal - that is, have it not be the case that there's...

One approach to creating useful AI systems is to watch humans doing a task, infer what they're trying to do, and then try to do that well. The simplest way to infer what the humans are trying to do is to assume there's one goal that they share, and that they're optimally achieving the goal. This has the problem that humans aren't actually optimal at achieving the goals they pursue. We could instea...

In this episode, Adam Gleave and I talk about adversarial policies. Basically, in current reinforcement learning, people train agents that act in some kind of environment, sometimes an environment that contains other agents. For instance, you might train agents that play sumo with each other, with the objective of making them generally good at sumo. Adam's research looks at the case where all you'...