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 'agency'? Today, I talk with Scott Garrabrant about his recent work on finite factored sets that...

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 conversations with Dylan that helped shape how I view AI x-risk research, so it was great to have...

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, what the most important parts are to get right, how the estimates should shape our behaviour, and...

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 absolutely necessary for achieving their objective. In this episode, Victoria Krakovna talks about her research on quantifying and minimizing side...

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 augment debate. Those who are already quite familiar with the basic proposal might want to skip...

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 depending on what we'll do then? Today's episode features Vanessa Kosoy talking about...

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. Link to the paper - Risks from Learned Optimization in Advanced Machine Learning Systems Link...

Link to the paper - Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making Link to the transcript Critch's Google Scholar profile

Link to the paper - On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference Link to the transcript The Alignment Newsletter Rohin's contributions to the AI alignment forum Rohin's website

Link to the paper - Adversarial Policies: Attacking Deep Reinforcement Learning Link to the transcript Adam's website Adam's twitter account