Recorded by Robert Miles: http://robertskmiles.com More information about the newsletter here: https://rohinshah.com/alignment-newsletter/ YouTube Channel: https://www.youtube.com/channel/UCfGGFXwKpr-TJ5HfxEFaFCg   HIGHLIGHTS Collaborating with Humans without Human Data (DJ Strouse et al) (summarized by Rohin): We’ve previously seen that if you want to collaborate with humans in the video game Overcooked, it helps to train a deep RL agent against a human model (AN #70), so that the agent “expects” to be playing against humans (rather than e.g. copies of itself, as in self-play). We might call this a “human-aware” model. However, since a human-aware model must be trained against a model that imitates human gameplay, we need to collect human gameplay data for training. Could we instead train an agent that is robust enough to play with lots of different agents, including humans as a special case? This paper shows that this can be done with Fictitious Co-Play (FCP), in which we train our final agent against a population of self-play agents and their past checkpoints taken throughout training. Such agents get significantly higher rewards when collaborating with humans in Overcooked (relative to the human-aware approach in the previously linked paper). In their ablations, the authors find that it is particularly important to include past checkpoints in the population against which you train. They also test whether it helps to have the self-play agents have a variety or architectures, and find that it mostly does not make a difference (as long as you are using past checkpoints as well). Read more: Related paper: Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination Rohin's opinion: You could imagine two different philosophies on how to build AI systems -- the first option is to train them on the actual task of interest (for Overcooked, training agents to play against humans or human models), while the second option is to train a more robust agent on some more general task, that hopefully includes the actual task within it (the approach in this paper). Besides Overcooked, another example would be supervised learning on some natural language task (the first philosophy), as compared to pretraining on the Internet GPT-style and then prompting the model to solve your task of interest (the second philosophy). In some sense the quest for a single unified AGI system is itself a bet on the second philosophy -- first you build your AGI that can do all tasks, and then you point it at the specific task you want to do now. Historically, I think AI has focused primarily on the first philosophy, but recent years have shown the power of the second philosophy. However, I don’t think the question is settled yet: one issue with the second philosophy is that it is often difficult to fully “aim” your system at the true task of interest, and as a result it doesn’t perform as well as it “could have”. In Overcooked, the FCP agents will not learn specific quirks of human gameplay that could be exploited to improve efficiency (which the human-aware agent could do, at least in theory). In natural language, even if you prompt GPT-3 appropriately, there’s still some chance it ends up rambling about something else entirely, or neglects to mention some information that it “knows” but that a human on the Internet would not have said. (See also this post (AN #141).) I should note that you can also have a hybrid approach, where you start by training a large model with the second philosophy, and then you finetune it on your task of interest as in the first philosophy, gaining the benefits of both. I’m generally interested in which approach will build more useful agents, as this seems quite relevant to forecasting the future of AI (which in turn affects lots of things including AI alignment plans).   TECHNICAL AI ALIGNMENT