How can humanity best reduce suffering?
Emerging technologies such as artificial intelligence could radically change the trajectory of our civilization. We are building a global community of researchers and professionals working to ensure that this technological transformation does not risk causing suffering on an unprecedented scale.
We do research, award grants and scholarships, and host workshops. Our work focuses on advancing the safety and governance of artificial intelligence as well as understanding other long-term risks.
6 March 2021
13 February 2021
Conflict is often an inefficient outcome to a bargaining problem. This is true in the sense that, for a given game-theoretic model of a strategic interaction, there is often some equilibrium in which all agents are better off than the conflict outcome. But real-world agents may not make decisions according to game-theoretic models, and when they do, they may use different models. This makes it more difficult to guarantee that real-world agents will avoid bargaining failure than is suggested by the observation that conflict is often inefficient. In another post, I described the "prior selection problem", on which different agents having different models of their situation can lead to bargaining failure. Moreover, techniques for addressing bargaining problems like coordination on […]
18 January 2021
One way that agents might become involved in catastrophic conflict is if they have mistaken beliefs about one another. Maybe I think you are bluffing when you threaten to launch the nukes, but you are dead serious. So we should understand why agents might sometimes have such mistaken beliefs. In this post I'll discuss one obstacle to the formation of accurate beliefs about other agents, which has to do with identifiability. As with my post on equilibrium and prior selection problems, this is a theme that keeps cropping up in my thinking about AI cooperation and conflict, so I thought it might be helpful to have it written up. We say that a model is unidentifiable if there are several […]
[Epistemic status: Strong opinions lightly held, this time with a cool graph.] I argue that an entire class of common arguments against short timelines is bogus, and provide weak evidence that anchoring to the human-brain-human-lifetime milestone is reasonable. In a sentence, my argument is that the complexity and mysteriousness and efficiency of the human brain (compared to artificial neural nets) is almost zero evidence that building TAI will be difficult, because evolution typically makes things complex and mysterious and efficient, even when there are simple, easily understood, inefficient designs that work almost as well (or even better!) for human purposes. In slogan form: If all we had to do to get TAI was make a simple neural net 10x the […]