Note: This is a quick write-up of some thoughts I've had recently about communicating AI risk.

There are two claims I wish to argue for concerning understanding and communicating AI risk. Neither of them is entirely original, but I thought it is worth articulating them clearly now that AI progress is starting to gain more public attention. The first is that communicating the difficulty of the different sub-areas of AI safety tends to involve many abstractions that might be confusing as well as creating future scenarios that at the moment might sound far-fetched. In this post, I'll assume that abstractions are conducive to reaching scientific insights. The second claim I'll put forth is a prediction that in the coming years, everything will be "normal" until something bad happens. I'll explain what "normal" means and I'll try to make specific bets and challenge myself to be as epistemically virtuous as possible.

Talking in terms of abstractions

I see technological forecasting as a rigorous version of philosophical thought experimenting. Writing a concrete AI risk story, for example, entails giving a lot of details about a possible world in the future, near-term, or long-term, where AI capabilities impact society at large in different ways. Stories of this sort require abstractions. Just like the rest of the research in AI safety. And if we want to be completely honest, this is not particularly different from the rest of science - we simply happen to care a lot more about understanding the various failure modes of advanced AIs and find ourselves scarily confused.

Notice that by "abstraction" I mean both the process of deriving a general concept from specific instances and the property of an idea to be abstract, i.e., not concrete or instantiated. I think that these are easy to conflate because they're not exactly dissimilar but they don't technically overlap either. The problem with abstractions is that they create a kind of map vs. territory gap. The better our abstractions become, the more likely it is that we will get things right about the world. In the meantime, we find ourselves in a dark forest of unprocessed conceptual tools that we try to manipulate so that we can move forward. We are, however, still in the dark.

While here, we use these abstractions to describe our situation. We try to construct explanatory frameworks and make pred...

Thinking about the future of ai