Katja Grace: The first future and the best future [opportunity costs]

The post:

It seems to me worth trying to slow down AI development to steer successfully around the shoals of extinction and out to utopia.

But I was thinking lately: even if I didn’t think there was any chance of extinction risk, it might still be worth prioritizing a lot of care over moving at maximal speed. Because there are many different possible AI futures, and I think there’s a good chance that the initial direction affects the long term path, and different long term paths go to different places. The systems we build now will shape the next systems, and so forth. If the first human-level-ish AI is brain emulations, I expect a quite different sequence of events to if it is GPT-ish.

People genuinely pushing for AI speed over care (rather than just feeling impotent) apparently think there is negligible risk of bad outcomes, but also they are asking to take the first future to which there is a path. Yet possible futures are a large space, and arguably we are in a rare plateau where we could climb very different hills, and get to much better futures.

My comment: YES. 

At the moment the A.I. world is dominated by an almost magical believe in large language models. Yes, they are marvelous, a very powerful technology. By all means, let's understand and develop them. But they aren't the way, the truth and the light. They're just a very powerful and important technology. Heavy investment in them has an opportunity cost, less money to invest in other architectures and ideas. 

And I'm not just talking about software, chips, and infrastructure. I'm talking about education and training. It's not good to have a whole cohort of researchers and practitioners who know little or nothing beyond the current orthodoxy about machine learning and LLMs. That kind of mistake is very difficult to correct in the future. Why? Because correcting it means education and training. Who's going to do it if no one knows anything else? 

Moreover, in order to exploit LLMs effectively we need to understand how they work. Mechanistic interpretability is one approach. But: We're not doing enough of it. And by itself it won't do the job. People need to know more about language, linguistics, and cognition in order to understand what those models are doing.