Isn't this akin to a protocol for securely monitoring private industry's experiments in thermonuclear weapons? It's better than nothing, but when something is dangerous enough, industrial regulation is never strict enough.
Some things are too dangerous to allow private competition in. The only sensible thing to do is nationalize them and have them run exclusively by extremely security-minded government agencies, if at all. And even that might not be good enough for AI, because we've never had tech whose base case scenario was "kill everyone".
Is this a plausible take?
It’s possible to make quantitative forecasting (forecasting by software) significantly more easy and fun by adding some missing pieces to the forecasting ecosystem. Let’s do it.
The models I’m interested in are “gears” and not “behavior” models: They should be motivated by theory, be built to inform our theoretical understanding, and have relatively few numeric parameters. The point is to do science, not just fit parameters to data.
In this post I focus on forecasting things that are regularly measured and published by institutions. Such data includes economic and population statistics, opinion and value surveys, statistics about violence, public health, etc. I propose that we can build tools that would make it easier to test... (read 1261 more words →)
Thanks for the pointer. I'll hopefully read the linked article in a couple of days.
I start from a point of "no AI for anyone" and then ask "what can we safely allow". I made a couple of suggestions, where "safely" is understood to mean "safe when treated with great care". You are correct that this definition of "safe" is incompatible with unfettered AI development. But what approach to powerful AI isn't incompatible with unfettered AI development? Every AI capability we build can be combined with other capabilities, making the whole more powerful and therefore more dangerous.
To keep things safe while still having AI, the answer may be: "an international agency holds most... (read more)