There are proverbs about how trying to generalize your code will never get to AGI. These proverbs are true, and they're still true when generalizing a driverless car. I might worry to some degree about free-form machine learning algorithms at hedge funds, but not about generalizing driverless cars.
I know people have talked about this in the past, but now seems like an important time for some practical brainstorming here. Hypothetical: the recent $15mm Series A funding of Vicarious by Good Ventures and Founders Fund sets off a wave of $450mm in funded AGI projects of approximately the same scope, over the next ten years. Let's estimate a third of that goes to paying for man-years of actual, low-level, basic AGI capabilities research. That's about 1500 man-years. Anything which can show something resembling progress can easily secure another few hundred man-years to continue making progress.
Now, if this scenario comes to pass, it seems like one of the worst-case scenarios -- if AGI is possible today, that's a lot of highly incentivized, funded research to make it happen, without strong safety incentives. It seems to depend on VCs realizing the high potential impact of an AGI project, and of the companies having access to good researchers.
The Hacker News thread suggests that some people (VCs included) probably already realize the high potential impact, without much consideration for safety:
Is there any way to reverse this trend in public perception? Is there any way to reduce the number of capable researchers? Are there any other angles of attack for this problem?
I'll admit to being very scared.