I think I have two disagreements with your assessment.
First, the probability of a random independent AI researcher or hobbyist discovering a neat hack to make AI training cheaper and taking over. GPT4 took 100M$ to train and is not enough to go FOOM. To train the same thing within the budget of the median hobbyist would require algorithmic advantages of three or four orders of magnitude.
Historically, significant progress has been made by hobbyists and early pioneers, but mostly in areas which were not under intense scrutiny by established academia. Often, the main achievement of a pioneer is discovering a new field, them picking all the low-hanging fruits is more of a bonus. If you had paid a thousand mathematicians to think about signal transmission on a telegraph wire or semaphore tower, they probably would have discovered Shannon entropy. Shannon’s genius was to some degree looking into things nobody else was looking into which later blew up into a big field.
It is common knowledge that machine learning is a booming field. Experts from every field of mathematics have probably thought if there is a way to apply their insights to ML. While there are certainly still discoveries to be made, all the low-hanging fruits have been picked. If a hobbyist manages to build the first ASI, that would likely be because they discover a completely new paradigm—perhaps beyond NNs. The risk that a hobbyist discovers a concept which lets them use their gaming GPU to train an AGI does not seem that much higher than in 2018 -- either would be completely out of the left field.
My second disagreement is the probability of an ASI being roughly aligned with human values, or to be more precise, the difference of that probability conditional on who discovers it. The median independent AI enthusiast is not a total asshole [citation needed], so if alignment is easy and they discover ASI, chances are that they will be satisfied with becoming the eternal god emperor of our light cone and not bother to tell their ASI to turn any any huge number of humans to fine red mist. This outcome will not be so different than if Facebook develops an aligned ASI first. If alignment is hard—which we have some reason to believe it is—then the hobbyist who builds ASI by accident will doom the world, but I am also rather cynical about the odds of big tech having much better odds.
Going full steam ahead is useful if (a) the odds of a hobbyist building ASI if big tech stops capability research are significant and (b) alignment is very likely for big tech and unlikely for the hobbyist. I do not think either one is true.
I think I have two disagreements with your assessment.
First, the probability of a random independent AI researcher or hobbyist discovering a neat hack to make AI training cheaper and taking over. GPT4 took 100M$ to train and is not enough to go FOOM. To train the same thing within the budget of the median hobbyist would require algorithmic advantages of three or four orders of magnitude.
Historically, significant progress has been made by hobbyists and early pioneers, but mostly in areas which were not under intense scrutiny by established academia. Often, the main achievement of a pioneer is discovering a new field, them picking all the low-hanging fruits is more of a bonus. If you had paid a thousand mathematicians to think about signal transmission on a telegraph wire or semaphore tower, they probably would have discovered Shannon entropy. Shannon’s genius was to some degree looking into things nobody else was looking into which later blew up into a big field.
It is common knowledge that machine learning is a booming field. Experts from every field of mathematics have probably thought if there is a way to apply their insights to ML. While there are certainly still discoveries to be made, all the low-hanging fruits have been picked. If a hobbyist manages to build the first ASI, that would likely be because they discover a completely new paradigm—perhaps beyond NNs. The risk that a hobbyist discovers a concept which lets them use their gaming GPU to train an AGI does not seem that much higher than in 2018 -- either would be completely out of the left field.
My second disagreement is the probability of an ASI being roughly aligned with human values, or to be more precise, the difference of that probability conditional on who discovers it. The median independent AI enthusiast is not a total asshole [citation needed], so if alignment is easy and they discover ASI, chances are that they will be satisfied with becoming the eternal god emperor of our light cone and not bother to tell their ASI to turn any any huge number of humans to fine red mist. This outcome will not be so different than if Facebook develops an aligned ASI first. If alignment is hard—which we have some reason to believe it is—then the hobbyist who builds ASI by accident will doom the world, but I am also rather cynical about the odds of big tech having much better odds.
Going full steam ahead is useful if (a) the odds of a hobbyist building ASI if big tech stops capability research are significant and (b) alignment is very likely for big tech and unlikely for the hobbyist. I do not think either one is true.