AI researcher Ken Stanley explains his 'stepping stone' principle and why objective metrics fail to identify innovative breakthroughs.
Insight
Stanley: Dismissing experts' subjective sense of 'interesting' cripples innovation
“It's actually the subjective judgments are the interesting ones because, like, the objective judgments are easy. You don't need a degree to just measure something. You know, some kid takes some test, you get a score. You can average them across everybody at th…”
Opinion
Stanley: Direct pursuit of human-level AI is a naive objective
“Like, at some level, there's, like, this really grandiose conception of, like, some human-like computer, and, like, that is, I think, a naive objective right now. Like, you just don't want to know how to do that. We don't know what the stepping stones are that…”
Insight
Stanley: AI research proves objective-driven achievement is deeply flawed
“And in the course of doing that research, we just started to see undeniable evidence that that approach to achievement has some serious flaws.”
Insight
Stanley: Major breakthroughs require positioning for serendipity rather than setting objectives
“And actually like a lot of what we do that facilitates making these kinds of important discoveries is to actually set us up, set ourselves up for having effective serendipity, which is not the way that we, you know, talk about things when you talk about settin…”
Insight
Stanley: Complex problems like education cannot be solved by direct metric optimization
“We cannot make progress in certain kinds of extremely complex problems, which education is one of those. Simply by just laying out some assessment system and then trying to follow it towards this global objective, which is just incredibly complex to get to.”
Insight
Stanley: Evolution solved all problems in one run, unlike typical ML
“Evolution, it's a very unique thing in the sense that it's kind of like a search or like a learning algorithm that discovered everything that was ever created in nature in a single run. This is very different from like what you see in typical machine learning,…”