Humans are net positive; that’s where all those words and grammar and ideas came from. As a practical example, Google says it sees about 15% new queries every y

Humans are net positive; that’s where all those words and grammar and ideas came from. As a practical example, Google says it sees about 15% new queries every year (queries that have never before been entered into Google). Is that a good proxy for how net positive people are in general? I don’t know, and the exact number may not be stable or even really matter. Human intelligence is kind of a red herring in this discussion, because our intelligence rides on something deeper, something we share with all animal life. Call it “will,” or “drive,” or even “soul,” the name doesn’t really matter. What matters is that we make decisions with that deeper thing, and then we apply our intelligence to execute and interpret those decisions. What’s your favorite food? Who are you attracted to? What sounds like a fun thing to do today? Why do you sometimes feel like not going to work? We can think about these answers, and how to get what we want, but we can’t really logically reason our way into changing what we want. Not all of them, anyway. That’s what is missing from ML systems, and why they are clearly tools. If you train up an ML system and then leave it alone, it just sits there until you prompt it. If you raise a human, they do NOT just sit there until you ask them to do something. And there’s a good chance they might not do what you want even if you ask, because they have “wants” as well. To go back to my mirror analogy, people don’t suddenly step out of mirrors and disagree with us. And ML systems don’t suddenly want to write a novel instead of respond to whatever question we input. They’re not alive, they’re just really good at mirroring back written language when we give them written language as an input. Edit to clarify: I’m not claiming that “will” has to be metaphysical or supernatural. Even if you believe humans are purely physical systems (as I do), we can characterize their behavior, compare it to ML systems, and see what’s missing. And more to the point, we know it is missing from ML systems since we designed those systems and we did not put it in there. We should not ignore our existing knowledge of how an ML system works when thinking about its output. Just like we shouldn’t ignore our knowledge of how mirrors work when looking at a perfect reflection of a person.