Robert Wright: belief

Transcript context

…The least valuable co-author, it should be said. Well, it was a very valuable paper. So yeah. So I did... I, you know, I'm basically a journalist. I have taught at the college level, but I am largely a journalist. I spent my life, to some extent, making technical concepts accessible to laypeople. Anyway, in that role, I did interview Geoffrey Hinton in 1983. And, uh, later on my podcast, I had Eliezer on. That was 2010. Uh, at that point, you know, a- as for the science fiction you mentioned, I think Eliezer by his own account starts out in that realm. I think as a kid he read science fiction and so on. Um, when I interviewed him, he was still kind of in transition from singularity enthusiast to doomer. He was more doomer than not, but he, he didn't yet sound as distraught as he sounds now. Hinton, I did not sense when I talked to him any kind of sci-fi vibe at all. I sensed a lot of optimism, but not really about the role AI would play in the future, more about just this paradigm that he was championing, which was then, you know, kind of a maverick paradigm, it wasn't mainstream, that it would prevail, that it would prove to be, uh, the one true path to artificial intelligence, which I think has turned out to be the case. In, in fact, the reason I interviewed him, and this is pre-internet, so to, to, to get your feet wet in a subject when you were researching it, you had to, you had to talk to people pretty much. I mean, you, you couldn't go online and learn anything about contemporary things. You couldn't email people. So I, I talked to a lot of people, and one of his colleagues, I forget who, possibly at Carnegie Mellon, said, "Oh, if you wanna hear the gospel about neural networks, you need to talk to Geoff Hinton." And that was very much the sense in talking to him. He was an evangelist at that point. I, I don't mean he sounded crazy, but he was a... He, he, he was really, uh, you know, a spokesperson for the worldview, and he was central to it. Uh, you know, he, he has played a very big role. I... I, I don't mean he sounded crazy, but he was a... He, he, he was really, uh, you know, a spokesperson for the worldview, and he was central to it. Uh, you know, he, he has played a very big role. I... You know, he's called the godfather of AI. Uh, he'd be the first to say that's a little too simple, but I don't know of anybody who's played a bigger role in the deep learning revolution in, in, in the overall, like, sweep of time, going back to, to the early '80s when it was this maverick view. Um, as everyone knows, he has since become something of a doomer himself, but at the time I don't even know that he was thinking along that dimension, that he was even thinking about the social implications of this. He was assured that this would be a fruitful model, and it, it was. Now, at that point, I certainly didn't get the picture, and in fact, when, you know, when, when ChatGPT 3.5 came out... I mean, I had kind of kept track of what was going on. Every once in a while I would write about AI. I wrote a lot about information technology, and I wrote Time magazine's cover story when Deep Blue beat Garry Kasparov, the world chess champion, and I wrote the, uh, New Republic's cover story on the internet. This was before, uh, web browsers were a thing even. It was, it was, it was early days. So I was intermittently in touch with relevant stuff, but I certainly had not been keeping track of AI prior to ChatGPT 3.5, and that really got my attention. And w- when, when 4.0 came out and got it in an, in an even bigger way, got my attention, um, I went back and read, I went back and read the piece I'd written that had, which had come out in 1984, and, uh, I realized how thoroughly I had failed to understand what really the secret sauce in deep learning was going to be. A- and I should say, like about the book, you know, I do hope that people, uh, who are steeped in AI, like you, will find ideas and provocations that are of interest to them, maybe even entire chapters. But the book is, to a large extent, written more for like, like your aunts and uncles and friends who are not in the AI community and are starting to, to get the sense that something big has been happening And they're wondering why it's gotten so big so fast, how much bigger it's gonna be, and, and who's right about how hard it's gonna be to control and all that. I try to, you know, present that in an accessible fashion, and that's why I, I start the book with Hinton, because this thing I had kind of backwards, I think is really the key to, to understanding the power of the, the deep learning revolution. And by the way, the, the misconception I had, I, I think was pretty common among AI people in that day, okay? And I wanna, I wanna read you a little bit from the, uh, the proposal for the 1956 Dartmouth conference, where the term artificial intelligence was coined. The, uh, I gotta find it, I guess. But basically, the idea seemed to be at that point that the, the way this would proceed is we would first figure out how the human mind works, and then, then we would, you know, instantiate that understanding in artificial intelligence, okay? And, uh, let's see. The, the quote from the proposal is...…

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