Tyler Cowen: prediction

Transcript context

…[chuckles] There’s a great breakdown on adoption of new technology that the science-fiction author Douglas Adams wrote about years ago. He says any new technology is received differently by three different groups of people. If you’re below the age of 15, it’s just the way things have always been. If you’re between the ages of 15 and 35, it’s really cool, and you might be able to get a job doing it. If you’re above the age of 35, it’s unholy and against the order of society and will destroy everything. AI, I think, so far is living up to that framework. What I would like to tell you is AI is going to be completely transformative for education. I believe that it will. Having said that, I did recently roll out ChatGPT to my eight-year-old. I was very, very proud of myself because I was like, “Wow, this is just going to be such a great educational resource for him.” I felt like Prometheus bringing fire down from the mountain to my child. I installed it on his laptop and said, “Son, this is the thing that you can talk to anytime, and it will answer any question you have.” He said, “Yes.” I said, “Well, no, this is a big deal. It answers questions.” He’s like, “Well, what else would you use a computer for?” I was like, “Oh, God, I’m getting old.” [laughter] I actually think there’s a pretty good prospect that kids are just going to pick this up and run with it. I actually think that’s already happening, right? ChatGPT is fully out, and Bard and Bing and all these other things. I think kids are going to grow up with basically — you could use various terms, assistant, friend, coach, mentor, tutor, but kids are going to grow up in this amazing back-and-forth relationship with AI. Anytime a kid is interested in something, if there’s not a teacher who can help with something or if they don’t have a friend who’s interested in the same thing, they’ll be able to explore all kinds of ideas. I think it’ll be great for that. I think it’s obviously going to be totally transformative and feels like warfare, and you already see that. The concern, quite honestly — I actually wrote an essay a while ago on why AI won’t destroy all the jobs. The short version of it is because it’s illegal to do that because so many jobs in the modern economy require licensing and are regulated. I think the concern would be that there’s just so much glue in the system now that prevents change. It’ll be very easy to not have AI healthcare or AI education or whatever because, literally, some combination of doctor licensing, teacher unions, and so forth will basically outlaw it. I think that’s the risk. If we think of AI and its impact in sociological terms, large language models, who will gain in status and who will decline in status? How should this affect how we think about policy? First of all, it’s important to qualify exactly what’s going on with large language models, which is super interesting. This thing has happened that you read about a lot in the press, which is, there was this general idea that there would be something called AI at some point, and then large language models appeared. And everybody said, “A-ha, that’s AI just like we thought it would be,” and then everybody extrapolates out. That’s true to a certain extent, but the success of large language models is very unexpected in the field. Actually, the origin story of even ChatGPT is, this is not what OpenAI actually started to do. They started to do something different. There’s actually one guy. Actually, his name is, I think, Alec Radford. He literally was off in the corner at OpenAI working on this in 2018, 2019. Then it basically was this revolution building on work that had been done at Google. It was this very surprising thing. Then it’s important to qualify how it works because it’s not just like some sort of robot brain. What it is, is you basically feed essentially, ideally all known human-generated information into a machine, and then you let it basically build a giant matrix of numbers and basically correlate everything. In a nutshell, that’s what these things are. Then basically what happens is, when you ask it a question or if you ask it to make a drawing or something, it basically traverses. It essentially does a search. It does a search across basically all of these words and sentences and diagrams and books and photos and everything that human beings have created. It tries to find the optimal path through that. That’s how it generates the answer that it gives you. Philosophically, it’s this really profound thing, I think, which is it’s basically staring. It’s like you as an individual using this machine to stare at the entirety of the creation of all human knowledge and then have it played back at you. It harnesses the creativity of thousands of years of human authors and artists and then derives new kinds of answers or new kinds of images or whatever. Fundamentally, you’re in interaction with our civilization in a very profound way. In terms of who gains and who loses status, there’s actually a very interesting thing happening in the research right now. There’s a very interesting research question for the impact on job scale, for example, for people who work with words or work with images and are starting to use these technologies in the workforce. The question is, who benefits more? The high-skilled worker — and think lawyer, doctor, accountant, whatever, graphic designer — the high-skilled person who uses these tools to become an additional quantum leap high-skilled. That would be a theory of separation. The other scenario is the average or even low-skilled worker who gets upgraded. Of course, just the nature of the economy there. There are more people in the middle.…

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