Arvind Narayanan

speaker

255 appearances 3 recordings 3 series first heard Aug 2024 last heard 25 Jan

Arvind Narayanan’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 1 in all, peaking in Jan 2026 with 1.

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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch · 20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton · 28 Aug 2024 podcast

I think that should continue. It still does leave us with the harder question of, you know, here in America, you know, if it takes me three weeks to get a GP appointment, it's very tempting to ask chat GPT a question about my symptoms. So what do we do about that? You know, is that can that actually be helpful with appropriate guardrails? Or should that be discouraged?

I don't know the answer to that.

I think there's different populations of students. There's a small subset of learners who are very self-motivated and will learn very well, even if there's no physical tutor. There are those kinds of learners at all different levels. And then there's the vast majority of learners for whom the social aspect of learning is really the most critical thing.

And if you take that away, they're just not going to be able to learn very well. And I think this is often forgotten, especially because in the AI developer community, there are a lot of these self-taught learners. I'm among them, right? I just paid zero attention throughout school and college and everything that I know literally is stuff that I taught myself. So I grew up in India.

The education system wasn't very great there. Our geography teacher thought that India was in the southern hemisphere. True story. Right. So again, I literally mean it when I say everything that I know I taught myself. And so, you know, you have a lot of AI developers who are thinking of themselves as the typical learner, and they're not.

And I think for someone like me, AI is on a daily basis, an incredible tool for learning. I use generative AI tools for learning. It's a new way of learning compared to a book or really anything else. You know, I can't summarize my understanding of my topic to a book and ask it if I'm right. These are things I can do with AI.

But I'm very skeptical that these new kinds of learning are going to get to a point anytime soon where they're going to become the default way in which people learn.

I think for now, they are very much overblown. My favorite example of the thing you said of technology creating jobs is bank tellers. When ATMs became a thing, it would have been reasonable to assume that bank tellers were just going to go away. But in fact, the number of tellers increased. And the reason for that is that it became much cheaper for banks to open regional branches.

And once they did open those regional branches, they did need humans for some of the things that you couldn't do with an ATM. And, you know, the more abstract way of saying that is, as economists would put it, jobs are bundles of tasks, and AI automates tasks, not jobs.

So if there are, you know, 20 different tasks that comprise a job, the odds that AI is going to be able to automate all 20 of them are pretty low. And so there are some occupations certainly that have already been affected a lot by AI like translation or stock photography. But for most jobs out there, I don't think we're anywhere close to that.

So I think it's a good question to ask. I think it's a bit of a category error there. I mean, a nuclear weapon is an actual weapon. AI is not a weapon. AI is something that, you know, might enable adversaries to do certain things more effectively. For example, find vulnerabilities, cybersecurity vulnerabilities in critical infrastructure, right?

So that's one way in which AI could be used on the quote unquote battlefield. So that being the case, I think it would be a big mistake to view it analogously to a weapon and to argue that it should be closed up for a couple of reasons. First of all, it's not going to work at all. So I think we have close to state of the art AI models that can already run on people's personal devices.

And I think that trend is only going to accelerate. We talked earlier about Moore's law, and it still continues to apply to these models. And even if one country decides that models should be closed, the odds of getting every country to enact that kind of rule are just vanishingly small.

So if our approach to safety with AI is going to be premised on ensuring that, quote unquote, bad guys don't get access to it, we've already lost. because it's only a matter of time before it becomes impossible to do that.

And instead, I think we should radically embrace the opposite, which is to figure out how we're going to use AI for safety in a world where AI is very widely available, because it is going to be widely available. And when we look at how we've done that in the past, it's actually a very reassuring story.

When we go back to the cybersecurity example, for 10 or 20 years, the software development community has been using automated tools, some of which you could call AI, to improve cybersecurity because software developers can use them to find bugs and fix bugs in software before they put them out there, before hackers even have a chance to take a crack at them.

So my hope is that the same thing is going to happen with AI. We're going to be able to acknowledge the fact that it's going to be widely available and to shape its use for defense more than offense.

Like a lot of people, I was fooled by how quickly after GPD 3.5, GPD 4 came out. It was just three months or so, but it had been in training for 18 months. That was only revealed later. So it gave a lot of people, including me, an inflated idea of how quickly AI was progressing.

And what we've seen in the nearly year and a half since GPD 4 came out is that we haven't really had models that have surpassed it in a meaningful way. And this is not based on benchmarks. Again, I think benchmarks are not that useful. It's more based on vibes. When you get people using these things, what do they say? I don't think models have really qualitatively improved on GPT-4.

And I don't think things are moving as quickly as I did 12 months ago.

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