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 monthsRecordings per month over the last 12 months — 1 in all, peaking in Jan 2026 with 1.
Appearances
Criminal justice, for instance, right? So I don't think we should be making decisions about people based on these crude statistical formulas with some caveats, like I was saying earlier. If it's the judge who is empowered to make that decision, that's a different story. There is a lot of the same coil in hiring.
There are companies that claim that by analyzing a 30-second video of a candidate, of a job candidate, not even talking about their skills for the job, but about their hobbies or whatever, that they can do video analysis and look at the candidate's facial expressions and body language and that sort of thing and use that to derive a personality score, which companies should do their hiring based on.
There's so much more. There is AI for detecting which students in a school or college might be at risk of suicide or mental health difficulties. There have been investigations of all these kinds of AI tools and they barely work better than the flip of a coin. So I think these are the kinds of things we should be very suspicious of.
And unfortunately, these are the kinds of things that are often used in order to make very high stakes decisions about people.
For ChatGPT, yes. But here's the difference between ChatGPT and trying to predict if someone will commit a crime. You know, ChatGPT is just trying to do things like, you know, a typical thing you might use ChatGPT for is to translate text from one language to another, right? That's not like a fundamentally impossible task. It's something that humans can do.
And AI over time is learning to do it better, right? Or write code or whatever it is. On the other hand... Predicting what's going to happen in the future, no one knows. The universe doesn't know. It doesn't matter how much data you can throw at it. What we're seeing is that these technologies are not really getting better. They haven't got better in decades.
And it should be common sense that we can't really predict the future, or at least not with anything close to perfect accuracy. And yet a lot of companies are telling us to suspend our common sense because AI, right? And that's what we're trying to push back on.
we're not going to have too many more cycles, possibly zero more cycles of a model that's almost an order of magnitude bigger in terms of the number of parameters than what came before and thereby more powerful. And I think a reason for that is data becoming a bottleneck. These models are already trained on essentially all of the data that companies can get their hands on.
So while data is becoming a bottleneck, I think more compute still helps, but maybe not as much as it used to.
I'm super excited for this conversation.
Sure. So, I'm a professor of computer science, and I would say I do three things. One is technical AI research, and another is understanding the societal effects of AI, and the third is advising policymakers.
So I spent years of my time on this. I really believed that decentralization could have tremendous societal impacts. How is this going to make society better? It was not the money angle. But by around 2018, I had started to get really disillusioned. And that was because of a couple of main things.
One is, in a lot of cases where I had thought crypto or blockchain was going to be the solution, I realized that that was not the case. While there is potential for crypto to help the world's unbanked, the tech is not the real bottleneck there. And the other part of it was just a philosophical aspect of this community.
I do believe that many of our institutions are in need of reform or maybe decentralization, whatever it is. And that includes academia, by the way, so many reforms so badly needed. And in an ideal world, we would have this, you know, hard but important conversation about how do you fix our institutions. But instead, these students have been sold on blockchain and they want to replace their
these institutions with a script. And that just didn't seem like the right approach to me. So both from a technical perspective, and from a philosophical perspective, I really soured on it. While there are harms around AI, I think it has been a net positive for society. I can't say the same thing about Bitcoin. Are we in an AI hype cycle right now? I think that's possible.
Generative AI companies specifically made some serious mistakes in the last year or two about how they went about things. What mistakes did they make, Harvind? So when ChatGPT was released, people found, you know, a thousand new applications for it, right? That OpenAI application. might not have anticipated. And that was great.
But I think developers, AI developers, took the wrong lesson from this. They thought that AI is so powerful and so special that you can just put these models out there and people will figure out what to do with them. They didn't think about actually building products, making things that people want, finding product market fit, and all those things that are so basic in tech.
But somehow, AI companies deluded themselves into thinking that the normal rules don't apply here.
So if we look at what's happened historically, the way in which compute has improved model performance is with companies building bigger models. In my view, at least the biggest thing that changed between GPT-3.5 and GPT-4 was the size of the model. And it was also trained with more data, presumably, although they haven't made the details of that public and more compute and so forth.
So I think that's running out. We're not going to have too many more cycles, possibly zero more cycles of a model that's almost an order of magnitude bigger in terms of the number of parameters than what came before and thereby more powerful. And I think a reason for that is data becoming a bottleneck.
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