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Arvind Narayanan

lines on the record: 58
first: 2024-08-28
most recent: 2024-08-28
sittings or episodes: 1
sources: podcast

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“This has been really, really fun. I apologize for rambling occasionally, but I hope that it's, yeah, I'm really looking forward to hearing it when it's out there.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“It's weird for me to be saying this, but I have to say think of the children. I'm never asked this because, and what I mean by that is that AI, the role of AI in kids' lives, kids who are born today, for instance, is going to be so profound. And it's something that technologists should be thinking about, every parent should be thinking about, policymakers should be thinking about because it can be profoundly good or profoundly bad or anything in between. And both as a technologist and as a parent, I think about that a lot”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“I have to say, I really like Jan Lacun's perspectives on various things, including his view that LLMs are a quote-unquote off-wrap to superintelligence, that, you know, in other words, we need a lot more scientific breakthroughs, as well as tamping down the fears of super advanced AI.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“A lot of technologists kind of have a disdain for policy. They see policymakers as, well, morons, to put it bluntly. But I don't think that's the case. I think there are a lot of legitimate reasons why policy is very slow and doesn't often go in the way that a tech expert might want it to. And that's the 90% frustration. And the reason I say it's only 90% is that the other 10% is really worth it. We really need policy and despite how frustrating it is. We need a lot of tech experts in policy.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“I do find it interesting that Nvidia itself has been trying to migrate really, really hard out of hardware into becoming a services company.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“So, my hope is that the kind of thing we saw in the movie HER, not the sci fi aspects of it, but the more kind of mundane aspects of it where you give your device a command and it interprets it in a pretty nuanced way and does what you wanted to do, right? Book flight tickets, for instance, or really build an app based on what you want it to look like. So these are things that are potentially automatable, don't have like massively dubious societal consequences. Those are things that I hope can happen.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Would resign. I don't think I would be a good CEO. But if there were one thing I could change about OpenAI, I think the need for the public to know what is going on with AI development overrides the commercial interests of any company. So I think there needs to be a lot more transparency.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Because the gap between benchmarks and the real world is big and it's only growing bigger. As AI becomes more useful, it's harder to figure out how useful it is based on these artificial environments.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“I think our intuitions are too powerfully shaped by sci fi portrayals of AI, and I think that's really a big problem, you know, this idea that AI can become self-aware. When we look at the way that AI is architected today, that kind of fear has no basis in reality. Maybe one day in the future, people are going to build AI systems where that becomes at least somewhat possible. And we should have visibility, transparency monitoring regulation around these systems to make sure that developers don't. But that would be a choice. That's a choice that society can make, that governments and companies can make. It's not that despite our best efforts, AI is going to become conscious and have agency and do things that are harmful to humanity. That whole line of fear, I think, is complete.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Making models bigger and bigger doesn't seem to be working anymore. I think new developments have to come from different scientific ideas. Maybe it's agents. Maybe it's something else.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Like a lot of people, I was fooled by how quickly after GPT 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 GPT-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.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“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, and 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 closed the 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 close, the odds of getting”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“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 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 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,”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“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. 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”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Useful for a diagnosis or for more mundane things like summarizing medical notes and so forth. So I think that work is really important. I think that should continue. It still does leave us with the harder question of, you know, here in America, if it takes me three weeks to get a GP appointment, it's very”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Sure. So I don't think you're wrong. I think the reason there is a lot of talk about this is it goes back to something we've observed over and over, which is that when there are problems with an institution like the medical system, right? Like the wait times are too long or it's too costly or in a lot of countries, you know, people don't even have access. In developing countries, there might be entire villages with no physician. Then this kind of technological band-aid becomes very appealing. So I think that's what's going on here. I think the responsible way to use in medicine is for it to be integrated into the medical system. And actually, the medical system has been a very enthusiastic adopter of technology, including AI. So you can consider CAT scans, for instance, to be a form of AI to be able to reconstruct what's going on inside a person based on certain imaging. And now with generative AI as well, there's a lot of interest from the medical system in figuring out, you know, can this be used?”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Account for the fact that students are doing this and there is no way really to catch AI generated text or homework answers. And so these are costs upon society. I'm not saying that the availability of AI makes education worse. I don't think that's necessarily the case. But it forces a lot of costs upon the education system and ideally AI companies should be bearing some of that cost.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“So, when we were talking about deepfakes, I'm much less worried about misinformation deepfakes and more worried about deepfake nudes that I was talking about, right? So those are things that can destroy a person's life. It's been shocking to me how little attention this got from the press and from policymakers until it happened to Taylor Swift a few months ago. And then it got a lot of attention. So there were deep fake nudes of Taylor Swift posted on Twitter slash X. And after that, you know, policymakers started paying attention. But it has been happening for many years now, even before the latest wave of generative AI tools. So that's the type of misuse that is very clear. And then there are other kinds of misuses that are not necessarily dangerous in the same way, but impose a lot of costs on society. So when students are using AI to do their homework, for instance, now high school teachers and college teachers everywhere have to revamp how they're teaching in order to”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“And so I think there should be more responsibility placed on social media companies. And my worry is that treating this as an AI problem is distracting from all of those more important interventions.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“For sure. Yeah. But I want to push on is this really an AI problem? These are deep problems in our society. So creating an image that looks like there were a lot more people there than there were. Yeah, it's become easier to do that with AI today. But you could have paid someone $100 to do that with Photoshop, you know, even before AI. It's a problem we've had. It's a problem we have been dealing with, often not very successfully. My worry is that if we treat this as a technology problem and try to intervene on the technology, we're going to miss what the real issues are and the hard things that we need to be doing to tackle those issues, which are, you know, which relate to issues of trust in society and to the extent it's a technology problem, it's more of a social media problem really than an AI problem because the hard part of misinformation is not generating it, it's distributing it to people and persuading them and social media is often the medium for that.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“So misinformation is a problem in a way. I think misinformation is more of a symptom than a cause. You know, misinformation slots into and affirms people's existing beliefs as opposed to changing their beliefs. And I think the impact on AI here, again, has been tremendously exaggerated. Sure, you know, you can create a Trump deep fake like you were talking about. But when you look at the misinformation that's actually out there, it's things that are as crude as video game footage. Because again, it's telling people what they want to believe in a situation where they're not very skeptical.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“That actually is our prediction. People we predict are going to be forced to rely much more on getting their news from trusted sources.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think that's fair. But I think the reason that that might fool a lot of people is because it came from a legitimate media company. So I think the ability to do this emphasizes some of the things that have always been important but have now become more important, like source credibility.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“I agree. So we call this the liar's dividend. People have been worried, for instance, about bots creating misinformation with AI and influence in elections and that sort of thing. We're very, very skeptical that that's going to be a real danger.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“From Wharton I broadly agree with that. I will add a couple of additions to that. One is there are many kinds of harms, which we already know about and are quite serious. So the use of AI to make non-consensual deepfakes, for instance, deepfake nudes, and this has affected thousands, perhaps hundreds of thousands of people, primarily women around the world. And governments are taking action now finally. So that's a good thing.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“AI regulation is better understood as regulating certain harmful activities, whether or not AI is used as a tool for doing those harmful activities. You know, 80% of what gets called AI regulation is better seen this way.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“So, in a sense, AI regulation is a misnomer. Let me give you an example from just this morning. The FTC has been worried about the Federal Trade Commission in the U.S., which is an antitrust and consumer protection authority, has been worried about people writing fake reviews for their products. And this has, of course, been a problem for many years. It's become a lot easier to do that with AI. So now, someone who thinks about this in terms of AI regulation might say, oh, you know, regulators have to ensure that AI companies don't allow their products to be used for generating fake reviews. And I think this is a losing proposition. Like, how would an AI model know whether something is a fake review or a real review, right? It just depends on who's writing the review. But instead, that's not the approach that the FTC took. They recognized correctly that it's a problem whether AI is generating the fake review or people are. So what they actually banned is fake reviews, right? And so what is often thought of as”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“I think that's a very serious possibility. And I think this is actually one area where regulators should be paying attention. What does this mean for market concentration, antitrust, and so forth? And I've been gratified that these are topics that, at least in my experience, U.S. regulators are considering. And I believe in the UK, the CMA, that competition and markets authority as well, and certainly in the EU. So, yeah, in many jurisdictions now that I think about it, this is something that regulators have been worried about.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I don't know, is the short answer, but at the same time, you know, we've been in this kind of historically interesting period where a lot of progress has come from building bigger and bigger models. That need not continue in the future. It might. Or what might happen is that the models themselves get commoditized. And a lot of the interesting development happens in a layer above the models. We're starting to see a lot of that happen now with AI agents. And if that's the case, great ideas could come from anywhere, right? It could come from a two-person startup. It could come from an academic lab. And my hope is that we will transition to that kind of mode of progress in AI development relatively soon.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“If they had to pick one, I think they should pick building products, but it certainly doesn't make sense for a company to be just an AGI company and not try to build products, not try to build something that people want, and just assuming that AI is going to be so general that it's just going to do everything that people want and that the company doesn't actually need to make products.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“In the past, you know, they didn't have this balance. They were so enabled by this prospect of creating AGI that they didn't think there was a need to build products at all. And the craziest example for me is when OpenAI put out ChatGPT, there was no mobile app for six months. And the Android app took even longer than that. There was this assumption that ChatGPT was just going to be this kind of really demo to show off the capabilities of the models. In the business of building these models and third-party developers would take the API and put it into products, but really AGI was coming so quickly, even the notion of productization seemed obsolete. I'm not trying to put words in anyone's mouth, but this was kind of a coherent, but in my view, incorrect philosophy that I think a lot of AI developers had. And I think that has changed quite a bit now. And I think that's a good thing.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“That's fair, and I think it would take a discipline from a management to be able to pull it off in a way that one part of the company doesn't distract another too much. And we've seen this happen with OpenAI, which is the folks focused on superintelligence didn't feel very welcome at the company, and there has been an exodus of very prominent people, and Anthropic has picked up a lot of them. So it seems like we're seeing a split emerging where OpenAI is more focused on products and Anthropic is more focused on superintelligence. While I can see the practical reasons why that is happening, I don't think it's impossible to have disciplines management that focuses on both objectives.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“So let's talk for a second about what AGI is. Different people mean different things by it, and so often talk past each other. The definition that we consider most relevant is AI that is capable of automating most economically valuable tasks. By this definition, you know, if automating most economically valuable tasks, if we did have AGI, that would truly be a profound thing in our society. So now for the CEO predictions, I think one thing that's helpful to keep in mind is that there have been these predictions of imminent AGI since the earliest days of AI for more than a half century. Alan Turing, when the first computers were built or about to be built, people thought, you know, the two main things we need for AI are hardware and software. We've done the hard part, the hardware, and now there's just one thing left, the easy part, the software. But of course, now we know how hard that is. So I think historically what we've seen, it's kind of like climbing a mountain.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“What we would use them for in the real world. So that's one reason why LLM evaluation is a minefield. And there's also just a very simple factor of contamination. Maybe the model has already trained on the answers that it's being evaluated on in the benchmark. And so if you ask a new question, it's going to struggle. We shouldn't put too much stock into benchmarks. We should look at people who are actually trying to use these in professional context, whether it's lawyers or really anybody else. And we should go based on their experience of using these AI assistance.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Big part of it is this issue of vibes, right? So you evaluate LLMs on these benchmarks, but then it seems to perform really well on the benchmarks, but then the vibes are off. In other words, you start using it, and somehow it doesn't feel adequate. It makes a lot of mistakes in ways that are not captured in the benchmark. And the reason for that is simply that when there's so much pressure to do well on these benchmarks, developers are intentionally or unintentionally optimizing these models in ways that look good on the benchmarks but don't look good in real world evaluation. So when GPT-4 came out and open AI claimed that it passed the bar exam and the medical licensing exam, people were very excited slash scared about what this means for doctors and lawyers. And the answer turned out to be approximately nothing, right? Because it's not like a lawyer's job is to answer bar exam questions all day. These benchmarks that models are being tested on don't really catch”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Sure, I think we are still in a period where these models have not yet quite become commoditized. There's obviously a lot of progress and there's a lot of demand on hardware as well. Hardware cycles are also improving rapidly. But, you know, there is the saying that every exponential is a sigmoid in disguise. So sigmoid curve is one that looks like an exponential at the beginning. So imagine the S letter shape. But then after a while, it has to taper off like every exponential has to taper off. So I think that's going to happen both with models as well as with these hardware cycles. We are, I think, going to get to a world where models do get commoditized.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Your inference cost decreases, but because it's the inference cost that dominates, the total cost is probably going to come down.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“So there's trading compute, which is when the developer is building the model, and then there is inference compute when the model is being deployed and the user is using it to do something. And it might seem like really the training cost is the one we should worry about since it's trained on all of the text on the internet or whatever. But it turns out that over the lifetime of a model, when you have billions of people using it, the inference cost actually adds up. And for many of the popular models, that's the cost that dominates. Let's talk about each of those two costs. With respect to training costs, if you want to build a smaller model at the same level of capability or without compromising capability too much, you have to actually train it for longer. So that increases training costs. But that's maybe okay because you have a smaller model you can push it to the consumer device or even if it's running on the cloud your server costs are lower so your training cost increases”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Moore's law, I think cost is going to be significant in the medium term. And then you get to applications like writing code, where what we're seeing is that it's actually very beneficial to let the model do the same task tens of times, thousands of times, sometimes literally millions of times, and pick the best answer. So in those cases, it doesn't matter how much cost goes down. You're going to just proportionally increase the number of retries so that you can get a better quality of output.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“So there's this interesting concept called Javin's paradox, and this was first in the context of coal in England in the 18th century, I think, when coal mining got cheaper. There was more demand for coal. And so the amount invested into coal mining actually increased. And I predict that we're going to see the same thing with models when models get cheaper. They're put into a lot more things. And so the total amount that companies are spending on inference is actually going to increase in an application like a chatbot, let's say. You know, it's text in, text out, no big deal. I think costs are going to come down, even if someone is chatting with a chatbot all day, it's probably not going to get too expensive. On the other hand, if you want to scan all of someone's emails, for instance, right? If a model gets cheaper, you're just going to have it running always on in the background and then from emails, you're going to get to all their documents. And some of those attachments might be many megabytes long. And so there, even when...”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“You're right. Cost is going down dramatically. In certain applications, cost is going to become much less of a barrier, but not across the board.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, thank you for asking that. That's not obvious at all. My view is that in a lot of cases, the adoption of these models is not bottlenecked by capability. If these models were actually deployed today to do all the tasks that they're capable of, it would truly be as striking economic transformation. The bottlenecks are things other than capability. And one of the big ones is cost. And cost, of course, is roughly proportional to the size of the model. And that's putting a lot of downward pressure on model size. And once you get a model small enough that you can run it on device, that, of course, opens up a lot of new possibilities, both in terms of privacy. People are much more comfortable with on-device models, especially if it's something that's going to be listening to their phone conversations or looking at their desktop screenshots, which are exactly the kinds of AI assistance that companies are”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“It's got to be more than passive observation. You have to actually deploy AI to be able to get to certain types of learning. And I think that's going to be very slow. And I think a good analogy is self-driving cars, of which we had prototypes two or three decades ago. But for these things to actually be deployed, you have to roll it out on slightly larger and larger scales while you collect data, while you make sure you get to the next nine of reliability, four nines of reliability to five nines of reliability. So it's that very slow rollout process. It's a very slow feedback loop. And I think that's going to happen with a lot of AI deployment and organizations as well.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think that's really spot on. I think one way in which people's intuitions have been kind of misguided by the rapid improvements in LLMs is that all of this has been, you know, in the paradigm of learning from data on the web that's already there. And once that runs out, you have to switch to new kinds of learning analog of riding a bike. That's just kind of tacit knowledge. It's not something that's been written down. So a lot of what happens in organizations is the cognitive equivalent of, I think what happens in the physical scale of writing a bike. And I think for models to learn, a lot of these diverse kinds of tasks that they're not going to pick up from the web, you have to have the cycle of actually using the AI system in your organization and for it to learn from that back and forth experience instead of just passively ingesting.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Is that the quality of data matters a lot more than the quantity of data? So if you're using synthetic data to try to augment the quantity, I think it's just coming at the expense of quality. You're not learning new things from the data. You're only learning things that are already there.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, let's talk about synthetic data. So there's two ways to look at this, right? So, one is the way in which synthetic data is being used today, which is not to increase the volume of training data, but it's actually to overcome limitations in the quality of the training data that we do have. So for instance, if in a particular language there's too little data, you can try to augment that, or you can try to have a model solve a bunch of mathematical equations, throw that into the training data. And so for the next training run, that's going to be part of the pre-training. And so the model will get better at doing that. And the other way to look at synthetic data is, okay, you take one trillion tokens, you train a model on it, and then you output 10 trillion tokens, so you get to the next bigger model, and then you use that to output 100 trillion tokens. I'll bet that that's just not going to happen. That's just the snake eating its own tail. And what we've learned in the last two years.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source
“Was trained primarily on English text, and they had actually tried to filter out text in other languages to keep it clean, but a tiny amount of text from other languages had gotten into it, and it turned out that that was enough for the model to pick up a reasonable level of competence for conversing in various other languages. So these are the kinds of emergent capabilities that really spooked people, that has led to both a lot of hype and a lot of fears about what bigger and bigger models are going to be able to do. But I think that has pretty much run out because we're training on all of the capabilities that humans have expressed, like translating between languages and have already put out there in the form of text. So if you make the data set a little bit more diverse with YouTube video, I don't think that's fundamentally going to change. Multimodal capabilities, yes, there's a lot of room there. But new emergent text capabilities, I'm not sure.”
2024-08-28 · The Twenty Minute VC · 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 · IDENTIFIED FROM THE TRANSCRIPT · source