Dwarkesh Patel
8 HN mentions · mentioned in 4 months, 2023–2025
47:41 · 790K views · Mar 27, 2023
Dwarkesh Patel interviews OpenAI chief scientist Ilya Sutskever on time to AGI, what comes after generative models, data limits, alignment, and competing with Google. The title centers on whether next-token prediction could surpass human intelligence.
HN commenters cite it mainly for Sutskever's statements that training data is far from exhausted.
8 comments from Hacker News
Thanks for that good summary. > I see how Ilya was right There are still some things Ilya[0] (and Hinton[1]). The parts I'm quoting here are an example of "that reddit comment" that sounds right but is very wrong, and something we know is wrong (and have known it is wrong for hundreds of years!). Yet, it is also something we keep having to learn. It's both obvious and not obvious, but you can make models that are good at predicting things without understanding them. Let me break this down for some clarity. I'm using "model" in a broad and general sense. Not just ML models, any mathematical model, or even any mental model. By "being good at predicting things" I mean that it can make accurate predictions. The crux of it all is defining the "understanding" part. To do that, I need to explain a little bit about what a physicist actually does, and more precisely, metaphysics. People think they crunch numbers, but no, they are symbol manipulators. In physics you care about things like a Hamiltonian or Lagrangian, you care about the form of an equation. The reason for this is it creates a counterfactual model. F=ma (or F=dp/dt) is counterfactual. You can ask "what if m was 10kg instead of 5kg" after the fact and get the answer. But this isn't the only way to model things. If you look at the history of science (and this is the "obvious" part) you'll notice that they had working models but they were incorrect. We now know that the Ptolemaic model (geocentrism) is incorrect, but it did
> What does it mean for computation to have a direction? Actually, all computation has a directionality! This is actually a subject that I get really excited about ^__^ Think about this, we have a function f, with input x, and output y. f(x) -> y We'd even use that notation! This is our direction. Now, the reverse actually gets a bit tricky. If the reverse is straightforward, our function has an inverse. But, it might not always. So our function f(x)=mx+b is invertible, because we can write x = (f(x)-b)/m (well... m can't be 0), which provides a unique solution. Every x corresponds to a unique f(x). But if instead, we have the function f(x) = x^2, this is not true! x = sqrt(f(x)), and here every f(x) corresponds to both x and -x! They are not unique. We can start adopting the language of images and preimages if you want to start heading down that route. There's a lot of interesting phenomena here and if you hadn't already guessed it, yes, this is related to the P=NP problem! An easy way to see this visually is to write down the computational circuit. Polylog actually has a really good video and will make the connection to P vs NP[0] In the context of machine learning, a Normalizing Flow would be invertible, while a diffusion model is reversible. A pet peeve of mine is that in ML people (I'm a ML researcher) call it the "inverse problem", such as GAN-Inversion, but that is a misnomer and we shouldn't propagate it... This also has to do with the naivety of these statements...[1
I do say the same think about JKR, btw. And for the same things, because the content she writes. I think you focused on the fanfic part and not the part where I'm criticizing where they say their stuff is the most important to keep humanity alive and they're charging money for it. Meanwhile, you may notice in academia we publish papers to make them freely available, like on arXiv. If it is that important that people need to know, you make it available. The second person, Hinton, is not as good of an authority as you'd expect. Though I do understand why people take him seriously. Fwiw, his Nobel was wildly controversial. Be careful, prizes often have political components. I have degrees in both CS and physics (and am an ML researcher) and both communities thought it was really weird. I'll let you guess which community found it insulting. I want to remind you, in 2016 Hinton famously said[0] | Let me start by saying a few things that seem obvious. I think if you work as a radiologist, you're like the coyote that's already over the edge of the cliff, but hasn't yet looked down so doesn't realize there's no ground beneath him. People should stop training radiologists now. It's just completely obvious that within 5 years that deep learning is going to be better than radiologists because it's going to get a lot more experience. It might take 10 years, but we've got plenty of radiologists already. We're 10 years in now. Hinton has shown he's about as good at making predictions as Mu
> The fact that we have figured out how to translate language into something a computer can "understand" should thrill linguists. I think they are really excited by this. There seems no deficiency of linguists using these machines. But I think it is important to distinguish the ability to understand language and translate it. Enough that you yourself put quotes around "understanding". This can often be a challenge for many translators, not knowing how to properly translate something because of underlying context. Our communication runs far deeper than the words we speak or write on a page. This is much of what linguistics is about, this depth. (Or at least that's what they've told me, since I'm not a linguist) This seems to be the distinction Chomsky is trying to make. > The main debate now is over the semantics of words like "understanding" and whether or not an LLM is conscious in the same way as a human being (it isn't). Exactly. Here, I'm on the side of Chomsky and I don't think there's much of a debate to be had. We have a long history of being able to make accurate predictions while erroneously understanding the underlying causal nature. My background is physics, and I moved into CS (degrees in both), working on ML. I see my peers at the top like Hinton[0] and Sutskever[1] making absurd claims. I call them absurd, because it is a mistake we've made over and over in the field of physics[2,3]. One of those lessons you learn again and again, because it is so easy to make t
I think many people are missing the core of what Chomsky is saying. It is often easy to miscommunicate and I think this is primarily what is happening. I think the analogy he gives here really helps emphasize what he's trying to say. If you're only going to read one part, I think it is this: | I mentioned insect navigation, which is an astonishing achievement. Insect scientists have made much progress in studying how it is achieved, though the neurophysiology, a very difficult matter, remains elusive, along with evolution of the systems. The same is true of the amazing feats of birds and sea turtles that travel thousands of miles and unerringly return to the place of origin. | Suppose Tom Jones, a proponent of engineering AI, comes along and says: “Your work has all been refuted. The problem is solved. Commercial airline pilots achieve the same or even better results all the time.” | If even bothering to respond, we’d laugh. | Take the case of the seafaring exploits of Polynesians, still alive among Indigenous tribes, using stars, wind, currents to land their canoes at a designated spot hundreds of miles away. This too has been the topic of much research to find out how they do it. Tom Jones has the answer: “Stop wasting your time; naval vessels do it all the time.” | Same response. It is easy to look at metrics of performance and call things solved. But there's much more depth to these problems than our abilities to solve some task. It's not about just the ability to do some