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2 HN recommenders · recommended in 11 months, 2024–2025
6:36 · 257K views · Sep 6, 2016
Freeman Dyson recounts how Enrico Fermi rejected his team's meson scattering model, noting that it had too many free parameters. It is the source of the quote about fitting an elephant.
Cited in modeling and machine learning threads as the classic story on overfitting and justifying parameters.
16 comments from Hacker News
> > In 1953, Enrico Fermi criticized Dyson’s model by quoting Johnny von Neumann: “With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.” For those who are interested, you can watch Freeman Dyson recount this conversation in his own words in an interview: this video
Freeman Dyson recounts the episode [1] that inspired this paper in his web of life interviews (prepositioned to the fitting an elephant bit) [2] [1] this video [2] this video
> that does not have a technical meaning I don't think the definition is very refined, but I think we should be careful to differentiate that from useless or meaningless. I would say most definitions are accurate, but not precise. It's a hard problem, but we are making progress on it. We will probably get there, but it's going to end up being very nuanced and already it is important to recognize that the word means different things in vernacular and in even differing research domains. Words are overloaded and I think we need to recognize this divergence and that we are gravely miscommunicating by assuming the definitions are obvious. I'm not sure why we don't do more to work together on this. In our field we seem to think we got it all covered and don't need others. I don't get that. > In this view, if a machine performs a task as well as a human, it understands it exactly as much as a human. And I do not think this is accurate at all. I would not say my calculator understands math despite it being able to do it better than me. I can say the same thing about a lot of different things which we don't attribute intelligence to. I'm sorry, but the logic doesn't hold. Okay, you might take an out by saying the calculator can't do abstract math like I can, right? Well we're going to run into that same problem. You can't test your way out of it. We've known this in hard sciences like physics for centuries. It's why physicists do much more than just experiments. There's the classic st
> only a small subset will contain actual understanding, a property that is independent of the model's input-output behavior? I think this is close enough. I'd say "a model's ability to make accurate predictions is not necessarily related to the model's ability to generate counterfactual predictions." I'm saying, you can make extremely accurate predictions with an incorrect world model. This isn't conjecture either, this is something we're extremely confident about in science. > I don't think I can agree. In your 2+a+b-2 example, that is an unnecessarily convoluted, but entirely correct model of addition. I gave it as a trivial example, not as a complete one (as stated). So be careful with extrapolating limitations of the example with limitations of the argument. For a more complex example I highly suggest looking at the actual history around the heliocentric vs geocentric debate. You'll have to make an active effort to understand this because what you were taught in school is very likely an (very reasonable) over simplification. Would you like a much more complex mathematical example? It'll take a little to construct and it'll be a lot harder to understand. As a simple example you can always take a Taylor expansion of something so you can approximate it, but if you want an example that is wrong and not through approximation then I'll need some time (and a specific ask). Here's a pretty famous example with Freeman Dyson recounting an experience with Fermi[0]. Dyson's model ma
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