| ▲ | Why don't machine learning research agents overfit?(amazon.science) |
| 45 points by Betelbuddy 2 hours ago | 20 comments |
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| ▲ | diddid 38 minutes ago | parent | next [-] |
| I always get annoyed when people misinterpret Occam’s razor. It’s not that the simplest is more likely to be correct, it’s that you should prefer it, because it’s simple. It’s just like the Hopper quote. She said it’s better to ask for forgiveness during the fog of war, doing something you thought was right, not to do something you knew they were going to say no to and now you are trying to get away with something. |
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| ▲ | srean 6 minutes ago | parent | next [-] | | > It’s not that the simplest is more likely to be correct, it’s I don't know what Occam meant, but if you accept the formalism of PAC learning, it is more likely to be correct http://www.cse.buffalo.edu/~hungngo/classes/2008/694/papers/... http://cs.ecs.baylor.edu/~hamerly/courses/5325_10s/papers/le... | |
| ▲ | beckhamc 6 minutes ago | parent | prev | next [-] | | And sadly, in academia, complexity (opposite of Occam's razor) is what gets you published. | |
| ▲ | gowld 30 minutes ago | parent | prev | next [-] | | That's not true. It's pretty clear that she meant "do something you knew they were going to say no to and now you are trying to get away with something." https://youtu.be/wHdHCoeUbU4?t=861s > So I want to tell something to all the young people here on many many occasions you'll find it is much easier to apologize than it is to get permission.
You do it then when somebody comes after you and say are you supposed to do that, "oh gee I didn't know I wasn't supposed
to do that" ...
so just remember it's frequently much easier to apologize than it is to get permission do it She goes on further, explaining how to deceive your superiors to manipulate them to get what you want. | | | |
| ▲ | sillyfluke 16 minutes ago | parent | prev [-] | | >It’s just like the Hopper quote. Not sure about Hopper, as I recall biographers of Lawrence of Arabia certainly made it seem like he was using the fog of war to do things he knew his superiors may object to. Regardless, even if its misinterpreted it still has a kernal of truth and separate utility than your version, that is: the people in the field closest to the action have an operational awareness that may result in better decisions in times of urgency. |
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| ▲ | demibabs an hour ago | parent | prev | next [-] |
| Even tech giants are putting out articles seemingly fully written by Claude. |
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| ▲ | ks2048 an hour ago | parent | next [-] | | The animated graphic labeled "Occam's razor, formalized" is bizarre. Is that really visualizing "Occam's razor, formalized"? | | |
| ▲ | mrbungie an hour ago | parent | next [-] | | Ah, over-the-top larger-than-life LLM-isms, they are really funny when you see them in a company blog, but they are vomitive when it's your coworker copy-pasting it and insisting you on reading it. | |
| ▲ | smashah 25 minutes ago | parent | prev [-] | | I was expecting Occam wearing a suit. |
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| ▲ | serial_dev 28 minutes ago | parent | prev [-] | | Time to first detected slop in this article is <1s. Claudisms per paragraph is also very high. Is it too much to ask from people to read their own article anymore? If anyone read this at all, they would have had the ick, and would have fired off a prompt to get rid of the most popular AI slop tells... | | |
| ▲ | cj 21 minutes ago | parent [-] | | What I really dislike is having to edit my own non-LLM assisted writing to make sure I'm not accidentally confused with AI. I caught myself writing "And that matters because..." in a HN comment but had to edit myself. Also miss uising emdashes. | | |
| ▲ | bee_rider 7 minutes ago | parent [-] | | These models are trained on human language, which belongs to us, we shouldn’t surrender it to them. Keep the em-dashes. IMO don’t overuse negative parallelisms though, they were always bad and lazy. |
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| ▲ | nyeah 38 minutes ago | parent | prev | next [-] |
| They tend not to overfit ... when there are way more data points than parameters. |
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| ▲ | dguest an hour ago | parent | prev | next [-] |
| arXiv link: https://arxiv.org/abs/2606.11045 |
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| ▲ | 32df179 34 minutes ago | parent | prev | next [-] |
| Wherein Claude gives an honest assessment that it genuinely does not overfit. I also had Grok telling me that it isn't quantized. Do the submitters really not notice that this is AI slop? Do they like this? It is a complete pain to read. |
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| ▲ | dominotw 2 hours ago | parent | prev [-] |
| > Machine learning, at its core, is about generalization, not memorization. Well they memorize the patterns. memorization doesnt mean rote learning. |
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| ▲ | tomrod 34 minutes ago | parent | next [-] | | I don't take issue with that. Attempting memorized pattern generalization through holdout / validation strategies is a big part of ML that you would not typically see with econometrics / psychometrics / possibly sabermetrics / most other -metrics. Philosophically the explain versus predict divide. https://www.stat.berkeley.edu/~aldous/157/Papers/shmueli.pdf | |
| ▲ | porridgeraisin 35 minutes ago | parent | prev [-] | | That's a bit pedantic no. Memorization in ML refers to the model having the wrong level of capacity such that it's too hard to optimise it such that it doesn't memorize the _training examples_ themselves. |
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