Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: o1-preview is pretty good at doing ML on an unknown dataset, published by Håvard Tveit Ihle on September 20, 2024 on LessWrong.
Previous post: How good are LLMs at doing ML on an unknown dataset?
A while back I ran some evaluation tests on GPT4o, Claude Sonnet 3.5 and Gemini advanced to see how good they were at doing machine learning on a completely novel, and somewhat unusual dataset. The data was basically 512 points in the 2D plane, and some of the points make up a shape, and the goal is to classify the data according to what shape the points make up.
None of the models did better than chance on the original (hard) dataset, while they did somewhat better on a much easier version I made afterwards.
With the release of o1-preview, I wanted to quickly run the same test on o1, just to see how well it did. In summary, it basically solved the hard version of my previous challenge, achieving 77% accuracy on the test set on its fourth submission (this increases to 91% if I run it for 250 instead of 50 epochs), which is really impressive to me.
Here is the full conversation with ChatGPT o1-preview
In general o1-preview seems like a big step change in its ability to reliably do hard tasks like this without any advanced scaffolding or prompting to make it work.
Detailed discussion of results
The architecture that o1 went for in the first round is essentially the same that Sonnet 3.5 and gemini went for, a pointnet inspired model which extracts features from each point independently. While it managed to do slightly better than chance on the training set, it did not do well on the test set.
For round two, it went for the approach (which also Sonnet 3.5 came up with) of binning the points in 2D into an image, and then using a regular 2D convnet to classify the shapes. This worked somewhat on the first try. It completely overfit the training data, but got to an accuracy of 56% on the test data.
For round three, it understood that it needed to add data augmentations in order to generalize better, and it implemented scaling, translations and rotations of the data. It also switched to a slightly modified resnet18 architecture (a roughly 10x larger model). However, it made a bug when converting to PIL image (and back to torch.tensor), which resulted in an error.
For round four, o1 fixed the error and has a basically working solution, achieving an accuracy of 77% (which increases to 91% if we increase the number of epochs from 50 to 250, all still well within the alloted hour of runtime). I consider the problem basically solved at this point, by playing around with smaller variations on this, you can probably get a few more percentage points without any more insights needed.
For the last round, it tried the standard approach of using the pretrained weights of resnet18 and freezing almost all the layers, which is an approach that works well on many problems, but did not work well in this case. The accuracy reduced to 41%. I guess these data are just too different from imagenet (which resnet18 is trained on) for this approach to work well. I would not have expected this to work, but I don't hold it that much against o1, as it is a reasonable thing to try.
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