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Comment by ryandamm | Hacker News Reader

If calibration is a temporary problem, it's because the people building those AI models understand imaging really deeply, and engage with some of the ideas in the linked post. To put it another way: if you want to build AI models that understand the world in 3D via 2D images, you should probably understand projective geometry, camera calibration, etc, extremely well.

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Or does it ? Drawing on a parallel from my experience in NLP during the early 2000s, the field was largely dominated by linguists trying to grok language and structuring rules manually. However, the most significant advancements came when we shifted towards using massive datasets to train models, without requiring explicit, deep linguistic knowledge.

Similarly, maybe the next frontier in 3D vision is employing large datasets to train a black-box AI without us having to understand and get the math right.

I think we have objective information about how protective geometry works.

Ergo, I would not bet against teams that understand that objective technical field; AI is not magic, and knowing the fundamentals will always help. Better models, smaller models, faster models; the less you leave for the model to solve in latent space the better you’ll do, I think.

If that was true, transformers would have been useless for NLP and deep learning for Go.

You can argue the exact opposite: the more you leave to the model to learn by itself, the more likely you are to find a solution that was not accessible to humans and their limited feature engineering capability.

Transformers and deeplearning was reconfigured to be in 2D for GO. Trying to apply 1D variants did not work, and people did try.

You cant compare a real technical field with hundreds of years of real world use to some social science nonsense. Linguists have never made useful or testable predictions of any kind. Geometric computer vision has been refining camera calibration to the point where we use stereo to find things billions of light years away.

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