Re:A Critical Field Guide for Working with Machine Learning ...
by wongarsu on Friday February 17, 2023 @12:23PM
(#34837489)
Some of them do, but others depend on labeled and augmented data. For example training facial recognition starts with images labeled by who appears in them, then you run a model to find the face in the image and crop to that, then another image tells you where the eyes are and you use that to rotate the image to make the eyes level, and that's the input to the actual facial recognition neural network. And that's for the "naive" approaches that don't have special handling for faces don't look at the camera. And the field still routinely has problems because it turns out that somebody's data set only contained certain lighting conditions, or severely underrepresented certain races, etc.
Re:A Critical Field Guide for Working with Machine Learning ...
by KRAKRISMOTT on Friday February 17, 2023 @12:49PM
(#34837891)
Not sure about image recognition but for the language models and audio space, semi/self-supervised methods are currently doing quite well.
Re:A Critical Field Guide for Working with Machine Learning ...
by avs733 on Friday February 17, 2023 @01:51PM
(#34838716)
That is a fairly brave statement given some of the research looking at how failed attempts at ML modeling fall apart [1]
[1] https://ai.googleblog.com/2021/06/data-cascades-in-machine-l...
100 years of statistical advances have not been able to solve image recognition at scale until convolutional neural networks. Large language models may be somewhat Bayesian but they do not resemble any traditional Bayesian techniques except at the highest level. Clean data and good sampling are incompatible with the real world outside of laboratory conditions (or large budgets).