Category: ai
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Warp Regression: Timing errors, warps, and a generative “terror”
For quite some time I’ve been interested in the idea of ‘time’ error, i.e. early, late, or in the time-series context, warped or unwarped. The machine learning field clings strongly to the idea using y-axis error or loss, but it’s not the only way to optimize. There is the whole error-in-variables field, for example, that…
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The Rise of AI Foundation Models
One surprising development, at least to me, in AI is the growing proliferation of foundational models. Text? Sure, makes sense. There’s lots of data out there. Then images, audio, video somehow seemed a natural continuation. But now time series, tabular data, geospatial data, graphs? Foundation models built on public and synthetic data are producing workable…
Aaron Pickering 