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by 7thaccount
2261 days ago
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Bahaha. It's a small world fellow HN user. As soon as ACM opened their digital library, I started looking for interesting APL papers and found that one and thought it was beautifully done. My takeaway is that you can make purpose-built AI in APL with very little code versus calling out to a large library like Tensorflow and having no idea what's going on. |
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Yes, the takeaway is that with APL or J is that you can see the mechanics in a paragraph of code, and it is not a very trivial example. If the libraries or verbs are created to deal with some of the speed or efficiency issues, it is promising as a way of understanding the concept better.
The dataframes of R and Python (Pandas) were always a thing in APL/J/k/q, so it is their lingua franca or basic unit of computation upon which the languages were built - arrays, not a library.
More importantly, almost along the lines of the emperor has no clothes, is a tack to get away from the black box, minimal domain knowledge, ML or DL that cannot be explained too easily - see newly proposed "Algorithmic Accountability Act" in US legislature. Differentiable Programming and AD (Automatic Differentiation)applied with domain knowledge to create a more easily explainable model, and try to avoid biases that may creep into a model and affect health care and criminal systems in a negative way [1][2].
And then there are those who use DL/ANNs for everything, even things that are easily applied and solved using standard optimization techniques. Forest from the trees kind of phenomenon. I have been guilty of getting swept up with them too. I started programming ANNs in the late 80s to teach myself about this new, cool-sounding thing called "neural networks" back then ;)
[1] https://arxiv.org/abs/1811.10154
[2] https://arxiv.org/abs/1907.07587