| It's more that programming with math is hard. When communicating in mathematics you often assume the reader can understand precisely which mathematical object you're talking by extrapolating from ellipses "...". For example "the naturals are 1,2,3,4,..." or "the evens are 2,4,6,8,..." Or I might tell you a shift matrix is something that looks like: [0 1 0 0 0]
[0 0 1 0 0]
[0 0 0 1 0]
[0 0 0 0 1]
[0 0 0 0 0]
And that single example with the suggestive naming should be enough for most people to understand what a shift matrix is for arbitrary size.But if you want to explain what a shift matrix is to a computer, you must use a formal definition (and to do it idiomatically you need to know esoteric details like that numpy.diagonal takes in an offset parameter so you can do something like https://stackoverflow.com/a/30230801). It would be nice if we can talk math with a computer in the same intuitive language we use with humans, just give it a few examples and it will generate the the rest and let you double check the formula is correct. So I see stuff like tf-coder as a great step, not for synthesizing programs we don't know how to solve, but for language translation: between math notation and programming notation. |
Anyway I guess I'm turnning into the typical stuffy academic who can't even begin to think of real-world applications, hah. Thanks for pointing out the practical uses of the work described in the article.