This is an interesting article if you read it like a howto for constructing a neural network for performing a practical task. But if you take it at face-value, and follow a similar method the next time you need to parse some input, then, well, I don't know what to say really.
The author takes a hard problem (parsing arbitrary input for loosely-defined patterns), and correctly argues that this is likely to produce hard-to-read 'spaghetti' code.
They then suggest replacing that with code that is so hard to read that there is still active research into how it works, (i.e a neural net).
Don't over-index something that's inscrutable versus something that you can understand but is 'ugly'. Sometimes, _maybe_, a ML model is what you want for a task. But a lot of the time, something that you can read and see why it's doing what it's doing, even if that takes some effort, is better than something that's impossible.
The author takes a hard problem (parsing arbitrary input for loosely-defined patterns), and correctly argues that this is likely to produce hard-to-read 'spaghetti' code.
They then suggest replacing that with code that is so hard to read that there is still active research into how it works, (i.e a neural net).
Don't over-index something that's inscrutable versus something that you can understand but is 'ugly'. Sometimes, _maybe_, a ML model is what you want for a task. But a lot of the time, something that you can read and see why it's doing what it's doing, even if that takes some effort, is better than something that's impossible.