alt Hacker NewsThe author is attacking the field of theoretical algorithm design broadly as being useless. If we take the author's logic to its conclusion, many algorithms we learn about in a standard Algorithm Design course such as "median of medians" (finding the median in a list in O(n) worst-case time) is useless because it is not used in practice. Even the original 2019 Harvey–van der Hoeven result, showing integration multiplication can be done in O(n log n), has a galactic overhead and no applications.
> theory gets so unmoored from the original question that an “open problem” becomes nothing more than an academic status puzzle...The theory of computer science has become a form of mathematics that doesn’t concern itself with computers, and it offers little reward for impact outside those mathematical pursuits
I thought it is common knowledge (and a common warning from profs to first-year CS students) that computer science has very little to do with computers. The very concept of Turing machines and decidability are mathematical in nature, far removed from how computers these days are run. The undecidability of Wang tiling, for example, is definitely a notable CS result, but it has nothing to do with computers." It is essentially a branch of mathematics. "Computer Science Is Not About Computers, Any More Than Astronomy Is About Telescopes" is a famous quote attributed to Dikstra. There is even famous MIT Open Courseware clip: https://youtu.be/2Op3QLzMgSY?t=30
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The irony being Dijkstra had lots of deep ideas about how programming ought to be done (from a basic science approach as informed by CS) and that the industry is still in the stone/bronze ages in terms of putting advanced ideas into practice.
A lovely theory, and yet the field is called "computer science".
Also, while I am not an expert, I suspect it is frequently useful to understand how telescopes actually work when you're trying to do astronomy with them.
I don't think that is necessarily true, the post is making a stronger point than what you describe
> Indeed, the algorithm requires you to multiply complex numbers whose magnitudes grow superpolynomially with n. If you stored these numbers as bits, the memory requirement would effectively be exponential in the input length. It assumes you can multiply such arbitrarily large complex numbers together in a single operation.
Another way to think about what the author said, imagine you had access to an oracle which could execute arbitrary precision arithmetic expressions in constant time. You could encode a wide range of computations in these and execute impossible algorithms.