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Comment by simonw | Hacker News Reader

Something I've learned: when non-engineers ask for an AI or ML implementation, they almost certainly don't understand the difference between that and an "algorithmic" solution.

If you solve "trending products" by building a SQL statement that e.g. selects items with the largest increase of purchases this month in comparison to the same month a year ago, that's still "AI" to them.

Knowing this can save you a lot of wasted time.

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Any sufficiently misunderstood algorithm is indistinguishable from AI.

Ha! I'm going to have to borrow this phrase.

I think it's a play on "Any sufficiently advanced technology is indistinguishable from magic".

In my AI class in college, we learned about first order logic. To me it didn't seem like we were really learning AI but I couldn't quite put my finger on it. I guess it's because it made too much sense so in my mind it couldn't be AI.

This is basically a form of the AI effect[1]:

> The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence.

[1]: https://en.wikipedia.org/wiki/AI_effect

Ah yes, the AI version of the no true scotsman fallacy

AI is what we call algorithms before we really understand them.

Some decades ago, that was AI to everyone.

In the future, I expect ML to also fall out of the "AI" umbrella - it gets used primarily for "smart code we don't know• how to write", so once that understanding comes, it gets a more-specific name and is no longer "AI".

•"know" being intentionally vague here, as obviously we can write both query planners and ML engines, but the latter isn't nearly as commonplace yet to completely fall out of the umbrella.

Right, this makes sense, because the "Artificial" part goes away once we have a fully understood algorithm. It's just part of intelligence to use algorithms when they work.

Exactly! I run a data science department at a corporation. I've had exactly one production level project that was sufficiently complicated to require deep learning. I am currently working on the second. I always start with the simplest approach. That's a message that I push hard for every recent grad and intern who comes here.

Engineers tend to overestimate how difficult machine learning is. That is exactly how a good data scientist would solve this problem. If (and only if) this initial solution is not sufficient then you can iterate on it (maybe we should also take into account monthly trends, maybe one category of products is overrepresented, ...).

hah yeah "dynamic programming" has turned out to have a fortunate name

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