[mrtksn]

But you don't gain ML/AI know-how by doing SQL, nor you discover previously unknown potential about your product buy sticking to your usual toolset.

Not that I necessarily disagree with the OP but I find it deeply uninspirational.

What's the difference between using ML/AI for problems traditionally solved by some other tool and using any other tool to solve the same problem unconventionally? Both can be "hacking". I guess my issue with this is the word "need", don't do what you need to do but what you want to do if you are looking for inspiration. After all, mankind never needed to leave the garden of Eden but left it anyway.

[rs86]

I think the OP just meant that you can get a lot done with databases queries and a bit of automation. There's no need to call that ML/AI.

[MaxBarraclough]

Something I hate about the business side of the tech world: the fad-chasing.

From the article:

> I hear these days for you to close that funding round quickly and early enough, you must throw in “Blockchain” even if it has no relevance in the grand scheme of things. A while ago, it was Machine learning and Artificial Intelligence.

Right on. No, blockchain won't help you with your corrupt voting system. If you don't understand the technology, you can't reason about its applicability, and there are more buzzword-chasers than serious technologists.

[sidlls]

A correct machine learning solution for a non-trivial problem is very likely to have higher complexity than a traditional approach known to work. For a toy or hobby problem, all's fair, but for a business application the added complexity can have significant impact on cost, time to market, etc.

[bastawhiz]

Well, I know I don't need to install GPUs on servers I install Postgres on, so there's that cost.

[threeseed]

You don’t need GPUs for machine learning.

In fact the majority of tools in this space are exclusively CPU based.

[joe_the_user]

But you don't gain ML/AI know-how by doing SQL, nor you discover previously unknown potential about your product buy sticking to your usual toolset.

If current ML/AI is the future and reveals more than anything else could, then it's logical for everyone to be piling onto it whether it's an applicable at the moment or not.

If current ML/AI is just another tool, then it's reasonable to use if and only it's applicable. Sure, not doing ML means you don't get ML insights but doing SQL means you get SQL insights. Back in the day, I recall clever queries could reveal interesting things, find outliner data and so-forth. Certainly, you don't get the powerful ad-hoc statistics power that ML give. But I suspect that power requires extremely large datasets.

[autokad]

I think its going to be like websites/internet back in the early 2000s. Everyone knew it was the future, but didn't know what to make of it. Many did it wrong and the profits (if there were any) didn't live up to expectations. then bubble burst. lots gave up, but the survivors, and the ones that learned how to do it right ended up with near unassailable moats. now a large portion of businesses today have an internet/app presence, and its seen as indispensable to their business.