3 ms·

While I see the author’s point, I fail to understand what any if this has to do with SQL. The problem ML solves isn’t querying databases, it’s making decisions.

While I see the author’s point, I fail to understand what any if this has to do with SQL. The problem ML solves isn’t querying databases, it’s making decisions. If a human came up with the idea “let’s lookup people X and send them email Y” and it works, great. But a human made that decision, and SQL is just a tool for making it happen. If you want to take the human out of the loop, SQL won’t save you.

r3bl 9y ago
I don't think you understand author's point. His point is highlighted in the first tweet, in which the author appears to be specifically annoyed by the potential founders and investors that can't understand that ML isn't a good solution for all of the problems. He then goes on and gives an example of such problem by explaining a shopping cart that doesn't actually need ML, but just some old-fashioned SQL. He doesn't claim that SQL is a solution to all ML problems, just this one.
- dotmanish 9y ago
  (I'm not the parent commenter who you replied to, but I think I understood what his/her point was). Taking the shopping cart example: "In a former life, I used to write SQL to extract customer of the week. Basically, select from orders table where basket size is the biggest." The author decided that 'customer of the week' will be selected by 'biggest basket size'. Not by 'biggest $ amount spent', 'fastest time from add-to-cart to checkout' (and numerous other attributes or combination of them). This decision (the "best attribute") was taken by a human, leaving a field open where a combination of attributes could've resulted in overall better business outcome (how much did 99% of these retained customers shop for, in $ value over lifetime?, etc) This is possibly what the parent commenter is hinting at - this human decision leaves a lot of optimization scope, where ML could have helped.

r3bl 9y ago

I don't think you understand author's point. His point is highlighted in the first tweet, in which the author appears to be specifically annoyed by the potential founders and investors that can't understand that ML isn't a good solution for all of the problems. He then goes on and gives an example of such problem by explaining a shopping cart that doesn't actually need ML, but just some old-fashioned SQL. He doesn't claim that SQL is a solution to all ML problems, just this one.

dotmanish 9y ago
(I'm not the parent commenter who you replied to, but I think I understood what his/her point was). Taking the shopping cart example: "In a former life, I used to write SQL to extract customer of the week. Basically, select from orders table where basket size is the biggest." The author decided that 'customer of the week' will be selected by 'biggest basket size'. Not by 'biggest $ amount spent', 'fastest time from add-to-cart to checkout' (and numerous other attributes or combination of them). This decision (the "best attribute") was taken by a human, leaving a field open where a combination of attributes could've resulted in overall better business outcome (how much did 99% of these retained customers shop for, in $ value over lifetime?, etc) This is possibly what the parent commenter is hinting at - this human decision leaves a lot of optimization scope, where ML could have helped.