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You don't need ML/AI, you need SQL

cyberomin.github.io

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Re: You don't need ML/AI, you need SQL

#51

Post by itronitron »

oh-kumudo wrote: post #26

The title is often true, but it doesn't mean too much or anything. And the same argument is brought up again and again in the past as well. What OP suggests, the so called SQL, is basically a heuristic based system. When done probably and carefully, it could of course work very well, and is indeed often used as baseline model to bootstrap a ML system. However, eventually the rule-based system will hit the wall, and M…

that is quite a leap to call SQL a rule-based system. SQL is a standard query language that you can use to discover how attributes and values relate to one another within data.

Re: You don't need ML/AI, you need SQL

#52

Post by m00x »

tylerjwilk00 wrote: post #37
benkarst wrote: post #35

There's no logical equivalency between SQL and ML/AI. SQL is a language that helps retrieve the data you're looking. ML/AI helps you predict the future (using past data). Maybe this is directed towards product people? But it has SQL in the title so it can't be. I'm confused as to who the audience is here.

You're missing the point. Much of the "intelligence" that AI/ML is touted as solving could be accomplished through standard SQL queries on well normalized data. But no one will invest in a company because we have proper data structures and accurate SQL reports that we learn from.

That's been done for ages. The point of AI is to adapt to the world instead of having humans spend time understanding the problem set.

SQL can apply a human understood model to data points. AI lets us develop new models and adapt them.

AI lets us solve problems that have abstraction, or problems that change over time. You can't have SQL detect cats in an image or drive a car.

Re: You don't need ML/AI, you need SQL

#53

Post by gonyea »

This post is downright bonkers. “We don’t need ML/AI! Proof: list of things you wouldn’t use ML for”

There are so many problems you can solve with a neural network. Should Waymo ETL sensor data and do a WHERE NOT IN for bicyclists?

This is blog post is pretty dismissive. Statistics software has been in use since the beginning; see SAS. Financial institutions, actuaries, etc, have been using these methods with SQL data as the input and it’s the only reason they’re still in business.

If this blog post simply suggested hiring a BI Analyst in your startup, I wouldn’t disagree.

Re: You don't need ML/AI, you need SQL

#54

Post by halflings »

shadowmint wrote: post #50
tfehring wrote: post #40

> say a person bought a pair of shoe, sunglasses and a book. For their newsletter, we will show include shoes, sunglasses and books. This was a lot more relevant than sending random stuff. I agree with the general sentiment of the article, but this seems like a poor example, since a more sophisticated approach can add a lot of value to a recommendation system. How do you know whether a customer is likely to want more…

Obviously ML can add a lot of value here, but its questionable to me if its trivial to build such a model with available data, keep said model up to date, or train variations on it easily, cheaply and quickly enough to A/B test the result and ensure you’re actually making any tangible difference. So you know... I don’t think it’s unfair to say that for smaller vendors, the cost/effort of setting up a ML model may dwa…

It's not unheard of to see +10-30% in revenue when adding a recommender system [0]; The system described by the author is arguably more complex than a recommender system, since he has to develop, maintain and evaluate a set of rules that are not based on real data, but only on his intuition of what users want. GP gave good examples of how this would easily fail (do you always want to recommend items from the same category; if not, how do you know which other category to recommend?)

[0] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.895...

Re: You don't need ML/AI, you need SQL

#56

Post by halflings »

joe_the_user wrote: post #8

Nice post, Here's a different way to think about the situation with current AI/deep-learning; if the current upsurge of methodologies was getting close to general AI, it would be getting closer and closer to a hammer that really did let you treat everything as a nail. IE, it would be general purpose. But I think I can say we're not seeing that even though deep learning seems to be continually expanding the domains th…

This doesn't sound related to the post, no? The post doesn't argue for general AI that learns like humans do, it only discusses the merits of AI as a whole vs hard-coded SQL queries and heuristics.

Re: You don't need ML/AI, you need SQL

#57

Post by visarga »

Counting items by value is a maximum likelihood estimation method too. It's still ML if you do a count, group by, max or threshold - just a less sophisticated way of doing things. The Naive Bayes algorithm is implemented by counting, at its base.

Re: You don't need ML/AI, you need SQL

#58

Post by anothergoogler »

visarga wrote: post #57

Counting items by value is a maximum likelihood estimation method too. It's still ML if you do a count, group by, max or threshold - just a less sophisticated way of doing things. The Naive Bayes algorithm is implemented by counting, at its base.

So any reduction is ML?

Re: You don't need ML/AI, you need SQL

#59

Post by visarga »

anothergoogler wrote: post #58
visarga wrote: post #57

Counting items by value is a maximum likelihood estimation method too. It's still ML if you do a count, group by, max or threshold - just a less sophisticated way of doing things. The Naive Bayes algorithm is implemented by counting, at its base.

So any reduction is ML?

Only if it fits to an existing statistical model.
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