[gwbas1c]

Maybe I'm niave, but are there really people who want to hop on the AI bandwagon just to do mundane lookups like this?

When I worked with machine learning many years ago, we learned that it was no better than the heuristics already in place. The thing is, it's much easier to diagnose a well written and understood heuristic than a machine learning model.

[whoisjuan]

Machine Learning is usually seeen as a magic black box by many people. So yes. There's a recent trend that seems to favor a machine learning first approach to solve very simple and mundane problems because people feel that they are missing some magic insight if they don't do it (FOMO).

For example, the author refers to a shopping newsletter where you personalize suggestions for certain products after a customer buys a particular product. This is very often a machine learning 101 example but really there's nothing preventing you from writing those heuristics yourself -no ML involved(e.g: if a customer buys a pillow, suggest pillow cases).

Machine learning does makes sense for something like that if your website is Amazon, but is definitely an overkill if your website is an e-commerce for house garments.

The funny thing is that usually you will end up writing those heuristics implicitly since you need to label your data anyways.

[apatters]

Another fun thing is that you will learn a lot more about your customer base if you do the research and write those heuristics yourself, vs. having ML do them. A business that understands the behavior of its customers is much more competitive than one which delegates that understanding to a black box. I don't contend that ML has no valuable (even transformative) applications but it's not a substitute for personally understanding every detail of your market.

[threeseed]

I don’t understand this at all. I’ve worked with a close to a hundred data scientists now and every one of them is an expert in the business problem they are trying to solve.

You can’t just throw an algorithm (even one like AutoML) at a problem and expect to be able to do magic with no knowledge of the domain. The technology simply doesn’t work like that.

[threeseed]

Machine learning can be implemented in just a few lines of code using something like Amazon Sagemaker to manage the process. In some cases it can be less work than hand crafting dozens and dozens of business rules.

And just because you have a small website doesn’t mean you have to behave like you have a small website.

[Spooky23]

Yes.

A few years ago I was called into save a dying project. They had built some big Hadoop cluster, had consultants on site, etc.

End of the day, they were doing something similar to assessing fines on library books. I wrote a prototype in about 3 hours.

[TeMPOraL]

People mentioned resume building and FOMO, I'll add to this: funding and sales. Investors and corporate managers are into this hype as much as engineers - if not more, so AI/ML is a label people want to use to get more money.

[itronitron]

Yeah, I think in most cases if an organization says they are doing AI then their user base is probably rather unsophisticated in regards to tech.

[sidlls]

Yes, the hype is strong and people want to stick ML or Data Science on their resume. They'll try to do anything that seems like it might touch statistics with ML first and not even consider whether it's necessary.

Kafka, Kubernetes, and things like Spark and machine learning are basically the next stage of the "data is King" hype cycle that Hadoop was a few years ago.

[adjkant]

https://news.ycombinator.com/item?id=17434254

The comment lists at least two "questions" that can be answered easily with SQL and a graph and even in ways that give more nuance than linear regression can capture.

[itronitron]

They are, and they are getting VC funds to do it. They may just be on the bandwagon to get the funding, but they still need to 'do AI' in order to satisfy their investors.