You probably don't need AI/ML. You can make do with well written SQL scripts

Fully agreed that in simple use cases simple solutions make sense; I've been arguing similarly for the NoSQL movement for years (i.e. NoSQL being great for large scale systems; but for most companies day-to-day needs SQL wins out).

However, it would be good to have a bit more in the article to say what AI/ML* is in this context, and a couple of scenarios where it beats SQL; i.e. otherwise it just sounds like the rantings of an old man "in my day we only had turnips; you needed a snack: turnip; you needed a pillow: turnip". By showing a few good use cases allows you to better contrast the product / get an understanding of where the boundaries are between the technologies.

*NB: When I first read this I assumed the author was talking about AIML (artificial intelligence markup language) rather than AI/ML (artificial intelligence / machine language)... as though the slash was included, there was no use of the full terms.

Appreciate the feedback! Regarding analytics, I think this is some A/B testing, which I don't think is even active in the current version (though still in the sources).

More importantly, we don't tie GA or any other usage analytics to individual users. We basically just use it to see where the extension is used and where people are blacklisting sites. We use this data to make the extension run better on more sites. We don't monetize user data in any way. You're right that we should make this point more salient. In our Privacy Policy, we do describe how to opt out by blocking GA, but we should put this somewhere more prominent.

Thanks for the feedback!

Why use the term "AI" when the GP specifically used the less ambiguous Machine Learning?

Machine Learning = statistics + linear algebra + computer science, mostly.

Naive Bayes and Graphical Models are pure statistics, but they are mostly used for toy problems. Machine Learning scales these approaches to high dimensionality problems, and tasks where data is abundant.

Am I misunderstanding something here?

Artificial Intelligence is about statistical analysis.

Such as: Is this picture of a man and his dog, actually a dog? Or is it a cat? Or is it a 4 legged creature? Or is it a turtle?

The AI is supposed to identify that the animal in the picture, is a dog with a 99.8% probability. And since it exceeded the 98% threshold, then it becomes accepted as a dog, until otherwise disproven.

Basically, it is a pattern matching mechanism, on a massive statistical scale.

And from this, then further actions can be taken.

Such as, the owner of the dog, can be mailed advertising and coupons that are related to dogs.

And then, the AI can go even further. What specific kind of dog is it? Is it a German Shepard? Is it a beagle? Is it a poodle?

The AI can determine the specific type of dog, and conclude that it is a German Shepard with a 99.7% probability. This exceeds the threshold, so then the computer system might mail out an advertising to the owner, about deals related to a German Shepard.

For something like this, then this is where social media can really shine. When you upload your pictures to Facebook, or Gmail, or Instagram, then Facebook or Google, can use an AI to analyze your picture. As well as reading your caption on it. And they can determine the context of your picture, such as whether you have a dog in it. Are you holding the dog? Are you walking the dog? Are you smiling in the picture? If the scenarios check out, then the AI can select you as a candidate, and send advertisements related to your dog.

In fact, I think our brain operates the same way, by using statistical analysis.

When we see a dog, in a picture or in real life, our brain is actually using a statistical analysis to determine that it is a dog. Our brain follows a neural network pathway to match that picture of a dog, to a similar variation of a dog that we have in our memory. It is thus statistically true, until otherwise disproven.

This of course, happens in the deep recesses of our brain, so it's currently impossible to know what really is happening there, until we have a better scientific understanding of how our neurons work in our brain.

On the flip side, SQL scripts has no mechanism to view the picture, to determine if the animal in it, is a dog, or a cat, or even if it is a human.

Yes, these are two main approaches to AI, with expert systems being the "old" AI and ML being the "modern" AI. In the old approach, you knew exactly what you wanted to achieve, and very often you had a relatively clear idea how to do it; the challenge was to construct the solution manually. In the new approach you are not so much concerned with how to do it as you train on some dataset anyway. The results are less predictable though, you might even not understand why certain things happen. The number of mistakes/false positives can be relatively high.

Still, with these methods, I think we're very far from real artificial intelligence, where you can really learn something new on the basis of something old. I believe until we have a profound understanding of what it means "to understand", such an advancement in AI won't be possible.