What AI companies hide until it's too late.

Listen, because this post is very likely to save you quite a bit of time or money or both.
 
Now you’ll see that I’m going to tell you something very honest that no AI company will tell you.

 

Just like the hotel didn’t take it upon itself to tell you that the friends’ trip to the beach wasn’t going to be what you expected.

Just like BMW is not going to be the one to tell you how much servicing costs.

Just like Thermomix will not warn you that you are not going to use it if you did not cook before.

 

Look, the thing about machine learning is that it will bring up a lot of associations between variables that you didn’t know about.

 
In theory.

 

But sometimes it won’t.

Because sometimes the variables that come out we already knew about by traditional methods.

 
This is nobody’s fault.
 
It is like if a clinical trial comes out negative. The science is in knowing that the drug does not work. It is what it is and it is not a failure.
 
It is true that we often solve this by adding a layer of genomics or histopathology, but we do not always have that.

But, but, but…

 
Something even worse can happen.

 

It may happen that you do bring out new predictor variables, but, since they are not in the literature, nobody believes you.

Neither the journal editor, nor your boss, nor the scientific committee of the conference believe you. Worse still, you don’t believe it yourself…

… until you can prove that the algorithm works by testing it on another uncontaminated group of patients.

 
That’s called validation.
 
And until you have validation, it’s all tall tales.

 

That’s why it’s important to give you a heads up.

 
Don’t do a project with AI.
 
Not unless you’re willing to go all the way.
 
Unless you have someone to guide you from model to validation.

 

If you want guidance, it’s right here.

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