The Three Horsemen Of The Machine Learning Apocalypse

an article by Ashutosh Jogalekar in The Curious Wavefunction brought to us by S. Abbas Raza of 3 Quarks Daily

My colleague Patrick Riley from Google has a good piece in Nature in which he describes three very common errors in applying machine learning to real world problems. The errors are general enough to apply to all uses of machine learning irrespective of field, so they certainly apply to a lot of machine learning work that has been going on in drug discovery and chemistry.

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Hazel’s comment

Artificial intelligence is only as “good” as the person writing the code. Biases and skewed rhinking will come into the mix because we are all human and not machines.

I had hoped for an image of some kind that would fit the article -- the three horsemen of

1. biases in the training data
2. hidden variables
3. ensuring your model is fit for purpose (at least I think that is what it says my science being a bit rusty).

but no such luck 

So I went looking for the four horsemen of the Book of Revelation Thank you Wikipedia


Four Horsemen of the Apocalypse, an 1887 painting by Viktor Vasnetsov. Depicted from left to right are 

1. Death, 
2. Famine, 
3. War, and 
4. Conquest.

The Lamb is visible at the top.

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