"Standard" statistical methods such as regression, cluster and factor analysis all require numerous decisions, many of which are judgmental.

Subject matter knowledge (e.g., marketing), project background and knowing who will use the results, and how and when they will be used are consequential.

Stats cannot be done just by the numbers, even when called machine learning, as these three methods frequently are.

AI can mean anything these days but often refers to some form of artificial neural network (#ANN). Form is the operant word here because, like regression, cluster and factor analysis, ANN come in many shapes, sizes and flavors and cannot be done just by the numbers either. See the link under Comment.

Humans design AI and must make many decisions, some of which are quite subjective. Different AI applied to identical data will not give us identical results. This is no different from statistics.

Moreover, today's AI are task-specific: Alpha Go (Go) and Alpha Zero (chess) are different programs and neither can drive a car or read an MRI scan. Or do regression, cluster or factor analysis.

🗣 @AI_Python_Arxiv
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How to Build a Simple Artificial Neural Network (#ANN)

#ArtificialNeuralNetwork

🌎 Artificial Neural Network

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Forest Fire Prediction with Artificial Neural Network (Part 2)
https://bit.ly/2G3iOte
#ANN

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A Very Short History of Artificial Neural Networks

🌎 History of Artificial Neural Networks

#ArtificialNeuralNetwork #neuralnetworks #ReinforcementLearning #ANN #NN

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#ArtificialNeuralNetworks (ANN) were supposed to replicate the architecture of the human brain, yet till about a decade ago, the only common feature between #ANN and our brain was the nomenclature of their entities (for instance – neuron). ANN architectures have become extremely useful across industries.

https://bit.ly/2Et1wpl

✴️ @AI_Python_EN