🕕 Back in the days, I had a professor that used to say: “𝐍𝐞𝐯𝐞𝐫 𝐝𝐨 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐡𝐚𝐯𝐢𝐧𝐠 𝐚 𝐦𝐨𝐝𝐞𝐥 𝐢𝐧 𝐦𝐢𝐧𝐝”.
► Let’s translate to in an example – Make a hypothesis about a behavior, like assuming a relation between some features (parameters) like height, weight, and age to heartrate variability for example. Then, view, perform some statistics and change or at least not accept your hypothesis.
► This is not how things are done using AI and ANN.
The main difference is that AI is able to tackle multiple features all at once while humans are limited by number of dimensions, they are able to percept and analyze. So, our theories and hypothesis are built according to our limitations.
► AI and Artificial Neural Networks in particular, are able to concurrently process high dimensional data and compress it to something we can absorb and interface with and thus build a theory that describe the distilled product of the ANN.
► Instead of building theories based on our direct perception, we now build them on top of an ANN broker that is compressing it so we can make some sense of it.
► Think of it like wearing a polarizing sunglass. They reduce the glare in a hot sunny day, but you may start seeing some annoying stress marks on your car’s windshield that are not seen with the naked eye.
► This is at the heart of the Explainable AI problem.
