Artificial intelligence as has four basic subject areas. They are as follows:

Statistics or, features

Linear algebra

Calculus

Probability

I would like to focus on statistics or AI eatures.

Information including statistics are derived from data ,data is real potential. It’s not calculated or surprised that will come later. And it most probably reflects some type of transaction

Information which is derived from data, can be standardized by reference data, or calculated of cours,e

Features can also be transformed, standardized and, consist of calculated information and be in many cases, change from its raw form

Then, of course we have to consider the algorithm you may choose to predict or especially recently todays generative content

It is also worth considering that in the early AI days much data concerns came from structured , sources, tables, or dimensions and columns, or as a attributes this data, what is the model and kept in and RDBMS recently we are able to process mature what we would call a structured data. This presents both great opportunity, and a greater chance for not understanding the context of the data.

My main conclusion is that you have to be careful that you consume data or feed it to your AI with a forensically traceable and verifiable source. In other words, he a chain of custody as you would in the real world. You cannot just take a prediction and not understand how it was. Arrived at. Like in the real world, there’s a difference between a guess and a well thought out prediction

Something you may want to know before you go that way , We have yet learned how our judgment works. Apparently, it’s a mixture of learned knowledge, experiences in human intelligence