ALL THINGS BIG AND SMALL 2.0

  AI can detect subtle patterns which the human brain may not be able to. Here are three examples. In these examples the AI converts the charts into pixels as well as uses OCR (Optical Character Recognition) to quantify changes in shades of colours, measure ‘distances’ between lines, rates of change and so on. Here are some of the hidden insights it uncovered.  1. FAN CHARTS  The fan charts above showing forecasted prices of gold and silver 60 days ahead were generated after 10,000 trials of a Monte Carlo Simulation. Q stands for quantile thus Q90 is 90th quantile. Gold's Q90–Q75 spread (47.6 points) is in fact wider than its Q25–Q10 spread (36.0 points).  Gold's dollar distribution is modestly top-heavy: about 21.7% of upside room to Q90 against 18.2% of downside room to Q10, measured from the median. Silver's is extreme: +59% of  room above the median versus −34% below. The model isn't merely uncertain about silver; it is saying that if it is wrong, it is far ...

Following my previous article on Life as a Complex Adaptive System [CAP], it is only natural that I should explore the nature of knowledge in a data-driven context When a CAP acquires data through its sensors [in human beings: our ears, eyes, nose, tongue etc]. it needs to process this mass of data to turn it into information. Information differs from pure data. By being organized in a contextual format, information yields insights. Some of the things the human brain tends to do to data are: Categorization. Human beings are fond of sorting things into pigeon holes. Thus, this is a Monarch butterfly and that is a Swallowtail butterfly. This is an insect and not a reptile because etc etc. Categorization implies groups of objects with common properties. When we categorize, we have fewer objects to deal with and Life becomes more simple. Sometimes categorization can be quite arbitrary. When is a human considered tall or fat, ugly or beautiful? The definitions differ from person to person, ...