Wouldn't it be exciting if computers could make decisions the same way we do? Because although our brains are amazing processors and decision-makers; they are terrible at storage and documentation. This means only a fraction of our consciousness is spent on:
- Collecting data.
- Modeling hypotheticals.
- Navigating through storms and/or looking for smooth sailing.
... while the remaining is wasted trying to blindly justify our decisions, whether good or bad - often leading us into hypocrisy and psychological shaky ground. Why? Because there's no way to go back in time and access what just happened between our ears; How amazing and productive would it be if we could just pause our thoughts, see all the possible routes we can take to achieve or miss our goals, share our insights and move ahead with full understanding... in advance.
More than ML/AI:
Flowchart on how to make a decision based on some data and goals:
We call it The Future of Data, because the StringCube allows you to play with data the same way your brain does - amplifying some types of answers while pushing others to the background... all in a flexible, powerful and elegant manner.
How it works:
When biases are applied to answers, the entire result set shifts in the direction you want to emphasize, giving you an idea of hypothetically how the world could work when you have your way; and also how far from the current world it would be. Also, everything starts relating to everything else - for example:
- Teachers can see all the ways the good students relate to the struggling ones.
- Team leaders can see all the ways the new members relate to the ones who've been there longer.
- Geologist can see all the ways a productive mine relates to unproductive ones.
- Investment bankers can see how the best performing stocks relate to all the others.
- ... etc.
When everything starts to relate to everything else the data explodes; so a simple spreadsheet of 20w x 100h produces many thousands of intersections of resulting relationships - showing how all the different replies from one column affects all the different types from all the others. To us, these relationships are more important than the actual replies in understanding how the data 'works' and how to achieve your goals.
Also, the relationships can be mapped onto a color spectrum and easily visualized; so when a result set is accelerated in the directions of biases, each data point intersection produces a smear - or rainbow. If you collide two bias sets, butterflies are produced at each intersection. The most important part of the rainbow/butterflies are in the middle - showing how far the 'needle' moves from no biases; but all the other parts also have meaning as well and can be used to model goal achievement.
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