And not 3 or 13. Albert Einstein was known for distinguishing 5 different things with one view (whereas most of us dabble around 3). Yet the most successful figure that we like for distinguishing differences, is 7. Take music, for example, the most widespread language across the world. Some 80% of all music in the world is based on a 7 tone scale. There are other scales, like 5 tone (as in Irish music) or 12 tone (as in modern classic). The lower the number, the more it appeals to large groups of people. 5-tone is very popular. But the majority of listeners wants more variation, modes, coloring of melodies. They land at 7 tone music. However, above 7 the air gets thin. In 12 tone scales, very few listeners find an emotional core. Most simply switch off.
But if you write a nice music and use a 7 tone scale, you can be sure, you won’t miss the audience. If you play around with 7 different modes in addition, you will be able to fulfill the musical dreams of nearly everyone.
It is kind of funny that this harmonizes with an overall look at results of our learning logic, as we use it in AI applications. Human perception goes like this: when it comes to finding the most efficient set of data, you are not at the end until you reached something around 7. You may even be able to drive it down to 5, but never lower, and you rare-rare-rarely find a set of 12, and never above.
Funny, isn’t it? It doesn’t tell us anything about the reality around us, but something about the perception of the home computer that we all carry with us. It can do many things, but in terms of distinguishing, it is short sighted.
It also tells a story about BigData. Dear CIO, if you haven’t identified your relevant set of 7, you can’t manage your data – except for storing it. For instance, if you cannot decide what is trash (which – as everybody knows – sums up to 99% of the data), you have to store it until someone decides. But because you need all your power to store the trash, you don’t find time to identify your value set of 7.
At this point, a friend, in charge for a data warehouse, intervenes with a comment: ‘All I can do is store the data that comes in. I am not paid for deciding what to throw away. This is what others need to do who take responsibility on a higher level.’
Hhm. And who is that? Many theories, but nobody knows. Finally nobody takes a decision.
What a bunch of “Creatures of the Night” there may be hiding in the data? Non-walking non-deads, non-discovered and non-existing, the shadow world of trash in modern customer warehouses. A final comment from my data warehouse friend: ‘in 4000 years, archeologists will dive into the data and take a look on us!’ Indeed, on black markets, few things find more appreciation than dinosaur shit. Let’s produce more of it for the sake of generations to come!
Or let’s make friends with the fact that there is no sense, where we don’t look for it, and find piece and satisfaction with our 7. Because we simply can’t make sense of everything.