One morning during the Fall 2017, as Rubber Ducky Buddha of Joliet and I watched Venus rise (for the last time on this sabbatical), he asked, “What are we seeing?” The first thing that came to my mind is a bright dot in the sky.
“Is that what it is?”, Rubber Ducky asked.
What didn’t come to mind first is that Venus is an entire planet with all sorts of complex systems and a relationship with the Sun and other things in the solar system, really everything in the Universe. Of course, I did know that Venus is not just a bright dot in the sky, but subconsciously, my mind first thinks “bright dot in the sky”, a very simple answer. For my normal life, the details of Venus never matter. All that matters to me about Venus is that it’s a pretty bright dot in the sky that I like to watch as I contemplate stuff.
If I were an astronomer researching Venus I may have answered differently. But Rubber Ducky still could have easily said, “Is that what it is?” That’s because Rubber Ducky is a blowhard, contrarian … hahaha … just kidding. That’s because our animal brains (not just human brains) abstracts very complicated stuff into salient points and remembers them – labels, categories, prejudices … When confronted with a situation in which we need to act quickly, it’s easier to process a few salient points than to consider every single detail. Animal brains, evolved in this system of competition, abstracts data to just the salient points. We wouldn’t be able to process knowledge of every single blade of grass and every rock under our feet as we chased down game in time to realize that’s a grizzly bear. The salient point is that it is solid and smooth enough for our legs to do its thing.
In the world of Big Data Analytics, we don’t perform analytics thinking about each individual sale or each heartbeat. Data is abstracted (using techniques such as Machine Learning) to salient points such as “sales of pho increase during colder weather” or these series of heartbeats is an erratic pattern associated with an impending heart attack. We abstract exabytes of raw data into a few megabytes of relatively few salient points, so that we can make fast decisions when that grizzly bear appears on our hike.
The difference between Big Data processing tons of elemental data into few salient points and animals abstracting countless photons in our eyes, molecules on our tongue, etc., is that we still store that elemental data in Hadoop clusters were we can re-abstract in case we missed something, but animals don’t store the raw information on every single photon and molecule.
The salient points in animal brains is something that lives only in there, artifacts of the past computing delusions of the future; it isn’t reality. Our energies are diffused in a tesseract of space-time (4-dimensional shape) which only exists in the wiring of our neurons. But by default we make decisions based on these salient points.
OK, OK … this post would take an entire book to fully explore. Rubber Ducky went on for three hours! The salient point of this lesson Rubber Ducky bestowed upon me is:
A big part of our angst is based on delusions of life that our brains reduced down to a few key points we use as shortcuts in our thinking. It takes effort to override these shortcuts, for example, a parent failing to remember her 30 year old son is no longer that little kid. If so many of the assumptions we subconsciously make in our decisions are inherently obsolete, our thoughts computed today are probably delusions. Therefore, if we’re constantly mindful of that, we can be comforted in knowing that our imagined angst most likely isn’t real, no matter how “real” it may seem.
Epilogue for Computer Folks: This lesson from Rubber Ducky is actually at the heart of better analytics as well as eliminating Dukkha. For most of the computer era, data structures are rigid, engineered once, even as the world around it changes, even over decades. Yes, programs and data structures are modified, but in very painful, Frankenstein manners. Mostly, more effort is made to keep the world in the same shape than to modify the software applications – “Oh, that’s not the way we do things here.” But the move from the “relational databases” of today with their 3rd normal forms to graph databases as the foundation of data enables the “fuzziness” of “things” in the world to the forefront.