Even Our Patterns Are Fractured
Understanding why we can’t get a handle on things and some people seem really dense
by Mike Meyer ~ Honolulu ~ November 30, 2020
Living in a shattered world makes it very hard to see our future. As pattern detectors, we have evolved by continuously making sense of our environment by matching its patterns to our memories. Sentience, at least on this planet, is pattern matching to determine threats or opportunities.
With destructive domination on this planet, we have proven the power of pattern matching. After several decades of false Artificial Intelligence starts, we have given up on various analytical logic forms and discovered that Machine Learning actually works.
Much of the problem was our own failure to see the simplistic nature of our intelligence. We were looking in the wrong places. It is not the complex processing power of our minds that have handed us the world but basic pattern matching at all levels of our experience.
We know we look for patterns continuously, we can’t stop looking for them, and we know that if there are no patterns, we will see some anyway, sometimes with disastrous results. We also know that if we see a pattern, we cannot unsee it. It will always be there somewhere.
While we can build and understand complex systems and apply sophisticated structural patterns as models, it’s all just pattern matching. The great leaps in Machine Learning represent understanding both our power and our limitations.
Machine Learning uses algorithms to define simple patterns and then use vast amounts of examples to learn the variations. That is not a biological process or an analytical process but pattern matching that does an amazing job of imitating our intelligence form.
The scary part is that our electronic systems operating at near light speed are much faster and can be equipped with vastly more memory. Despite our long desire for brilliant devices that think and act as we do, we screwed up and figured out the real secret of our intelligence.
Experience is the basis of knowledge, but that means how many years and how many patterns you’ve been able to match. People who spend their lives looking for a pattern in as much information can find a lot more than those who do not experience as much.
People with more patterns can project new patterns as variations of the old. That makes them much more interesting than those people who never got past the publications at the checkout counter in the supermarket in their quest for patterns. Interestingly, we are dealing with a new reality that machine learning intelligence is not fun or entertaining, but it is getting there.
While our experience with Machine Learning is in its uses for facial recognition but usually applying a bad pattern beyond simple recognition, the algorithms are the product of people who don’t even realize the patterns they are inadvertently using to build matches. Our structural racism, misogyny, and ethnocentrism show up. After all, we are the products of cultural algorithms that we use to classify the patterns we match.
Just because our intelligence is much simpler than we like to admit does not mean the results are simple. But it does mean our inability to keep track of cultural patterns and conditional errors in those patterns that we may be at the limit of abilities.
Wait that doesn’t sound good! You are correct. We need augmented memory storage to even keep up with what is happening. And some people are limited in the number of daily patterns they can handle, while others are overwhelmed in dealing with electronic tools to boost our memory.
The basic problem we are facing in the mid 21st century has been developing for a long time. Intelligence is how many patterns you can actively match with the number of outcomes (matches) you can retain. But since we are way past our biological storage limits, we need to use mnemonics and flags to know where to look for matches we have forgotten. Those memes and flags are the keys to our external processing and storage.
Our educational systems came to grief because they were institutionalized over a hundred years ago when knowledge was based on individual memory supplemented by physical libraries. We are still teaching people as if they need to remember everything. That is so far out of reason that it is no wonder our youth are abandoning colleges and going for select experiences augmented by internet memory.
Our bigger problem is the fractured nature of our universe. The gaps that divide people are the near-complete loss of shared patterns with different subgroups in our societies. This is a bit like the discovery of Machine Learning in making machine intelligence useful. The sheer quantity of the pattern matching process produces qualitative knowledge far beyond the quantitative matches' total.
Not surprisingly, this has a close relationship to patterns we see in modeling our universe's initial expansion and to, of all things, philosophy in understanding that patterns are all that there is. We’ve lost control of our patterns. We always thought they were representative of things, but our idea of things is simply a representation of patterns.
But there are far too many patterns that fade between being abstract patterns that we insist on seeing as things in the old sense that confuse us. We need to give up remembering things and worry about getting very good at using our machine intelligence for organizing our patterns.
That is the problem with identity and diversity also. Traditionally we have always made people into things that fit a fixed pattern, although we really knew that was not true. By having to abandon things as the primary objects in our lives, we are stuck with inherently dynamic and unstable patterns, and a lot of people don’t like that. Sorry, that’s the way the universe expands.
This means that we no longer have any workable ‘big pictures’ that can be easily identified as shared. We have only dynamic patterns, and too many people still haven’t broken the old patterns of things.
Unfortunately, things as fixed and stable objects are now an illusion. Once you see that pattern, there is no going home again. That means we need to educate ourselves on a very different relationship with our intelligent machines, not as anthropomorphized threats but as the tools we need to exist in the new algorithmic age.