In a world where everything seems chaotic and it seems that many things happen randomly, it is quite comforting to hear that “machine learning, and prediction, is possible because the world has regularities. Things in the world change smoothly.” Of course, in this case, Ethem Alpaydin is speaking about the ways in which we can train our AI in order to complete a task or make a prediction, but nevertheless, these systems are trained on data from the world we live in. In fact, the smoothness assumptions of our sensory organs and brain are so important because they are necessary for our learning algorithms, which make a set of assumptions about the data to find a unique model. And while we probably all hold the viewpoint that many of the technologies we see today are extremely complex (which they are), the reason we can train our data to make predictions is that we are unconsciously trying to find a simple explanation for this data. Beyond technology itself, preferring simple explanations is human nature; philosopher Occam’s argues that we should eliminate unnecessary complexity for more favorable interactions. In fact, barcodes and single fonts are the ideal images because there is no need for learning, we can simply template match them

One could argue simplicity is why the binary system works so well for our electronics. Because this system is discreet, aka able to be distinct and differentiated. “We need designer electronics to impose a shape, a pattern, a structure, on a type of natural energy that is nothing like human logic or meaningful symbolic patterns” Professor Irvine states. And the simplest electrical pattern we can design and control is switch states (like on/off, open/closed, etc). Given this, the binary system, which only has two positions and two values, is an efficient way to transform digital binary computers into symbol processors. Binary and base 2 math lead to a mapping system for a one-to-one correspondence and overall present a solution to a symbolic representation and symbolic processing problem. Through this process, we can make electricity hold a pattern in order to represent something not electronic (i.e. something more human). The binary system provides us with a unified subsystem with which we can build many layers on and thus create data structures in a defined pattern of bytes.

When applying this to deblackboxing, in which we remove the notion that a computer/program’s inputs and operations are not visible to the user or another interested party, we can see that at its heart simple systems are used to create our technologies. The principles of computing (communication, computation, coordination, recollection, evaluation, design) in this case are useful, as “some people see computing as computation, others as data, networked coordination, or automated systems. The framework can
broaden people’s perspectives about what computing really is.”

Again, when specifically applied to the principle of computation, we can see that at the heart our systems are composed of layers of binary maps – yes/no’s, 0/1’s, on/offs. It’s beautiful, but there is no ‘magic’ underneath the hood of our systems. We store and train on data, use math, and develop our algorithms to create the technologies we have today.