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# Abstraction

Abstraction (from Latin abstraho, to draw away) is an important concept in programming, mathematics and other fields of science, philosophy and art, which in simple terms means "viewing something from a distance", thinking in higher-level concepts, paying less attention to fine detail so that one can better see the bigger picture. In programming for example we distinguish programming languages of high and low level of abstraction, depending on how close they are "to the hardware" (e.g. assembly being low level, JavaScript being high level); in art high abstraction means portraying and capturing things such as ideas, feelings and emotions with shapes that may seem "distant", not resembling anything concrete or familiar. We usually talk about different levels of abstraction, depending on the "distance" we take in vieweing the issue at hand -- this concept may very well be demonstrated on sciences: particle physics researches the world at the lowest level of abstraction, in extreme close-up, for example by examining individual atoms that make up our brains, while biology resides at a higher level of abstraction, viewing the brain at the level of individual cells, and finally psychology shows a very high level of abstraction because it looks at the brain from great distance and just studies its behavior. Scientific papers always begin with an abstract, a short "high altitude" overview and summary of the paper.

Mathematics is a discipline best exemplifying abstraction: it deals purely with abstract concepts. Initially this abstraction is mild -- numbers and sets for example -- and the more it advances, the deeper and harder to grasp the abstraction becomes, towards difficult to imagine concepts such as differential equations, categories, quaternions, different types of infinities, decidability etc. Some subjects go as far to almost lose any connection with the real world, leaving us completely without any intuition or a way to even visualize what we're dealing with. Ever growing abstraction is probably the inevitable purpose and objective of mathematics, but now let's rather ask how abstraction relates to programming.

In programming we have to be more careful: abstraction here means basically "making stuff easier by hiding and ignoring lower levels of abstraction", i.e. making libraries, languages and other tools that facilitate work with the astronomically complex computer hardware. In mainstream programming education it is generally taught to "abstract as much as possible" because that's aligned with the capitalist way of technology -- high abstraction is easy to handle for incompetent programming monkeys, it helps preventing them from making damage by employing billions of safety mechanisms, it allows them to quickly learn to do poorly what should be done properly, it also perpetuates the cult of never stopping layering of the abstraction sandwich, creating bloat, hype, bullshit jobs, it makes computers slower, constantly outdated and so drives software consumerism. As with everything in capitalism, new abstractions are products hyped on grounds of immediate benefit: creating more comfort, being something new and "modern", increasing "productivity", lowering "barriers of entry" so that ANYONE CAN NOW BE A PROGRAMMER without even knowing anything about computers (try to imagine this e.g. in the field of medicine) etc. -- of course, long term negative effects are completely ignored. Abstraction is useful but what's happening here is twisting its meaning: instead of ignoring details where it's acceptable and useful, abstraction is now used as an excuse meaning ignorance of details, i.e. whereas originally a programmer knew the details and would decide to ignore them where it's of benefit, nowadays the programmer doesn't know the details at all because he thinks he is allowed to by the existence of abstraction, and so he ignores them in any situation. This is BAD. It is basically why technology has been on such a huge downfall in the latest decades and why so many incompetent people flooded the industry. Opposing this, LRS advocates to employ only as little abstraction as needed, so as to support minimalism. Too much abstraction is bad. For example a widely used general purpose programming language should only have as much abstraction as to allow portability, it should definitely NOT resort to high abstraction such as object obsessed programming.

Upon closer inspection we find that abstraction is not one-dimensional, we may abstract in different directions ("look at the issue from different angles"); for example functional, logic and object paradigms are different ways of programming languages abstracting from the low level, each one in different way. So the matter of abstracting is further complicated by trying to choose the right abstraction -- one kind of abstraction may work well for certain kinds of problems (i.e. solving these problems will become simple when applying this abstraction) but badly for other kinds of problems. The art of choosing right abstraction (model) is important e.g. in designing computer simulations -- if we want so simulate e.g. human society, do we simulate individual people in it or just societies as whole entities? Do we simulate wars as a simple dice roll or do we let individual soldiers fight their own battles? Do we represent roads as actual surfaces on top of which cars move according to laws of physics, or do we simplify to something like mathematical graph connecting cities with mere abstract lines, or something in between like a cellular automaton maybe? Do we consider beings living on a round planet, with possibilities like meteor impacts and space flights, or do we simply consider people living on a flat 2D sheet of paper? Similar though has come to designing games (another kind of simulation).

Let's take a look at a possible division of a computer to different levels of abstraction, from lowest to highest (keep in mind it's also possible to define the individual levels differently):

- physics: Computer is a collection of atoms and subatomic particles such as electrons, operating with terms such as energy, charge, spin or quantum effects.
- electronic circuit: Computer is an analog circuit in which electricity flows through wires and electronic components, operating with terms such as voltage, current, transistor, resistor or electronic interference.
- logic circuit: Computer is a binary digital circuit; this is abstracting electricity away, now we are only considering two possible values carried by the wires: 1s and 0s. Operating with terms such as logic gate, logic function, multiplexer or sequential circuit.
- machine code/assembly: Computer is a machine with a specific instruction architecture, executing an algorithm encoded as simple binary instructions, such as "add two numbers" or "write a number to memory", in a specific format that's different for different types of computers. Operating with terms such as CPU cycle, opcode, register, memory or interrupt.
- low level portable language: Computer is a machine capable of performing algorithms written in a structured language resembling human language and it's a machine that's essentially the same as other computers, even of different types, i.e. all computers can understand the same language (programs are portable), typically e.g. C. Operating with terms such as structured data type, procedure, signed/unsigned type, memory management etc.
- high level language: Computer is a machine capable of performing algorithms while handling many things (such as memory allocation or ensuring safety) automatically and dynamically (on-the-go) and understanding more complex and abstract descriptions of problems, allowing for very fast and comfortable programming in languages like Python or JavaScript. Operating with terms such as objects, dictionaries, pure functions and polymorphism.
- very high level, artificial intelligence: Computer is a machine capable of simulating human thinking and therefore able to lead a conversation with human, it can perform commands given in natural language and even reason and create on its own. Operating with terms such as prompts, training, data sets and ethics.

## See Also

- https://unixsheikh.com/articles/we-have-used-too-many-levels-of-abstractions-and-now-the-future-looks-bleak.html

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