Non-Operating Systems, part 2: State of the Art

The Anthropology of Prediction and Control

The interfaces we use to navigate our world, our resources and our selves are useful clues to how we think about the world and how we got to our current state of the art of living.

Proceeding in chronological order is misleading. It is more instructive to consider situations and systems we all take for granted and think we have mastered mechanical control over. Then we can go further back into our history to see if we’ve ever had inklings of a different approach.

Assumed Mastery of Control


C-130T Instrument Display, US Navy

The dashboard of a typical and/or an atypical airplane is full of various knobs, dials, meters, sticks, switches and dashboards. All of these interfaces have evolved overtime to be efficient for a human operator with typical ten fingered hands, two eyes, two legs, a brain, etc. They are efficient for a human with extensive training to fly an aircraft. We cannot make any general claim about the efficiency of this system outside of all those contingencies.

And yet we do.

Now consider the control interfaces we’ve evolved to operate Drones (unmanned air vehicles aka UAVs). In addition to the ideas of control systems in cockpits we borrowed whatever a computer operating system uses as knobs, switches, windows, forms. Slammed all of those interfaces together with abstracted satellite view maps, qwerty keyboards, LCD monitors, OS notifications, search bars, joysticks (JOY sticks)

A team from NAVAIR simulates the operation of the future MQ-25 during a demo of the Unmanned Carrier Aviation Mission Control System (UMCS) at NAS Patuxent River, Md. in April 2017. U.S. Navy photo

How should we consider the plane itself? In one sense we consider the plane as a control system itself, as the US Military does with the E-3. This is a craft providing signals about various air and ground phenomena and is used to signal to other aspects of the military and battlefield infrastructure.

The E-3 Sentry Airborne Warning and Control System is based on the Boeing 707

Now the plane itself exactly is what? How much control do we actually have over the physical plane itself and when is that control exerted? In the design phase? While it’s being flown? Only once something “goes wrong?”

Consider the raw Boeing 707 specs as provided by the Boeing Company ( )

Cockpit of the 707

A Computer Aided Diagram of the 707 facade.

None of this is near enough to fully operate the 707. We need to understand all of its mechanics, assembly and repair ( . We have to understand how to operate the experience of riding in the 707. We have to understand how to operate airports and holding infrastructure.

How to hold cargo on a 707

We also need to be able to coordinate between planes and airports and other resources.

An air traffic control tower (wikimedia commons)

Interior of Air Traffic Control (wikimedia commons)

How much room is needed to turn the plane on the ground

For a single commercial carrier flight there are 20–30 people required to operate the airplane.

Captain, First Officer, 3–7 flight crew, air traffic controllers, airport personnel, computer system operators, ground engineers, etc.

The captain has to have substantial experience before commanding even a single flight. Beyond the flight operation training a commercial pilot must also maintain medical training, being in great physical health and maintain various “character” levels.

This is all in addition to our creation of “auto pilot” which has introduced new operational concerns for the operators of the operations.

“As one pilot whom Wiener interviewed put it: “I know I’m not in the loop, but I’m not exactly out of the loop. It’s more like I’m flying alongside the loop.””

What exactly can and cannot be operated and automatically operated on a plane? Or any complex system? How do we even assess how much of the space of behavior a system has can be observed much less operated?

The Computer and Its Non-Operation

More of us are familiar with modern computer operations. Go a little deeper into them.

The history of the computer operating system is well documented. If a reader is unfamiliar with it there are many books on it and the Wikipedia page is an acceptable jumping off point. The core to understand is simple. Computer operating systems come from a long line of small efforts to manage mechanical resources at tighter and tighter scales.

Windows 1 through Windows 10+ represents some stylistic changes but not really any new control paradigms. A strange evolution considering what has happened since Windows 1 was released and how much computing systems have changed. ( )

In 1985 this was how we viewed working with a computer.

Windows 1, 1985

Windows 10, 2014

The iOS and iPhone hardware and software evolution in 10 years

These interfaces tell a different evolutionary and control story than the mechanics they operate.

In the last 8 years alone the memory, CPUs and hard disks are all faster, smaller, and harder to understand than ever. The displays and manual interface tools (mice, touch screens, keyboards, etc) have all dramatically changed. More users are using touch screens that traditional input devices, which changes how people expect the computer work and how the user interface on the display could and should work.

In the last 30 years the internet has connected 15 billion devices, had all sorts of security challenges and opened up huge “computing clouds.”

Go back a bit further. The Analytical Engine, a series of gears that could calculate routinely was constructed in the 1870s, while its conception was nearly 50 years earlier, based on learnings from using looms and other factory machines. It could compute simple mathematical things.

Science Museum | Science & Society Picture Library

This engine would be controlled via a set of cards with holes them that allowed different gears to move

Punch cards had a very long history as an operating interface for a variety of reasons.

In 1977 Radioshack introduced the TRS-80 and Apple with the Apple 1 and so on….

The iPad hits popular markets in 2010, after 17 years of development.

More history:

Marketing of the computing revolution (not actual reality, but a good story, and good forensics for how we think we got here)

But knowing some of the history of the hardware and seeing what the “interfaces” do is not really enough to understand the function of the computer. We have to look at how computers are actually operated. When you click or touch something what makes the hardware compute? Software and programs.

We saw earlier the earliest programming language was holes in a card that corresponded to different gears etc. For obvious reasons this eventually became tedious for longer instruction sets and paper punch cards aren’t durable enough to last so we had to invent digital punch cards, called programs. And we write those using different programming languages.


Programming languages are a mess of evolution. There are many competing theories as to why some have taken off and others haven’t. But it mostly doesn’t matter. There are basically 3 main phases in the history of languages. Phase 1) mimic the punch cards in the CPU (assembly/machine code/circuit logic) Phase 2) low level, but abstracted languages (like c) and Phase 3) human readable/proto human natural languages (like Java, javascript, python, etc).

“Hello World” in the different phase languages makes the distinction clear:

Phase 1: assembly (literally tells the computer which physical location to jump and move to)

Phase 2: hello world in C. (a bit simpler but still not human like)

Phase 3: Python (just print it out.)

The language evolution appears to be a reasonable progression, something we can claim operational efficiency over. Unfortunately it’s an illusion. Making some interface more human readable doesn’t mean it’s easier or better for the computer or leads to a better outcome in terms of programming and operating computing machines.

Summary of Bug % in programs by Language from ACM Study

A longitudinal study from 18 years of programs provides insight into the strange loopiness of the trade offs in different paradigms of programming languages.

Taking it all in we can see that while the computer has undoubtedly become ubiquitous it has not been some obvious, forward progress, efficiency in operation history. The metaphors of interfaces and the approaches to hardware are mixed and matched. No doubt that we can compute large numbers and spit out images faster and connect things in interesting and surprising ways… but can anyone say how or why or whether it’s efficient or going anywhere or has any idea how all or much of it actually works?

Why are we trying to make computers work like paper and pencil? What is it about pencils that work so well? Why do we want to touch the stuff in the computer rather than type it? Why do we need user interfaces? Why is programming a computer so hard? How are we somehow assuming we’re going to go from facsimile pencil+touchscreen paper, error prone programming, weird UI metaphors and machine learning to automation of all human activity?

These questions and their inevitable non-answers are not actually that particular to computing, computers, flying nor airplanes. They are particular and general to all of reality. We will go deeper into general reality later.

Despite All This Evidence

Despite all this evidence that most people and certainly specialists are aware of we still call our systems Operating. We still think we can predict what’s next. We still expect our programs to do what we tell them to do. We assume that planes will fly themselves and cars will drive us one day and all our jobs will be lost to thinking machines.

Certainly this isn’t the first moment we’ve thought our existence could be outsourced to something larger than us other?

This is part 2 of a n-part series. part 3, herein. part 1, recently.

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I be doing stuff. and other stuff. More stuff. I believe in infinite regression of doing stuff.

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