Understanding Automation

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How nice it would be to automate every little boring job that we currently now have?

You, reader, might be thinking that yours is an administrative affair at the office that could be completed within one or two hours with a basic understanding of a computer programming language1. Or you could be a sewer hunter2 of our modern times, wishfully thinking, “When do we finally invent a machine that can complete the job, instead of me going down the drain?” Unless it’s something you enjoy doing. In which case, more power to you!

Automation of jobs are key to a more fulfilling life, especially for others whose jobs constantly threaten their safety and well-being, perhaps resulting to endangering others as well. It has the potential to free people from their regular miseries. Imagine all the things one could do after regaining the eight-hours-a-day six-days-a-week time that was previously allocated for “work”. More people would then look into other avenues of creating value.

Is it even possible?

There’s a lot to unpack here, and I might not be able to discuss them all. Here are a few thoughts.

To be Sick and Tired

For blue collar jobs: doing something for one-third of a day is exhausting, physically and mentally.

For white collar jobs: pretending to be doing something for one-third of a day is exhausting, physically and mentally.

For every other job in between: see either of the above two.

By the end of your work, when you’re really tired, it’s hard to be in a mental space to venture to other hobbies.

Capitalism pushes us to work until we drop dead, because it doesn’t want us to do other things that have no profit motive. It destroys freedom.

Since automation would allow the working class more freedom, it is heavily regulated by the ruling class.

High-tech dystopia?

Automation of everything requires an access to a certain level of technology. The consensus is to develop artificial intelligence and/or machine learning. Engineers and scientists have been exploring fields into which AI integration would vastly improve the quality of work.

But, as with all technology, its capabilities are a function of the biases of those that develop them. Like how dictionaries can be racist.34 Or like how facial recognition software can be racist. 56

Moreover, the idea that machines are more efficient in doing tasks than humans is important to acknowledge here. We must remember that while the Industrial Revolution (arguably) increased productivity by employment of machines, it has also allowed destruction of natural environment which, in my opinion, offsets the benefits gained.

Consider this: The future of automation is likely to rely on the development of artificial intelligence and machine learning. AI and ML relies on the processing prowess of computers, which rely on their hardware. Electronics rely on mining industry, supply chain logistics, and oil industry, all of which harms the environment on a global scale.

There’s may be healthy compromise to be made among Effectivity, Efficiency, and Environmentalism. Our focus, then, should be in figuring that one out soon.

Besides, everything neither can be or has to be fully automated.

What is possible?

For now, we have to recognize that we cannot automate every boring task there is, not even at the rate which our technology upgrades. In fact, at the current rate,

Machines are still not reliable (as of this writing) to make all the important and, dare I say, humane decisions.

Suggestions for doing the Dirty Work

In the meantime, we need to consider several things when doing work that nobody wants to do:

  1. If nobody does it, and nothing happens when it doesn’t get done, it probably didn’t need to be done in the first place.
  2. If the outcome does need to be achieved, we might look at alternative methods of achieving that end. For example, if nobody wants to clean the clogged estero, then we should be mindful of our domestic waste and stop throwing them in the estero!
  3. If that task is the only way of achieving that end, then we should look at why it’s unenjoyable, and how we might make it more bearable. We might also look at how it can be broken up into more bearable chunks. Still maintaining the clogged estero as an example, it’s understandable why attempting to clean it can be unpleasant. Organizing the community to participate in a clean-up drive helps distribute the work load. To prevent fatigue, such clean-up drive would only last between three to six hours, scheduled over a period of time (say, thrice a week). Other members of the community could also provide free food for the cleaners as an incentive.
  4. If nobody still wants to do it, it has to be done, there’s no other available options, and it’s as optimized and bearable as we can make it, whoever is able probably just takes turns. TNU

  1. Even if you’re a non-programmer, you will still benefit a lot by learning a programming language. If you have no idea where to start, why not try Python (https://python.org)? ↩︎

  2. Dash, M. (2012, June 29). Quite Likely the Worst Job Ever. Smithsonian Magazine. https://www.smithsonianmag.com/history/quite-likely-the-worst-job-ever-319843/ ↩︎

  3. Abagond, J. (2016, April 5). “The dictionary was written by White people.” Abagond. https://abagond.wordpress.com/2016/04/05/the-dictionary-was-written-by-white-people/ ↩︎

  4. “BUT THE DICTIONARY SAYS—” (2012, October 20). FUCK YOUR RACISM. https://fuckyourracism.tumblr.com/post/33943946460/but-the-dictionary-says ↩︎

  5. Najibi, A. (2020, October 26). Racial Discrimination in Face Recognition Technology. Science in the News. https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/ ↩︎

  6. Breland, A. (2017, December 4). How white engineers built racist code – and why it’s dangerous for black people. The Guardian. https://www.theguardian.com/technology/2017/dec/04/racist-facial-recognition-white-coders-black-people-police ↩︎