Writer and filmmaker Astra Taylor explains fauxtomation as "the process that renders invisible human labour to maintain the illusion that machines and systems are smarter than they are." (Faux means false or fake in French).

Taylor gives an example of standing in line to collect food and the person in front of her expressing amazement at how the app knew his order was done 20 minutes early. The server replies, "I sent you a message."

With the rise in incredible abilities driven by AI chatbots and tools, it's tempting to believe that AI can run anything. But, in many situations, claims of AI magic and sophisticated automation disguise work done by people—often outsourced and poorly paid.

An example of fauxtomation is content moderators, whose job is to review and filter objectionable content from the web when the claim is that sophisticated algorithms and AI are doing the work. Other examples are tagging or searching images, transcribing audio, responding to customer queries, making decisions, or, ironically, training machine learning models. In another twist, ordering on the app or touchscreen at McDonalds or the self-checkout at a supermarket, you, as a customer, become the labour of a server or waiter.

Some have argued that automation polarises work into vast quantities of small, menial tasks feeding the machines on one end and sophisticated, high-skilled work in designing and maintaining the machines at the other.

Amazon has long offered its Mechanical Turk service, which crowdsources assignments with a distributed workforce—they called it artificial, artificial intelligence. The original Mechanical Turk was a hoax from the 1700s where a concealed human chess player fooled onlookers into believing a machine was playing.

As a design and UX professional, I've used the Wizard of Oz prototype technique. Named after the infamous wizard secretly controlling impressive machines from behind a curtain, the idea is to give the appearance of a working software prototype by discreetly controlling what people see based on their actions.

AI promises instant automation, but that's rarely the case. As you work to automate and remove inefficiencies, it's natural that systems should be supported by people.  Human-augmented work may be a more appropriate term. In many domains, especially where safety is at stake, such as in self-driving cars, reviewing legal documents, or automated background checks, oversight by people may remain critical.

Alongside the AI revolution, a quiet revolution nearly as significant for our everyday experience is the tying together of different tools for automation. Tools like Zapier, Google Sheets, Notion, and the growth of APIs allow us to automate workflows across systems, saving massive amounts of time and effort.

While the vision of machines reducing our labour persists, history shows that they often drive increased productivity instead of reducing work. This echoes Jevon's paradox, where fuel efficiency gains usually result in increased fuel usage rather than less.

I saw an excellent talk by machine learning and AI expert James Smith, in which, before you try machine learning ("I hear you have Magic Pixie dust. Can I have some?"), he would ask, "Have you tried 50 IF statements?" These programmed rules will be more maintainable, less complex, and result in responses that are easy to understand.

I learned about fauxtomation from Tom Humberstone's remarkable visual essay What Luddites Can Teach us About Resisting an Automated Future in the MIT Technology Review. Fauxtomation is similar in concept to Potemkin AI, a term from researcher Jathan Sadowski.

You can read Astra Taylor's article, The Automation Charade, or watch her discuss fauxtomation at the AI Now 2018 symposium.

Sketchplanations continues to be done by me, by hand. If you'd like to support me, I'm improving my Patreon, which makes such a difference in helping creators like me keep creating. Thank you.

Here's a fauxtomation sketch by ChatGPT in case you're interested =)

As well as fauxtomation prints, you can get AI department prints

Fauxtomation

We find it handy to count in the decimal system using 10 numbers from 0,9, known as base 10, before we have to put two together to make 10 to keep counting further — 10 fingers and toes and all. But it turns out you can represent all numbers equally using just two digits, known as base 2, a 0 and a 1. This is called the binary system and is credited to Gottfried Wilhelm Liebniz in the 1600s.

The binary system is handy because 1s and 0s can be represented by simply by on/off and reproduced in as simple means as pebbles in trays , the sign of a magnetic field eg positive/negative, or a gate as open or closed. This made them the choice for designing computers and is how digital information is stored and transmitted today.

When we write a decimal number we use positional notation with each successive position representing 10 to the next power. So 739 is understood as (7 x 10^2) + (3 x 10^1) + (9 x 10^0). 10 to the power 2 is 10 x 10, so is the number of 100s in the number. Anything to the power 0 is 1.

The same is true in binary, so 1101 is understood in decimal as:

1101 = (1 x 2^3) + (1 x 2^2) + (0 x 2^1) + (1 x 2^0)

1101 = (1 x 8) + (1 x 4) + (0 x 2) + (1 x 1)
     = 8 + 4 + 0 + 1
     = 13

Each 0 or 1 in a binary number is known as a bit — named by Claude Shannon as short for binary digit — and 8 bits is known as a byte.

Translating to decimal looks fiddly, but computers don't have to do that, they can just add, subtract or multiply the binary numbers directly. Amazing to think that the device you may be reading this on now is just incredibly efficient at manipulating 1s and 0s.

For a readable and visual introduction to the history and operation of computers — from binary, logic gates, transistors, circuits, and Moore's law through to software and AI — you could do a lot worse than my Dad's book The Computing Universe 😀

Binary

The Two Generals Problem is a fun thought experiment highlighting the challenges of communicating over an unreliable channel. Your message may or may not get through, and how will you know if it didn't?

The situation is that two armies on either side of a hostile valley are trying to coordinate an attack, and they want to attack simultaneously to be sure to succeed. The trouble is, to coordinate, they have to send brave messengers through the hostile valley to the other camp, and the messengers may or may not be captured along the way.

To coordinate the attack, you have to know that your message got through, which you’d know if you received a confirmation. But how would the general sending the confirmation know that their confirmation got through unless they received a confirmation of the confirmation? And on…

The two generals problem

Idempotence is essentially an operation that, no matter how many times you do it, you’ll still get the same result, at least without doing other operations in between. A classic example would be view_your_bank_balance being idempotent, and withdraw_1000 not being idempotent. It’s a property that’s often handy as you can retry the operation without worrying about unintended effects.

Idempotence is a concept from mathematics and computer science but can be applied more generally. For example, simple on and off buttons are idempotent, a property that can be very useful in emergencies.

Idempotence

Koomey's law roughly translates to: The energy efficiency of computers doubles roughly every 18 months.

In a new twist beyond Moore’s Law, which shows signs of reaching it’s limits, there’s Koomey’s Law. In his own words, the idea is:

“…at a fixed computing load, the amount of battery you need will fall by a factor of two every year and a half.”

Why does this matter? It means we can fit more computing power into smaller and smaller devices. This is much of the reason the phone in your pocket is probably more powerful than the EDVAC there in the chart.

Koomey’s Law

This chart is from the original he published - just the five data points. Pretty simple huh? The “observation, originally made in 1965, that the number of transistors on an integrated circuit would continue to double every year. Although this increase has slowed to a doubling in eighteen to twenty four months, Moore’s law has held true for nearly fifty years and is the foundation for the astounding Information Technology revolution we see around us.”

For those interested in Moore’s law, remarkably, I think the best information is my Dad’s book, The Computing Universe, which is also the source of the quote.

Moore’s Law

Big data + Machine learning + Cloud = Informed, intelligent decisions. As a very minor example of this I have been enjoying the Dark Sky app.

Computing sketches