Six eponymous laws that apply to automation and AI
Many people have given their name to a law, adage, principle or observation. Some coin the prediction they make, others have the nuggets of observation ascribed to them.
These eponymous laws come from technicians, scientists, developers, philosophers and inventors. And they cover everything from life and computing, to crickets chirping and the electrical conductivity of rocks.
Naturally, then, there are also eponymous laws that can apply to automation and AI. Here are six of them.
Asimov’s Three Laws of Robotics
Starting with a famous example, Asimov’s three laws of robotics made their debut in a 1942 short story titled “Runaround”. They have remained popular with science fiction writers ever since.
Formulated by American writer Isaac Asimov, the three laws of robotics are as follows:
First law: A robot may not injure a human being or, through inaction, allow a human being to come to harm.
Second law: A robot must obey the orders given it by human beings, except where such orders would conflict with the First Law.
Third law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
Zeroth Law: A robot may not harm humanity, or, by inaction, allow humanity to come to harm.
Neither automation nor AI today actively incorporate Asimov’s laws. But they have still proved influential in ethical discussions surrounding the growing technology. These eponymous laws outlined a basic ethical guide for robots, before robots were much more than a science fiction dream.
Clarke’s third law
Arthur Clarke was a British science fiction writer, inventor, and futurist. As part of his exploration into the future, Clarke formed and gave his name to three eponymous laws. But it’s the third of these that most applies to automation and AI.
“Any sufficiently advanced technology is indistinguishable from magic.”
When set up to work well, automation can feel like magic. Your dull, repetitive tasks get completed on their own. You see productivity boosts and a higher sense of accomplishment when you complete higher-value work. It’s important, however, to remember that it’s automation, not automagic.
Artificial intelligence has a similar effect. The dreams for what AI programs could one day achieve would seem like magic today. But, as artificial intelligence unlocks ever-more advanced abilities, this earlier sense of wonder sometimes wears off.
Our next eponymous law comes from engineer and inventor Douglas Engelbart:
“The intrinsic rate of human performance is exponential; that while technology will augment our capabilities, our ability to improve upon improvements is a uniquely human endeavour.”
Engelbart’s law is a reminder that automation and AI are not nearly so powerful as the human brain and our human tenacity to succeed. AI doesn’t care about its performance. It doesn’t agonise over achievements.
Though we can now train machine learning algorithms to get better at getting better, this drive to improve is ultimately not a robotic attribute. It’s a product of human motivation and yearning to progress.
Kranzberg’s laws of technology
Melvin Kranzberg was a historian who taught the history of technology at Georgia Tech. He was also one of the founders of the Society for the History of Technology in the US.
An impressive six laws of technology come with Kranzberg’s name attached. But it’s the first of these eponymous laws that’s particularly applicable in discussions about automation and AI:
“Technology is neither good nor bad; nor is it neutral.”
Automation and AI are tools. They cannot be intrinsically good or evil — they do nothing out of malice. Though they may seem omniscient at times, it’s important to remember that AI and automation are merely instruments to be picked up and used by humans as needed.
But they’re not completely neutral, either. Technology can be used for either good or evil, and it can propagate disparate outcomes. Particularly in the case of AI, these tools can amplify unconscious biases.
Perhaps the most well-known and universal of the eponymous laws on this list, Murphy’s law is one of those unavoidable features of life. And, as it turns out, of automation and AI.
Murphy’s law, attributed to Edward Murphy (though not coined by him) states that:
“Anything that can go wrong will go wrong.”
As a rule-based system, automation does what you tell it to do. If, then, you give it faulty or incorrect instructions, it will either not work at all, or complete the task incorrectly. It won’t notice anything is wrong. The same applies in the case of trying to automate a broken process.
Similarly, AI is not yet at a state where it can notice things such as biased data or incorrect output. This means that care must be taken when training artificial intelligence. Accidentally biased data can, and has, result in biased results from AI programs.
Papert’s principle is named after Seymour Papert, a mathematician, computer scientist and educator. He worked on both artificial intelligence and a movement in education known as constructionism.
Papert’s principle is often used in child psychology. But it can also apply to AI, specifically machine learning.
“Some of the most crucial steps in mental growth are based not simply on acquiring new skills, but on acquiring new administrative ways to use what one already knows.”
For AI, Papert’s principle demonstrates a way to think about how we teach machines. It’s not only about teaching machines to do new things. Rather, a part of intelligence is being able to build on your knowledge and find ways to adapt to new challenges.
Eponymous laws of automation and AI
These are just six of the most pertinent eponymous laws to apply to AI and automation. But automation and AI are technology tools that slot into many aspects of modern life.
Countless eponymous laws exist to cover all sorts of disciplines, ideas, and observations. As such, there are plenty more that could apply to the technologies.
And, as time goes on, more people will likely lend their name to new rules and truisms that apply to the growing field of artificial intelligence. Watch this space for future wisdom.