Should I be scared a smart algorithm will take my job?

Written By: Eric Kimberling
Date: May 20, 2022

Defining an algorithm

Wikipedia defines an algorithm in mathematics and computer science, as a self-contained sequence of actions to be performed. Algorithms can perform a calculationdata processing, and automated reasoning tasks.

An algorithm can be viewed as series of steps which performs a particular computation or task. Algorithms were originally born as a child of mathematics – the word “algorithm” coming from the Arabic writer Muḥammad ibn Mūsā al-Khwārizmī, – but recently, the word is strongly associated with computer science. Algorithms are not a new invention, but whereas previously it was created for humans it is now created for machines or AI.

The role of algorithms in modern society

As articulated by Chandra Johson in the article -How algorithms affect our way of life -, on our phones, tablets and other devices in our digital world, algorithms hail our Uber rides, decide whom we should befriend on social media, and choose shows, movies and products for us to consume. They can even help companies decide what candidates to hire and help universities diversify their student bodies. Johson further articulates that one day, automakers hope, complex algorithms will drive cars more safely than humans do.

If you are still fantasising about having a machine to decide on your medical treatment, on whether you could insure your house if you should be hired, or what news stories you read, be assured it may be happening to you already. Think about the fact that every time you go online to make a purchase, search for a restaurant, access your bank account or simply interact with your mobile device, you are creating a digital trail of data that is being tracked and stored. This “big data” is fodder for machine learning algorithms that will, for example, suggest what to buy. There is no denying algorithms are all around us and active in all spheres of society. This deluge of Big Data is what the AI world needed to fuel their smart algorithms. And the more data they have, the more intricate the algorithms can be.

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In a report prepared by the previous Obaman administration on: "PREPARING FOR THE FUTURE OF ARTIFICIAL INTELLIGENCE", it states that the current wave of progress and enthusiasm for AI began around 2010, driven by three factors that built upon each other: the availability of big data from sources including e-commerce, businesses, social media, science, and government; which provided raw material for dramatically improved machine learning approaches and algorithms; which in turn relied on the capabilities of more powerful computers. Now, remember we are in the age of exponential growth so exactly where are algorithms headed?

The future of algorithmic business and society - machine learning

In his book: "The master algorithm", Pedro Domingos writes that society is changing, one learning algorithm at a time. He further articulates that machine learning is remaking science, technology, business, politics, and war. Satellites, DNA sequencers, and particle accelerators probe nature in ever finer detail, and learning algorithms turn the torrents of data into new scientific knowledge according to Domingo who has been in the field for over twenty years.

We are teaching our machines to "think" for themselves. Edward C. Monoghan writing in Wired magazine stated that our machines are starting to speak a different language now, one that even the best coders can’t fully understand. He goes on to state that over the past several years, the biggest tech companies in Silicon Valley have aggressively pursued an approach to computing called machine learning. In traditional programming, an engineer writes explicit, step-by-step instructions for the computer to follow, however with machine learning, programmers don’t encode computers with instructions but train them, writes Monoghan. He goes on to state that if you want to teach a neural network to recognise a cat, for instance, you don’t tell it to look for whiskers, ears, fur, and eyes. You simply show it thousands and thousands of photos of cats, and eventually, it works things out. If it keeps misclassifying foxes as cats, you don’t rewrite the code. You just keep coaching it. Can you see where this is heading?

Ray Kurzweil (inventor, author, futurist) says that machines will follow a path that mirrors the evolution of humans. Ultimately, he goes on to say, that self-aware, self-improving machines will evolve beyond humans ability to control or even understand them.

Should I be scared?

The Boston Consulting Group has predicted that by 2025 as much as a quarter of jobs currently available will be replaced by either smart software or robots. A study out of Oxford University also suggested that as much as 35 percent of existing jobs in the U.K. could be at risk of automation inside the next 20 years. Maybe this is hype, but I personally believe that many of these types of predictions will be close to the mark or even exceed it. I do however think that it will be a symbiotic relationship initially which we can evolve alongside. As stated by Domingo in his book "The master algorithm" that your digital future begins with a realisation: every time you interact with a computer—whether it’s your smartphone or a server thousands of miles away—you do so on two levels.

The first one is getting what you want there and then: an answer to a question, a product you want to buy, a new credit card. Domingo goes on to state that at a second level, and in the long run the most important one, is teaching the computer about you. The more you teach it, the better it can serve you—or manipulate you. Life is a game between you and the algorithms that surround you. You can refuse to play, but then you’ll have to live a twentieth-century life in the twenty-first. Or you can play to win. Domingo sums it up by saying "What model of you do you want the computer to have?"

In other words, we are going to have to reinvent ourselves and the jobs we currently do. After all my tribe of humans has evolved over millennia to take advantage of whatever niche opened up on planet earth. We cannot stop this onslaught of AI so best we prepare for what is coming.

In summary, Picasso might have said that computers can only give you answers, but in the near future they will be providing answers to questions that we as homo sapiens could not even conceive to ask.

James Barrat writing in his book "Our final invention" states that the failure to explore and monitor the threat is almost society-wide. Barrat goes on to state that this failure does not in the least impact the steady, ineluctable growth of machine intelligence. Nor does it alter the fact that we will have just one chance to establish a positive coexistence with beings whose intelligence is greater than our own. Some experts are speculating that society’s own sense of survival will promote more of a balance between technology and humans before the economy enters the realm of science fiction. Homo Sapiens, after all, is still in charge of technological innovation for now, with the ability to correct course as the digital landscape of algorithms changes.

“We are finally reaching a state of symbiosis or partnership with technology. The algorithms are not in control; people create and adjust them,” Microsoft principal researcher Jonathan Grudin has stated. “I’m optimistic that a general trend toward positive outcomes will prevail.”

Food for thought - or food for the algorithm?

Kimberling Eric Blue Backgroundv2
Eric Kimberling

Eric is known globally as a thought leader in the ERP consulting space. He has helped hundreds of high-profile enterprises worldwide with their technology initiatives, including Nucor Steel, Fisher and Paykel Healthcare, Kodak, Coors, Boeing, and Duke Energy. He has helped manage ERP implementations and reengineer global supply chains across the world.

Eric Kimberling
Eric is known globally as a thought leader in the ERP consulting space. He has helped hundreds of high-profile enterprises worldwide with their technology initiatives, including Nucor Steel, Fisher and Paykel Healthcare, Kodak, Coors, Boeing, and Duke Energy. He has helped manage ERP implementations and reengineer global supply chains across the world.
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