The Robots in Our Lives: The subtle side of Artificial Intelligence

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When’s the last time you asked a robot to do something for you? I can tell you I do it almost constantly. My most consulted robot lives in a mainframe somewhere in the great Google warehouse in the sky. At least, I like to think so.

Artificial intelligence has been a theme in science fiction since the genre’s beginning. And all aspects of the idea of a “thinking machine” are out there. For most classic stories, it never seemed like something possible. With computers like ENIAC taking up an entire room, coding taking place on cards, the idea that a human might someday be unable to keep pace with a thinking machine was sheer lunacy. But thanks to Moore’s Law, we now have a link to powerful artificial intelligence in our pockets nearly 24/7.

Fans of science fiction can go into any way-back machine they like and find conjecture on AI. From Kuttner’s Ego Machine to Mari Wolf burning the term droid into our collective consciousness, the greats of science fiction love those thinking robots. But with the end of the holidays looming, there’s another aspect of artificial intelligence that we may be less aware of, even though it affects us every day.

We are all at the whim of a great beast that lives in the heart of our society. It lurks, just beneath the surface, waiting to rise and snarl us without warning. At least, that’s one interpretation.

Where we get the things we use every day is a mystery to most, beyond when we purchase an item. The age of Amazon has brought some people a little closer to understanding supply chains, but for most, it’s done the opposite, simply giving us an easy button to click and our good showing up a few days later. So what, exactly, is the supply chain, how does it work? And the most important question to us science fiction writers is, what does the future look like?

To understand the nature of the beast, we first have to spend a few minutes diving into the idea of complexity. And here, we don’t mean how a heating and cooling system on a jet engine works. That’s undeniably complicated, but it’s not complex.

Complex systems are those in which the same inputs don’t necessarily yield the same results. Take farming, for example. There are so many different variables and probabilities at work determining if a field will produce a bumper crop that the farmer can control everything precisely the same, year after year. Still, there’s no way to predict rainfall with certainty or predict storms, blight, and flooding. So farming is a complex system by nature. Science fiction fans are likely intimately familiar with complexity; look at some fantastic short stories about predicting the unpredictable.

So is the global supply chain. Sure, if you aggregate enough data and a high enough level, you can get some amount of predictability. This art of prediction is called forecasting, and it is a critical part of how many advanced industries operate. But these forecasts aren’t certainties. And that’s a problem for the human element of the equation.

People don’t do well with uncertainty. As humans, we all want to know things for sure. So the tendency is to take high-level forecasts that are probabilities and look at them as omens of the future. Oracles, if you will, are applied to the lower levels of a supply chain, and this application leads to many problems.

Chronic over or under-ordering applies a bullwhip effect back up the supply chain. Each level has a little bit longer tail on it, to the point where manufacturing sometimes takes a year or more to order raw materials. It all has to come from somewhere.

Heavy hitters in the tech industry, from Lockheed Martin to IAI, rely on these forecasts to manage their supply chains. Historically, they’ve optimized each level independently and let the market forces do what they will. But there’s a new idea in town.

Genetive algorithms, machine learning, all forms of artificial intelligence allow a revolutionary take to supply chain management. The buffer is the key.

There is a buffer of goods or materials waiting to be either sold or processed at each level. This buffer represents waste, as these materials aren’t being actively used and take up storage space. The automotive lot is the classic example; acres of cars built in the hopes that someday, someone will want them.

AI allows cascading analysis of the supply chain demand in real-time, which means that the buffers are no longer analyzed quarterly or even annually. They can be adjusted minute to minute based on actual market demand signals and the constraints of the specific supply chain.

This application of AI may seem like science fiction, but some companies already use these algorithms with incredible results. With this technique, manufacturing giants like Toshiba have seen a nearly two-fold increase in their supply chain efficiency.

So when you go out and buy that next thing you need, remember that someday, you might place an order, and it’s made the same day and delivered. After all, most tech is on-demand; why shouldn’t the future be on-demand?

N.T. Narbutovskih is a bestselling author and writes in the tech and security fields. His work has appeared in Metastellar, Air and Space Power Journal and Over the Horizon Journal for both fiction and non-fiction, and he has spoken on leadership and geopolitics at the USAF Squadron Officer School and NavyCON. His first book, Steel in the Blood, has received rave reviews and is available now wherever books are sold. Join his mailing list for exclusive early access and an opportunity to pre-order his next book at