The rise of the machines and the fourth industrial revolution

Hollywood has been predicting the rise of the machines for a long time. You may have seen movies like The Matrix or the The Terminator where machines get intelligent, somehow decide to wage war on humans and we all become nearly extinct. While this doomsday scenario is only science fiction, in the recent years some of the most prominent business leaders and technologists, including Elon Musk, have expressed deep concerns about artificial intelligence (AI). Perhaps the reasons for those concerns are that AI has gotten dramatically smarter over the last couple of decades. It was previously unthinkable that a computer could win against a world champion of Go (an ancient Chinese game), yet that’s exactly what occurred recently. What is even more remarkable is that the computer (aka AlphaGo) was not really programed to play the game. Instead, it essentially learned to play using its neural network basically mimicking the way humans learn. Like humans, AlphaGo can also constantly improve without being told how to do that. It’s no doubt that AI beating humans in games is quite remarkable. However, can this technology deliver greater benefits to society? Also, where could it lead us — other than a total and complete annihilation of the human race, of course? Before I fully explore that, let me quickly summarize where we are today. In the past few decades we essentially digitized what was previously a very analog world. Before, just about everything that existed on a piece of paper can now be found in a digital world. We are all connected through a global network.

We have devices in our pockets that enable us to effortlessly communicate with people across the globe. Moreover, we began connecting not just people, but also ordinary physical objects. Soon, every physical object from our cars to homes, household appliances, clothes and even our eye contacts will be online and communicating with us — and each other. This will represent an explosion in the amount of data that will be created. In fact, some predictions are that this digital universe will double in size every year and reach close to 44 trillion gigabytes by 2020. All of this data will need to be stored, and in many cases, analyzed. The big opportunity is that we can uncover hidden patterns, correlations and trends that were previously hidden in plain sight. With this “Big Data” we will be able to better understand our customers, employees and ourselves.

While we have certainly been able to do some of this before, the vastness of data that is now being generated and our technological sophistication for analyzing it are unprecedented. In the past (especially in the corporate world), data analysis typically meant looking at some sort of report, decision support system or a spreadsheet and having to figure out patterns manually. Today, many companies use Big Data analytics to uncover previously difficult to find patterns. Through the use of data mining we can look for patterns and establish relationships between things and events. For instance, using association algorithms, we can examine if one event is related to another event. We can also classify and group objects or events and by leveraging predictive analytics, we can even forecast the probability of future occurrence of some events. If you have ever Googled an image of a dog, you have experienced this kind of data mining yourself. Google does not manually tag each image — that would take too long. Instead, it uses a form of data mining that allows its search engine to learn the difference between a dog and something else and classify it as such.

Most classification algorithms like this employ a form of supervised machine learning. This is where data scientists first train the system by showing it pictures of something and teaching it what that something is. When new pictures are analyzed, the algorithm automatically spots patterns and classifies images by itself. Supervised machine learning can be applied to variety of problems and its algorithms can analyze different types of data. Many companies leverage these techniques to examine their server logs for security breaches or financial transactions for fraud. It used to be that organizations needed to employ statisticians to do this sort of work, but that is no longer the case. The likes of IBM, Google, Microsoft and Amazon have opened up many of their internal machine learning technologies to the public via their respective cloud offerings. There are also many open source tools that IT departments can leverage including Hadoop, Apache Spark and others. While supervised machine learning can be very useful, it’s limited in the amount of “intelligence” it has. To replicate human intelligence, scientists think they need to copy the human brain itself. That technique leverages neural networks which somewhat resemble the synapses in the brain. What’s known as deep learning is an unsupervised form of machine learning that can recognize patterns in data and deduce relationships without having to be programmed with a specific algorithm. Deep learning systems can, for instance, read the entire Wikipedia and learn that Texas and California are states, not cities, all without having to be taught [3]. Deep learning algorithms can now win against the masters of Go.

As one might imagine, benefits behind Big Data analytics, including supervised and unsupervised machine learning, could be enormous. By analyzing data, companies could better understand their customers, for example. This could lead to better marketing, better customer service and increased operational efficiency. Big Data can answer questions like: “Are customers happy with our services?” “What products or services are they most likely to buy?” “What is the best way to communicate with them?” or “What is the optimal price of my product?” Those questions could not only be answered, but answered at scale.

Navistar, a leading commercial vehicle manufacturer, currently monitors their trucks and buses for engine health, fuel consumption and overall performance. It analyzes 20 million records per day and leverages the insight to better understand and predict which vehicles may break down and will need maintenance. According to Navistar, the company’s repair and maintenance costs have dropped 30% for the average customer because of the way the company analyzes its data and resolves issues in advance [1].

Another company that leverages machine learning is Starwood Hotels. Specifically, they are able to price their hotel rooms according to demand, much like what airlines have been doing for ages. Their system is able to “decide” when to send promotional offers, raise or lower prices, or how long to hold prices at a given rate. Because unused hotel rooms represent missed revenue opportunities, Starwood uses Big Data analytics to essentially determine price and other conditions that can lead to highest occupancy rates [2].

At the beginning of the article I posed a question about where I think these new technological advances such as Artificial Intelligence and Big Data analytics may lead us. I believe they will culminate into what may be called a Fourth Industrial Revolution. The first industrial revolution was mechanical. We learned how to create a steam engine to power our tools and machines instead of using human or animal muscles. The second industrial revolution created mass production. Factories and assembly lines were able to produce massive amounts of products, enabling us to manufacture inexpensive cars and everyday objects. The third industrial revolution was the computer age. We digitized everything. Instead of using pen and paper, our communication and automation needs were aided by computers. We still had to think, but applications like calculators, spreadsheets and supply chain management tools just made it easier.

The Fourth Industrial Revolution, however, will replace humans as the thinking machines. They will significantly aid in decision-making processes in most cases, or in some cases, will think for us. We will not need to drive because self-driving cars will do that for us. We will not need to do mundane tasks like scheduling meetings because virtual assistants will handle that, too. We will not need to learn foreign languages to communicate with each other because natural language translators will seamlessly do that as well. Ordering a cab, booking a flight, buying groceries or doing taxes? Machines will handle all of that. We have some of this today, but it’s not yet seamless. In the future it will be so seamless, so ever-present and natural that it will simply blend into our regular lives like electricity does today.

While it all sounds great, we also need to recognize that revolutions tend to be disruptive. With each industrial revolution there were displaced workers and bankrupt businesses. Ultimately, the revolution led to higher productivity, but while the transition was in progress, it was certainly painful for some.

Think about what happened to horse-drawn carriages when automobiles entered mass production. It bankrupted many businesses and put many drivers out of work. For those revolutions, displaced workers were manual laborers. In the Fourth Industrial Revolution there will probably be very few jobs intelligent machines could not replace, be it drivers, waiters or even computer programmers. However, as with all previous technological and economic shifts, I have no doubt that the next shift will ultimately be better for society as a whole. The key is not to fear it, but to embrace it wholeheartedly.

This change may not happen today. It may not happen tomorrow or in the next 10 years, but it is inevitable. In the meantime, businesses should embrace technologies that are available today — technologies that can improve products and services to ultimately make customers’ lives better. Start taking a look at Big Data now and see if you can leverage it to improve your business today.


1. says-open-data-expands-market-for-analyticsservice/

2. using-big-data-to-boost-revenue/ 
 3. 8-big-trends-in-big-data-analytics.html

About Vladimir Collak

Vladimir currently serves as president and CEO of Ignite Media. Ignite builds mobile and web solutions primarily for the Oil & Gas industry that includes clients such as Mansfield Oil, Enbridge, Total Safety, Universal Plant Services and others. Prior to Ignite, he served at FuelQuest as manager of research and development and at Xerox Connect as principal consultant providing technology solutions to clients including Continental Airlines and Equifax. Vladimir holds a Bachelor of Science degree in Information Technology. He also holds an MBA degree from the University of Texas at Tyler. He can be found on his blog at and at