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written by John Schmidt January 2, 2018

Even with all the coffee and energy drinks in the world, humans require sleep. Doctors suggest a good seven to eight hours a night for optimum performance. But machines don’t have the same restrictions. No rest is required, no holidays. They’re generally on 24/7. And that means they are sensing, analyzing and transmitting data 24/7.

Put another way: The world has more than seven billion people. Each of those people, on average, has five connected devices. Billions of machines around the world keep those devices running, and these machines need to run continuously to keep up with demand. It’s the stuff of machine-to-machine dreams.

Clearly, devices outnumber people. When devices and computers start talking to each other, they create extreme stress on any network. In 2018 and beyond, we see this stress only becoming more profound. Moreover, it’s also a growing worldwide market. IDC predicts the market could reach $7 trillion by 2020.

Looking to the immediate future in 2018, we see data centers affected in the following three ways regarding machine-to-machine communication:

  1. Laying the foundation for 5G: Yes, it will happen in data centers, too. All the devices that must communicate with each other and humans will drive a massive amount of fiber, especially as we look to 5G’s arrival in the next 5 to 10 years. There’s much to be done behind the scenes beforehand. Wireless networks need a lot of “wired” assets to effectively deliver fiber backhaul to the core and edge. Enabling 5G also requires densification of cell sites (small cells, for example). Additionally, we’ll see several types of power solutions come to the market, allowing operators to cost-efficiently power up many devices at the network edge.
  2. Low latency: Machines can process information nearly as fast as they receive it. Humans can’t. In the data center especially, decision making is almost instantaneous, and it needs a strong network backbone. This situation is a change from past data centers that simply provided data storage. Now they’re computing, analyzing and processing information, and they must do it in real time. IDC sees the “modernization” of data centers as one of its top predictions for 2018, making “heavy use of predictive analytics to increase accuracy and reduce downtime.”
  3. Higher density and speed: Deploying copious amounts of fiber is a best-case solution. But it’s not always feasible. The most efficient scenario is to deploy high-density fiber from the get-go to allow fast machine-to-machine conversations. A modular high-speed platform that can support multiple equipment generations is the best option.

It’s a machine-learning example used repeatedly, but it’s a good one: self-driving cars are becoming a reality, thanks in part to a pilot project in Pittsburgh. Backed by a strong network and nearly perfect sensors, the project is going well. The cars can process the data much quicker than any human could. It’s like an entire data center on wheels!

And the cars stay sober. They don’t text and drive. They stay awake at the wheel. And they have a quicker reaction time. As long as they make the right decision at the right time, they can drive well into the future.

But humans have been driving for a century. We all make mistakes, and should a computer replace a human behind the wheel? What about compassion and empathy, emotions that machines can’t feel? Is the human element lost in this situation?

The answer depends on your perspective. Machines are only as good as their algorithms and programming. They’re vulnerable to manipulation (hacking) by humans and perhaps even other machines. Gartner predicts that by 2022, most people in mature economies will consume more false information than true information. It even goes as far to say that false information will “fuel a major financial fraud.” With more devices than people in the world, we have become more vulnerable to hackers and data thieves. Data privacy is a concern. One school of thought believes machines will take over jobs that once only humans could do. But the same Gartner report suggests that machine learning will create 2.3 million jobs by 2020 while eliminating only 1.8 million jobs. There’s still plenty of work for us humans, though it may be different than what we’re doing today.

And problems will arise as well. The world won’t run on robots alone. Machine-to-machine technology requires a change in mindset, giving up some control. It certainly won’t be perfect, but it’s a huge step in this “fourth industrial revolution.” Now is a great time to be part of this ever growing industry, and CommScope is ready for 2018 and beyond.