There’s only one way for machines to get smarter, and that’s to learn. Machine learning is a form of artificial intelligence that gives computers the ability to learn without being explicitly programmed.

For example, this could involve a device picking up signals, or hints, about how its human operator functions, along with the effects of the environment, in order to take future action on its own.

Let’s say that every morning I get to my cubicle somewhere between 7:10 and 7:30 a.m., and the first things I do are turn on my computer, turn on my portable fan (we don’t have the best air movement), and open up my email. To be useful to me, my machine assistant would have all that done, so I just sit down and get to work.

But – and here’s the important part – if I’m running late, or if have to get to the office early, or if I’m traveling and not in the office, the machine assistant knows that. It senses when I pull into the parking lot, whatever time that may be, or if I’m not driving to work at all that day. That’s learning, by determining patterns and acting on them.

Digital personal assistants

The kind of digital personal assistants that will become essential in the business world will do far more than answer questions, Siri style, or perform a few helpful tasks like turning on your computer and fan in your cubicle. Once they really come into the organization, the demand will be immediate for them to be more comprehensive. What we will be looking for will be, essentially, intuitive robots.

But it may not be the prototypical robot, not like R2D2 or anything we’ve seen so far in the workplace. Cloud-connected digital assistants could be tiny. Their physical form will often be unimportant. What it must have, though, is massive storage capacity, along with the ability to interface reliably with an ultra-powerful network. These digital assistants will serve us by learning what we do and what we need (and when we need it), and delivering on that to allow us to work as efficiently as possible.

Beyond assisting humans

From a business perspective, machine learning will accomplish things such as scheduling, to make us more efficient. Planning, in ways that put us in front of our customers faster and more effectively. Always seeing, feeling, and hearing, and taking the algorithms and executables that we have programmed into them and improving upon them, figuring out the best paths for us.

But the scope of machine learning applications extend far beyond assisting humans with tasks. They will make investment decisions, improve factory production, and help us produce new drugs.

One of the vital elements of machine learning will be the capacity of the network to handle all that these machines will need to connect with other devices and machines; to pass on and receive information. 5G networks, with their promise of vast transmission speeds in both directions and low latency, are going to be key.

Needs network capacity

The autonomous vehicle will demand this kind of network capacity. One estimate is that the average autonomous car will be transmitting and receiving 9 petabytes of data per year. That’s 9,000 terabytes, or 9 million gigabytes. That is a machine that takes learning to a new level – and, as we have talked about previously, to the point of deep thinking.

With deep thinking, for instance, a machine-assisted vehicle can take all that it has learned about its surroundings and perhaps warn me as the driver with time to spare so that I might avoid a collision.

How do you think these learning machines will affect your life and business?  Leave us a comment below.


Back to all blog posts