Technicians or Robots? How to Invest Your IT Spend in 2018 (Part 2: Machine Learning – The Pros and Cons for Business Agility)

Technicians or Robots? How to Invest Your IT Spend in 2018 (Part 1: What Is AI and Why Can’t You Buy It?)
December 7, 2017
Technicians or Robots? How to Invest Your IT Spend in 2018 (Part 3: 4 Common Scenarios That Determine Your IT Strategy)
December 21, 2017
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Technicians or Robots? How to Invest Your IT Spend in 2018 (Part 2: Machine Learning – The Pros and Cons for Business Agility)

These days, it seems like everywhere you look there’s always a newer, better tech option coming out. From cobots that work alongside humans to intelligent machines that learn as they go and productivity enhancement software powered by the human voice, every product claims to be the solution your business needs to maintain your competitive stance in the future.

And if you’re not a techie, but are, instead, a hiring manager trying to figure out how to allocate your tech funds the best way, all this conflicting data can get confusing.

That’s why ISG has put together this short series to help you make the right decision about how you should plan to allocate your company’s IT spend in 2018. Here in Part 2, we’ll be discussing machine learning and how it can help your business.

If you’ve wondered whether you should focus your investments on increasing workplace automation through robots or AI, or if it’s a better idea to hire skilled IT technicians, you’ll get the answers you need here. Let’s figure this out together.

Note: In this series, we interchangeably use the terms “machine,” “computer,” “AI” and “robot.” For our purposes right now, please note that they all refer to the same thing: the advanced technology your business can use.

What Is Machine Learning?

In Part 1 of this series, we talked about artificial intelligence and how, basically, it doesn’t exist. Not now and possibly not ever. However, the machines are making great strides in their capabilities, and their development is powered by something called “machine learning.”

Today’s technology empowers us to train our machines using machine learning tools, which provide algorithms to help computers learn the logical or ethical reasoning skills humans already have. In addition to building mental capabilities, machine learning also helps machines perform complex physical tasks that we take for granted, such as the ability to navigate our surroundings, perform simple motor skills and interact with humans and other machines.

Not sure what we’re talking about here? Consider just one of the tasks you performed today: making your morning coffee.

To make your coffee, you had to:

  • Walk into the kitchen and correctly identify the coffee pot
  • Pick up your coffee pot, rinse it and fill it with water
  • Pour the water into the water reservoir in your coffee machine to the fill line
  • Select a coffee filter, open the grounds section of your coffee machine and place the coffee filter in there
  • Find the fresh coffee grounds, open the package and then measure and transfer the grounds to the coffee filter, without spilling
  • Press “brew,” pour the finished beverage into an appropriate cup, pick up the cup, add cream and sugar, stir… etc.

You get the point. Even in the simplest of human actions, we perform a lot of tasks. And, in the case of coffee, we can perform these tasks when we’re still bleary-eyed from sleep. Machines, even though they’re never sleepy, can’t perform these functions without hours of training.

And even when they’ve completed hours of training, machines still get tripped up by tiny details that don’t faze us humans one bit: things like the texture and weight of the coffee mug; the pressure you need to use to safely hold the glass coffee pot without shattering it; unscrewing the lid on a new, pressurized tub of coffee grounds versus an old, nearly empty one.

Machines are, in fact, really dumb. And they’re really hard to train. But once they learn something, they never forget it – and they can share their knowledge with other machines, effortlessly, using machine learning.

Since machine learning is a largely open source endeavor, our machines can all be hooked up to the same system so that, when a single machine acquires new knowledge of a task or behavior, that knowledge is uploaded instantly to a shared network (like a shared machine brain) that every connected machine can access and use. Instantly.

Admittedly, this is pretty incredible.

Can Machines Learn Creativity?

If we humans had access to neural networks like the machines do, we’d never have to struggle through 8th grade Algebra or college-level Organic Chemistry. We’d simply plug ourselves into a network, download the knowledge we need and apply it. (This is a lot like The Matrix, when Keanu Reeves’s character was able to learn Kung Fu without any practice.)

However, if we were to download all our learning without having to think and reason our way through it, we wouldn’t have the creative abilities to think of new possibilities, new options, new tasks, new realities. As we’re learning, we need those small mental vacations we all take, in which our minds lead us on a tangent and we daydream for a while in class. It would probably be a valid argument if someone claimed that daydreams are where all our new ideas came from.

Actually, we’re going to claim that. Right now.

As the old saying goes, “Whatever man can imagine, man can do,” but it follows that if man isn’t imagining much, man isn’t doing much either. If we’re not daydreaming and creating new possibilities in our heads as we learn, we won’t come up with new ways to do things as well as new things to do – we’ll simply keep doing the same tasks over and over, mindlessly.

You know, like robots do.

This was all a very long way of pointing out that machines are very good at performing trained tasks over and over, but they’re not good at coming up with new, innovative ways to do things.

Remember, in Part 1 of this series we talked about how the main problem separating artificial intelligence from real intelligence is the fact that machines, almost by definition, cannot be creative. And, since creative solutions are what drive forward momentum in this world, humans will always be necessary to keep a business competitive and innovative.

Tap into Tech Innovation at Your Organization

Today’s business environment thrives on disruption. More than ever before, companies that innovate new tech solutions for increased brand visibility, customer service and client convenience are taking over their status-quo-following counterparts.

Speed and agility determine your success in today’s highly competitive global online market.

If you’re looking for an on-demand experienced tech consultant to help you create a strategy for pulling ahead of the curve in 2018, or if you’re searching for the perfect, easy-to-manage tech development team—on-shore or off-shore, and fully managed for you—ISG can help.

Contact ISG online or at (800) 739-2400 to learn how your business can secure the tech skills you need, hassle-free and fast.


Coming Up Next:

Without tech experience yourself, it can be nerve-racking to decide how your company should specifically spend your IT allocations in the coming year. In Part 3 of this series coming next, we’ll call out four specific, common IT challenges that your company may have experienced this year so you can determine how you should spend your IT funds for next year to overcome those obstacles.