As a technical director the largest question business executives tend to ask (other than “Why isn’t this working?”) is what’s the next big thing on the horizon and how can we take advantage of it?

It’s an important question.  More so today than ever before as EVERY business requires some form of technology to compete and succeed in modern day.  This is not a fad and is not likely to change in the foreseeable future.  The answer to that question is an easy one for anyone inside the innovation industry and blisteringly complex to almost anyone else.  Not because it’s a secret among corporate closet R&D departments, or even a lack of documentation, but quite simply it’s the first time that the answer to the question of what’s next is ‘questioning’ itself.

Learning machines.  The history of digital learning is vast.  It has gone through a number of grammar iterations as its evolved under the scientific and media limelight which has not made this any easier.  Not to mention we’re talking about thinking (something many vehemently argue is a unique skill to only humans) and how to solve for teaching something to not just learn, but understand.  Starting out as simple algorithms, then machine learning, followed by business intelligence, predictive modeling and big data – we’re finally to today where there are no real missing pieces in this field of technology science.  Just time and work, putting it all together.  There is no question that this will revolutionize the world just as networking (now called the Internet) has.

So how do machines come up with answers?  You can find those answers here in a set of 4 outstanding articles below.  CAUTION: these are not for the casual reader and have plenty of caffeine handy.  I will be honest however, it’s far more important to understand which pill to take when, than exactly how the chemical compounds in modern medication interact with the human body.  Understanding the macro more so than the micro.

Why will learning machines inherently do this better, faster, and more accurately than a whole party bus of executives?  Due to essentially the unrestricted processing speed of today’s computers.  Machines don’t care if they only get to look at the best option.  If fact, you don’t want them to.  You ask them to look at EVERY option.  Just think of that. . .

If you, as a person, was tasked with figuring out a new way to expand your landscaping business, you’d think of all the possibilities and options that YOU are capable of knowing in the given amount of time available.  What if there was a business partnership you discovered three years down the road for another 50 clients if only you’d looked into partnering with other local office cleaning services?  “What if” is going to be a revolution of understanding.

Funny thing about this type of solution understanding – you just now scratched the surface of why Google and Google-like companies are so important.  They already KNOW many of these things – at least when the question is related to marketing and business revenue.  The challenge is knowing who and how to ask the right question so businesses can expand from what learning machines that are already out there in the wild, already know.

Jon Liebertz Technology Business Direction, Innovation, Development, and Management.
                          “Over 15 years’ experience in IT and business special projects.”
                                                     Blog: http://www.JLiebertz.com

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