Subject area: Artificial Intelligence Part 1
What do chat bots, self-driving cars, and predictive maintenance have in common?
They each involve the application of artificial intelligence (AI).
In recent years the development of AI has become a hot topic.
It seems that AI is being talked up everywhere. At every conference, online event, and even industry magazines.
Vendors of equipment and software have also been loosely using the term to describe their offerings for some years now. Some might say perhaps a little too loosely.
And why wouldn’t we be keen to adopt artificial intelligence?
The simulation of human thinking, and the mimicking of actions, in machines that can produce seemingly far more accurate results in a fraction of the time, seems like an operational nirvana. Not to mention that they operate 24/7 without sleep, holidays, or pay.
But is it really everything that it is made out to be? Perhaps not yet.
What people talk about as AI is more often not much more than just a fancy algorithm.
Sure, with more computing power it can handle more data in a shorter period and solve problems that would take a person much, much longer but it relies none-the-less on the thinking and intelligence of the people that designed it. And that may be an algorithm developed decades ago.
Does that mean that it won’t be useful? Of course not.
The connectivity created by the ‘Internet of Things’ (IoT) and ‘Big Data’ enables a level of decision support analysis not imaginable early in my career.
However, before we get all excited about the extension of this to AI, we do need to recognize three main problems.
These are addressed in Part 2 of this article.
Posted by Phillip Slater