Seriously though – there is a place in this business for all manner of solutions providers be they human or virtual. As anyone who has used Siri or Cortana on a smartphone – they can be very adept at understanding and responding to what they have been programmed to understand.
When you are asking for the time, or the weather…, or the time… they work perfectly well. I can see a benefit to AIs jumping in to help solve simple text book problems. However, before we all jump on the bandwagon and just assume that by giving a virtual assistant a handbook to digest and then setting them loose on end users, we should take a step back.
Think of all the times you have been frustrated with the simple assistant in your phone. If you ask what time a movie is playing it will tell you, if you ask it if you can make it there in time for that next show – it is confused. Simple changes in basic syntax might as well be a foreign language if the system has not been programmed to understand. Now imagine why people contact the help desk – they have a problem and they want a solution. You do not want that person to become more frustrated.
All this being said it will happen – and sooner than we all think. Eventually these systems will learn by listening in on human to human support calls, predicting the correct response and then comparing with the actual response. Over time they will learn how to provide more acceptable responses.
"Next time your PC locks up, would you want another computer helping you out?
Automation company IPsoft is betting you would, or at least believes your boss will think automated assistants are good enough to replace humans on helpdesks.
The field of cognitive computing - getting machines to replicate the ability of humans to learn from the world around them - is being researched at the world's biggest tech companies. Perhaps the most famous system to emerge has been IBM Watson, the machine that beat two of Jeopardy champions at the quiz show in 2011. Google has also devoted much time to this area, with its Google X labs devising a deep-learning algorithm running on a neural network capable of detecting human faces in YouTube videos four in five times."
This article was originally published in TechRepublic.com on September 29, 2014, and written by Nick Heath. You can read full article here.