When debating what qualifies as artificial intelligence (AI), Evan Stubbs, chief analytics officer for SAS Australia, believes the definition is a set of moving goal posts, and every time a machine that can think as well as a human is made, it is immediately redefined as something along the lines of pattern detection in an automated system of real-time data feeds, not AI.

For this reason, Stubbs believes asking when it will be that a machine can think as well as a human is a loaded question.

"The mistake we made with artificial intelligence generally back in the '60s and '70s was thinking that for artificial intelligence to be useful it had to be exactly the same as a human; it had to be as intelligent as a human," Stubbs said.

"We don't have to have devices which can think as well as a human, they just have to think as well as a human in a very limited context and that's enough to get them to do what they need to do."

According to Stubbs, there has been a shift in thinking when it comes to AI, thanks to the combination of the Internet of Things (IoT), machine learning, and big data.

Looking to the future, the analytics officer said in his lifetime we will experience large amounts of automation driven by information, the IoT, and machine learning to give an enterprise the ability to automate large chunks of what is currently treated as manual activity.

"Whether it's payments processing, whether it's in traffic management ... all of these will happen; it's not a case of if, it's a case of when," he said.

Having general purpose AI -- such as Apple's Siri or Microsoft's Cortana -- is really challenging, Stubbs said, because consumers and developers do not know where it is going to lead.

"Our expectations are so great now because of how embedded this has already become. We expect our computers to be this intelligent because they are this intelligent -- we've been conditioned to expect this level of intelligence," Stubbs said.

"Things are moving fast and things are changing around us.

"Once something has been invented or come into the world, the ending is almost inevitably written. We already know largely speaking what the future is going to look like; it's just now a matter of when."

Stubbs said when it comes to IoT in isolation it is quite boring.

"The exciting thing about the IoT is not the IoT; the exciting thing about the IoT is the fact that it can create an entire army of autonomous devices linked to central intelligence that can make decisions for you," he explained. "The fact that your toaster has an IP address -- who cares at the end of the day -- this is not transformative."

What is transformative, Stubbs said, is the ability to link the IoT with machine learning, with the uniting information he believes is "stirred by digital disruption".

"We can create devices which effectively think for themselves and adapt to everything that's going on around them. We can fundamentally change the way we view information and analytics, and create a situation where we have a real feedback loop in the real world," he said.

"This is challenging because we don't entirely know how we're going to deal with this yet ... all of a sudden you can create devices that can interact with the real world and devices which can effectively mirror human behaviour."

Stubbs said that two years ago everyone was talking about big data; now innovation is the buzzword.

"You couldn't throw a rock without hitting a conference about big data, or without talking to someone who is talking about big data. Now it's all about innovation and digital disruption," he said. "This isn't a new thing, it's been happening the whole time. The big difference is that it's hit a point where it's happening so fast."

According to Stubbs' colleague, Kevin Kalish, IoT Domain Lead at SAS, focusing on collecting big data in regards to a business' IoT strategy often results in an organisation becoming a data hoarder.

Instead, Kalish believes the innovation gold lies in the filtered data an organisation has extracted from the intermediate layer between the devices and the cloud -- what he refers to as the "fog".

"The view of big data in IoT is that it is more a commodity and that sometimes can lead businesses to the desire to become a bit of a data hoarder," he said.

"The misconception is that storage is a commodity and big data will solve these problems but the volumes and the costs are quickly becoming unsustainable. Unless part of your business model is data monetisation, it's highly likely that you can afford to only send back filtered data."