By Divina Paredes
Aug. 30, 2016
Technology around us will provide an “augmented intelligence” that will help humans to make smarter decisions, improve business models and solve problems that were previously intractable.
“The ways in which we are able to interact with computers is going to make people a lot more efficient and more effective, and build digital models.”
This, says Richard Paris, senior data scientist at KPMG New Zealand, is the future of digital.
We are increasingly seeing the digital world interact in our everyday lives, says Paris, who spoke at the inaugural KPMG Technology Series in Auckland.
People interact with smartphones and these devices are becoming our intelligent assistants.
“We are moving into the Internet of Things (IoT),” he adds. “We are surrounded by devices getting data from us, so we interact with them.”
We are moving into the Internet of Things... We are surrounded by devices getting data from us, so we interact with them. - Richard Paris, KPMG
This convergence leads to the rise of cognitive computing.He defines cognitive computing as artificial intelligence that feeds on Big Data.
“Cognitive computing redefines the nature of the relationship between people and their increasingly pervasive digital environment.”
The notion of cognitive implies being biologically inspired, such as with speech recognition software.
It covers language, vision, speech, prediction, knowledge, data insights and search.
“You can take existing systems and enhance them,” he says.
The machines can learn from data and come out with strategies and solutions from the data.
This is demonstrated by chatbot, he says. “With chatbot, you actually have an intelligent conversation with a machine that is able to get facts and offer solutions.”
“What makes it significant is that machines are able to transfer their learning based on their specific context, so that the context gets wider and deeper and much faster than one machine in a specific environment,” says Paris.
“We will have these machines in different environments and when you combine them like a kind of brain, you are distributing the learning … and when it is distributed, that learning becomes much broader and more robust.”
He cites how machine learning is already being applied by the legal profession.
“Machines get very good at learning about contract structures and understanding that for a given contract structure, what the risk or weak points in the contract could be,” he says. “Under what conditions could one party sue the other and most likely be successful?”
Lawyers are starting to use it for litigation to basically, based on the previous legal arguments, determine what is the likelihood of even winning the case.
Before you even go to court, you can run it through the software, and based on machine learning, look at all the precedents and the factors that will show the likelihood of winning the case using a particular line of legal argument.