Oh, this sounds like a great idea. Artificial Intelligences are now being instructed in the finer points of being angry.
(Resident Evil: Retribution - The Red Queen)
The new machine learning research project, which Touchpoint is investing $500,000 to develop, is being built with input from one of Australia’s big four banks, which is supplying reams of real-life customer interactions that have been collated over the past two years. Telecommunications companies and insurance firms are also contributing data.
Data scientists in Australia and New Zealand will spend the next six months uploading the dataset into the platform and tweaking its learning algorithms with an expectation that it will be live by the end of the year.
Once complete, the project will simulate hundreds of millions of angry customer interactions that will help companies better understand the behaviours and processes that trigger customer outbursts.
Touchpoint Group chief executive Frank van der Velden said the research would help with the complex task of understanding how customers were affected by the various products, systems, policies, processes and people they interacted with in the lead-up to reaching breaking point.
Mr van der Velden said the program would constantly run “what if” scenarios to see if a particular scenario was likely to enrage or benefit the customer.
“The end goal is to build an engine that can recommend solutions to companies — and we’re talking about the people at the frontline here — how they can improve particular issues that customers are facing,” Mr van der Velden said.
“This will be possible by enabling our AI engine to learn right across a whole range of interactions of what has and has not worked in past examples.”
So far, so good. But then they named it the Radiant, after the prime radiant in Isaac Asimov's Foundation Series.
The project has been dubbed Radiant, which takes its name from Isaac Asimov’s seminal Foundation series of science fiction novels.
In the Foundation series, Prime Radiant was a supercomputer that could predict the future behaviour and development of humanity through the analysis of history, sociology and mathematical statistics.