Take two astrophysicists, an Apollo engineer, a guy who designed parts of the International Space Station, a professor of robotics, and a random science fiction writer, and what do you have? It sounds like a dream sequence from the TV show, “The Big Bang Theory,” or the start of a science nerd joke. In fact, it was the make-up of a talk panel at a recent science fiction convention where I was one of the guests. The panel was ostensibly meant to be a retrospective look back at the days of Apollo, but like many such conversations, it soon turned to thinking about the future, which led to the subject of machine learning (ML) driven artificial intelligence (AI) and its current capabilities.
I expected an enthusiastic discourse, and so I was surprised when most of these actual rocket scientists seemed more ambivalent about the technology and its potential impacts.
A couple of observations caught my attention enough to tweet them out at the time:
“ML is great at recognizing patterns but not much else.”
“ML assumes tomorrow is going to be the same as today.”
Yet it seems these technologies are being received more enthusiastically elsewhere. Nearly every customer experience discussion and the majority of CX projects my team is engaged in these days includes some mention of machine learning and artificial intelligence (and often the two are used synonymously although they are different). Which got me thinking, how do the somewhat downbeat observations of a panel of space experts play into the world of customer data, and the ways we try to infer context from it?
‘ML Is Great at Recognizing Patterns but Not Much Else’
Machine learning is usually defined as “a set of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions.” It’s a subset of artificial intelligence that relies on patterns and inference to drive conclusions. In other words, as the scientists observed, it’s great at doing what it is meant to: Pattern recognition.
That means it can see what is happening in a data set, but not why it’s happening. That still (at the moment anyway) needs human interaction to derive context based on experience, knowledge, and a degree of intuition.
Machine learning can greatly reduce the workload and automate the process of recognizing patterns of behavior in large sets of customer data, but it is not a magic panacea for developing an understanding of why customers do what they do.
‘ML Assumes Tomorrow Is Going to Be the Same as Today’
The data we get from machine learning is a reflection of what happened the day the data was captured. For the purpose of pattern matching, there is an underlying assumption that the next set of data is going to be similar enough for the patterns and models it recognized to still be applicable.
Machine learning is not a predictive tool. It is a great way to analyze a lot of data and an efficient way to learn about repetitive behavior. But that’s it. The danger can be we take that baseline and believe that is how things will always be. Our customers acted that way yesterday, so they will act the same way tomorrow. If that was truly the case, to paraphrase Henry Ford’s observation, we’d still be riding horses. ML does not take into account the impact of disruptive social or technological influences. Overreliance on technologies like ML without understanding their role in developing a broader understanding of our customers can be just as much a blocker to delivering a good customer experience as any older system or technology.
We’ve Got a Long Way to Go With Machine Learning
When my wife and I get into my car on a Saturday morning, the ML system connected to my phone that analyzes my movements assumes we are heading for our favorite local diner. While that’s true around 80% of the time, on the odd weekend we head off in another direction, and the phone and GPS literally get lost for a while.
We have a long way to go (both figuratively and literally) with machine learning before it drives a true artificial intelligence-driven customer experience.