For our final blog in this series about the myths and misconceptions surrounding machine learning, we’re looking at how machine learning can set your business up for long-term success. And to do that, we all need to lay to rest a misconception that’s all too often attached to machine learning -- the belief that machine learning technologies will naturally improve as we throw more data at it.
This isn’t the case. Not at all.
In reality, this is almost always the opposite of the truth, as we see in the report “Shatter the Seven Myths of Machine Learning,” by Kjell Carlsson, Ph.D. of Forrester Research, a leading advisory and research firm that works with business and technology leaders. The report, which we’re offering at no cost, shows how your business’ AI efforts can’t rely on machine learning to do the work itself.
If left unattended, learning models are likely to become less accurate over time. That’s why you have to retrain your machine learning models as months and years go by and as your business grows, your goals change, and your customers’ needs evolve. Sure, your machine learning model may work for a while, but it will soon become, as the report says, “frozen in time when it is deployed and will inevitably degrade.”
At Verint, we use Conversational Intelligence tools to help your AI solutions -- like an Intelligent Virtual Assistant, for example -- to process both structured and unstructured data, because, again, machine learning doesn’t work on its own.
“Machine learning has taken the spotlight for so long – in both academia and business – that people have forgotten it is just one part of an intelligent system,” says Verint Chief Scientist Dr. Ian Beaver, Ph.D.
In Verint’s work within the customer service space, these tools help IVA managers, developers and business analysts better identify, prioritize, and validate optimal areas for focus and deployment. With the ability to learn, unlearn and relearn, continuous improvement and refinement is essential for the long-term success of any AI project.
This comes back to another reality that we see repeatedly outlined in the Forrester report: humans are vital to the success of your AI projects. While machine learning can indeed process enormous amounts of data and find patterns far more quickly than humans ever could, it’s humans who help retrain the way the data is processed. This notion of a “human in the loop” is vital to preparing your AI project to succeed not only upon launch, but also years down the road.
Again, we’re offering Forrester’s report, and you can download it here. It’s available through the end of April, so take a look.