Blog

Building customer-centric support one blog post at a time.

Keep AI From Feeling Like Sci-Fi With Our Terminology Guide

When the conversation turns to AI, there’s often a Sci-Fi novel’s worth of terminology and jargon that the uninitiated reader has to decode. If you’re looking at using automation for service, then here’s a quick guide to the difference between AI, Machine Learning, and Deep Learning.

Watch Our Webinar with Solvvy here – The Truth About Bots and Intelligent Automation

Artificial Intelligence as a concept has been around since at least the ancient Greeks, who designed some mechanical devices that could be loosely-termed as intelligent. However the term itself is around 60 years old, and the first applicable AI technologies have only just started coming to market in the last few years.

Machine Learning is a more specific subset of AI. It describes machines’ ability to learn from their mistakes and improve over time. A good example of Machine Learning in practice example is the recent Google AI that beat a world champion at Go. The more the AI plays, the better it becomes at spotting patterns and predicting its opponents’ moves.

Deep Learning is a further iteration of machine learning. It describes machine learning algorithms that run on multiple layers, mirroring how our own neurons function. A now common example of deep learning is the way that smart assistants like Alexa or Siri process speech.

Also important is Natural Language Processing. NLP is the ability for a computer program to understand human speech, regardless of slang or dialect. By being able to make sense of written or spoken language in the messy and error-filled ways humans normally express it, AI capabilities become much more applicable to everyday life.

What does this mean for service? Artificial intelligence and intelligent automation can take over existing tasks and create new efficiencies that your organization couldn’t dream of previously. Machine Learning is just one example. By suggesting responses agents can use to common customer queries, a partially-automated system could learn the most effective replies and language for your customer base. Deep learning capabilities should extend to IVR trees, and put an end to the common “Sorry, I didn’t get” response from many systems that currently rely on processing speech. And NLP is crucial for chatbots, and for analytics that look at all of the conversations your agents have across chat, social, and any other text-driven medium.

It’s important to build a solid understanding of these exciting technologies as they become more prevalent and relevant to the service and customer experience sphere. To learn more, listen to our webinar with Solvvy: The Truth About Bots and Intelligent Automation.

Request a Demo