Article | 3 min read

What’s the difference between machine learning and deep learning?

By Brett Grossfeld

Last updated July 18, 2017

Understanding how today’s AI works might seem overwhelming, but it really boils down to two concepts you probably have heard of before: “machine learning” and “deep learning”. Neither are brand new ideas, but the way they’re used seems to constantly evolve. Machine learning and deep learning are how Netflix knows what you might want to watch next, or how Facebook can recognize your friends’ face in a photo, or how a support agent can figure out if you’ll be satisfied with your customer service.

So what are these buzzwords that still dominate the conversations about AI, and how exactly are they different? And what do they mean for customer service?

What’s the difference between machine learning and deep learning?

Here’s a basic definition of machine learning:

“Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions”

An easy example is an on-demand music streaming service. An app like Pandora or Spotify use an algorithm to learn about your music preferences, and then uses that information to make a prediction about what other music you might enjoy. Machine learning spans across multiple industries to automate both basic and complex tasks, from finding malware for data security firms to helping financial professionals recognize favorable trades.

So what’s the difference between machine learning and deep learning then? Deep learning technically is machine learning, but while a standard machine learning model would need to be told how it should make an accurate prediction (by feeding it more data), a deep learning model is able to learn that on its own. It’s similar to how a human would perceive something, think about it, and then draw a conclusion. To achieve that, deep learning uses a layered structure of algorithms called an artificial neural network. It’s design is inspired by the biological neural network that the human brain uses.

A great example of deep learning is Google’s AlphaGo: Google created a computer program that learned how to play the abstract board game Go, a game famous for requiring sharp human intuition. By giving AlphaGo a deep learning model, it learned how to play at a professional level by playing against other professional Go players (instead of being told when it should made a specific move, as it would in a standard machine learning model).

So to recap:

  • Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned
  • Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make decisions on its own
  • Deep learning is a subfield of machine learning. While both fall under the broad category of artificial intelligence, deep learning is the term that’s often used to describe how human-like artificial intelligence works

What do machine learning and deep learning mean for customer service?

Many of today’s applications of AI in customer service utilize machine learning algorithms to help drive self-service, increase agent productivity, and ultimately make customer service more reliable. They learn to make accurate predictions pretty quickly, thanks in part to a constant flux of incoming customer queries. In these early days of AI proliferation, industry leaders have noted that the most practical application of AI for businesses is in customer service.

It’s worth noting that as deep learning becomes more refined, we’ll see even more advanced applications of artificial intelligence in customer service. A great example is Zendesk’s own Answer Bot, which incorporates a deep learning model to better understand the meaning and context of a support ticket. Expect to see even more innovative applications of deep learning in the near future, and expect better customer service as a result.