A Simple Way to Understand Machine Learning vs Deep Learning

A Simple Way to Understand Machine Learning vs Deep Learning

Understanding how today’s artificial intelligence 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 to describe intelligent machines 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 artificial intelligence, and how exactly are they different? And what do they mean for customer service?
 

What is machine 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.

Deep learning vs machine learning

Deep learning is a subset of machine learning. While a machine learning model needs to be told how it should make an accurate prediction (by feeding it more data), a deep learning model is able to learn that through its own computing “brain”. 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). It caused quite a stir that a machine could not only grasp such a complex game, but also use it’s neural network to beat the world-renowned “masters” of Go.

So to recap deep learning vs machine learning:

• 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 intelligent 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

A simple explanation

We know – all of this might still seem complicated. The easiest takeaway for understanding the difference between machine learning and deep learning is to know that deep learning is machine learning. More specifically, it’s the next evolution of machine learning.
 

An analogy to be excited about

Another exciting thing about deep learning, and a key part in understanding why it’s becoming increasingly popular, is that it’s powered by massive amounts of data. This “Big Data Era” of technology will inspire new innovations in deep learning; we’re bound to see things in the next 10 years that we can’t even fathom yet. 

Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain project, has a great analogy for deep learning that he shared with Wired: “I think AI is akin to building a rocket ship. You need a huge engine and a lot of fuel,” he told Wired journalist Caleb Garling. “If you have a large engine and a tiny amount of fuel, you won’t make it to orbit. If you have a tiny engine and a ton of fuel, you can’t even lift off. To build a rocket you need a huge engine and a lot of fuel.”

“The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.”

– Andrew Ng (source: Wired)

So 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 the machines to bring better customer service as a result.

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