Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two concepts you’ve likely heard of: machine learning and deep learning. How these terms are thrown around can make them seem like interchangeable buzzwords, hence why it’s important to understand the differences.
And those differences should be known! Examples of machine learning and deep learning are everywhere. It’s how Netflix knows which show you’ll want to watch next or how Facebook recognizes your friend’s face in a digital photo. Or how a customer service representative will know if you’ll be satisfied with their support before you even take a CSAT survey.
So what are these concepts that dominate the conversations about artificial intelligence and how exactly are they different?
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 of a machine learning algorithm is an on-demand music streaming service. In order to make a decision about which new songs or artists to recommend, those algorithms associate the listener’s preferences with music containing similar information. Machine learning fuels all sorts of automated tasks and spans across multiple industries, from data security firms hunting down malware to finance professionals setting parameters for favorable trades.
The thing about basic machine learning algorithms is that as complex as they may seem, they’re still… well, machinelike. They’re only capable of what they’re designed for; nothing more, nothing less. For AI designers and the rest of the world, that’s where deep learning holds a bit more promise.
Deep learning vs machine learning
In practical terms, deep learning is a subset of machine learning. A machine learning model needs to be told how it should make an accurate prediction, as done so by feeding it more data, while a deep learning model is able to learn through its own method of computing – its own “brain”, if you will.
A deep learning model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. To achieve this, deep learning uses a layered structure of algorithms called an artificial neural network (ANN). The design of an ANN is inspired by the biological neural network of the human brain. This makes for machine intelligence that’s far more capable than that of standard machine learning models.
It’s a tricky prospect to ensure that a deep learning model doesn’t draw incorrect conclusions (which is probably what keeps Elon up at night), but when it works, functional deep learning is a scientific marvel and the potential backbone of true artificial intelligence.
A great example of deep learning is Google’s AlphaGo. Google created a computer program that learned to play the abstract board game called Go, a game known for requiring sharp intellect and intuition. By playing against professional Go players, AlphaGo’s deep learning model learned how to play at a level not seen before in artificial intelligence, and all without being told when it should made a specific move (as it would with a standard machine learning model). It caused quite a stir when AlphaGo defeated multiple world-renowned “masters” of the game; not only could a machine grasp the complex and abstract aspects of the game, it was becoming one of the greatest players of it as well.
To recap the differences between the two:
- 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 usually what’s behind the most human-like artificial intelligence
A simple explanation
We get it – 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 thing to be excited about with deep learning, and a key part in understanding why it’s becoming popular, is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for 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, shared a great analogy for deep learning with Wired Magazine: “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 AI applications in customer service still utilize machine learning algorithms, primarily to drive self-service, increase agent productivity, and make customer service workflows more reliable. The data fed into those algorithms comes from a constant flux of incoming customer queries, which in turn leads to quick and accurate predictions. With AI in business proliferating at an increased rate, industry leaders are speculating that the most practical applications of business-related artificial intelligence will be AI for 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 about better customer service.