Understanding the latest advancements in artificial intelligence (AI) can be daunting, but if you are only interested in the basics, many AI improvements boil down to two concepts: machine learning and deep learning. These concepts may seem like interchangeable buzzwords, so it is important to understand the differences. And these differences should be understood – examples of machine learning and deep learning abound. This is how Netflix determines what show you might want to watch next, Facebook recognizes whose face is in a photo, self-driving cars become a reality, and a customer service representative finds out whether you are satisfied with their service even before you complete a customer satisfaction survey.

What are these concepts that dominate discussions about artificial intelligence, and how do they differ?

Machine Learning

Machine learning is a subfield of artificial intelligence that includes algorithms that analyse data, learn from it, and then apply what they have learned to make better decisions. A simple example of a machine learning system is an on-demand music streaming service. Machine learning algorithms compare a listener’s preferences with those of other listeners with similar musical tastes, allowing the service to decide which new songs or artists to suggest to the listener. Many services that provide automatic recommendations use this method, which is commonly referred to as AI.

Machine learning is the foundation of many automated tasks across a variety of industries, from data security organizations detecting malware to financial specialists searching for trading alerts. AI algorithms are designed to continuously learn and mimic a virtual personal assistant – a skill they excel at. Machine learning requires a variety of complex computing and coding processes that ultimately serve the same mechanical function as a torch, a car, or a computer screen.

When we say that something is “machine learning capable,” we mean something that can perform a function with the given data and improve over time. It is like having a torch that turns on every time you say, “It’s dark,” and recognizes different expressions that include the word “dark.”

Deep Learning

When we talk about deep learning and deep neural networks, the way robots learn new tricks becomes much more interesting (and exciting).

Deep learning is a form of machine learning in which algorithms are organized into layers to build an “artificial neural network” that can learn and make decisions on its own. A deep learning model is designed to analyze data in real time using a logical framework similar to how a human would draw conclusions.

Deep learning applications achieve this through a layered structure of algorithms known as artificial neural networks. The design of an artificial neural network is based on the biological neural network of the human brain, leading to a much more powerful learning process than traditional machine learning models.

Ensuring that a deep learning model does not draw incorrect conclusions is a difficult task—like other types of AI, it takes a lot of practice to get the learning processes right. However, when it works as intended, functional deep learning is considered by many to be a scientific marvel and the backbone of true artificial intelligence.

What is the Difference?

Deep learning is practically a subfield of machine learning. Deep learning is a form of machine learning that works similarly to traditional machine learning (hence the occasional confusion between the terms). However, its capabilities differ significantly. Simple machine learning models improve with each task they are given, but still require a certain level of training. If an AI algorithm makes an incorrect prediction, an engineer must intervene and make adjustments.

With a deep learning model, an algorithm can use its own neural network to decide whether a prediction is correct or not.

Machine learning is a technique for analysing data, learning from it, and making intelligent decisions based on what has been learned. Deep learning creates an “artificial neural network” that can learn and make intelligent decisions on its own through the layering of algorithms. Machine learning has a subfield called deep learning. Although both fall under the umbrella of artificial intelligence, deep learning is the engine that drives the most human-like AI.

Computers that learn from data and use algorithms to perform tasks without being explicitly programmed are called machine learning. Deep learning is based on a complex set of algorithms modelled after the human brain. This allows it to process unstructured data such as documents, photos, and texts.