Machine Learning vs. Deep Learning: What's the Difference?

Artificial intelligence is everywhere these days, but the fundamentals of how this influential new technology work can be difficult to wrap your head around. Two of the most important fields in AI development are “machine learning” and its sub-field, “deep learning,” although the terms are sometimes used interchangeably, leading to a certain amount of confusion. Here’s a quick explanation of what these two important disciplines are, and how they’re contributing to the evolution of automation.

First, what is AI?

Proponents of artificial intelligence say they hope to someday create a machine that can “think” for itself. The human brain is a magnificent instrument, capable of making computations that far outstrip the capacity of any currently existing machine. However, software engineers involved in AI development hope to eventually make a machine that can not only do everything a human can do, intellectually, but can also surpass it. Currently, the applications of AI in business and government largely amount to predictive algorithms, the kind that suggest your next song on Spotify or try to sell you a similar product to the one you bought on Amazon last week. However, AI evangelists believe that the technology will, eventually, be able to reason and make decisions that are much more complicated. This is where ML and DL come in.

Machine learning, explained

Machine learning (or ML) is a broad category of artificial intelligence that refers to the process by which software programs are “taught” how to make predictions or “decisions.” One IBM engineer, Jeff Crume, explains machine learning as a “very sophisticated form of statistical analysis.” According to Crume, this analysis allows machines to make “predictions or decisions based on data.” The more information that is fed “into the system, the more it’s able to give us accurate predictions,” he says.

Unlike general programming where a machine is engineered to complete a very specific task, machine learning revolves around training an algorithm to identify patterns in data by itself. As previously stated, machine learning encompasses a broad variety of activities.

Deep learning, explained

Deep learning is machine learning. It is one of those previously mentioned sub-categories of machine learning that, like other forms of ML, focuses on teaching AI to “think.” Unlike some other forms of machine learning, DL seeks to allow algorithms to do much of their work. DL is fueled by mathematical models known as artificial neural networks (ANNs). These networks seek to emulate the processes that naturally occur within the human brain—things like decision-making and pattern identification.

The key difference between ML and DL

One of the biggest differences between deep learning and other forms of machine learning is the level of “supervision” that a machine is provided. In less complicated forms of ML, the computer is likely engaged in supervised learning—a process whereby a human helps the machine recognize patterns in labeled, structured data, and thereby improve its ability to carry out predictive analysis.

Machine learning relies on huge amounts of “training data.” Such data is often compiled by humans via data labeling (many of those humans are not paid very well). Through this process, a training dataset is built, which can then be fed into the AI algorithm and used to teach it to identify patterns. For instance, if a company was training an algorithm to recognize a specific brand of car in photos, it would feed the algorithm huge tranches of photos of that car model that had been manually labeled by human staff. A “testing dataset” is also created to measure the accuracy of the machine’s predictive powers, once it has been trained.

When it comes to DL, meanwhile, a machine engages in a process called “unsupervised learning.” Unsupervised learning involves a machine using its neural network to identify patterns in what is called unstructured or “raw” data—which is data that hasn’t yet been labeled or organized into a database. Companies can use automated algorithms to sift through swaths of unorganized data and thereby avoid large amounts of human labor.

How neural networks work

ANNs are made up of what are called “nodes.” According to MIT, one ANN can have “thousands or even millions” of nodes. These nodes can be a little bit complicated but the shorthand explanation is that they—like the nodes in the human brain—relay and process information. In a neural network, nodes are arranged in an organized form that is referred to as “layers.” Thus, “deep” learning networks involve multiple layers of nodes. Information moves through the network and interacts with its various environs, which contributes to the machine’s decision-making process when subjected to a human prompt.

Another key concept in ANNs is the “weight,” which one commentator compares to the synapses in a human brain. Weights, which are just numerical values, are distributed throughout an AI’s neural network and help determine the ultimate outcome of that AI system’s final output. Weights are informational inputs that help calibrate a neural network so that it can make decisions. MIT’s deep dive on neural networks explains it thusly:

To each of its incoming connections, a node will assign a number known as a “weight.” When the network is active, the node receives a different data item — a different number — over each of its connections and multiplies it by the associated weight. It then adds the resulting products together, yielding a single number. If that number is below a threshold value, the node passes no data to the next layer. If the number exceeds the threshold value, the node “fires,” which in today’s neural nets generally means sending the number — the sum of the weighted inputs — along all its outgoing connections.

In short: neural networks are structured to help an algorithm come to its own conclusions about data that has been fed to it. Based on its programming, the algorithm can identify helpful connections in large tranches of data, helping humans to draw their own conclusions based on its analysis.

Why is machine learning important for AI development?

Machine and deep learning help train machines to carry out predictive and interpretive activities that were previously only the domain of humans. This can have a lot of upsides but the obvious downside is that these machines can (and, let’s be honest, will) inevitably be used for nefarious, not just helpful, stuff—things like government and private surveillance systems, and the continued automation of military and defense activity. But, they’re also, obviously, useful for consumer suggestions or coding and, at their best, medical and health research. Like any other tool, whether artificial intelligence has a good or bad impact on the world largely depends on who is using it.