Differences Between Artificial Intelligence, Machine Learning, and Deep Learning

Differences Between Artificial Intelligence, Machine Learning, and Deep Learning
Differences Between Artificial Intelligence, Machine Learning, and Deep Learning

In many contexts, artificial intelligence, machine learning, and deep learning are used interchangeably, but in reality, machine and deep learning are subsets of AI. We can think of AI as the branch of computer science focused on building machines capable of intelligent behavior, while machine learning and deep learning are practices of using algorithms to sift through data, learn from the data, and make predictions or take autonomous actions. Therefore, instead of programming specific constraints for an algorithm to follow, the algorithm is trained using large amounts of data to give it the ability to independently learn, reason, and perform a specific task.

Figure 1: Timeline of Artificial Intelligence, Machine Learning, and Deep Learning

 

So what's the difference between machine learning and deep learning? Before defining deep learning - which we'll do in part 3, let's dig deeper into machine learning.

Machine Learning: Supervised vs. Unsupervised

There are two main classes of machine learning approaches: supervised learning and unsupervised learning.

Supervised Learning. Currently, supervised learning is the most common type of machine learning algorithm. With supervised learning, the algorithm takes input data manually labeled by developers and analysts, using it to train the model and generate predictions. Supervised learning can be delineated into two groups: regression and classification problems.

Figure 2: Supervised Regression Example

 

Figure 2 demonstrates a simple regression problem. Here, there are two inputs, or features (square feet and price), that are used to generate a curve fitting line and make subsequent predictions of property price.

Figure 3: Supervised Classification Example

 

Figure 3 is an example of a supervised classification example. The dataset is labeled with benign and malignant tumors for breast cancer patients. The supervised classification algorithm will attempt to segment tumors into two different classifications by fitting a straight line through the data. Future data can then be classified as benign or malignant based on the straight-line classification. Classification problems result in discrete outputs, though that does not necessarily constrain the number of outputs to a fixed set. Figure 3 has only two discrete outputs, but there could be many more classifications (benign, Type 1 malignant, Type 2 malignant, etc.)

Unsupervised Learning. In our supervised learning example, labeled datasets (benign or malignant classifications) help the algorithm determine what the correct answer is. With unsupervised learning, we give the algorithm an unlabeled dataset and depend on the algorithm to uncover structures and patterns in the data.

Figure 4: Unsupervised Learning Example

 

In Figure 4, there is no information about what each data point represents, and so the algorithm is asked to find structure in the data independently of any supervision. Here, the unsupervised learning algorithm might determine there are two distinct clusters and make a straight-line classification between the clusters. Unsupervised learning is broadly applied in many use cases such as Google News, social network analysis, market segmentation, and astronomical analysis around galaxy formations.