What is the Difference between AI, Machine Learning, and Deep Learning

Artificial intelligence is a field of computer science that aims to create computers that can mimic the human brain. In a nutshell, human intelligence is defined as the ability to make judgments based on information obtained via our five senses: sight, hearing, touch, smell, and taste. Artificial intelligence (AI) is not a new science; it has been popular since the 1950s. There have been several waves of joy and pain inside this area since then. Following significant advances in computers, data availability, and a better grasp of theoretical foundations, AI has witnessed a rebirth in the twenty-first century. Deep learning and machine learning are AI subfields that are increasingly being utilized interchangeably.

The link between AI, ML, and DL is shown in the diagram below:

Machine Learning

Machine learning is a kind of artificial intelligence that identifies patterns in data and draws conclusions. The conclusions drawn from the data are then utilized to forecast outcomes on previously unknown data. In its approach to solve particular problems, machine learning varies from conventional computer programming. To get the desired results in classical computer programming, we define and execute certain business rules and heuristics. The rules and heuristics of machine learning, on the other hand, are not clearly specified. By supplying a dataset, these rules and heuristics may be taught. A training dataset is a collection of data used to learn the rules and heuristics. Training refers to the whole process of learning and inferring.

Different algorithms that employ statistical models for this purpose are used to learn rules and heuristics. For learning, these algorithms use a variety of data formats. Each of these types of data representations is referred to as an example. A feature is a term used to describe each element in a given example. The renowned IRIS dataset is exemplified in the following example. This dataset represents many iris flower species based on several properties such as the length and breadth of their sepals and petals:

Each row of data in the above dataset represents an example, and each column indicates a feature. These characteristics are used by machine learning algorithms to form conclusions from data. The reliability of the models, and hence the expected results, are heavily reliant on the data's characteristics. The odds of achieving a satisfactory result are great if the features supplied to the machine learning algorithm are a good representation of the issue statement. Linear regression, logistic regression, support vector machines, random forest, and XGBoost are examples of machine learning methods.

Traditional machine learning methods are effective for a variety of applications, but they rely heavily on the quality of the features to provide better results. Creating features is a time-consuming process that requires a great deal of subject expertise. Even with extensive domain expertise, however, there are constraints to translating that information to extract features, therefore capturing the intricacies of data generation processes. Also, as the issues that machine learning is used to solve get more complicated, it might be almost hard to build features that reflect the complicated functions that produce data, especially with the arrival of unstructured data (pictures, speech, text, and so on). As a consequence, finding a new strategy to tackling complicated issues is often required; here is where deep learning comes into play.

Deep Learning

Deep learning is a subset of machine learning and an extension of Artificial Neural Networks, a kind of algorithm (ANNs). Neural networks aren't a brand-new concept. In the early part of the 1940s, neural networks were developed. The understanding of how the human brain functions sparked the creation of neural networks. There have been various ups and downs in this industry since then. The invention of an algorithm called backpropagation by veterans in the field like Geoffrey Hinton was one pivotal milestone that reignited interest in neural networks. Hinton is known as the "Godfather of Deep Learning" as a result of this.

Deep learning is based on ANNs with numerous (deep) layers. The capacity of deep learning models to learn characteristics from input data is one of its distinguishing qualities. Deep learning excels in learning diverse hierarchies of features over numerous layers, unlike classical machine learning, which requires the creation of features. Let's say we're trying to recognize faces using a deep learning model. As demonstrated in Figure 1.3, the model's early layers will learn low-level approximations of a face, such as the borders of the face. Each subsequent layer combines the elements of the previous levels to create more complex characteristics. If the first layer has learned to recognize edges in the instance of face detection, the succeeding layers will join these edges to construct components of a face such as a nose or eyes. This technique is repeated for each subsequent layer, culminating in a picture of a human face:

Note: The preceding image is sourced from the popular research paper: Lee, Honglak & Grosse, Roger & Ranganath, Rajesh & Ng, Andrew. (2011). Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks. Commun. ACM. 54. 95–103. 10.1145/2001269.2001295.

Over the last decade, deep learning algorithms have advanced significantly. Deep learning approaches have exploded in popularity due to a variety of variables. The availability of enormous amounts of data is at the top of the list. The digital age has created a lot of data, particularly unstructured data, thanks to the growing network of linked gadgets. As a result, deep learning methods have been widely adopted because they are well-suited to handle vast amounts of unstructured data.

The advancements in computer infrastructure are another key element that has fueled the growth of deep learning. Deep learning models with several layers and millions of parameters need a lot of processing power. Deep learning has been widely used because of advancements in processing layers such as Graphical Processing Units (GPUs) and Tensor Processing Units (TPUs) at a reasonable cost.

Deep learning's ubiquity has also been aided by the open-sourcing of many frameworks for developing and implementing deep learning models. The TensorFlow framework was open-sourced by the Google Brain team in 2015, and it has since become one of the most popular deep learning frameworks. PyTorch, MXNet, and Caffe are the other main frameworks available.