Artificial Intelligence, Machine Learning, and Deep Learning Explained

Introduction

With the recent advancements in the tech industry, the terms artificial intelligence, machine learning, and deep learning have become quite popular in conversations. While most people are using these words interchangeably and sometimes ambiguously, one should note that each of these terms has its distinct meaning.

In broad terms, think of Russian dolls and how each doll is a component of the previous one. It’s the same way that these terms fit into one another. Deep learning is a subset of machine learning which, in turn, is a subset of artificial intelligence. They are closely related, but with real differences.

This article tries to explain what each of the terms entails, where they overlap, and how they differ.

Artificial Intelligence

Artificial intelligence (AI) is such a broad term and is misunderstood by most people. Part of the problem is the lack of a uniformly agreed-upon definition. Generally, AI is a discipline that focuses on developing computers that can perform tasks that require human intelligence. Some of these tasks include learning, decision-making, and problem-solving.

Early AI creations involved mapping human intelligence to static rules. These rules defined how the AI was expected to behave in every possible situation. However, this approach was limited to problems where the rules were well-defined. Complex problems like identifying images and recognizing speech were hard to crack through this classic approach, hence the rise of machine learning.

Machine Learning

Machine learning (ML) is a subfield of artificial intelligence. It is focused on teaching computers how to learn from data and improve with experience, without being explicitly programmed to do so. The more a machine learning model ingests data, the more it’s able to identify patterns and improve its performance.

Training a machine learning model involves the collection of historical data. Features are then extracted from the dataset by human experts. These features are used to help the model understand the differences between data inputs. With increased data and experience, the results are more accurate. This means that given a particular input, a model can make predictions with precision.

Machine learning models mostly rely on structured, labeled data to make predictions. Even though they can also handle unstructured data, a manual feature extraction stage is required to organize it into a structured format. The models are also inefficient when it comes to handling data that involves text, images, videos, and speech. This has given rise to deep learning, a subset of machine learning.

Deep Learning

Deep learning algorithms solve problems using deep neural networks. Neural networks are algorithms that are loosely modeled on how the human brain works. A neural network is considered to be a deep neural network if it has three or more layers. With deep learning, each layer feeds off the previous layer to optimize the performance of the algorithm.

Deep learning algorithms eliminate the data pre-processing stage required in machine learning models. This is achieved by ingesting large amounts of unstructured data and automating the process of feature extraction. A deep learning algorithm is capable of singling out features that are important for a particular problem. It is then able to adjust and fit itself for accuracy and can make predictions with increased precision.

These powerful capabilities of deep learning come at a cost, as the algorithms require large datasets, storage, and computing capabilities to efficiently train them. It is for this reason that deep learning has seen slower growth over the years compared to recent times. The recent surge in deep learning innovations could be attributed to the availability of large amounts of data, the affordability of data storage, and advancements in computing technology.

Conclusion

In a nutshell, artificial intelligence is a blanket term used to define machines that can mimic cognitive functions associated with human minds. For classic AI systems, this involves programming the machine using static rules that involve conditional statements. An important distinction between AI and machine learning is that even though all machine learning is AI not all AI is machine learning. It is the ability to improve performance that sets machine learning apart.

The differences between machine learning and deep learning algorithms lie in how each one of them learns and the amount of data required to train them. While machine learning includes a manual feature extraction process, the process is redundant when it comes to deep learning. This is because deep learning algorithms can automatically detect features within a dataset. However, the downside of this automation is that it needs large amounts of data and large computational requirements to extract the features.