Introduction to AI: Machine Learning, Deep Learning, Neural Networks, and More

June 23, 2022
6 min read time
Introduction to AI: Machine Learning, Deep Learning, Neural Networks, and More

Engineers are making massive strides in the field of artificial intelligence (AI). There is no denying that AI will have a massive impact on how we interact with computing systems in the future and on what we can do with them.

This guide will help you to understand what exactly AI—and, more specifically, ML (machine learning)—is and how it works so we can begin our journey into building systems that use this exciting technology.

Myth and Reality

For many people, AI or ML represents this mystical element of sentience, where machines can learn and think for themselves. However, the reality is much less exciting. At its core, ML is still essentially coded algorithms. Yes, we’re training computers how to learn and perform certain operations, but this learning is still structured code. We tell a system what to learn, what rules to apply to its learning, and most importantly what the fundamental objectives of its learning are.

At its core, AI is the design of understanding how a machine can best take the given data and decide the outcome. It combines both code-driven and data-driven development techniques that allow machines to solve problems faster than any human could. And thanks to how much of the world’s behavior and thinking is built around a methodical thought process, it’s possible that machines can use this learning to predict certain patterns and help us to identify and solve problems before they even arise.


Now, we might think of AI as a new concept in computer science and software development. In fact, Alan Turing, who invented the very first computer, laid out a paper in 1950 about computing machinery and intelligence, setting goals for how we can get machines to replicate human behavior. So, even in the infancy of programming and computing, the thought of AI has always been there. Essentially, the foundation hasn’t changed—it’s just that we finally have computers that can do this sort of processing now with enough data to enable many of the algorithms that we’re conceiving.

Different types of AI systems

The above points may serve as great explanations for trying to explain how computer learning and AI work, but there is much more to this topic worth exploring. The world of AI actually consists of multiple components, which I want to briefly explain.

However, while each type of AI is a distinct field with its own levels of specialization, there is often overlap between them. Many techniques applied in one field of AI may also work in another.

Rule-based systems

A rule-based system (e.g., production system, expert system) uses rules as the knowledge representation. These rules are coded into the system in the form of logic statements. The main idea of a rule-based system is to capture the knowledge of a human expert in a specialized domain and embody it within a computer system. It’s essentially trying to automate more routine or trivial-based human decisions.

Machine Learning | Learning from experience

Machine learning, or ML, is an application of AI that provides computer systems with the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on developing algorithms that can analyze data and make predictions. Beyond predicting what Netflix movies you might like or the best route for your Uber, machine learning is being applied to the healthcare, pharma, and life sciences industries to aid in disease diagnosis, interpret medical images, and accelerate drug development.

Deep Learning | Self-educating machines

Deep learning is a subset of machine learning that employs artificial neural networks that learn by processing data. Artificial neural networks mimic biological neural networks in the human brain.

Multiple layers of artificial neural networks work together to find a single output from many inputs, such as identifying the image of a face from a mosaic of tiles. Machines learn through positive and negative reinforcement of the tasks they carry out, which requires constant processing and reinforcement for progress.

Another form of deep learning is speech recognition, which enables the voice assistant in phones to understand questions like, “Hey, Siri, how does artificial intelligence work?”

Neural Network | Making associations

Neural networks enable deep learning. As mentioned, neural networks are computer systems modeled after neural connections in the human brain. The artificial equivalent of a human neuron is a perceptron. Just like bundles of neurons create neural networks in the brain, stacks of perceptrons create artificial neural networks in computer systems.

Neural networks learn by processing training examples. The best examples come in the form of large data sets, such as, say, a set of 1,000 cat photos. By processing the many images (inputs), the machine can produce a single output, answering the question, “Is the image a cat or not?”

This process analyzes data many times to find associations and give meaning to previously undefined data. Through different learning models, such as positive reinforcement, the machine is taught that it has successfully identified the object.

Cognitive Computing | Making inferences from context

Cognitive computing is another essential part of AI. Its purpose is to imitate and improve interactions between humans and machines. Cognitive computing seeks to recreate the human thought process in a computer model, in this case by understanding human language and the meanings of images.

Together, cognitive computing and AI strive to endow machines with human-like behaviors and information-processing abilities.

Natural Language Processing (NLP) | Understanding the language

Natural language processing, or NLP, allows computers to interpret, recognize, and produce human language and speech. The goal of NLP is to enable seamless interaction with the machines we use every day by teaching systems to understand human language in context and produce logical responses.

Real-world examples of NLP include Skype Translator, which interprets the speech of multiple languages in real-time to facilitate communication.

Computer Vision | Understanding images

Computer vision is a technique that implements deep learning and pattern identification to interpret the content of an image, including graphs, tables, and pictures within PDF documents, as well as other text and video. Computer vision is an integral field of AI that enables computers to identify, process, and interpret visual data. This differs from the example I shared earlier with the cat, where it is not merely trying to make an image associate but an interpretation of it. Recognition and understanding are two very different things—and these types of systems will require a very different testing approach.

Applications of this technology have already begun to revolutionize industries like research & development and healthcare. Computer vision is being used to evaluate patients’ X-ray scans and produce a faster diagnosis.

The above types of AI are by no means exhaustive, and there are many other branches of AI out there that we are still discovering. However, understanding that the field, itself, is quite broad and that each aspect of it uses a different type of algorithmic behavior should help us appreciate the complexity. Using machines to solve many of our problems is a long development path that requires significant investment and is not something that can be realized quickly or easily.

Lifecycle of AI innovation

Each of these phases of AI, though, is in a distinct space of development—an expected timeline, as highlighted by this graph from Gartner.


This figure reveals how most AI technologies form in assorted phases.

  1. Rapid initial development through innovation and hype
  2. An equally rapid fall into the trough of disillusionment as we grasp the difficulties
  3. Gradual rise as we discover and apply practical solutions and eventually find a useful niche.

If you’re interested in following AI more closely, it’s important to understand this cycle. During the early phases, there’s lots of change and rapid development, which makes understanding the quality aspects of software particularly difficult. It’s only at the later stages where the development slows down that we typically come to grips with how to better develop technology and test it effectively.

As you can see from this graph, many popular AI techniques, such as NLP and ML, are entering a phase of disillusionment where we’ll be realizing the limitations of the technology before fully grasping what we can do with it.

For most AI technology, though, there is still a lot of growth potential. It remains an exciting space to follow and try to understand these aspects of computing and where they can lead us in the future.

An exciting time for AI

This is an exciting time in the field of artificial intelligence and the potential that it has for our technological future. It is an ever-expanding field filled with many exciting permutations that we have yet to realize. Hopefully, this brief introduction to the topic has only whetted your appetite further and prepared you for a time of great learning and exploration into the field of AI and what is possible in our computing future.

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