Today, in the world dominated by digital technologies, such buzzwords as “Artificial Intelligence” and “Machine Learning” are actively used during the presentations and commercials of US companies’ boards, in articles from the press. Usually, people use them interchangeably when referring to any kind of innovative technologies. This, however, is a serious misconception of the actual meaning of these terms.

Though both Artificial Intelligence and Machine Learning are the crucial aspects of the modern information revolution and have a lot in common, there is a significant difference between them. To be clear about their definitions, architectures, and implementations, read further and understand the actual gap between these technologies.

1. Core Concepts Definition

As stated above, one needs to think of a hierarchy where Machine Learning can be viewed as a part of Artificial Intelligence. In other words, Artificial Intelligence is a vision of a future, while Machine Learning serves as a means to achieve that vision.

What is Artificial Intelligence?

Artificial Intelligence is a large, multi-disciplinary domain of computer science that deals with the creation of machines able to perform tasks usually done by a human being, thus possessing certain intelligence. Its main goal is to make computers think, reason, solve problems, comprehend natural language, process visual information and make decisions on their own.

AI is a huge field of computer science comprising both old technologies of expert systems with hard-coded deterministic rules written by engineers and new ones such as computer vision, NLP, robotics, and optimizers. All of these systems are characterized by their ability to behave intelligently in a particular application domain.

What is Machine Learning?

In contrast to a conventional approach to software development based on a set of rules and predetermined instructions that should be followed by the computer exactly, Machine Learning takes another perspective on building programs.

ML is a practice where statistical algorithms and models allow teaching a computer based on large datasets, making it understand hidden patterns, learn from history and make predictions about future actions autonomously. Within the context of Machine Learning, a program explicitly learns and adjusts itself without any outside interference from humans.

2. Relationship Visualization

In order to better perceive the relationship between these two terms, one can imagine nested concentric circles. In this representation, the outer layer stands for Artificial Intelligence, symbolizing the general idea of the technology aimed at creating machine intelligence. Inside this layer, we can find Machine Learning, which is the major component responsible for most AI achievements today. Lastly, there is a subdomain within ML called Deep Learning (DL) that makes use of multi-layer artificial neural networks inspired by the human brain to process data of great complexity such as video streams and audio signals.

3. Operational Difference

The most striking distinction between the general concept of Artificial Intelligence and specialized Machine Learning lies in its way of operation and implementation.

Within the framework of the classic, non-ML implementation of AI, all of the data and rules required for solving the task are fed into the computer by human developers. As a result, the program performs all operations precisely according to these instructions and delivers a result accordingly. As soon as it faces a situation it cannot handle within the scope of pre-defined rules, it crashes.

Within the ML framework, this process turns upside down. The developer only provides a machine with raw data and a set of desired outcomes for this data. The learning algorithm finds correlations between this data and targets and automatically develops a mathematical model to predict the results in advance.

4. Differences Table

In order to evaluate their functionality more effectively, compare some features of these technologies below.

5. Applications in the Wild

Having defined what we mean when we talk about AI and ML, one can understand their role in the modern world.

AI in Action: Smart Virtual Assistant

Think about a modern smart home hub or customer service chatbot. If you ask a virtual assistant to organize a meeting for you, it uses multiple AI components. First of all, it employs natural language processing algorithms for parsing the syntax, then it correlates the purpose of a request with your settings in the cloud and communicates with your databases to create appointments. Such a complex interaction of various technologies represents the usage of Artificial Intelligence.

ML in Action: Recommendation Engines

If you browse a personalized page of Netflix, Spotify, and Amazon, you are experiencing Machine Learning first-hand. The application does not operate upon generalized rules such as “show all action movies to a client older than 30”. An algorithm analyses your personal preferences, calculates the duration of time spent on particular media items, compares your behaviour to those of millions of other people and builds a mathematical model to predict exactly what you would watch next.

FAQ

Is all Machine Learning considered Artificial Intelligence?

Yes, every ML project can be called a subset of the AI domain, because all Machine Learning solutions teach the system to behave intelligently in its own way. Conversely, Artificial Intelligence does not necessarily involve any Machine Learning.

Can Artificial Intelligence exist without Machine Learning?

Certainly. There are so-called expert systems or rule-based AI algorithms that depend entirely on deterministic logic written by human experts. Although these solutions work perfectly in cases involving closed domains and strict conditions (e.g., tax returns calculators), they lack flexibility as they cannot evolve without direct external help.

What is Deep Learning? How does it differ from AI and ML?

Deep Learning is a highly-specialized type of ML algorithms that employs multi-layer artificial neural networks called deep neural networks. Deep Learning enables processing unstructured data such as images, videos, text messages and audio recordings. This technology makes possible many impressive accomplishments such as facial recognition, automatic speech transcription, and large-scale generative models.

Which technology should a business consider investing in first, AI or ML?

It is impossible to build a working AI solution without having ML infrastructure already set up. ML is what makes a machine to learn from data, classify clients into clusters and deliver valuable insights. After getting powerful Machine Learning modules, a company can incorporate them into larger AI frameworks like automated agents or chatbot interfaces.

What are the programming languages for AI and ML projects?

Currently, Python is the leading programming language used for both domains due to its large set of libraries (Pandas, TensorFlow, PyTorch, etc.). Other notable languages include C++ (used for computer vision and robotics) and R (statistical computations and research).