Artificial intelligence (AI) and machine learning are key tech concepts today. We interact with the applications of these innovative technologies every day, be it through modern search engines, automated processes, chatbots, or intelligent assistants on our smart devices.
AI and machine learning are closely intertwined, and the terms are also often used interchangeably. However, they aren’t exactly the same. Here’s what you should know about artificial intelligence and machine learning and the key differences between the two.
What Is the Difference Between AI and Machine Learning? The Short Answer
Artificial intelligence is the ability of computers to emulate human cognitive functions like learning, problem-solving, and decision-making. This is achieved through numerous tools, one of which is machine learning. Machine learning is thus a subset or application of AI. It is the technology that enables computers to absorb information without direct instructions. Through machine learning, the system will be able to identify patterns and gather experience, which helps the system improve itself.
What Is AI?
Artificial intelligence is a complex concept as it encompasses a broad topic. The term was coined as early as the middle of the 20th century by an American computer scientist named John McCarthy. Although the history of AI reaches far back, it wasn’t until we understood how human minds work that the discipline could truly develop.
Today, we can consider AI both a multidisciplinary engineering field that creates systems that mimic certain elements of human behavior and a set of technologies that make it possible. By artificial intelligence, we refer to machines capable of acting “intelligently.”
AI is a vast field that encompasses numerous subfields. The tools used by AI to complete tasks in a human-like manner include the following means:
- Natural language processing – a computer’s ability to understand and interpret human language, which enables it to respond appropriately
- Machine learning – processes that enable computers to learn based on datasets
- Deep learning – an advanced type of machine learning that instructs computers to analyze layers of data and learn by experience
- Computer vision – the computers’ ability to interpret images by breaking them down and analyzing them
- Neural networks – systems designed based on the human brain with the goal of classifying information
- Cognitive computing – algorithms designed to interpret text, images, speech, and more, as the human brain does
Types of AI
Although there is still a lot of vagueness surrounding the field of AI, artificial intelligence is typically split into two categories. These are applied and general AI.
Applied AI is what most people think of when they hear the word “AI.” These are the application of AI in systems designed to replicate human intelligence. Applied AI is what’s used, for example, in self-driving cars. However, general AI is where the most advanced innovations are taking place today.
General AI is an emerging field that aims to create general-purpose systems that can handle any task. This is where machine learning steps into the picture.
According to another division, we can also make the distinction between general and narrow AI.
Artificial general intelligence (AGI, strong AI, full AI, etc.) is the ability of intelligent agents to learn intellectual tasks like a human can. Some also refer to systems that possess (or at least mimic) consciousness and sentience as strong AI.
In contrast, narrow (or weak) AI focuses on solving only a specific problem. Academics also refer to programs that do not possess human-like cognitive abilities as weak AI.
What Is Machine Learning?
Machine learning is a field that creates methods that use data to learn and improve processes. It is all about using data to create real knowledge. Machine learning is a key element in artificial intelligence.
Despite the concept being cutting-edge, the idea of machine learning was considered ahead of its time when it was conceived. It dates back to 1959, when Arthur Samuel, an IBM employee, considered the possibility of teaching a computer to teach itself instead of feeding information to it.
The earliest version of a “learning” machine was developed in 1960. It was designed to analyze electrocardiograms, sonar signals, and speech patterns. This early machine was taught through rudimentary reinforcement learning. A human was required to teach the machine and prompt it to reevaluate wrong decisions, which is similar to some methods used even today.
How Does Machine Learning Work?
As mentioned, machine learning is a subset of AI. Machine learning helps intelligent systems perform tasks by making predictions based on previous experiences.
Machine learning uses algorithms to build a model. The initial phase requires the user to input sample (or “training”) data. Once sufficient information is supplied, the machine learning algorithm will be able to make predictions and decisions without intervention. For this reason, machine learning is also often referred to as predictive modeling or predictive analytics.
Naturally, a large amount of data is necessary for this to work. The emergence of the internet provided the perfect source for machine learning, as it makes massive amounts of digital information readily available. Machine learning is also aided by neural networks, which are designed to classify data in the same manner as our brains do. By classifying and re-classifying information, the system continues to improve itself. The machine can learn from previous data without programmers having to do anything.
Types of Machine Learning
Machine learning has three main types: supervised, unsupervised, and reinforced machine learning. Additionally, some also consider semi-supervised machine learning another category.
Supervised machine learning is built on examples. The user provides the machine with a set of data, which contains not only the input but also the desired output. Based on this example, the machine will determine the method that leads from A to B. The machine identifies the patterns that explain the processes between the input and the output and will use them to make predictions the user can correct. The longer the process goes on, the higher the accuracy.
Semi-supervised learning uses labeled and unlabeled data to teach the machine to label data accurately.
Unsupervised learning, as the name suggests, operates without human intervention. The user won’t have to correct incorrect predictions. In this model, the machine learning algorithm studies data to identify patterns. As it processes more and more data, its decision-making ability improves.
Finally, reinforcement learning essentially involves trial and error. The machine learning algorithm explores various options based on a set of inputs and compares results to determine the most optimal process. This model lets the algorithm learn from experience.
Key Differences Between AI and Machine Learning
Artificial intelligence and machine learning are complex disciplines. Here are some main differences between them to help you better understand their relation.
AI
- AI is an interdisciplinary field.
- AI has an extensive scope.
- The goal of using AI is to create intelligent systems that can perform a range of complex tasks.
- AI provides a computer with the ability to mimic human cognition.
Machine learning
- Machine learning is a subset and application of AI.
- It has a narrower scope.
- Machine learning disciplines aim to design machines that perform the specific tasks for which they are trained.
- Machine learning allows computers to learn based on data and patterns.
How Do AI and Machine Learning Interact?
As we mentioned, AI and machine learning are closely intertwined. While AI is used by computers to perform tasks in a human-like way, machine learning provides the means for it to develop its intelligence. AI systems are designed using a range of techniques, including machine learning. In fact, machine learning has become one of the most important ways artificial intelligence has been used in recent years.
Applications of AI and Machine Learning Across Industries
Today, both AI and machine learning applications are all around us. Whether you’re doing a Google search, scrolling your algorithm-driven social media feeds, or checking out Netflix’s recommendations, you’re inadvertently interacting with machine learning models. Have you noticed that the recommendations on such platforms get better and better the longer you use the service? This is because the machine learning algorithm keeps collecting more data and making more accurate predictions based on your habits.
These technologies have found their way into almost all industries, including retail, healthcare, banking, customer service, transportation, and manufacturing. According to an IBM survey, 35% of businesses have already implemented AI in their processes globally. Another 42% are exploring the option. AI and machine learning are the driving force behind chatbots, language translation apps, predictive text, self-driving cars (like Tesla), AI assistants (like Alexa and Siri), and many more tools that have become a part of our everyday life in the 21st century.
The Future of Intelligent Machines
As AI and machine learning are becoming necessary technologies for all businesses hoping to keep pace, learning the difference and relation between the two concepts is crucial.
AI implementation can not only help businesses collect and analyze data more efficiently, but it also increases operational efficiency and improves decision-making. Its rapid development is already opening up new business possibilities and will undoubtedly shape our future.
