Machine learning and artificial intelligence (AI) are often used interchangeably in discussions about the future of computing, but there’s actually a big difference between the two technologies.
Artificial intelligence (AI) is about creating systems that behave like humans, while machine learning is simply about giving machines the ability to learn without being explicitly programmed.
It’s easy to mix up these definitions, so if you find yourself confused by these terms, don’t worry—you’re not alone! Let’s take a closer look at how machine learning compares to AI and how these two technologies are shaping the future of digital services and products worldwide.
Definitions
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. It allows software applications to grow and improve as they are used, without requiring extensive human intervention.
Artificial intelligence (AI) is an area of computer science focused on giving machines the ability to do things that would require intelligence if done by a human, such as sensory perception, speech recognition, and problem solving.
It also includes creating systems that have some form of autonomous behavior, though it does not necessarily encompass all machine learning techniques. Some fields within artificial intelligence (AI) include robotic engineering, machine learning, computer vision and natural language processing.
There is no consensus among scientists about how broadly artificial intelligence (AI) can be applied or what it will be capable of in future decades; this topic makes headlines with seemingly every new advanced robot creation coming out of Silicon Valley these days.
Similarities
Both machine learning and artificial intelligence (AI) are computer programming techniques. The two terms are often used interchangeably, but they actually refer to different things.
Similarities Between AI and ML
Both machine learning and artificial intelligence (AI) involve making predictions based on data. With ML, computers learn from patterns in large datasets, while with AI, computers use algorithms to find patterns in data.
In both cases, the goal is to make more accurate predictions over time. With ML, a computer learns by taking actions; this is called supervised learning. For example, if you want a machine to recognize images of cats and dogs for tagging purposes, it will first need training data of cats and dogs tagged as such.
As the machine reviews more pictures, it will eventually get better at distinguishing between cats and dogs. Meanwhile, in unsupervised learning, which works without any human input (the computer acts on its own), an algorithm can automatically identify groups of similar pictures that might indicate what type of object is being depicted or what emotional state someone was in when they took a selfie.
Unsupervised learning has led to breakthroughs in facial recognition technology like Snapchat filters!
Differences
One of the major differences between machine learning and artificial intelligence is that machine learning is a subset of artificial intelligence (AI). Machine learning is focused on developing algorithms to allow computers to teach themselves how to do things, such as recognizing objects in images or understanding natural language.
In contrast, artificial intelligence (AI) covers a broad range of technologies, including computer vision, speech recognition, and natural language processing. Artificial intelligence (AI) can be further broken down into three different types: Weak artificial intelligence (AI), Strong artificial intelligence (AI), and Super-Intelligence.
Weak artificial intelligence (AI) means an artificial intelligence (AI) system exhibits some degree of human intelligence but will never match it.
Strong artificial intelligence (AI) refers to an artificial intelligence system that has strong capabilities with reasoning, perception, planning, learning etc.
Superintelligence is when machines are able to outperform humans at every cognitive task. It is difficult to define what superintelligent might look like; however, we could look at it from two perspectives: either we're going to develop systems with the same intellectual ability as humans (or even exceed this ability), or we're going to create something much more intelligent than us.
Applications
Artificial intelligence (AI) is a broad term that has evolved to encompass many different technologies. Machine learning is one of these technologies and it has become very popular in recent years, largely due to its applicability across many industries.
One major difference between machine learning and artificial intelligence is the breadth of applications for each technology.
Machine learning typically only needs data to train algorithms while artificial intelligence needs a large quantity of data as well as computing power. The lack of computational power necessary for machine learning could be why this type of algorithm became so popular among businesses in recent years.
Another difference between machine learning and artificial intelligence is their respective objectives. For example, machine learning focuses on improving an already existing system whereas artificial intelligence develops new ways to solve problems or think more efficiently.
Despite being quite different in terms of design and application, they share some overlap when it comes to subject matter such as natural language processing (NLP) or autonomous vehicles (AVs).
Summary
Artificial intelligence and machine learning are often used interchangeably. But they aren't one in the same. Machine learning is one way of defining artificial intelligence, but it is not the only way.
There are many other definitions for artificial intelligence (AI), such as planning algorithms (a type of algorithm that creates an optimal solution to a problem), natural language processing (a computer's ability to understand human speech), or neural networks (computer systems modeled after how the brain works).
There is no single definition for what constitutes artificial intelligence (AI); therefore, there can be multiple different types of artificial intelligence (AI). One could also say that every type of artificial intelligence (AI) has some degree of machine learning involved.
