Artificial intelligence (AI) and machine learning (ML) are two terms that are coming to the forefront. More organizations are adopting these new technologies, which are now all around us but not always immediately visible. Businesses with e-commerce sites are taking advantage of the ability to increase sales by recommending products based on product images viewed. The healthcare industry is treating patients faster because machines can recognize patterns in high-resolution medical imagery. Manufacturers are improving quality control and catching small errors before they become major issues.
The list goes on, but all of these examples employ machine learning, which is a subset of artificial intelligence. Although the labels AI and ML are sometimes used interchangeably, they’re not quite the same.
What Is Artificial Intelligence?
Alan Turing is frequently credited with originating the concept of AI. His 1950 article “Computing Machinery and Intelligence” begins with the line, “I propose to consider the question, ‘Can machines think?’” Turing’s original belief was that if a machine could convincingly replicate human communication, “then the machine could be considered as intelligent as a human being.” The term artificial intelligence was first coined six years later by John McCarthy, a professor at Massachusetts Institute of Technology.
Basically, artificial intelligence is an umbrella term for the idea that machines can operate in a smart way, meaning they can do more than just follow simple instructions.
One of the goals of AI is to mimic human decision-making. However, given that humans make decisions in a number of ways, there is no single approach that works for all types of thinking. For example, on a given day a human might make a sandwich, drive a car, put on a pair of shoes, analyze a report, and decide what movie to watch in the evening. Each of these activities requires countless decisions. It feels natural to us because it’s the way our brains work, but even the smallest decisions involve massive underlying processes. You might not remember the specific moment you learned that sugar is sweet, but this knowledge informs your choices every day.
AI encompasses all of the various methods used to get machines closer to human thought. Initially, the primary approach to AI was to teach a computer everything it needed to know to accomplish a certain task, essentially trying to build a massive database of knowledge. This process is manageable for basic tasks, but as the decisions become more complicated, inputting the data becomes unwieldy. More recent advancements have led to a different approach—machines learning for themselves.
What Is Machine Learning?
Put simply by Karen Hao in MIT Technology Review, “Machine-learning algorithms use statistics to find patterns in massive amounts of data. ... If it can be digitally stored, it can be fed into a machine-learning algorithm.” Hao adds that the process of machine learning is “quite basic: find the pattern, apply the pattern.”
Essentially, machine learning is one application of AI that is based on the idea that, given a set of data, machines can learn. In simple terms, machine learning works by:
- Feeding data to a machine
- Having the machine make decisions based on that data
- Telling the machine whether it was right or wrong
The machine learns based on this feedback until an acceptable level of accuracy is reached. If weak areas are recognized, more relevant data can be added to the training set to help the machine learn more comprehensively.
Machine learning can be either supervised or unsupervised. With supervised learning, the training data is labeled and the machine can essentially check to see if it’s right after the task has been performed. Accuracy will improve over time as the machine learns which decisions are correct and which are incorrect. With unsupervised learning, the data set is not labeled, and it’s up to the machine to learn based on patterns it identifies in the information it has access to.
Why Are the Two Terms Often Used Interchangeably?
Machine learning is a major subset of AI and one that is used to develop other types of AI. For example, natural language processing (NLP) uses machine learning to become proficient in the nuances of human language. Both ML and NLP are types of AI, but because ML is the foundation of NLP, it’s easy to confuse it with the broader concept of AI.
The same is true for computer vision, which is a type of AI that uses machine learning to analyze images and videos. Applications include classifying images, detecting quality issues in pharmaceutical labels, and evaluating damage for insurance claims.
Another reason the terms often get confused with each other is that machine learning is one of the aspects of AI that is currently most under development, and therefore most exciting in the technological world. Machine learning is a more modern buzzphrase, and although it doesn’t encompass all of AI, the term is being readily adopted. This is perhaps because it feels more new than AI, which has been around a long time.
How Did AI Lead to Machine Learning?
Arthur Samuel, a trailblazer in the realm of AI in the 1950s and ‘60s, believed in the innovative idea that machines could learn. Using the game checkers as a testing ground, he demonstrated that, given a set of data about which moves were considered good or bad, the computer could make decisions and win the game against a human.
The data set for the computer that could play checkers came from existing volumes of annotated games, a resource that was not always available for other types of applications, stalling the growth of this new technology. However, the advent of the internet has dramatically increased the amount of available data and, importantly, made it easier to obtain. Machines now have more access to quality information, which allows them to learn more quickly and thoroughly, creating rapid growth in the field of machine learning.
How Do the Two Differ?
The difference between machine learning and AI is nuanced, similar to the concept that every square is a rectangle, but not every rectangle is a square. Machine learning is a type of AI, but not all AI is machine learning.
For example, reactive machines, another type of AI, are not capable of learning. All they can do is react to certain stimuli based on the instructions they have been given. They don’t have the ability to make memories or learn from them, so the reactions will always be the same, even if the results are not the most desirable. IBM’s Deep Blue chess computer is a classic example of AI that is not capable of learning.
What Does Skyl.ai Do?
Skyl.ai is a unified platform that relies on a type of supervised learning known as deep learning artificial intelligence. With this type of learning, the machine recognizes patterns based on training, validation, and test data. The platform allows you to do every step needed to create machine learning workflows in a single place, including data collection, labeling, visualization, training, deploying, and monitoring.
With Skyl.ai, you can implement machine learning automation for unstructured data across a range of industries and applications with solutions such as:
- Data labeling: Transform raw data into a high-quality labeled data set that can be used for other types of machine learning.
- Computer vision: Teach a machine to analyze digital imagery, recognize patterns, and react to specific stimuli.
- Natural language processing: Extract insights from unstructured text data by classifying information, analyzing sentiments, and identifying entities.
In practical terms, these solutions can be used by organizations to become more efficient, improve the customer experience, accelerate research, reallocate valuable resources, and more. If you are interested in exploring artificial intelligence and machine learning, Skyl.ai lets you do it without hiring a data scientist or majorly investing your internal resources.
When you work with Skyl.ai, you don’t necessarily need to understand the difference between artificial intelligence and machine learning—the platform has everything you need to achieve your AI goals so your organization can adopt AI through ML. To learn more about all of the steps, download our free machine learning checklist.