You’ve no doubt heard the terms “artificial intelligence” and “machine learning” before. And if you haven’t, you will soon. By 2021, it’s estimated that 80% of new technologies will be AI-based. And 37% of organizations globally are using some form of AI to improve their daily operations.
Amazon, for example, used machine learning to reduce its shipping time by over 225%. So, if you aren’t sure what these terms mean, and what the difference between them is– don’t worry, we’re here to help.
Over the next few paragraphs, we’re going to dive into the difference between machine learning and artificial intelligence(1), hopefully shedding light on this sometimes confusing topic. We’ll also briefly go over what each term means and give a few examples of different types of artificial intelligence and different types of machine learning. Lastly, we’ll discuss why it is that the two terms are used interchangeably in the first place.
A Brief overview of AI
Artificial intelligence, or AI, refers to the mimicking of human intelligence by a man-made machine. The machine possesses a computerized brain that is capable of learning and problem-solving in a similar capacity to the human brain.
Artificial intelligence is a rather broad umbrella term that encompasses several subsets– something that’s important to remember because we’ll be coming back to it later.
The goal of AI is really to replicate not only the problem-solving but also the decision-making abilities of the human brain. This can be achieved through the use of algorithms, which are essentially a set of rules outlining what the computer does in a given situation.
You can look at algorithms as a sort of recipe that the computer must follow when all of the ingredients are present.
Artificial intelligence can be broken down into three types:
Narrow AI, as the name suggests, has a very narrow focus. It’s sometimes also referred to as “weak AI”. An example of narrow AI would be Siri or Google Assistant. Narrow AI represents where we are currently at with artificial intelligence in technology.
The second type of AI is artificial general intelligence (AGI). This type of AI occurs when the abilities of a computer can match the abilities of the human brain. Under AGI, computers would be capable of independent problem solving and reasoning, decision making, and even creative thinking.
he third type of AI is artificial superintelligence (ASI). You’re probably pretty familiar with this type, although it does not currently exist. Under ASI, machines develop intellectual abilities that go beyond what the human brain can achieve.
If you’ve ever seen the Terminator series, you understand why this could be problematic. The reality is, however, that many experts predict ASI would actually hugely benefit the human race.
A Brief overview of ML
Remember when we talked about how artificial intelligence had several different subsets? Well, machine learning, or ML, is one of them. Machine learning is the ability of a machine to learn from data. Of course, the machine must be programmed first. But once the proper algorithms are in place and the machine is given access to data, it can begin to learn.
Machine learning exists and is actually pretty common in our world today. Autocorrect is one example of ML in modern life, as is a spam filter. These programs are far from sentient, but they possess the ability to change their behavior based on new data. If that sounds an awful lot like narrow AI, that’s because it is. Machine learning is an example of narrow AI.
Machine learning can be broken down into four different categories:
This type of ML involves the use of labeled datasets. Once the data teaches the machine a certain pattern or set of characteristics, the machine can predict an outcome.
Unsupervised machine learning is all about sorting existing data that is unlabeled. An unsupervised machine learning algorithm can teach a computer to separate data into different groups based on relationships or patterns.
Semi-supervised machine learning falls somewhere between the two. This type of machine learning comes into play when data sets have both labeled and unlabeled components. The predictions given under semi-supervised machine learning tend to be the most accurate of all types of machine learning.
This type of ML is similar to the kind of reinforcement learning that humans participate in. Under reinforcement learning, a reward is given when the best course of action is determined. The goal of the machine is to make decisions that maximize the reward.
Key differences between Artificial Intelligence and Machine Learning
After all that, you may be wondering: how are these things different? There are a few key characteristics that may make the distinction easier to remember.
One thing to keep in mind is the scope. Artificial intelligence has a very broad scope. Machine learning, on the other hand, has a much narrower scope– these machines can master a given task, but they can’t do much else.
Another key difference between artificial learning and machine learning is that the two have very different goals. When it comes to artificial intelligence, particularly AGI or ASI, the goal is to create a computer capable of decision-making and sentient thought. With machine learning, the goal is simply for the machine to be able to accurately predict an outcome based on past data.
Type of dataset
Additionally, artificial intelligence can deal with all types of data– structured, unstructured, and semi-structured. Machine learning, alternatively, can only make sense of structured and semi-structured data. Furthermore, while both AI and ML involve self-correction, only AI involves reasoning.
Wisdom vs Knowledge
You could also say that artificial intelligence involves the procurement of wisdom and intelligence, whereas machine learning aims for knowledge.
Artificial intelligence will look at multiple outcomes and pick the one that is best. Machine learning will pick what it sees as the only solution, regardless of whether it is the best one.
Really, at the core of the difference between machine learning and artificial intelligence is sentient thought. Machine learning does not require a computer to develop its own consciousness. Artificial intelligence requires the machine to be able to feel and think independently of its programming in order to match the capabilities of the human brain.
Why do tech companies tend to use AI and ML interchangeably?
Tech companies use artificial intelligence and machine learning interchangeably because decades ago, the focus was primarily on developing true artificial intelligence– AGI and ASI. At that time, a negative stigma began to develop around the term. This stigma may have had to do with the portrayal of ASI in movies, TV, and the media.
For that reason, other terms began to emerge as the technology advanced. Terms such as machine learning and deep learning began to crop up, with people using them interchangeably with narrow AI.
The problem is that ML is really only synonymous with narrow AI. Once artificial general and superintelligence become a contender, it’s likely that the distinction between ML and AI will become more important and the terms will naturally become less interchangeable.
Machine learning is where AI technology is today. Artificial intelligence represents where it could be tomorrow. If you need help keeping the terms straight, just remember that machine learning involves teaching a machine to learn.
These machines perform a single task extremely well. Artificial intelligence, on the other hand, involves replicating the human mind. These machines could, in theory, perform a variety of tasks just as well– if not better– than a human.
Ultimately, the difference between the two will become wider and easier to distinguish as years go on.
Other Useful Resources:
How Artificial Intelligence Is Posed to Take Robotic Process Automation to the Next Level
Best Artificial Intelligence Focused Platforms that Increase Conversions
Future of Cybersecurity with Artificial Intelligence
Top 5 Public Datasets for Machine Learning