Are Artificial Intelligence (AI) and Machine Learning (ML) the same thing? Many people confuse these two concepts, using one instead of another and vice versa. Unfortunately, companies mislead their customers by promising AI instead of ML or some unrealistic combination of the two.
In the realm of big data, AI and ML are often used interchangeably. With all the hype going on about these two ideas, it’s easy to get lost and fail to see the difference. For example, just because you use a certain algorithm to calculate information, it doesn’t mean that you have AI or ML at work. What does? Let’s start with the basics.
What Is An Algorithm?
An algorithm is simply a set of actions to be followed in order to get to a solution. When it comes to ML, the algorithms involve taking data and performing calculations to find an answer. The complexity of these calculations differs depending on the task. The best algorithm allows you to get the right answer in the most efficient manner.
If an algorithm works longer than a human does, it’s useless. If it offers incorrect information, it’s unnecessary. Algorithms get training to learn how to process information. The efficiency, the accuracy, and the speed depend on the training quality.
When you use an algorithm to come up with the right answer, it doesn’t automatically mean using AI and/or ML. But if you are using AI and ML, you are taking advantage of the algorithms.
All humans have eyes, but not all creatures who have eyes are human.
These days, we hear about AI and ML being used whenever an algorithm exists. Using an algorithm to predict event outcomes doesn’t involve machine learning. Using the outcome to improve the future predictions does.
What Is Artificial Intelligence?
AI is a widely used term. It’s a science of making the computer behave in such ways which are commonly thought to require human intelligence. Basically, making a computer act human in some ways.
Even though the above definition is rather precise, the AI field is still broad. For example, in the 1980s, anyone would tell you that a pocket calculator was an artificial intelligence. Today, it’s a common program which doesn’t seem to have anything to do with AI.
Artificial intelligence takes advantage of numerous technological advances. Machine learning is just one of them.
Unlike machine learning, the definition of artificial intelligence changes as new technological advances come into our lives. It’s likely that in just a few years, what we consider to be AI today will look as simple as a pocket calculator.
Experts at Miromind offer another definition of AI, which can be easier to grasp. It is a study of training a computer to execute tasks which humans seem to do better today. In the digital realm, it’s largely applicable in marketing and SEO efforts
What Is Machine Learning?
Machine learning is simply a branch of AI. It’s a study of computer algorithms that automatically become better through experience. ML is one of the ways to achieve AI. Machine learning requires large data sets to work with in order to examine and compare the information to find common patterns.
For example, if you give a machine learning program many photos of pregnancy ultrasounds together with a list of indications to identify the gender, it’s likely to learn to analyze ultrasound gender results in the future. ML programs compare different information to find common patterns and come up with correct results.
Machine learning comes with advanced sub-branches, such as deep learning and neural networks. Some people have a tendency to compare neural networks and deep learning to the way human brains operate. However, there are many differences between them.
Overall, ML is a learning process, which the machine can achieve on its own without being explicitly programmed to do. Machine learning involves computer learning from experience.
AI vs ML
The key difference between the two concepts involve
• Goal – The goal of AI is to increase the chances of success. Meanwhile, ML’s aim is to improve accuracy without caring for success.
• Nature- AI is a computer program doing smart work. ML is the way for the computer program to learn from experience.
• Future – The future goal of AI is to stimulate intelligence for solving highly complex programs. The ML’s goal is to keep learning from data to maximize the performance.
• Approach – AI involves decision-making. ML allows the computer to learn new things from the available information.
• Solutions – AI looks for optimal solutions. ML looks for the only solution.
AI and ML
Even though many differences exist between AI and ML, they are closely connected. AI and ML are often viewed as the body and the brain. The body collects information, the brain processes it. The same is with AI, which accumulates information while ML processes it.
When people use these two terms interchangeably, they fail to have a deeper understanding of the concepts while intuitively understanding how closely related they are.
AI involves a computer executing a task a human could do. Machine learning involves the computer learning from its experience and making decisions based on the information. While the two approaches are different, they are often used together to achieve many goals in different industries.