What is the Difference Between AI and Machine Learning?

Introduction

Both machine learning (ML) and artificial intelligence (AI) are fundamental parts of humanity’s quest to develop brilliant, self-educating, learning machines. While the two terms may sound similar, in reality many differences define them. It is enough to ask ourselves this simple question to better understand the difference between AI and ML:
How do we teach a machine?

We can look for an answer in the basic definition of what artificial intelligence and machine learning is.

What is Artificial Intelligence (AI)?

AI or Artificial intelligence is a term that describes machines capable of learning from previous experience. It uses that learning to make decisions based on those experiences. Artificial intelligence can be broad or narrow depending on its use. AI constitutes a much broader term than Machine Learning.

A more in-depth explanation about AI and how it's changing the world today can be found in this knowledgebase article:
What Is AI? A Beginners Guide

What is Machine Learning (ML)?

Machine learning is a machine’s ability to learn by using advanced algorithms. In time, we can teach machines to recognize patterns, objects, and other input data we provide to a device. We can say that if we genuinely wish to have artificial intelligence, machine learning is the primary component.

Types of Machine Learning

When we talk about machine learning, there are three types we will consider.

Supervised Learning

This type of machine learning is one of the most basic types of machine learning. A supervised learning machine algorithm is trained on a small data set that needs to be labeled by a human. It will learn from it and try to apply solutions on more massive datasets. The training dataset will provide the fundamental algorithm problem, data points, and the solution.

While in this type of machine learning, as we mentioned previously, data sets need to be labeled. This is a useful solution, as small scale initial data can be applied to a larger, more significant data set. To simplify, machines can learn using a small example and apply that learning in a larger manner.

Unsupervised Learning

As a contrast to supervised learning, unsupervised learning does not require its input data sets to be labeled. Therefore, we remove the human intervention component. In this model, a machine will be given an unlabeled data set. It will attempt to extract some structure from the data provided. It will try to learn how to create outputs from the given inputs. And since it’s datasets will not be labeled, it will not be as accurate as supervised learning. Since a machine must learn on its own, it can better adapt to dynamically changing data structures using this knowledge.

Reinforcement Learning

This type of education is the most similar to humans learning from experience. It revolves around the concept of an algorithm that improves using a trial and error method. Favorable outputs are reinforced or “rewarded” and unfavorable are discouraged or “punished”. Since we are talking about a machine, many will ask how a device is rewarded or punished in terms of machine language?

It will merely be something like:

1. This action was good (X percentage)
2. This action was bad (Y percentage)

In this model, the machine will not only learn, but it will be efficient at the same time. It’s reward will increase or decrease depending on the overall effectiveness in terms of a percentage value. It will not only learn how to get to the right solution, but also how to get to it the fastest.

What is the Difference Between Machine Learning and Artificial Intelligence?

While on the surface, it may seem simple. There are however complex relationships between deep learning, machine learning, and artificial intelligence. In the following diagram, we see the differing layers of intelligence.

• Deep learning (DL), while similar to ML, also learns from experience but uses much larger data sets. An example is Autonomous cars: Researchers use deep learning to automatically detect and respond to objects like stop signs, traffic lights and pedestrians.
• Machine learning (ML) refers to a system that learns by experience. Examples include speech and image recognition systems.
• Artificial intelligence (AI) is a broad term whose primary goal is to create an intelligent machine. Examples include manufacturing robots and AI assistants like Alexa or Google Home.

Artificial Intelligence vs. Machine Learning

Some examples of machine learning are programs that monitor stock exchange and use historical data to predict the stock price or programs that track weather patterns and will try to predict the weather.

We can say that artificial intelligence will try to simulate the human brain and apply intelligent solutions to a problem presented by looking at all options weighing the consequences, and then proposing the answer. In comparison, machine learning will represent a cold, dark AI that we know from sci-fi movies.

Is this an actual possibility? Not really. However, it works similarly. It will only collect the data in machine learning and then apply the solution without second-guessing or rethinking it. However, it is essential to understand that machine learning is designed to be efficient and productive and, as such, has a different overall purpose then artificial intelligence.

Conclusion

In conclusion, while artificial intelligence and machine learning are connected in a sense that machine learning is a necessary component of creating artificial intelligence, they are each designed to fulfill a different purpose. Should we be worried that machine learning is a component of artificial intelligence? Not really since it is only a part of it and can represent basic instincts that we can observe on humans. If we see someone burning their hand on the surface, our instinct is not to touch it. In a sense, our brain behaves as a learning machine. We had an input of data where we saw someone burned their hand, and our output or conclusion was not to touch it.

Artificial intelligence is the next logical step in computers’ evolution, and machine learning dramatically advances that goal. In the right direction and having a distinct and unique purpose that can benefit all humanity while developing true AI is underway.

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