Artificial Intelligence and Machine Learning. Are they the same thing?

Original article was published on Artificial Intelligence on Medium

MACHINE LEARNING 101

Artificial Intelligence and Machine Learning. Are they the same thing?

The answer is NO. And I’ll tell you why.

Photo by Comfreak on pixabay

Understanding Machine Learning is a matter of coping with modern life: intelligent virtual assistants, self-driving cars, recommender systems of your preferred streaming platform, your bank policies, the government’s plans, and maybe the statistics gathered by your nearby supermarket potentially are all examples of applications of machine learning. I hope to shed some light on this topic with this series of introductory articles. If you want to learn more, follow me through these stories.

First of all, let’s define the two terms we’re going to talk about:

  • Artificial Intelligence
  • Machine Learning

Artificial Intelligence

Artificial Intelligence (A.I.) is a skill, the ability of the machine to solve problems, behaving like a human on thinking, make decisions, interact with the environment. The studies on Artificial Intelligence aim to reach, one day, what is called the Artificial General Intelligence (A.G.I.), the ability to solve any kind of problem without human intervention.

So, when we talk about artificial intelligence in general, we’re talking about a broader field compared to Machine Learning, which is only one of the possible solutions to the A.I. paradigm. The others are far beyond the scope of this article.

Machine Learning

Machine Learning ( abbreviated ML ) is a topic in Artificial Intelligence that tries to reach the goals of A.I by mimicking the human ability to learn by experience and solve problems.

Arthur Lee Samuel, a pioneer of Artificial Intelligence, invented the term Machine Learning in 1958, giving the following definition:

Arthur Samuel — Photo by XI2085 on Wikimedia

“machine learning is a field of study that gives computers the ability to learn without being explicitly programmed”

He believed that teaching machines to play games was an excellent way to develop strategies to solve generalized problems. He then built his first self-learning application, a program able to play checkers on an IBM 701.

A game of checkers — from GIFER

A more modern definition by Tom Mitchell, a university professor at Carnegie Mellon University, in 1998, shed some light on what machine learning is:

Tom Mitchell — from Carnegie Mellon website

“machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience”

This definition leads to Tom Mitchell’s description of a well-posed machine learning problem:

“A computer program is said to learn from experience E, for some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”

This way, it’s relatively easy to identify any kind of machine learning problem. In fact, for any ML problem, we need to specify:

  • an experience
  • the task
  • a performance measure

Experience

What means experience? By definition is some kind of interaction with reality; in ML, what’s defined experience could be in the form of:

  • a bunch of data collected from previous observations of actual phenomena; this is made by classical machine learning
Example of machine learning with Neural Network — from Carnegie Mellon University
  • analyzing how the environment interacts with the machine when working on solving problems; this defines a reinforcement learning problem
Deepmind playing space invaders — from Deepmind

Task

A task is what the program is trying to do. Classifying objects, predict some value, recommend a movie, find anomalies, etc. are all tasks, and for each task, we have to apply a different model of ML. Many times, in machine learning, it is possible to combine different solutions and applications to solve problems. Which one the best is a matter of performance.

Performance

Performance is a way to compute how good the machine learning model is at solving the given problem. Therefore, it’s necessary to apply some kind of measure or metric, mathematically significant in comparing what the goal was and what we achieved with our machine learning method.

Conclusions

In this article, we aimed to depict the substantial difference between the terms of Artificial Intelligence and Machine Learning. Now we are one step ahead on the path for machine learning comprehension.

Lao Zi, an ancient Chinese philosopher, once said:

Lao Zi — unknown on Wikimedia

“A journey of a thousand miles starts with a single step”

We made this single step now.

In the next story, we’ll talk about different machine learning models and how they apply to specific problems. Thank you for reading, pat yourself on the back and see you next time!