How to distinguish Artificial Intelligence (AI) and Machine Learning (ML) ?

Published on: 31 Jan 21:42

Photo by Franck V. on Unsplash

How to distinguish Artificial Intelligence (AI) and Machine Learning (ML)

Artificial Intelligence is becoming more intelligent and efficient – its impact on our daily life will be more obvious. Martin Ford, a futurist and writer of the book „The rise of the robots“ leads us to the following example: „Imagine your own reaction, if you order a Taxi at the airport but the driver’s seat is empty.“

At this point we realize, that within the next 10 years Artificial Intelligence will become part of the political discussion because million jobs will be replaced by modern technologies and alternatives have to be made.[1]

With other words, Artificial Intelligence is of very high importance. When you deal with the expressions of Artificial Intelligence (AI) and Machine Learning (ML) you might realise that it is not always simple to distinguish them. The original thought of the superior category „Artificial Intelligence“ was the so called independent problem solving, whereby time after time subcategories evolved. Examples for subcategories are strong and weak Artificial Intelligence as well as Deep Learning.

Artificial Intelligence belongs to the computer science which aims to transfer particular capabilities of human mind on computer systems so that machines will arise which can automatically identify and solve problems,“[2] Machine Learning was defined from Arthur Samuel as „the ability to learn without having been explicitly programmed“ and belongs as a subcategory to Artificial Intelligence. Especially in processing the natural language, translation and by identifying pictures it represents its importance and future growth.

Let’s lighten up the Machine Learning:

Machine Learning enables computers without having been programmed, based on data, to learn and bring solutions to the exisiting problem. The more data you provide, the more efficient and exact does the computer respond. The problem solving improves time by time!

The research team of Google Brain was able to use Machine Learning on a computer: The computer could identify cats on thousands of pictures within milliseconds (see here: ).

Also, through Machine Learning you can analyse huge amounts of data and evaluate them as well as derive measures which are impossible compared to a human being to reach in such a short time and such an enormous capacity!

Photo by Hal Gatewood on Unsplash

What about Deep Learning (DL) in relation to Machine Learning?

As mentioned already, Deep Learning is a spread of the Machine Learning and established in the past the firm foundation for the improvement of Artificial Intelligence and Machine Learning. Furthermore, Deep Learning has a great meaning for the growth of the field of “”augmented reality” and virtual reality because of its smart combination and process of pictures and language.

Deep Learning is a neuronal technological network which was adjusted to the human brain. It strives to imitate the neuronal connections as it is in the human brain. Therefore, it needs a massive volume of data to upskill the system to its most in order to reach the best results. The technology and computer of our time as well as the meaning of Big Data play a high valued role to help Deep Learning to improve and make its results coming closer to reality.[3]

The five most important fields related to Machine Learning:

Medical diagnosis, natural language processing, online search engines, smart cars and personalization in marketing.

In general speaking, when you talk about Machine Learning, it depends on the volume of data which you provide the computer to learn. Another example to understand how it works is like a new born baby, which learns from its environment. A Machine Learning System learns the same way with the difference that its environment just is the data. The more data in the system, the better it can learn.

The following link below will provide access to a channel with detailed information about Machine Learning and similar interesting topics: