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There are many people who get confused when hearing about Computer Science (CS), Artificial Intelligence (AI), and Machine Learning (ML). These (along with Data Science, Big Data, Deep Learning, Neural Networks, Computer Vision, Natural Language Processing, etc.) are all the buzz-words that are being extensively used for marketing. There are many different definitions of them as you search throughout the internet, which is a reason for you to be even more … confused. In this blog, I shall present the most simple but also concise view about the meaning of these terms.
First of all, let’s talk about: Computer Science, Artificial Intelligence and Machine Learning.
As you can see from the picture above, CS is the umbrella definition. AI is a sub-field of CS while ML, in turn, is a sub-field of AI.
Computer Science: is about the study of computational systems (that is, using computers for computing). CS mostly involves software but not hardware (the field that deals with hardware is called Computer Engineering). Computer scientists study what computers can do. By “computers” here, we mean all the electrical devices that can be programmed, like your television or your air conditioner.
The main focuses of CS are Software Engineering (create software programs), Artificial Intelligence (program the computers so that they can do intelligent work), Database (store data on computers), Networks (communication among computers), Security (protect computers from malicious software and attackers), Graphics (display content) and Human-Computer Interaction (how to help users use the computers in the easiest way).
Artificial Intelligence: is a sub-field of Computer Science, which targets to make computers do what humans do. For example, playing chess, determining which stocks to buy or sell, correcting your grammar mistakes can all be done by AI.
Machine Learning: is a child of AI, it is also considered a smarter version of AI. In machine learning, we give data to our program and instruct the program to learn from the data. It is just like you to give a book to your child and guide him to learn from the book.
As many people feel uneasy to distinguish between Artificial Intelligence and Machine Learning, let us compose a table to indicate their differences:
| Artificial Intelligence | Machine Learning |
| Not involve data. | Involve data. |
| You tell your program what to do. The program learns from you. | You give your instruction to your program, tell it how to learn from data. The program learns from data. |
| The target is: the program mimics humans and can do as good as humans do. | The target is: the program excels humans and does better than humans. |
You can see from the above that when AI only aims to produce a program to act just like a human, ML’s purpose is to create a superintelligence that can surpass humans. And how do we do that? we give the program some data (note that we ourselves do NOT fully understand this data), hoping that the program can learn more from the data than we can and be more intelligent.
Ok, we are done with the definition of essential terms. Now we can move on to the more marketing-purpose words.
Data Science and Big Data: Being trendy from around 2010, data science refers to everything related to data, from cleaning, preparation up to deducing useful information from data. Big data is more or less the same as data science, the only difference is that it deals with a large amount of data. What is large? Well, large is just a qualitative term and it changes from time to time and case by case. In the past, 10 GB of data can be considered large, today some people do not call it large even if it reaches 10 TB.
Neural Networks and Deep Learning. Neural Networks (or more precisely, Artificial Neural Networks) are the systems created by (loosely) simulating the human brain, with the aim is to recognize the pattern of data. Deep Learning refers to Neural Networks that have many layers (if you don’t know what layer means, you may simply assume that Deep learning is a more complex type of Neural Networks). Deep learning and Big data have become popular since the same time (one is deep, one is big. Ok.).
Computer Vision and Natural Language Processing. These 2 are the biggest breakthroughs that made the above terms famous. Computer Vision is the science that studies how computers see things (include seeing pictures and videos) and understand what they see. Natural Language Processing, on the other hand, is about teaching computers to understand our language (in terms of text). Both fields obtained many successes in recent years, thanks to Deep Learning and Big Data.
Have you tried the website that predicts your age based on your 1 image? Have you got suggested by Facebook when trying to tag a friend in your photos? Have you played an AR-game? Those are all from computer vision. Do you chat with a chat-bot? Check your grammar mistakes on the Grammarly website? Got translated on Google Translate? That is Natural Language Processing.
Disclaimers:
As I said at the beginning of the post, there are many different meanings and interpretations of the above terms. We have yet to have any formal explanation of them. So, the way I explain things reflects only my point of view, nonetheless, I did try to do it the most objectively as I can. Hope this post helps you get a somewhat clearer view of what it contains.
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