Difference between Data Science, Machine Learning and AI

We help in understanding the difference between DS, ML and AI and when to use each of them
Fri 06 Mar 2020

Data Science

Data science is the course of extracting relevant insights from data. It borrows concepts and ideas from several fields. Statistics, computer science, machine learning, data engineering, and mathematics can be used within Data Science.
Data science aims at structuring, analyzing, and processing multiple data points and sources in order to uncover patterns and deliver insights. The information extracted through data science applications can be valuable insights used to guide business processes and improve decision making.

Machine Learning

Machine learning enables computers to learn a set of rules without needing to explicitly program those rules, as opposed to traditional computer science.

Example : - In traditional programming, I will teach a program to recognize an apple with a few criteria, such as an apple being round and red; - In Machine Learning, I will give a program many photos of apples, and teach the program to learn by itself how to recognize an apple.

Machine Learning focuses on enabling algorithms to learn patterns from data, and it often benefits from learning out of a large volume of data - it thus strongly thrives in today's Big data era. A Machine Learning algorithm can analyze data from different types of data sources and formats such as: images, videos, tabular data, audio recordings, or DNA sequences. While Machine Learning is a subset of Artificial Intelligence, both denominations are often used interchangeably, for instance in the media.

Artificial Intelligence

Overall, Artificial Intelligence refers to the simulation of human brain functions by machines. Some AI systems often rely on neural networks that can partly mimic human intelligence. The primary human function that an AI machine can perform are: logical reasoning, learning, and self-correction. Artificial Intelligence is a wide field with many applications, yet it is classified into two parts: General AI and Narrow AI.

General AI refers to the matter of making machines intelligent by a wide set of activities that involve thinking and reasoning.

Narrow AI, on the other hand, involves the use of artificial intelligence for a very specific task. For example, general AI would mean an algorithm that is capable of playing all kinds of board games, whereas narrow AI would limit the capacity of the machine to a specific game, like chess or scrabble. Currently, only narrow AI is in the reach of developers but this is an immense field of research where new research papers are published every day.

AI, ML, and Data Science: when to use each of them?

Here is a memo that will be useful to identify which is which : - Data science is a wide term that covers the whole spectrum of data processing. - Artificial Intelligence is a very wide term with applications ranging from robotics to text analysis. - Machine Learning is a subset of AI. In most organizations, when AI is applied, it is actually Machine Learning.

Kantify helps companies succeed in their AI journey. We help our clients develop their AI strategy, and we develop best in class AI solutions using the latest technology. If you are curious about how your organization can use AI to make smarter and data-driven decisions, leave us a message and our team will answer all your questions. You can also book a 15min slot to discuss your idea and have a first exchange (in French, English or Dutch).