Artificial Intelligence, Machine Learning and Data Science

Key Differences Between AI, Machine Learning and Data Science

By AcingAI     |     Updated May 16th, 2021
Whether you are new in tech, or you’ve been writing code for the past decade, you’ve probably encountered the words Data Science, Machine Learning, Artificial Intelligence and Deep Learning.

While these kinds of technologies (and respective jobs) are becoming more and more common, it’s a good idea to understand their differences, especially if you’re aiming to find a job in one of those fields.
Table Of Contents

What is AI?

One of the hottest buzzwords in the past decade, Artificial Intelligence by definition is intelligence demonstrated by machines, as opposed to natural intelligence demonstrated by humans and other living creatures.

In the context of this blog, AI is used to describe computer programs that mimic cognitive functions - such as learning, problem solving, and pattern recognition.

AI In Different Places

AI applications are used in a variety of fields from robotics to agriculture, through healthcare and finance. As a rule of thumb, anything that seems “smart” uses AI.

Being an AI developer/engineer usually means applying your proficiency into creating and deploying algorithms which solve a complex problem. AI practitioners can be found in areas such as algo trading, robotics, automated systems and predictive models.

How about Machine Learning?

Machine Learning, often referred to as ML, is a sub-field of AI. The basic concept behind Machine Learning is creating a program not in order to solve a specific problem, but rather to improve with experience in a given task.

Machine Learning uses statistical and probabilistic methods, usually to predict an output associated with new inputs. Examples include predicting the future price of a certain stock, a person’s preference of a song or movie, or the appearance of a cat in an image.
The basic concept behind Machine Learning is creating a program not in order to solve a specific problem, but...
The 3 major types of Machine Learning algorithms are:

1) Supervised learning - where the training data set includes the desired output for each of the input examples (labeled data) and so the algorithm learns to map an unseen input to an output.

2) Unsupervised learning - in which the training data contains the only inputs, and the algorithm has to find or learn a pattern in the data set.

3) Reinforcement learning - the area concerned with how software programs take actions to maximize a cumulative reward

Being a Machine Learning developer/engineer is not very different than working as such in the field of AI, and both terms are often used interchangeably. In a nutshell, a Machine Learning engineer will program a machine to learn, rather than to perform an explicit operation.

Last but not least, Data Science

Out of all the titles we covered, Data Science has the best PR and is sometimes used to (wrongly) describe any of the above.

Simply put, Data Science is the science of data. It is an interdisciplinary field whose purpose is to turn data into knowledge and insights. The Data Scientist’s toolbox includes mathematics, statistics, information science, computer science and domain knowledge in the field relevant to the project they will work on.

A Data Scientist’s work is consisting of a wide range of tasks, from defining data requirements and preparing data sets before they can be used, to researching for new models and optimizing the current ones, and even business analysis tasks such as data visualizations, dashboards and data analyses, to translate conclusions on data to business insights.

Python is definitely the most required skill for a Data Scientist, while some of their tasks can be done with R, SQL and even Excel but these alone will not be sufficient.

Data Science was nominated the best job in the US in the years of 2016-2019