It’s hard to imagine the modern technology landscape without AI, ML, and Data Science. Almost every technological breakthrough of recent years has to do with them. But the difference between these three concepts is sometimes subtle, and they’re often used interchangeably. Unfortunately, that’s not always correct.
In this post, you’ll learn what each of these terms stands for and how they’re all connected.
What Is Data Science?
First off, you should clearly understand that Data Science is an umbrella term. It covers a whole range of disciplines like data analytics, data mining, and other related fields.
Even though Data Science combines many related disciplines, they all have one goal — to extract insights from raw data and improve decision-making for businesses.
Data specialists work with structured and unstructured data using machine learning algorithms and various tools to achieve that goal.
The simplest example of data science is recommendations: similar products on Amazon or people you may know on Facebook. Every recommendation system you see is one way or another based on Data Science. They all require input data to work.
Would you like something more specific? To create a streaming service like Netflix the data analytics should be applied to track the users’ behavior and improve decision-making.
Here’s a quick list of things they keep an eye on:
- The time and date a user watched a show
- How often or if the show was paused
- If users continue watching after the pause
- If users watch the entire show or not
- How long does it take for users to finish the show
What Is Artificial Intelligence?
Artificial Intelligence (AI) is a broad branch of computer science connected with building machines trained to simulate human intelligence and imitate human actions.
AI can cope with a great diversity of tasks and is used across different industries. For example, AI in FinTech is often used for fraud detection. Among other famous use cases are voice recognition and natural language processing (NLP). The latter is often used in translators, including Google Translate.
It’s much more than that: intelligent devices, autonomous cars, robotic solutions.
There’s an example of AI we all face pretty often — ride-hailing apps like Uber or Lyft. Most of the ride-hailing apps are AI-enabled: they understand traffic to build the best possible routes. That’s also why you can see the drop-off time without even being in a car.
What Is Machine Learning?
Machine learning (ML) is a concept that machines can learn and adapt to new data without any human intervention. ML is the field of Artificial Intelligence.
Let’s get back to the recommendation example. Data Science is in charge of data there, while AI and ML process that data and show you the items you might like. It can be based on your browsing behavior, things picked or bought previously, and many other factors.
What’s the Connection Between Them?
There’s a connection between Data Science, AI, and ML.
First off, Machine Learning is a part of Artificial Intelligence. Yet, it can be used in Data Science to build predictive algorithms.
Being focused on learning from data over time, Machine Learning serves as a kind of bridge connecting AI and Data Science. In other words, AI is a tool that helps Data Science get results and find solutions to specific problems. Still, Machine Learning is what helps to achieve that goal.
Let’s take a look at a made-up example of an autonomous car meant to show how these three concepts are different yet connected with each other.
First of all, the car should be able to recognize road signs and traffic lights using its cameras. The algorithm has to be fed with millions of related photos and videos with stop signs and traffic lights to achieve this.
Now, there should be a decision-making algorithm. Once the stop sign or red light is recognized, the algorithm applies brakes. They should be applied right in time, not too early or too late. In addition, the algorithm should keep in mind weather conditions like a slippery road or fog.
Let’s imagine that after running some tests, engineers find out that the car doesn’t react to stop signs sometimes. By analyzing the data, they find out that the number of false results depends on the time of day. During the night time, the car sometimes skips stop signs. Taking a closer cool, engineers defined that the training data predominantly comprise objects in daylight. So they have to add nighttime content, get the algorithm back to learning, and run those tests all over again.
As you can see, these three concepts are often inseparable. In other words, it’s impossible to build something as complex as an autonomous car with AI only. This process requires the combination of all three concepts and their joint work.