What’s the Difference Between AI, Machine Learning and Data Science?

Today, we hear about data science, machine learning, and artificial intelligence from everywhere. Sometimes these terms are even used interchangeably.

But it’s not the right way to treat them, and in this post, we’re explaining why. We’re going into all the details about the difference between data science, machine learning, and artificial intelligence. And show how these technologies are interconnected.

But first, let’s have a quick look at what each of them stands for.

The central aspect of data science is getting new results from data. DS is based on strict analytical evidence and works with structured and unstructured data.

In fact, everything connected with data selecting, preparation, and analysis relates to data science.

Data science allows us to find the meaning and required information from large volumes of data. As there are tons of raw data stored in data warehouses, there's a lot to learn by processing it.

What is it used for?

• Tactical optimization (improving marketing campaigns, business processes)
• Predicted analytics (forecast of demand and events)
• Recommendation systems (like those of Amazon, Netflix)
• Automatic decision-making systems (like face recognition or drones)
• Social research (processing of questionnaires)

For instance, Netflix uses its data mines to look for viewing patterns. This allows staff to understand users’ interests better and make decisions on what Netflix series they should make next.

Who’s responsible for DS implementation? There’s always a human behind the technology – a data scientist who understands data insights and sees the figures.

Mostly, data scientists should be capable of:

• Understanding of SAS and other analysis tools
• Skills in programming (R, Python, SQL, RapidMiner)
• Ability to process data
• Skills in statistical analysis

But that’s just the tip of the iceberg.

DS specialists may also need expertise in domains like simulations and quality control, computational finance, industrial engineering, and even number theory.

The core purpose of artificial intelligence is to impart human intellect to machines.

AI can relate to anything – from apps for playing chess to speech recognition systems. Just like the Amazon Alexa voice assistant, which recognizes speech and answers questions.

Artificial intelligence focuses explicitly on making smart devices that think and act like humans. These devices are being trained to resolve problems and learn in a better way than humans do.

AI application examples include:

• Game-playing algorithms (like Deep Blue)
• Robotics and control theory (motion planning, walking a robot)
• Optimization (like Google Maps creating a route)
• Natural language processing
• Reinforcement learning

One of the best examples of AI appliance is self-driving cars and robots.

And here's how Amazon uses smart robots. Amazon Prime used to be powered by people whose jobs revolved around getting products from warehouses to customers' doorsteps.

It's a predictable algorithm that didn’t change at all. So the company decided to optimize this repetitive and boring job – and hand it over to robots.

Amazon built distribution centers to enable same-day delivery closer to customers’ homes and put robots into these centers.

Artificial intelligence experts work with AI frameworks like Pytorch & Torch, TensorFlow, Caffe, Chainer, and lots of others.

Machine learning is one of the areas of artificial intelligence.

It’s the science of getting computers to learn and act like humans do and improve their learning over time in an autonomous fashion.

Instead of writing code, you feed data to the generic algorithm, and it builds its logic based on that information.

Simply put, in machine learning, computers learn to program themselves.

ML makes programming more scalable and helps us to produce better results in shorter durations. And if programming is considered to be an automation process, machine learning is double automation.

How companies use machine learning? Netflix takes advantage of predictive analytics to improve recommendations to site visitors. That's how the platform involves them in more active use of their service.

All recommendations are provided to site visitors using machine learning algorithms that analyze users’ preferences and ‘understand’ which films they like most.

Machine learning experts are responsible for applying the scientific method to business scenarios, cleaning, and preparing data for statistical and machine learning modeling.

They work with analytical algorithms to build models that better explain data relationships, predict scenarios, and translate data insights into business value.

Specialists who work with ML have:

• Hands-on experience with MALLET
• Knowledge of Apache Tomcat/Open Source
• Experience with C++, Python
• Experience with GraphLab Create, scikit-learn, scipy, NetworkX, Spacy, NLTK

Finally, it’s time to find out what is the actual difference between ML and AI, when data science comes into play, and how they all are connected.

And here's a tip:

Machine learning and statistics are parts of data science. So there’s plenty of relations between data science and machine learning.

The machine learning algorithms train on data delivered by data science to become smarter and more informed in giving back business predictions.

Thus, ML algorithms depend on the data; they won't learn without using it as a training set.

But sure, data science applies to much more than machine learning. In DS, information may or may not come from a machine or mechanical process. Survey data, for example, can be collected manually.

Sometimes it may have nothing to do with learning.

The main difference lies in the fact that data science covers the whole spectrum of data processing. It’s not limited to the algorithmic or statistical aspects.

Here are some fields data science covers:

• data integration
• distributed architecture
• data visualization
• data engineering
• deployment in production mode
• data-driven decisions

So while ML experts are busy with building useful algorithms throughout the project lifecycle, data scientists have to be more flexible switching between different data roles according to the needs of the project.

Data science is more of a tech field of data management. It uses AI to interpret historical data, recognize patterns in the current, and make predictions. In this case, AI and ML help data scientists to gather data about their competitors in the form of insights.

Data science involves analysis, visualization, and prediction. It uses different statistical techniques. While AI implements models to predict future events and makes use of algorithms.

With the help of data science, we create models that use statistical insights. AI works with models that make machines act like humans.

First, let’s review the basics of AI and ML difference:

• Artificial intelligence means that the computer, in one way or another, imitates human behavior.

• Machine learning is a subset of AI which consists of methods that allow computers to draw conclusions from data and provide them to AI applications.

AI is a broad scientific field working on automating business processes and making machines work like humans. Areas like machine learning (which are AI branches) are pushing data science into the next automation level.

Machine learning and AI difference is better understood through their use cases.

Today, AI is mostly associated with Human-AI interaction gadgets like Google Home, Siri, and Alexa. While we consider video and audio prediction systems like Netflix, Amazon, Spotify, and YouTube to be ML-powered.

Despite the difference between machine learning and artificial intelligence, they can work together to automate customer services (using digital assistants) and vehicles (like self-driving cars).

These technologies help companies to make huge cost savings by eliminating human workers from these tasks and allowing them to move to more urgent ones.

Let's imagine we're building a self-driving car and trying to make it stop at stop signs. For that, we need all three – data science, artificial intelligence, and machine learning.

That’s it. That’s how the whole machine learning vs. artificial intelligence vs. data science correlation works.

The thing is, you can't just pick one of the technologies like data science and ML. Data science and machine learning go hand in hand: machines can't learn without data, and data science is better done with ML.

As well as we can’t use ML for self-learning or adaptive systems skipping AI. AI makes devices that show human-like intelligence, machine learning – allows algorithms to learn from data.

Have any questions about one of these technologies? Let us know – we’ll be glad to answer them.