Besides Blockchain, AI must be the most overused term in the industry today. It has been popularized by movies such as the Matrix and Ex Machina, as well as by utopians like Mark Zuckerberg and dystopians like Elon Musk.

But what is artificial intelligence, and how does it differentiate from machine learning and deep learning which are often used interchangeably?

Elevator Pitch

Simply said: Artificial intelligence (AI) is the ability of a computer program or a machine to think like humans do.

AI is using technology to do things which used to require human intelligence, for example problem solving or learning, and will likely shape our future more than any technology that has come before.

Machine learning (ML) is a subfield of AI and gives machines the skills to ‘learn’ from examples without being explicitly programmed to do so. Deep learning (DL) is a specialized ML technique that mimics the behavior of the human brain and enables machines to train themselves to perform tasks.

“AI is the goal; AI is the planet we’re headed to. Machine learning is the rocket that’s going to get us there. And Big Data is the fuel.” – Pedro Domingos

🤯 AI is already able to defeat the world’s best poker players, predict crime, read your lips, clone any voice, beat human doctors on clinical exams as well as better predict the death risk of their patients.


As should be clear from the above, machines are increasingly getting smarter and are picking up more and more tasks which could previously only be done by humans.

A lot of people therefore have this misconception that artificial intelligence was designed to replace humans and whatever we do in our daily work or at home. However, AI was (and is being) developed for the sole purpose of augmenting our lives and amplifying our skills and capabilities in all that we do. We are entering an age where man and machine will collaborate ever more closely. Many countries have published or announced national AI strategies to capitalize on the technology’s potential and become a global leader.

Core concepts

  • Machine learning (ML) is a subfield of AI and is its most common application. A computer system is fed data which it uses to recognize patterns and make decisions or predictions without being explicitly programmed.
    Think for example of understanding speech or automatically classifying email as ‘spam’ or ‘non-spam’. Machine learning is one of the most desirable IT skills today.
  • Deep learning (DL) is a subfield of ML and is the hot topic of the day as it aims to simulate human thinking. It basically is machine learning on steroids and allows the crunching of vast amounts of data with improved accuracy. As it’s more powerful it also requires considerably more computing power.
    Algorithms can determine on their own (without intervention of an engineer) whether a prediction is accurate or not. Think for example of providing an algorithm with thousands of images and videos of cats and dogs. It can look at whether the animal has whiskers, paws or a furry tail, and use learnings to predict whether new data fed into the system is more likely to be a cat or a dog.
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There are three main algorithms or techniques you should be aware of:

  • Supervised learning – You are given a training data set with clearly defined labels and thus already know the ‘right answers’. The algorithm will help provide more of the same answers. Supervised learning is for example used to classify email as spam or non-spam and to detect fraud.
  • Unsupervised learning – You are not given a training data set, but only have input data which has not been labeled/categorized, yet the algorithm must automatically try to find patterns. The model will cluster data based on relationships among the variables.
    Unsupervised learning is where you’ll hear most of the excitement when people talk about ‘the future of AI’ due to its limitless potential. It’s for example used for market segmentation (i.e. clustering groups of customers based on common characteristics) and to provide product recommendations based on a shopper’s historical purchase behavior.
  • Reinforcement learning – An algorithm interacts with a dynamic environment in which it must perform a certain goal, like driving a car, without a teacher explicitly telling it whether it has come close to its goal. For each action it takes, a reward (positive or negative) is received. The goal is find the best actions which maximize the long-term reward. The algorithm thus learns by trial and error. An example is learning to play a computer game by playing against an opponent.

Big data acts as an ingredient. Think of it as when you are making a cake – the data represents the flour and the actual process of baking the cake is represented through machine learning. Artificial intelligence will then be the output, or the cake if you will.

Key Benefits

  • Improve decision making

  • Hyper-personalization

  • Business process optimization

  • Free up time for more substantive and creative work

How Artificial Intelligence is disrupting industries

Artificial intelligence (AI) and machine learning (ML) have wide applicability, going from your Netflix content recommendations to detecting fraud with PayPal and even allowing drones to fly autonomously.

Let’s look at the role they are playing as the next big industry disruptors.

  • Financial Services

  • Retail

  • Automotive

  • Healthcare

  • HR & Recruitment


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