“Curiosity keeps leading us down new paths” ~ Walt Disney. 

It is indeed the curiosity of man that has led him to create technological marvels and make discoveries that have helped him in everything from daily chores to outer space flights. Be it any sphere of life, research and advancements keep on happening every minute, every hour.


It is the inquisitiveness and intellect of humans that makes them soldier on with the same. With all this, man has successfully created machines that display intelligence giving rise to the field of Artificial Intelligence (AI). AI or Artificial Intelligence is a buzzword in the current scenario. The term has prevailed since years, but has presently become more common than ever! Other popular terms that come along with AI are Machine Learning and Deep Learning. While these terms are frequently used with each other, one needs to understand the differences between the three. Since AI is the present and future of technology, it becomes very important to correctly understand what it actually is and how does it differ from Machine Learning and Deep Learning. Read through to find out!

AI, Machine Learning, & Deep Learning: What are the Differences? 

While Artificial Intelligence and Machine Learning are the terms used in place of each other, it becomes significant to understand the differences between the two.

Artificial Intelligence: ‘Human Intelligence Evinced by Machines’

Let’s begin with defining Artificial Intelligence. Artificial Intelligence is a wider concept than machine learning and deep learning. AI can be defined as the capability of a machine to imitate human intelligence or intelligent human behavior. It entails everything from planning, recognizing sounds and objects, to understanding language, learning, and problem-solving. The history of Artificial Intelligence can be dated back to antiquity. For example, the myths of Hephaestus and Pygmalion. But it was until 1913, that the ‘Principia Mathematica’ was published by Bertrand Russell and Alfred North Whitehead which further revolutionized formal logic.

And today, AI has become powerful more than ever. Some of the most common examples of AI from our day-to-day lives include face recognition on Facebook and image classification on services like Pinterest. All these are classified under narrow AI which includes technologies that can perform several tasks even better than humans.

But the most important question is yet to be answered. How do machines get this intelligence? This is when the concept of machine learning came into the picture. 

The Advent of Machine Learning: ‘An Approach to Execute AI’

Now moving on towards Machine Learning, first of all, you should know that it is a subset of Artificial Intelligence. It can be stated as a way of achieving of Artificial Intelligence. Machine learning can be defined as the practice of making use of algorithms in order to parse data, learn from it, and eventually making a prediction or determination about something in the world. Machine learning does not include hand-coded software routines in order to perform a particular task. Rather, it ‘trains’ the machine by making use of substantial amounts of data and algorithms. In this way, the machine becomes able to understand how to perform the task.

Well, the fact is that the concept of Machine Learning emerged from the minds of AI researchers only. In the beginning, the algorithmic approach included everything from decision inductive logic programming to tree learning, Bayesian networks, and reinforcement learning- to name a few. Unfortunately, the ultimate goal of General AI was not achieved by this approach. Also, Narrow AI was still out of reach with the early approaches of machine learning. 

And then started the journey of machine learning! For a number of years, computer vision ruled to be one of the very best application areas for machine learning. But there was still a plight in this, and that was the fact that it still required a good amount of hand-coding to accomplish the task. In order to make it happen, developers would compose hand-coded classifiers, edge detection filters- for an instance, in order to let the program identify where an object started and where it actually stopped. They would also use codes for shape detection so as to determine if it has 8 sides, a classifier to identify the letters ‘S-T-O-P’. In a nutshell, it can be said that hand-coded classifiers were used in order to develop algorithms in order to determine whether it was a ‘STOP’ sign by making sense of the image.

The aforementioned technique was doing good, but not great! Imagine a foggy day when nothing is clearly visible. In such a scenario, will image detection and computer vision beat humans? Obviously, not and errors were much common in such cases.

Well, the curious minds of humans did find out a way out of it. Gradually, right learning algorithms made everything possible! 

The Birth of Deep Learning: ‘A Technique Used for Implementing Machine Learning’

Technology evolves as time passes. Since human mind never sleeps, newer ideas keep on popping giving birth to newer and better technologies. Same happened with machine learning. To make the machines more intelligent, researchers and developers came out with the idea of Artificial Neural Networks. As gets clear much from the name itself, ANNs are inspired by the biological neural networks that are found in animal brains. As our brains are made up of neurons, ANNs or connectionist systems also make use of artificial neurons. The difference lies in the fact that biological neurons can connect to the other neurons within a specific physical distance; ANNs are composed of discrete connections and layers, along with the directions of data propagation.

 The birth of Artificial Neural Networks gave rise to the concept of Deep Learning. In fact, it emerged as a part of a broader family of machine learning methods. Instead of task-specific algorithms, deep learning makes use of methods based on learning data interpretations. Deep learning exploits neural coding which is aimed at defining a relationship between different stimuli and related neuronal responses in the brain.

Now, let’s understand how deep learning can be distinguished from machine learning, and how does it get better than the previously existing technologies. As already mentioned, deep learning makes use of artificial neural networks, which is composed of artificial neurons. These are made up of discrete layers. This can be better explained by taking the example of an image. Chop the image into a number of tiles that will form the first layer of the neural network. The first layer will then pass the data to the second layer. This will keep on repeating until it reaches the final layer in order to produce the final output. 

When it comes to neural networks, each input is assigned by a weighting by its neuron. The idea is to find out how correct or incorrect it is according to the task being performed. When it comes to the final output, it is determined by the total of those weightings. This is actually called a ‘probability vector’ which is nothing but a highly educated guess which is based on the weighting. Taking the example discussed above, the task of the neural network will be to find out whether it is a 'STOP' sign or not. The features of the sign image will be chopped off and then ‘examined’ by the artificial neurons, right from the distinctive letters to its octagonal shape.

But the journey of neural networks and deep learning has not been a smoother one. In its early days, it was shunned by the AI research community. It was until 2012 when Andrew Ng at Google made neural networks huge. He increased the number of layers and the neurons and imparted large amounts of data in order to train the system. The data was nothing but several images from 10 million YouTube videos which were used to identify the ones with cats. It was Ng who took the concept of deep learning way too deep.

AI Has a Bright Future: Credit Goes to Deep Learning!

In the present day, deep learning has reached a whole new level. Deep learning is everywhere in today’s world whether it’s Google Assistant Speech Recognition, or dynamically personalizing layouts & movie thumbnails by Netflix. Thanks to Deep Learning, that the future of AI is still bright. It is because of deep learning that a number of applications of machine learning have become possible. All those sci-fi dreams of driverless cars, more intelligent robots are now on the verge of transforming into reality. And who knows, sometime soon the C-3PO will come to life?