Artificial Intelligence Vs. Machine Learning Vs. Deep Learning: What’s the difference?

4:05 am
October 15, 2021
cogent infotech
Dallas, TX
Cogent University

Artificial Intelligence Vs. Machine Learning Vs. Deep Learning: What’s the difference?

Since the beginning of the invention of technology, humans have been obsessed with the idea of automation. 

Automation has skyrocketed over the past few years, with more and more companies using automation to reduce dependency on humans. You might hear people talking about Machine Learning, Deep Learning, Natural Language Processing, Neural Networks, Virtual Reality, etc. All of this comes under the umbrella of Artificial Intelligence (AI).

But what do they mean? What is the difference between Machine Learning, Deep Learning, and Artificial Intelligence? 

In this article, we will highlight the key differences between these three fields and explore their applications.

What is Artificial Intelligence?

Artificial Intelligence is an umbrella term related to making machines more intelligent, and it enables machines to develop cognitive intelligence with minimal human intervention.

Common applications of AI are Google’s AI-powered algorithm, Uber and Lyft apps, commercial flights with AI-powered pilots, etc.

AI is classified into three types: 

Artificial Narrow Intelligence (ANI):

ANI is goal-oriented and is programmed to perform a single task.

Artificial General Intelligence (AGI):

AGI helps machines understand, learn, and act similarly to humans in a given situation.

Artificial Superintelligence (ASI):

ASI is a hypothetical AI whose intelligence surpasses even the brightest individuals

What is Machine Learning?

Machine Learning is a subset of AI that uses statistical learning algorithms to build smart systems. During Machine Learning, AI systems “learn” to recognize patterns from massive data sets to then predict outcomes in similar situations. 

Training here entails providing machines with lots of data to learn to use pre-processed information. For example, Machine Learning algorithms can be used to identify apples and oranges.

Machine learning examples are Netflix recommendations, voice recognition, image recognition, social media, etc. 

Machine Learning is classified into three types: 

Supervised Learning:

Supervised learning is used when we have correctly labeled data. 

Unsupervised learning:

Unsupervised learning is used when we do not have adequately labeled data. 

Reinforcement Learning:

In reinforcement learning, a machine continuously interacts with the environment to train the algorithms. e.g., military robots.

What is Deep Learning?

Deep Learning is a subset of Machine Learning which is inspired by the working of the human brain. The algorithms used in Deep Learning are called artificial neural networks. Several Deep Learning algorithms are in use, including CNNs (convolutional neural networks), RNNs (recurrent neural networks), LSTMs (long short-term memory networks), and others. 

In Deep Learning, machines learn from examples, just like humans do. It filters the input data into layers of information. 

However, unlike Machine Learning, Deep Learning requires high-end machines and large amounts of data to deliver accurate results.

The best example of Deep Learning would be Tesla’s driverless cars. 

Few examples of Deep Learning Algorithms are:

Convolutional Neural Networks:
Recurrent Neural Networks:
Recursive Neural Networks:
Modular Neural Network:

In this article, we have quickly walked through the concepts of Artificial Intelligence, Machine Learning, and Deep Learning. 

In short, Deep Learning is a subset of ML, and both of them together are a subset of Artificial Intelligence. It’s amazing to see how they are interconnected. To know more on topics like this, visit the Cogent Infotech website.


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