Artificial Intelligence, Machine Learning, Deep Learning: What’s the Difference?

Over time, there has been an astronomical rise in the number of ways Artificial Intelligence can be utilized for the betterment of everyday lives. The technology is almost everywhere- in traffic predicting Google Maps, in video surveillance cameras, in face recognition apps, in speech recognition & language translation solutions, etc. In fact, AI is one of the trending technologies during the COVID-19 pandemic.

The healthcare industry as been smartly utilizing the AI technology to fight against the coronavirus outbreak. And not just the healthcare, other industries such as finance, retail, education, etc. have been making the most Artificial Intelligence and Machine Learning to stabilize their businesses and make predictions for their future. 

If we take a look at the ways AI is being used, it certainly turns out to be the most versatile technology. However, due to its wide scope, it is usually confused with its sub-sets, i.e. most of the AI technologies are considered the same and people are not able to differentiate between them. 

We, at Daffodil, have acknowledged this confusion in understanding AI and its technologies, many times from our prospects. In most cases, they are unable to differentiate between AI, Machine Learning, & Deep Learning. Considering that this could be the confusion of masses, here we are with a thorough description of what these technologies are and how they are inter-related and differentiate from each other. Let’s get started. 

AI vs ML vs Deep Learning: A Quick Overview

Artificial Intelligence (AI) is the simulation of human intelligence by machines. Whenever a machine completes a task based on a set of specified rules (algorithms), then that intelligent behavior is called artificial intelligence. 

“Artificial Intelligence’ is an umbrella term that refers to the human-like intelligence exhibited by machines.”

Now, technology Artificial Intelligence can be used in different ways to act like humans. It can showcase cognitive learning skills wherein the machine learns from its past experiences by using the data defined in a dataset. It can act like a human who is multilingual and is capable of translating phrases/sentences in one language into another. Or, it can listen to what others are saying, analyze sentiments, and respond accordingly. 

Depending upon how machines act like humans, AI technology is further divided into sub-sets. These sub-sets of AI are referred to as Machine Learning, Natural Language Processing, Deep Learning, Machine Vision, Speech Recognization, etc. 

In this blog post, we are going to discuss Machine Learning and Deep Learning technologies that are usually confused with each other and with Artificial Intelligence. 

Briefly, Machine Learning (ML) is an approach to achieve Artificial Intelligence and Deep Learning is a technique for implementing Artificial Intelligence. Let’s talk about this in detail for a better understanding of both technologies. 

Understanding Machine Learning

Machine Learning is an application for AI that enables the machines to automatically learn from a data set and improve from experiences. For this, machine learning models are developed that are trained to learn from data and make informed decisions on the basis of prior experiences (quite similar to the human brain). 

To understand how machine learning works, just consider the example of a music streaming app. The app usually recommends artists and songs to the users on the basis of music choices that the user made. Here, the ML algorithms are working at the backend, analyzing preferences of listeners and understand what other listeners of similar taste are listening to. 

On the basis of this analysis, the ML model makes recommendations that help to significantly improve user experience. All of this takes place automatically, without human intervention. Just imagine, how difficult it would be for a music streaming service to provide personalized recommendations to all the users (say 2 million users). But with ML working at the backend, the same task is handled for 2 million users, Without having to engage a workforce for it. 

Moreover, we see Machine Learning examples in our day-to-day lives. Traffic predictions, video surveillance, face recognition, email spam filtering, product recommendation, online fraud detection, are some of the examples that illustrate the potential of machine learning.  

Understanding Deep Learning

Deep learning is a subset of machine learning wherein the computer systems utilize artificial neural networks to analyze data, learn, and make decisions (just like the human brain does). An artificial neural network is a layered structure of algorithms. 

One of the finest examples of deep learning is Google’s AlphaGo. It’s a computer program based on a neural network that learned to play the abstract board game called Go. AlphaGo’s deep learning models performed against professional players, illustrating an exceptional level of artificial intelligence.  

In a neural network architecture, the layers are stacked upon each other. The first layer has minimum information while the next layer will combine with the previous information to make more complex information. 

One of the benefits of such approaches is it allows transferring information from one layer of the model to another. Transfer Learning is one such approach that helps AI engineers to create intellectual models in less time. 

Machine Learning vs Deep Learning 

Deep Learning, in short, is an advanced version of machine learning. In the case of machine learning, the models progressively learn from the available set of data. If an algorithm makes incorrect predictions, then it can be fixed by making changes in the model. On the contrary, a deep learning model learns on its own. Even in scenarios when the model makes incorrect predictions, the algorithm self-determines the problem and fixes it. 

As compared to deep learning, machine learning needs less data for training a model. Since deep learning requires resolving complex problems, they need an extensive and diverse set of data to train the model. 

ALSO READ: What is Machine Unlearning? 

Machine Learning or Deep Learning: What does your Project Need? 

Project type, complexity, features, time for training are some of the deciding factors behind choosing an AI sub-set for application development. At Daffodil, our team analyzes the right AI application before getting started with the project. If you have an idea of an AI-based project, then connect with our experts through a free consultation session to know which is the right technology to make your computer systems/applications intelligent.