Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are the most widely used interchangeable words creating confusion among many people globally.
Although, these three terminologies are typically used interchangeably, but they all are different from each other especially in terms of their applications, capabilities, and results.
Understanding the difference between AI, ML, and deep learning is important to utilize the precise applications of these jargons and take the right decision while dealing with AI, ML, or DL related projects.
Before we start, I would like to show you few images (see below) that will give an overview, how AI, ML, and DL are different from each other or how these three terminologies are related to each other.
The easiest way to understand their relationship is to visualize them as concentric circles with AI — which is a broader area, then ML — which is the branch or subset of AI, and finally deep learning — which is a part of the subset of ML, fitting inside both or you can say — DL is driving today’s AI explosion due to more complex inputs and outputs.
I think these highly illustrative images cleared some doubts and misconceptions about these jargons. But you need to go through more definitions with a few sets of useful examples and use cases that will help you understand these concepts better.
As the name denotes, AI is a broader concept used to create an intelligent system that can act like human intelligence. The terms — “Artificial” and “intelligence” means “a human-made thinking power”.
Basically, AI is the field of computer science used to incorporate human intelligence into machines, so that such machines or systems can think (not exactly) and take sensible decisions like humans.
And such AI-enabled machines can perform specific tasks very well and sometimes even better than humans — though they are limited in scope. And to develop such machines AI training data sets are processed through machine learning algorithms.
To be more precise, AI-enabled systems don’t need to be pre-programmed, instead such algorithms are used, that can work with their own intelligence. And machine learning algorithms such as reinforcement learning algorithms and deep learning neural networks are used to create such systems.
Smart Home Devices, automated mail filters in our Gmail, Self-driving cars, Chatbots, AI Robots, Drones and AI Security Cameras are the popular examples where AI in integrated. Though, there are many more other applications, devices, systems and machines works on AI principles helping humans in various areas across the globe.
As the name suggests, machine learning empowers the computer system to learn from past experiences earned through training data. As of now, you got to know machine learning is the subset of artificial intelligence, in fact, it is the technique used to develop AI-enabled models.
Machine Learning is used to create various types of AI models that learn by themselves. And as much as it gets more data, it gets better at learning and gives more accurate results.
Let’s take an example of how machine learning and algorithms work while making predictions. ML is actually a process of training the algorithms to learn and make the decisions as per the learning.
While training an ML-based model, we need certain machine learning training data sets to feed into the algorithm allowing it to learn more about the processed information.
Recommendation on your Mobile or Desktop based on your web search history, Virtual Assistance, Face & Speech Recognition, Tag or Face Suggestion on Social Media Platforms, Fraud Detection, Spam Email Filtering, are the major examples of machine learning in our daily life. Most of the AI devices are developed through machine learning training.
It is the subset of machine learning that allows computers to solve more complex problems to get more accurate results by far out of any type of machine learning.
Deep learning uses the Neural Network to learn, understand, interpret and solve crucial problems with a higher level of accuracy.
DL algorithm-based neural networks are roughly inspired by the information processing patterns that are mainly found in the human brain.
While learning, understanding, and predicting just like we use our brains to recognize and understand certain patterns to classify various types of information, deep learning algorithms are mainly used to train machines for performing such crucial tasks easily.
Whenever we try to perceive new information, the brain tries to compare it with the items known to the brain before making sense of it.
In deep learning — neural network algorithms employ to perceive new information and give results accordingly.
Actually, the brain usually tries to decode the information it receives and archives this through classification and assigning the items into various categories.
Let’s take an example — As we know DL uses a neural network which is a type of algorithms aiming to emulate the way human brains make decisions.
The notable difference between machine learning and deep learning is that the later can help you to understand the subtle differences. Because DL can automatically determine the features to be used for classification, while ML needs to make understandable these features manually.
Finally, the point is compared to ML, DL requires high-end machines and a substantially huge amount of deep learning training data to give more accurate results.
Automated Translation, Customers Shopping Experience, Language Recognition, Autonomous Vehicles, Sentiment Analysis, Automatic Image Caption Generation & Medical Imaging Analysis are the leading examples of deep learning in our daily life.
Machine learning is already being used in various areas, sectors, and systems but deep learning is more indispensable for the healthcare sector where the accuracy of results can save the lives of humans.
Though, countless opportunities lie for machine learning and deep learning to make the machines more intelligent and contribute to developing a feasible AI model.
In the healthcare and medical field, AI can diagnosis disease using the medical imaging data that are fed into deep learning algorithms to learn the tumors or other life-threatening diseases. Now deep learning is giving excellent results, even performing better than radiologists.
Finally, in all types of AI, ML or DL models working on computer vision-based technology needs a huge amount of training data for object detection. These datasets help them to learn the patterns and utilize similar information for predicting the results when used in real-life.
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