In this blog post, we will explore the difference in Artificial Intelligence, Machine Learning, and Deep Learning (AI vs ML vs DL) in short. The post uses basic Machine Learning terminologies if you are completely new to this topic you can refer here.
Artificial Intelligence is one of the subjects that has taken the technological world by storm and it is a term that you must be currently hearing all the time. If you have been reading articles on Artificial Intelligence then I am sure that you must have seen Machine Learning and Deep Learning terms used almost synonymously or sometimes representing some different meaning from each other. In this post, we will try to learn the difference in AI vs ML vs DL by reading through the definitions and understanding their relationship hierarchy.
Artificial Intelligence (AI)
Artificial Intelligence (AI) is a computer technology that is used to make a machine intelligent enough to carry out any work as you and I would do. In simple terms, make a machine act like a human. In order to be an intelligent machine, it needs three basic capabilities, first, it should be able to learn as it gains more experiences by performing a task. Second, it should be able to think from its learning and third, it should be able to perform the action based on its learning and thinking capabilities to complete the task as a human would do.
Based on the task to be accomplished Artificial Intelligence can be classified into many subfields, like, if the machine is designed to process human languages then it is classified as Natual Language Processing or NLP or if it built for processing image just like our own eyes and much more is classified as vision. Another notable classification or subfield is Robotics, here the focus is to design and build robots to perform tasks that are difficult for humans or to perform with accuracy all the time like for example the famous Tesla Motors assembly line.
Machine Learning (ML)
Machine Learning (ML) is a subfield of Artificial Intelligence that focuses on building software applications that automatically learn and improves from its experiences without being assisted or in other words without being explicitly programmed. The focus here is to enable software application learning capabilities so that it can use them and figure out the actions needed to complete the task.
Machine Learning uses algorithms (countable steps to solve any problem) as a pathway to reach to a solution using data (dataset) as an input. Here data is the bread and butter for any type of machine learning algorithm, the choice and quality of the data directly affects the accuracy of the result of the algorithms used. Data can be of any type, for example, an image of specific species, employee data in tabular format, movement of a butterfly as a graph data, etc. These data are distributed into a training dataset and test dataset and the former is used to train the algorithm and the latter is used to test the accuracy of the data.
A few real-life examples where machine learning is being used actively used is Speech recognition, Medical diagnosis (detecting cancer), Financial loan fault detection, Classification identifying and grouping of data, etc.
Deep Learning (DL)
Deep Learning (DL) is a subfield of Machine Learning that focuses on algorithms that try to imitate the functioning of a human brain. The algorithms follow unsupervised learning and try to find patterns from unstructured (unlabeled) data and use them in decision making. The algorithms used are created taking inspiration from brain functioning and decision making. Deep Learning is also known as “Deep Neural Learning” or “Deep Neural Network”.
Deep Learning requires a huge amount of data and a substantial amount of computing processing power. Like for example, Automated Driving requires a million driving scenario images (stop at the red light, initiate cruise control on freeways, etc) and similar hours of videos to learn and drive the car automatically. A few other fields where deep learning is being actively used are Language Detection and Automated Translation, Computer Vision, Chat Bots to improve customer experience, etc.
AI vs ML vs DL: the difference of hierarchy
To summaries AI vs ML vs DL, Artificial Intelligence is a more generalized subject, Machine Learning is the specialization of Artificial Intelligence and Deep Learning is a further specialization of Machine Learning. The more specialization work you need the more extensive data and processing power will be required. The image below may clear the differences.
Given below is a summary to define the difference between Machine Learning and Deep Learning.
|Machine Learning||Deep Learning|
|Computation power||Less||Significantly Large|
So here are the basic difference of AI vs ML vs DL, I hope you found this post useful. Thanks for visiting, Cheers!!!
[Further Readings: ASP.NET Core Blazor Server Application Project Structure | ASP.NET Core – Blazor Application an Introduction | Top 5 Machine Learning Frameworks to learn in 2020 | Visual Studio 2019 Output Window | Visual Studio 2019 Code Navigation (Ctrl+T) | 10 Basic Machine Learning Terminologies | Introduction to Machine Learning | How to Publish a NET Core application | How to change Visual Studio 2019 Theme | How to create an ASP NET Core MVC Web Application using dotnet-cli | How to create an ASP.NET Core Web Application using dotnet-cli | How to create a dotnet core NUnit Test Project using dotnet-cli ]