Artificial Intelligence vs Machine Learning vs Deep Learning
AI vs Machine Learning vs Deep Learning, these terms have confused a lot of people. If you too are one among them then, this article is definitely for you.
Artificial Intelligence is the broader umbrella under which Machine Learning and Deep Learning come. And you can also see in the diagram that even deep learning is a subset of Machine Learning. So all three of them AI, machine learning and deep learning are just the subsets of each other. So let us move on and understand how exactly they are different from each other.
Starting with Artificial Intelligence
The term artificial intelligence was first coined in the year 1956, but AI has become more popular these days why? Well, it’s because of the tremendous increase in data volumes, advanced algorithms, and improvements in computing power and storage.
The data we had was not enough to predict the accurate result. But now there is a tremendous increase in the amount of data. Statistics suggest that By 2020, the accumulated volume of big data will increase from 4.4 zettabytes to roughly 44 zettabytes or 44 trillion GBs of data.
Now we even have more advanced algorithms and high end computing power and storage that can deal with such large amount of data. As a result, it is expected that 70% of the enterprise will implement AI over the next 12 months, which is up from 40% in 2016 and 51% in 2017.
What is Artificial Intelligence?
Artificial Intelligence is a technique which allows the machines to act like humans by replicating their behavior and nature.
Artificial Intelligence makes it possible for the machines to learn from their experience. The machines adjust their response based on new inputs thereby performing human-like tasks by processing large amounts of data and recognizing patterns in them.
AI Explained with an Analogy: Construction of a Church
You can consider that building an artificial intelligence is like building a church.
The first church took generations to finish, so most of the workers working on it never saw the final outcome. Those working on it took pride in their craft, building bricks and chiseling stones that were to be placed into the Great Structure. So, as AI researchers, we should think ourselves as humble brick makers, whose job it is to study how to build components (e.g. parsers, planners, learning algorithms, etc) that someday someone, somewhere, will integrate into intelligent systems.
Some of the examples of Artificial Intelligence from our day to day life are Apple’s Siri, the chess-playing computer, tesla’s self-driving car and many more. These examples are based on deep learning and natural language processing.
Well, this was about what is AI and how it gained its hype. So moving on ahead let’s discuss machine learning and see what it is and why was it even introduced.
Machine Learning came into existence in the late 80’s and early 90’s. But what were the issues with the people which made Machine Learning come into existence?
- Statistics: How to efficiently train large complex models?
- Computer Science & Artificial Intelligence: How to train more robust versions of the AI systems?
- Neuroscience: How to design operational models of the brain?
What is Machine Learning?
Machine Learning is a subset of artificial intelligence. It allows the machines to learn and make predictions based on its experience(data).
Understanding Machine Learning with an Example
Let’s say you want to create a system which could predict the expected weight of a person based on its height. The first thing you do is collect the data. Let us say this is how your data looks like:
Each point on the graph represents one data point. To start with we can draw a simple line to predict the weight based on the height. For example, a simple line:
W = H — 100
Where W is weight in kg and H is height in cm
This line can help us to make predictions. Our main goal is to reduce the difference between the estimated value and actual value. So in order to achieve it, we try to draw a straight line that fits through all these different points and minimize the error and make them as small as possible. Decreasing the error or the difference between the actual value and the estimated value increases the performance.
Further, the more data points we collect, the better will our model become. We can also improve our model by adding more variables (e.g. Gender) and creating different prediction lines for them. Once the line is created, so in future, if a new data (for example height of a person) is fed to the model, it would easily predict the data for you and will tell his predicted weight.
I hope you got a clear understanding of machine learning. So moving on ahead let’s learn about Deep Learning.
What is Deep Learning?
Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts or abstraction.
You can consider deep learning models as a rocket engine and its fuel is the huge amount of data that we feed to these algorithms.
The concept of deep learning is not new. But recently its hype has increased, and deep learning is getting more attention. This field is a special kind of machine learning which is inspired by the functionality of our brain cells called artificial neural network. It simply takes data connections between all artificial neurons and adjusts them according to the data pattern. More neurons are needed if the size of the data is large. It automatically features learning at multiple levels of abstraction thereby allowing a system to learn complex functions mapping without depending on any specific algorithm.
Understanding Deep Learning with Analogies
Let me start with a simple example which explains how things work at a conceptual level.
Example 1:
Let us try and understand how you recognize a square from other shapes.
The first thing is to check whether there are 4 lines associated with a figure or not (simple concept right!). If yes, we further check, if they are connected and closed, again if yes we finally check whether it is perpendicular and all its sides are equal (Correct!). Well, this nothing but a nested hierarchy of concept.
What we did, we took a complex task of identifying a square in this case and broke it into simpler tasks. Now, this Deep Learning also does this but on a larger scale.
Example 2:
Let’s take an example of a machine which recognises the animals. The task of the machine is to recognize whether the given image is of a cat or of a dog.
What if we’re asked to resolve the same issue using the concepts of machine learning, what we would do? First, we would define the features such as check whether the animal has whiskers or not, or check if the animal has pointed ears or not or whether its tail is straight or curved.
In short, we will define the facial features and let the system identify which features are more important in classifying a particular animal.
Now when it comes to deep learning. It takes this to one step ahead. Deep Learning automatically finds out the features which are important for classification, comparing to Machine Learning where we had to manually give the features.
By now I guess my article has made you clear that AI is a bigger picture, and Machine Learning and Deep Learning are its subparts, so concluding it I would say t he easiest way of understanding the difference between machine learning and deep learning is to know that deep learning is machine learning. More specifically, it’s the next evolution of machine learning.
If you wish to check out more articles on the market’s most trending technologies like Python, DevOps, Ethical Hacking, then you can refer to Edureka’s official site.
Do look out for other articles in this series which will explain the various other aspects of Data Science.
2.Math And Statistics For Data Science
9.Introduction To Machine Learning
12.How To Create A Perfect Decision Tree?
13.Top 10 Myths Regarding Data Scientists Roles
15.Data Analyst vs Data Engineer vs Data Scientist
16.Types Of Artificial Intelligence
17.R vs Python
18.Artificial Intelligence vs Machine Learning vs Deep Learning
20.Data Analyst Interview Questions And Answers
21.Data Science And Machine Learning Tools For Non-Programmers
22.Top 10 Machine Learning Frameworks
23.Statistics for Machine Learning
25.Breadth-First Search Algorithm
26.Linear Discriminant Analysis in R
27.Prerequisites for Machine Learning
28.Interactive WebApps using R Shiny
29.Top 10 Books for Machine Learning
Originally published at https://www.edureka.co on June 8, 2018.
