Machine Learning vs Artificial Intelligence vs Deep Learning
These terms have confused a lot of people. Considering the latest advancements in artificial intelligence it seems really very overwhelming, but it really boils down to two concepts you’ve likely heard of before: machine learning and deep learning.
These 3 terms are often used interchangeably especially in the realm of Big Data. But why these terms are used interchangeably, are the same? So what are these concepts that dominate the conversations about artificial intelligence and how exactly are they different? That’s what we discuss further in this article.
Before talking about these three terms, First, let’s talk about humans. Because through AI our main vision is to mimic the human.
Yes, I mean the guy who wrote this article and the one who is reading this article. You and me…
We human are super amazing, are the finest creation of God. We went to the moon, to space and now we are trying to explore this universe. Humans have the capability to learn & since from birth we are learning, Indirectly saying we are continuously storing & processing data in our brain. The way we are aging we adding and upgrading this data.
This whole information makes human capable:
- Communicate with the people
- Identifying the situation and patterns.
- Remember what people have said that time, revisualize what people did during that situation. Based on past stored information we take present Decision.
In short, we can say that –
Are among the predominant capabilities that we humans are equipped with & this all be possible just because of the information, the data that our brain holds.
Now what we are trying to create, A system or machine that could perform all these tasks same way as a human did & we call it Artificial intelligence.
Artificial intelligence is the broadest term. Originated in year 1950s and the oldest terminology used among all which we will discuss. In a one-liner, Artificial intelligence (AI) is a term for simulated intelligence in machines. The concept has always been the idea of building machines which are capable of thinking like humans, mimic like humans. The simplest example of AI is a chess game when you play against a computer. Recent AI example would include self-driving cars which have always been the subject of controversy. Artificial Intelligence can be split between two branches –
- One is labeled “applied AI” which uses these principles of simulating human thought to carry out one specific task.
- The other is known as “generalized AI” – which seeks to develop machine intelligence that can turn their hands to any task, much like a person.
Simply saying AI is a broader umbrella under which we are trying to create an artificial brain, that behaves similarly to the human brain. And machine learning is a subset of AI While Deep learning is a subset of Machine learning.
" That’s probably the reason the term artificial intelligence /Machine learning/ Deep Learning are often used interchangeably "
Machine Learning — An Approach to Achieve Artificial Intelligence.
Machine learning is the subset of AI which originated in 1959. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence. ML gives computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. The machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.
You encounter machine learning almost every day, think about
- Ride-sharing apps like Lyft & Uber – How do they determine the price of your ride?
- Google maps – How do they analyze traffic movement and predict your arrival time within seconds?
- Filter spam – Emails going automatically to your spam folder?
- Amazon Alexa, Apple SIRI, Microsoft Cortana & Google Home – How do they recognize your speech?
How model designed in ML : – Titanic: Machine Learning from Disaster
Deep learning (also known as Hierarchical learning, Deep machine learning or Deep structured learning) is a subset of Machine Learning where learning method is based on data representation or feature learning. , Deep Learning was meant to simulate neural networks found in our brains. It is essentially mimicking the human brain’s neural process.
The development of neural networks has been key to teaching computers to think and understand the world in the way we humans do, while retaining the innate advantages they hold over us such as speed, accuracy, and lack of bias and can work without fatigue.
A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain.
Set of methods that allow a system to automatically discover the representations needed for feature detection or classification from raw data. Examples like
- Mobile check deposits – Convert handwritings on checks into actual text.
- Facebooks – face recognition – Seen Facebook recognizing names while tagging?
- Colorization of black and white images.
- Object recognition
Deep Learning owes its effectiveness to the vast amount of Data. Using DL, One can harvest information that typically wouldn’t have been possible using classical ML algorithms.
A great example of deep learning is Google’s AlphaGo. Google created a computer program that learned to play the abstract board game called Go, a game known for requiring sharp intellect and intuition. By playing against professional Go players, AlphaGo’s deep learning model learned how to play at a level not seen before in artificial intelligence, and all without being told when it should be made a specific move (as it would with a standard machine learning model). It caused quite a stir when AlphaGo defeated multiple world-renowned “masters” of the game; not only could a machine grasp the complex and abstract aspects of the game, it was becoming one of the greatest players of it as well.
Let’s recap the differences :
Artificial Intelligence is just a bigger picture for machines which deal with vision someday, a machine can work & behaves similarly to humans.
Machine learning comprises a set of an algorithm, which is used to parse data, learn from that data, and make informed decisions based on what it has learned from the given data.
Deep learning goes yet another level deeper and can be considered a subfield of machine learning. While both fall under the broad category of artificial intelligence. Inspired by the structure and function of the brain, that can learn and make intelligent decisions on its own.
In a nutshell, AI is the grand goal works to achieve a concept where it is possible for a machine to “think” or react like humans.