An Introduction to Artificial Intelligence (AI), Machine Learning (ML) & Deep Learning (DL)
The terms artificial intelligence, machine learning, and deep learning can be very unclear and muzzy at times even by its practitioners as it is used in the same context interchangeably. Let's begin with the popular “Artificial Intelligence”.
The term “Artificial Intelligence” has been around for over 60 years. Of course, it triggers a lot of connotations to people when they hear the term, especially at the first instance. Some of the ideas describe computers as being smart from probably getting some good article or data from the internet like Wikipedia. We wouldn’t totally rule out the popular idea channelled to the movies like “The Terminator” where machines in most cases robots learn the ways of humans and respond with similar behaviours and of course probably conquer the world in the later years. Funny, isn’t it?
Well, that isn’t quite the concept of AI.
The concept of AI
AI involves machines that can perform tasks that are characteristic of human intelligence. — John McCarthy 1956
The concept of AI can basically be pictured as computers mimicking human behaviour. More like the simulation of human intellect in machines that are programmed to think like humans and mimic their actions. The machines don’t magically take a peek at some human actions and replicate them. No!
This basically works by merging large amounts of data with continuous processing and following some structured algorithms to enable the machine to learn and pick out patterns to come up with an hypothesis from the data.
The replication of these human actions can be seen in self-driving cars, smart assistants, manufacturing robots, recommender systems, and a lot more.
What is Machine Learning (ML)?
The concept can be briefly described as the system of programming computers so they can learn from data. We could say it is a way to accomplish Artificial Intelligence or more like a subset of Artificial Intelligence.
Here are some more general definitions:
Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed. — Arthur Samuel, 1959
From a more technical approach:
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. — Tom Mitchell, 1997
According to Arthur Samuel we could generalize that machine learning actually beats or is a better means to achieve Artificial Intelligence compared to the conventional programming method which would involve some bulky code base containing complex rules which require high-level code optimization and management. That already sounds like a tedious routine.
With machine learning we could basically train an algorithm to learn by feeding in some large amounts of data into the algorithm to discover or recognize patterns from the data, adjust and optimize the algorithm to come up with a model to predict new data in future.
Netflix is a Good Example
Netflix’s complex recommendation system helps users find shows and movies that interest them. Netflix gets data from its users through their interactions with its service such as the user’s viewing history and how the user rates other titles, information about the titles, such as their genre, categories, actors, release year, etc. These pieces of data are processed and fed to a complex machine learning algorithm which learns and come up with predictions of tv shows and movies which may be of interest to the user.
This kind of machine learning is called Recommender systems used not only by Netflix but by other popular companies. Companies like Amazon, LinkedIn, and Pandora help users discover new and relevant items (products, videos, jobs, music) by creating a delightful user experience while driving incremental revenue.
What is Deep Learning (DL)?
Deep Learning just as Machine Learning is also a means of achieving Artificial Intelligence. It could be seen as a subset of ML.
Deep learning is basically inspired by the structure and working functions of the brain. Which is the interconnecting of many neurons.
It is pictured as a neural network with three or more layers. Each layer picks out a specific feature to learn. An example in the case of image recognition, It picks several features like curves/edges etc.
Deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction as compared to Machine Learning which is made of Linear algorithms.
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