What's the difference? Artificial Intelligence, Machine Learning and Deep Learning

Welcome
to the world of artificial intelligence! It’s a huge world with many concepts,
methods and applications. It’s totally normal to be confused by technical
terminology, especially if you have no background in computer science. But
don’t let this hold you back, by the end of this article you’ll fully
understand the differences and similarities of top 3 concepts in technology.
Even though most people use them interchangeably, artificial intelligence,
machine learning and deep learning don’t have the same meanings. Let’s dig in
deeper.

Simple definitions
of the fundamental concepts and their abbreviations¹ :

Artificial Intelligence (AI): Any technique which enables
computers to mimic human behavior.

Machine Learning (ML): AI techniques that give
computers ability to learn without being explicitly programmed to do so

Deep Learning (DL): A subset of ML which make the
computation of multi-layer neural networks feasible.

What is Artificial Intelligence?

ability of computer program to function like intelligent human brain

The term
“Artificial Intelligence” was first coined by John McCarthy in 1956 when he
held the first academic conference on the subject². It is a broader concept
that involves Machine Learning and Deep Learning.

Some
familiar examples of applications of AI:

  • Self-driving
    cars
  • Robots
  • Chatbots

The term
AI doesn’t say anything about how computers act smart. There are different
techniques to make computers “smart”, including machine learning.

What is Machine Learning?

empowering computer systems with the ability to “learn”

You may
have realized that an ad you see while you’re surfing on the web is not the
same ad on your friends’ phone. Actually, it might be something you had already
searched for, or similar. These ads “learn” your buying behavior and recommend
you similar products. This is machine learning.

Machine
learning is a subset of AI which enables the computer to make a data-driven
decision rather than explicitly programming for carrying out a specific task.
In order to make data-driven decisions, computer analyzes data and understands
the patterns in it. As a result, computers can take decisions with minimal
human intervention which is very useful for automation tasks.

Examples
of applications of machine learning:

  • Product
    recommendation
  • Search
    engine results
  • Personal
    assistants

Machine
Learning is the answer when you need more than acting like a human: learning
like a human. Fixed instructions does not accomplish tasks like extracting
meaning out of text that are very simple for humans. Machine Learning doesn’t
use a fixed way to solve a problem, it changes and improves the solution as it
learns more about the problem.

Chihuahua or muffin?

What is Deep Learning?

an approach to ML that mimics the network of neurons in a brain

Deep Learning is the
next evolution in machine learning. Training computers to learn
like humans is achieved partly through the use of (deep) neural networks. Deep
neural networks are a series of algorithms modeled after the human brain. Just
as the brain can recognize patterns and help us categorize and classify
information, neural networks do the same for computers.³ Deep neural networks have many layers which create unique capabilities that enable them to solve tasks that Machine
Learning models could never solve.

All
recent advances in intelligence are due to Deep Learning. Without Deep Learning
we would not have self-driving cars, chatbots or personal assistants like Alexa
and Siri. Google Translate app would remain primitive and Netflix would have no
idea which movies or TV series we like or dislike.⁴ In all these applications
you experience some form of artificial intelligence. Behind the scenes, that
AI is powered by some form of deep learning.

Examples
of applications deep learning:

  • Image
    recognition
  • Self-driving
    cars
  • Translators
Structure of a neural network

Deep Learning vs. Machine Learning

  1. Machine Learning models need some human intervention for guidance. If a machine learning model fails, someone needs to fix that problem, but deep learning models fix themselves.
  2. Machine Learning models require explicit, manually provided features while Deep Learning models automatically discover the features in data.
  3. Deep Learning requires more data for training compared to Machine Learning. Deep learning networks need to see large quantities of items in order to be trained.

Final Notes…

Artificial Intelligence is the all-inclusive concept that first rose and followed by Machine Learning and lastly Deep Learning. Machine learning and Deep learning are ways of achieving Artificial Intelligence but they are not Artificial Intelligence themselves. Deep Learning is state-of-art version of Machine Learning and all recent advances in intelligence are due to Deep Learning.

Source: blogs.nvidia.com

References:

¹ https://blogs.oracle.com/bigdata/difference-ai-machine-learning-deep-learning

² https://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf

³ https://www.datasciencecentral.com/profiles/blogs/artificial-intelligence-vs-machine-learning-vs-deep-learning 

https://www.deeplearning-academy.com/p/ai-wiki-machine-learning-vs-deep-learning

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