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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