What is the difference between Artificial Intelligence & Machine Learning?

Paraphrased from a Oracle Blog article & other online sources -

Artificial Intelligence means getting a computer to mimic human behavior

Machine Learning is mimicking how humans learn.

If machine learning is about mimicking how humans learn, why not go all the way and try to mimic the human brain? That’s the idea behind neural networks. Deep learning is all about using neural networks with more neurons, layers, and interconnectivity

These networks imitate the human brain’s connectivity, classifying data sets and finding correlations between them. With its newfound knowledge (acquired without human intervention), the machine can then apply its insights to other data sets. The more data the machine has at its disposal, the more accurate its predictions will be.

Machine Learning vs Deep Learning -

	All machine learning	Only deep learning
Number of data points	Can use small amounts of data to make predictions.	Needs to use large amounts of training data to make predictions.
Hardware dependencies	Can work on low-end machines. It doesn't need a large amount of computational power.	Depends on high-end machines. It inherently does a large number of matrix multiplication operations. A GPU can efficiently optimize these operations.
Featurization process	Requires features to be accurately identified and created by users.	Learns high-level features from data and creates new features by itself.
Learning approach	Divides the learning process into smaller steps. It then combines the results from each step into one output.	Moves through the learning process by resolving the problem on an end-to-end basis.
Execution time	Takes comparatively little time to train, ranging from a few seconds to a few hours.	Usually takes a long time to train because a deep learning algorithm involves many layers.
Output	The output is usually a numerical value, like a score or a classification.	The output can have multiple formats, like a text, a score or a sound.

AI applications have human-like intelligence. The pure definition of AI is an application that can pass the Turing test. All the major voice assistants are AI-powered.

ML results in a prediction of some possible outcome. ML is where a machine learns to perform a specific task without using explicit instructions, relying on patterns and inference instead.

Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

The most common place today to find all three (AI, ML and DS) at work is in autonomous driving vehicles. All three disciplines work together to help train an algorithm to recognize obstacles (ML), then to provide real-time actions (AI) to the vehicle, all based on large amounts of information that data science (DS) analyzed to determine patterns, actions, and the physics to perform safely.