One of my jobs at work is providing training about Machine Learning (ML), Artificial Intelligence (AI), and Data Science (DS). Here’s how I distinguish these, in my own mind anyway, from simplest to most complex:
1. Data Science – Analyzing data using classical statistics techniques. Example: a hypothesis test to infer if the average salary of men is the same as women, when you only have sample data.
2. Machine Learning – Making predictions using data, often using a basic neural network. Example: predicting the outcome of a football game.
3. Deep Learning – Making predictions using data using a complex (deep) neural network. Example: predicting if an image of a digit is a ‘1’ or a ‘7’.
4. Artificial Intelligence – Doing something with a computer that is typically associated with a human. Often use a deep neural network. Examples: sight (identifying a picture), hearing (Siri and Cortana), speech (Siri and Cortana again), touch (robotics), and reasoning (asking a program a question and getting an intelligent answer).
I believe that an understanding of basic neural networks is a key to understanding machine learning and artificial intelligence. I recently gave a talk about some of these topics.