This week, we’re going to take a look at the ways that computers are used to make sense of a media collection. Let’s start by putting a finer point on a number of terms you hear in this space.
What is the difference between Computational Tagging, Artificial Intelligence, Machine Learning, and Deep Learning?
While the definitions of these processes have a lot of overlap, we can draw some useful distinctions.
Computational Tagging refers to any system of automated tagging that is done by a computer. This includes the metadata added by your camera. It also includes information like a Wikipedia page that could be added by simple linking.
Artificial Intelligence (AI) encompasses any computer technology that appears to emulate human reasoning. AI could be as simple as a set of rules that can create an intelligent-looking behavior (e.g., a self-driving car could be taught the “rule” that you don’t want to cross a double yellow line). And AI can include some cutting edge services as outlined below.
Machine Learning (ML) is a subset of AI that is more complex. Instead of just following a set of rules created by a programmer, in an ML environment, the system can be trained to discover the rules. An ML system for identifying species, for instance, uses a training set of tagged images to figure out what a Labrador Retriever looks like.
Deep Learning is a specific type of ML that makes use of a predictive model in its learning process. This process actually mimics the way the brain works. In deep learning, the system does not just look at results, but it uses a predictive model to train itself.
In our next post, we’ll look at two system configurations for making use of AL/ML tools.