The broad category of “artificial intelligence” comprises of multiple sub-categories with 2 standing out among the rest; Machine Learning and Deep Learning.
Machine Learning
Machine Learning constructs mathematical algorithms that are able to parse data, learn from that data and make data-related predictions based on what’s been learned to date.
A popular application of A.I. that is a good example of this is Netflix. When it creates viewing suggestions tailored to each subscriber’s preferences it’s using A.I. But it applies machine learning to update those recommendations after learning the subscriber’s interests and habits, what they watch, how long they watch it for, and what they don’t watch.
Deep Learning
Deep learning is a more sophisticated refinement, of machine learning. The key difference is that it can learn on its own, independently, through iterative learning or training process similar to how the human brain, processes information and arrive at intelligent decisions.
Deep learning models must organize its processes into structured layers that form an ‘artificial neural network’ that can learn from the data it was presented with and can make intelligent decisions and informed predictions based on what it has learned.
Artificial Neural Networks
Artificial neural networks (ANNs) are modeled after human brains to best mimic this process. A major reason why deep learning with artificial neural networks has become so popular recently is that they are powered by huge amounts of data which increases their ability to learn from that data and make highly accurate forecasts.
There have been considerable advancements made in computing hardware related to how ANNs crunch its data which has sparked renewed interest in artificial intelligence and its applications in numerous industries such as medicine,
More recently, Internet-connected cloud computing technology emerged, in which neural network software code and big data can be stored and run on powerful physical or virtual servers that make up the ‘Cloud’. This development made the application of deep learning with neural networks more practical and far more cost-effective. Now, large-scale projects involving big data can utilize cloud computing to store its data.