There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all-encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data. There are whole swaths (not swatches) of techniques that have been developed over the years like Linear Regression, K-means, Decision Trees, Random Forest, PCA, SVM and finally Artificial Neural Networks (ANN). Artificial Neural Networks is where the field of Deep Learning had its genesis from.
Some ML practitioners who have had previous exposure to Neural Networks (ANN), after all, it was invented in the early 60’s, would have the first impression that Deep Learning is nothing more than ANN with multiple layers. Furthermore, the success of DL is more due to the availability of more data and the availability of more powerful computational engines like Graphics Processing Units (GPU). This, of course, is true, the emergence of DL is essentially due to these two advances, however, the conclusion that DL is just a better algorithm than SVM or Decision Trees is akin to focusing only on the trees and not seeing the forest.
Anyway, despite being ignored, DL continues to be hype. The current DL hype tends to be that we have these commoditized machineries, that given enough data and enough training time, is able to learn on its own. This of course either an exaggeration of what the state-of-the-art is capable of or an oversimplification of the actual practice of DL. DL has over the past few years given rise to a massive collection of ideas and techniques that were previously either unknown or known to be untenable. At first, this collection of concepts seems to be fragmented and disparate. However, over time patterns and methodologies begin to emerge and we are frantically attempting to cover this space in “Design Patterns of Deep Learning“.
Deep Learning today goes beyond just multi-level perceptrons but instead is a collection of techniques and methods that are used to building composable differentiable architectures. These are extremely capable machine learning systems that we are only right now seeing just the tip of the iceberg. The key take away from this is that Deep Learning may look like alchemy today, but we eventually will learn to practice it like chemistry. That is, we would have a more solid foundation so as to be able to build our learning machines with greater predictability of its capabilities.