(Human) Domain Expertise in the Age of Deep Learning
It’s a burgeoning area of AI—but where is it headed?
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It’s a bit of an understatement to say that Deep Learning has recently become a hot topic. Within a decade alone, the field has made significant strides on problems once thought to be impossible, including facial recognition, generating text that mimics human writing, making art, and playing games involving strategy and intuition.
Given the buzz surrounding the (seemingly unreasonable) effectiveness of these algorithms, it’s easy to get lost in the extremes of speculation and skepticism towards Deep Learning. Instead, I’d like to focus on the following issues.
First, what is the role of human expertise in a world where Deep Learning is becoming more prevalent as a problem-solving tool? Second, where is the field of Deep Learning headed, and how can (human) expertise help move it forward?
As we will soon see, these two questions are intimately related. But first, let’s start with the basics.
Deep vs. human learning
To get a better scope of the picture, it helps to get a common understanding of what we mean when we refer to Deep Learning.
Simply put, Deep Learning refers to a broad range of algorithms, very loosely inspired by the human brain.
These algorithms take the form of networks, known as Deep Neural Networks (DNNs), which iteratively improve as they encounter new examples. It’s important to note that the kind of learning we refer to when we talk about Deep Learning is a very narrow subset of what we as humans might consider learning.
Human learning often involves the ability to explain, generalize, and even teach concepts that we have come to understand, all of which encompass different forms and levels of learning. It turns out that a lot of the questions surrounding these variants of learning are still open problems in Deep Learning, ones where domain expertise may play a large role.
Explainability
While Deep Learning algorithms outperform other machine learning methods at a wide variety of…
