1.3 Understanding the limitations of deep learning
As we’ve seen, deep learning has achieved some remarkable feats, and it’s undeniable that it’s revolutionizing the way that we deal with data and predictive modeling. But deep learning’s short history also comprises darker tales: stories that bring with them crucial lessons for developing systems that are more robust, and, crucially, safer.
In this section, we’ll introduce a couple of key cases in which deep learning failed, and we will discuss how a Bayesian perspective could have helped to produce a better outcome.
1.3.1 Bias in deep learning systems
We’ll start with a textbook example of bias, a crucial problem faced by data-driven methods. This example centers around Amazon. Now a household name, the e-commerce company started out by revolutionizing the world of book retail, before becoming literally the one-stop shop for just about anything: from garden furniture to a new laptop, or even...
