Waymo Reminds Us: Successful Complex AI Combines Deep Learning And Traditional Code

As AI has become one of the hottest must-have technologies, companies have rushed to build deep learning solutions to almost every problem. The industry has become particularly fixated with monolithic models and end-to-end learning in which algorithms are simply fed a database of training examples and turned loose on their problem domain without ever requiring human assistance. Yet, as Waymo reminds us, when it comes to building truly robust complex deep learning systems that must interact with the chaotic cacophony of the real world and seamlessly operate alongside humans, the most successful systems today combine multiple deep learning models together with traditional hand-coded rulesets. Like all forms of machine learning, deep learning offers a seductively simplistic beginner experience that requires little effort from newcomers to produce reasonably high-quality results right from the start. The problem lies in the long road of incremental improvements to make those out-of-the-box models sufficiently robust and accurate for production use. This seductive beginner's simplicity, coupled with almost a century of science fiction portrayals of machine intelligences that can learn like humans, has led many companies to focus their efforts on building massive singular all-encompassing end-to-end AI models that can do absolutely everything the company needs with one model without ever requiring a moment of human assistance.