Constructing Deep Learning Models Is Still A Bespoke Process

Despite the plethora of marketing hype touting "universal" deep learning models that can be magically applied to any domain and any question without requiring additional training, the reality is that today's deep learning systems are largely bespoke creations, custom built for each individual application. Transfer learning, automated neural architecture selection and automatic tuning can reduce much of the manual labor required in constructing and tuning the algorithm itself and minimize the amount of novel training data required, but at the end of the day, every new deep learning application requires the creation of a new custom-built model. It is one of the great ironies of the deep learning revolution that despite the biggest advances today increasingly coming from machine-assisted development, humans are still needed to create and curate the vast training datasets required and to oversee the development process. While there are an increasing number of point-and-click model generators, they still require labeled training data and still require that a new model be created for each application. Moreover, transfer learning is still extremely limited, encoding only low-level pattern relationship knowledge rather than the high-level abstract knowledge representation and transfer that humans make use of when learning new tasks.