Deep learning has been applied to many problems that are too complex to solve through an algorithm. Most of these problems have not required the specific expertise of a certain individual or group; most applied networks learn information that is shared across humans intuitively. Deep learning has encountered very few problems that would require the expertise of a certain individual or group to solve, and there has yet to be a defined class of networks capable of achieving this. Such networks could duplicate the intelligence of a person relative to a specific task, such as their writing style or music composition style. For this thesis research, we propose to investigate Artificial Intelligence in a new direction: Intelligence Duplication (ID). ID encapsulates neural networks that are capable of solving problems that require the intelligence of a specific person or collective group. This concept can be illustrated by learning the way a composer positions their musical segments -as in the Deep Composer neural network. This will allow the network to generate similar songs to the aforementioned artist. One notable issue that arises with this is the limited amount of training data that can occur in some cases. For instance, it would be nearly impossible to duplicate the intelligence of a lesser known artist or an artist who did not live long enough to produce many works. Generating many artificial segments in the artist's style will overcome these limitations. In recent years, Generative Adversarial Networks (GANs) have shown great promise in many similarly related tasks. Generating artificial segments will give the network greater leverage in assembling works similar to the artist, as there will be an increased overlap in data points within the hashed embedding. Additional review indicates that current Deep Segment Hash Learning (DSHL) network variations have potential to optimize this process. As there are less nodes in the input and output layers, DSHL networks do not need to compute nearly as much information as traditional networks. We indicate that a synthesis of both DSHL and GAN networks will provide the framework necessary for future ID research. The contributions of this work will inspire a new wave of AI research that can be applied to many other ID problems.