Which system identifies solutions to abstract problems by determining which simpler concepts are applicable?

The correct answer is deep learning, as this approach is a subset of machine learning that focuses on neural networks with many layers, which are capable of identifying patterns and solutions in complex data. Deep learning excels at processing large sets of unstructured data and can discern simpler underlying concepts from abstract problems. This ability to break down complex information into simpler, more manageable components allows deep learning systems to effectively recognize and solve intricate problems that serve as the foundation for more straightforward concepts.

In contrast, big data refers to the extensive volumes of data that traditional data processing software can't manage, emphasizing data management and analytics rather than problem identification. Machine learning overall encompasses various techniques for teaching algorithms to learn from data, but it does not specifically focus on the hierarchical depth and pattern recognition that deep learning provides. Lastly, deep fakes involve utilizing deep learning techniques primarily for generating realistic but artificial media content, which does not directly relate to the abstract problem-solving context asked in the question.