Neural Networks and Deep Learning: A Paradigm Shift in Information Processing, Machine Learning, and Artificial Intelligence

Recent years have brought a revolution in the field of Artificial Intelligence on an unprecedented scale. Advances in hardware, availability of large data sets, as well as innovation in architectural and algorithmic design, enabled successful application of Machine Learning models based on multi-layered Artificial Neural Networks to a variety of problems of practical interest. Unsupervised problems, as well as applications outside of mainstream computer science, such as computational social science, psychometrics, econometrics, people analytics, stock market prediction, social engineering, biology, and even art became the new frontiers for deep neural networks. We believe that advancements in neural information processing systems will likely revolutionize the field of finance in the coming years. This chapter provides an introduction to the basic ideas, methods, and architectures on which most modern neural AI systems are based. After reading this chapter, the reader should gain appreciation and understanding of neural AI systems, and anticipate future developments in research and applications of AI, and Deep Learning in particular. The appendix grounds the main concepts presented here by combining them in a case study involving the design of a real-world neural AI system. Applications of introduced concepts to alternative finance are stressed throughout. The reader should anticipate a high impact of deep learning systems within alternative finance in the coming years. This chapter, together with the appendix, form a good basis for understanding the core principles behind these future applications of AI in alternative finance, and will enable the reader to grasp the main themes that are likely to persist in the near future.