36 Deploying Artificial Intelligence ^Top Because of its multiple connections of complex mathematical functions, DL allows for understanding data variables with relationships that are complex, non-obvious, non-linear, diverse, and unstructured.83 That is, DL can find pat- terns in data that has no obvious relationship. Virtual assistants such as Siri and Alexa run on DL algorithms. Machine/deep learning approaches can be broadly classified into supervised and unsupervised algorithms depending on how complex or unpredictable the input-output relationships are. In supervised learning the architecture of the model is specified by humans.84 But when the architecture is decided by the machine itself, when relationships among variables are hard to detect for predicting an outcome, it becomes unsupervised learning. For example, unsu- pervised learning is best for detecting fraudulent transactions and anomalies. CI is an evolving field, and some of the most successful AI systems are based on CI’s three main pillars: neural networks, fuzzy systems, and evolutionary computation.85 Artificial neural networks, or simply neural networks are a sim- ulation of human brain with neurons or nodes connecting one another to create a sophisticated algorithm of analyzing data. Like Google’s search algorithm. Fuzzy systems are designed using fuzzy sets (imprecise quanti- ties) and fuzzy rules (common sense and inference) to model the concepts of the world and make decisions about it (fuzzy logic), like 83 Bernard Marr, “What Is Deep Learning AI? A Simple Guide With 8 Practical Examples,” Forbes: Enterprise Tech, October 1, 2018, https://www.forbes.com/sites/bernardmarr/2018/10/01/what-is-deep-learning-ai-a-simple- guide-with-8-practical-examples/. 84 For instance, a neural network with four layers having 4, 16, 16, and 2 neurons or a classifier with a specific functional form. 85 IEEE Computational Intelligence Society, What is Computational Intelligence, retrieved on Dec 18, 2020, https:// cis.ieee.org/about/what-is-ci. Matthew Roos, “Evolutionary approaches towards AI: past, present, and future,” Towards Data Science, October 15, 2019, https://towardsdatascience.com/evolutionary-approaches-towards-ai-past-present-and-future-b23c- cb424e98.