# Artificial Intelligence Imagery Analysis Fostering Big Data Analytics
## 2 Artificial Intelligence Imagery Analysis: Background, Evolution, and Illustration
### Azure Cognitive Services from Microsoft
When using Artificial Intelligence Imagery Analysis, one computer replaces many human raters, and thus significantly reduces the complexity of data management. **Facilitation of longitudinal research designs. ** Research designs that involve human participants for analyzing images are often cross-sectional because it is challenging and costly for research practice to have the same human participant available at numerous points in time (as is necessary for longitudinal research designs). Due to its low costs, high availability, and computer-based execution, Artificial Intelligence Imagery Analysis makes it easier to conduct longitudinal research. **Reduction of study complexity and researcher cognitive overload. ** Conducting empirical studies that involve human participants often ties up significant person power in researcher teams or even requires additional team members just to administer and execute the empirical study. Artificial Intelligence Imagery Analysis, in contrast, frees up human researchers from many details around the gathering of the empirical data, and thus leaves more room for the research design and interpretation of the data. **Increase in rating precision. ** During the last two decades, systems for Artificial Intelligence Imagery Analysis have continuously improved and now provide a high rate of precision that outperforms human raters [8]. The system makes fewer objective errors, such as missing objects that should be classified or wrongly classifying objects due to inattention. **Allows for \"serendipity\" and better explorative data analysis in the early stages of research projects. ** Artificial Intelligence Imagery Analysis supports typical data sciences' research efforts as it allows for quickly testing datasets for certain patterns, and thereby more easily allows for finding something \"interesting\" or unexpected in a dataset, which might ultimately stimulate a research project that would not otherwise have existed. However, there are also challenges arising from applying Artificial Intelligence Imagery Analysis in research. **Makes machine learning models interpretable. ** While systems for Artificial Intelligence Imagery Analysis can perform sophisticated rating and classification tasks, the underlying model obtained from machine learning is usually highly complex and not interpretable. Therefore, when researchers not only want to perform certain rating or classification tasks, but also gain some insights into how the rating process came to its conclusions, Artificial Intelligence Imagery Analysis alone is not sufficient. At least up to now, Artificial Intelligence Imagery Analysis itself does not offer any insights on how it solves a problem. **Coping with researcher skill reduction. ** When insights are just \"one click away\" and the tools around systems for Artificial Intelligence Imagery Analysis become more sophisticated, this may lead to an overall reduction in researcher skills. Here, researchers have to balance on the one hand the benefits from using a technology that frees them up from some complexity in their work, and on the other hand, the potential drawbacks of \"outsourcing\" an important part of their intellectual and cognitive skills to a machine and accept the \"responses\" from the black box as given. **Machine encoding of knowledge/information. ** The machine executing the Artificial Intelligence Imagery Analysis takes certain ontologies for granted when performing the rating tasks. Examples of such ontologies are the categories used to for categorize certain images cues. As the input drives the output, deploying Artificial Intelligence Imagery Analysis requires questioning the underlying ontologies--ideally before running any analysis and not just upon interpreting results. **Coping with empiricism. ** Artificial Intelligence Imagery Analysis speeds up and eases the process of obtaining large empirical datasets at low cost, compared to, for instance, running experiments with human participants.