# Artificial Intelligence Imagery Analysis Fostering Big Data Analytics
## 2 Artificial Intelligence Imagery Analysis: Background, Evolution, and Illustration
### Azure Cognitive Services from Microsoft
The underlying systems first need to \"learn\" how to accomplish a complex imagery analysis task based on known data relations before they can work on unknown datasets. ### Supporting Research Designs and Processes

We envision that Artificial Intelligence Imagery Analysis has applicability in a wide variety of research areas. Four exemplary settings include: (1) design science studies on human-computer interaction, (2) studies on the adoption and usage of information systems and technology, particularly with regard to the visual components, (3) studies on visual-content-driven social media, and (4) studies on systems for content recommendation, segmentation and filtering. Many of these research areas have experienced a large growth of pictorial input data. New touch-based devices such as smartphones and tablets come with simplified interfaces that favor pictorial over text-based information. Digitization lowers the barrier to producing pictorial data in any context, for instance around user-generated content in social media. In all research areas, Artificial Intelligence Imagery Analysis allows for the large sample sizes required to draw robust statistical inferences from real-world contexts. Thus, it constitutes a valuable extension of the methodological pool available to researchers, particularly as it overcomes the sample size drawbacks related to research designs that rely on manual human rating and classification of pictorial data. The least common denominator for applying Artificial Intelligence Imagery Analysis services is targeting any research question that asks for a relation between any antecedents such as, for instance, human behavior, pictorial data, and their outcomes. Overall, Artificial Intelligence Imagery Analysis supports the research process in several respects. **Reduction of biases. ** Traditional image rating and classification tasks, as employed for experimental research designs that involve humans, are subject to numerous biases, such as anchoring, attentional biases, contrast effects, courtesy biases, distinction biases, framing effects, negativity biases, salience biases, selective perception, or stereotyping. In addition, human performance on large series of \"dull\" image rating and classification tasks tends to decline over time. Artificial Intelligence Imagery Analysis significantly reduces these biases. The \"system\" that performs a task is not subject to previous biases and its rating performance does not decline with an increasing number of tasks. **Reduction of costs. ** Compared to traditional, experimental research designs for the rating and classification of images by humans, Artificial Intelligence Imagery Analysis reduces the costs of empirical studies by a factor of 100 to 1000. This is because human time is a comparatively costly resource. Also, compared to computers, humans are significantly slower when it comes to image rating and classification tasks. When using Artificial Intelligence Imagery Analysis, however, costs arise only for the initial development and training of the system. This is usually not done on the basis of the individual research project, but only _once_ by an external service provider. After the system is set up, marginal costs for analyzing additional images converge to zero. **Reduction of time. ** Artificial Intelligence Imagery Analysis can quickly analyze millions of images and thereby provide empirical results \"by the end of the day\", as opposed to studies involving human participants that often require months for planning, execution, data preparation, and analysis. **Simplification of data management. ** Experiments involving traditional image rating and classification tasks often come with a significant overhead of data management requirements. Researchers need to set up questionnaires or rating tools, administer these to study participants and collect their responses, and often also need to collect demographic data for the different human participants.