In developing AI (artificial intelligence) applications, it is important to play close attention to human-computer interaction (HCI) and design each application for specific users. To make a machine intelligent, a developer uses multiple techniques from an AI toolbox; these tools are actually mathematical algorithms that can demonstrate intelligent behavior. The course examines the following categories of AI development: algorithms, machine learning, probabilistic modelling, neural networks, and reinforcement learning. There are two main types of AI tools available: statistical learning, in which large amount of data is used to make certain generalizations that can be applied to new data; and symbolic AI, in which an AI developer must create a model of the environment with which the AI agent interacts and set up the rules. Learn to identify potential AI users, the context of using the applications, and how to create user tasks and interface mock-ups.
define Human Computer Interaction as a multidisciplinary field essential to computer science and describe its importance for the success of software companies
list the components involved in human-computer interaction (HCI) studies and specify their role
identify main objectives of HCI studies
recognize the multidisciplinary nature of HCI and list the areas most involved in the studies
describe the development of HCI studies and their significance to the overall software development process
specify recent trends in HCI research and list ideas that can become most impactful
list the tools commonly used for HCI studies and specify their purpose
identify potential users of an AI application and define the context for its use
describe the role of a user-oriented approach in the success of AI applications
specify why explainability research in AI is required for developing user friendly applications
apply common tools used in HCI to choose appropriate tasks for an AI application
work with simple mock-ups and identify techniques useful for developing interfaces