If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Semantic applications can help commercial applications perform quickly and reliably by improving ecosystem interoperability. Converting and integrating current standards specifications to OWL models could support the adoption of semantic models, as well as machine-processable standards compliance and data interoperability.
End-user programming environments for the IoT such as IFTTT rely on a multitude of low-level trigger-action rules that categorize devices and services by technology or brand. EUPont is a Semantic Web ontology that enables users to meet their needs with fewer, higher-level rules that can be adapted to different contextual situations and as-yet-unknown IoT devices and services.
Empowering students to become socially responsible professionals is a desirable result of computing education. Humanitarian Free and Open Source Software (HFOSS) projects provide an opportunity for computing educators to inspire their students to tackle global humanitarian challenges while also learning about software engineering.
Deep learning can enable Internet of Things (IoT) devices to interpret unstructured multimedia data and intelligently react to both user and environmental events but has demanding performance and power requirements. The authors explore two ways to successfully integrate deep learning with low-power IoT products.
A hybrid learning framework uses a collective anomaly to analyze patterns in denial-of-service attacks along with data clustering to distinguish an attack from normal network traffic. In two evaluation datasets, the framework achieved higher hit rates relative to existing anomaly-detection techniques. Mohiuddin Ahmed, "Thwarting DoS Attacks: A Framework for Detection based on Collective Anomalies and Clustering", Computer, vol.