Abstract:

Delineating the boundaries of the discourse on machine or artificial intelligence (AI) may help in defining and understanding its most discussed concept, the concept of intelligence. Furthermore, better insights into both definitions and how to define them well has proven to be essential for a better understanding of concepts, intelligence included.

These and related cognitive abilities (e.g. defining, analyzing, understanding, discussing, and comparing definitions of intelligence, among others) are expected for AI researchers and practitioners in the first place. Yet, they are also central to extending or at least providing the basics of AI literacy to other stakeholders of our society. Intelligent systems are transforming the way we interact with technology, with each other, and with ourselves, and knowing at least what AI or intelligence mean is becoming essential for designing, developing, deploying, using, and even regulating intelligent artefacts.

However, defining intelligence has been one of the most controversial and studied challenges of both ancient and modern human thinking. A lack of consensus on what intelligence is has remained almost constant over the centuries. Interested scholars have not come up with a consensus or cross-domain accepted definition of intelligence. Neither in the ancient Eastern nor in the ancient and contemporary Western conceptions of intelligence. Nor in the more recent perspectives from the last 70 years within the field of AI.

We argue that a better understanding of contemporary technologies, AI-based but not only, starts with a grounded exposure to their conceptual pillars. These include fundamental concepts like the concept of intelligence, in general, and of AI, in particular. Learners and decision makers at all levels should face them, as well as be able to discuss their importance and limitations critically and in an informed way. For doing that, they must be confronted with definitions of intelligence and understand their meaning well, for instance. If these contents are already part of study programs, the better.

It is the main goal of this paper to present how a few hundreds of definitions of intelligence were annotated, i.e. their properties and characteristics systematically analyzed and commented, in order to construct a corpus (i.e. a collection) of definitions of intelligence for further uses in AI and other fields. The work and particular application domain presented here has not yet been considered in the extended work on linguistic annotation. Even though, both the annotation and the data merit special attention, for they deal with the elusive, important concept of intelligence, i.e. with definitions of both human and machine (or artificial) intelligence.

Undergraduate Computer Science students carried out the annotation process and several research activities. They were involved in an AI research project led by faculty and included their findings and work as part of their undergraduate student research projects in their last study year. We provide details about how the student research projects were conceived, conducted, and mentored. We also describe the properties or quality criteria that were considered for annotating the definitions from the intelligence corpus.

Keywords:

AI literacy, annotation, artificial intelligence, corpus, intelligence, student research projects.