This article systematically analyzes the problem of defining “artificial intelligence.” It starts by pointing out that a definition influences the path of the research, then establishes four criteria of a good working definition of a notion: being similar to its common usage, drawing a sharp boundary, leading to fruitful research, and as simple as possible. According to these criteria, the representative definitions in the field are analyzed. A new definition is proposed, according to it intelligence means “adaptation with insufficient knowledge and resources.” The implications of this definition are discussed, and it is compared with the other definitions. It is claimed that this definition sheds light on the solution of many existing problems and sets a sound foundation for the field.

To investigate gender differences in pre-attentive processing of facial expressions we recorded the expression mismatch negativity (EMMN) in the deviant-standard-reverse oddball paradigm. For female participants, sad faces elicited larger EMMN than happy faces, but this difference disappeared in the left hemisphere. For male participants, EMMN was not modulated by facial expressions, regardless of in the left or right hemispheres. While the source analysis indicated that for both genders prefrontal activations were observed for sad EMMN, more sources were involved for male than female participants. For happy EMMN, the current sources were located in the occipital lobe and parietal lobe for females and the temporal lobe and the frontal lobe for males. The present findings emphasized the importance of considering gender as a factor in the study of the pre-attentive processing of facial expressions.

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It is an open question whether social stereotype activation can be distinguished from nonsocial semantic activation. To address this question, gender stereotype activation (GSA) and lexical semantic activation (LSA) were directly compared. EEGs were recorded in 20 participants as they identified the congruence between prime-target word pairs under four different conditions (stereotype congruent, stereotype incongruent, semantic congruent, and semantic incongruent). We found that congruent targets elicited faster and more accurate responses and reduced N400 amplitudes irrespective of priming category types. The N400 congruency effect (i.e., the difference between incongruity and congruity) started earlier and had greater amplitude for GSA than for LSA. Moreover, gender category priming induced a smaller N400 and a larger P600 than lexical category priming. These findings suggest that the brain is not only sensitive to both stereotype and semantic violation in the post-perceptual processing stage but can also differentiate these two information processes. Further, the findings suggest superior processing (i.e., faster and deeper processing) when the words are associated with social category and convey stereotype knowledge.

Pei Wang's paper titled "On Defining Artificial Intelligence" was published in a special issue of the Journal of Artificial General Intelligence (JAGI) in December of last year (Wang, 2019). Wang has been at the forefront of AGI research for over two decades. His non-axiomatic approach to reasoning has stood as a singular example of what may lie beyond narrow AI, garnering interest from NASA and Cisco, among others. We consider his article one of the strongest attempts, since the beginning of the field, to address the long-standing lack of consensus for how to define the field and topic of artificial intelligence (AI). In the recent AGISI survey on defining intelligence (Monett and Lewis, 2018), Pei Wang's definition, The essence of intelligence is the principle of adapting to the environment while working with insufficient knowledge and resources. Accordingly, an intelligent system should rely on finite processing capacity, work in real time, open to unexpected tasks, and learn from experience. This working definition interprets "intelligence" as a form of "relative rationality" (Wang, 2008), 1. Most striking in these numbers is the glaring absence of female authors. A common reason among female academics for rejecting our invitation to contribute was overcommitment. As a community, we may want to think of new, different ways of engaging the full spectrum of AI practitioners if we value inclusion as an essential constituent of a healthy scientific growth. Self determination and willingness to participate are also essential. This is an open access article licensed under the Creative Commons BY-NC-ND License.

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