Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Abstract

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.

Going All in on AI

“…Notable computer science scholar John McCarthy simply defined AI as "the science and engineering of making intelligent machines" (McCarthy, 2007, p. 1). However, this simplicity has led to myths and misnomers about the value of AI, particularly to industry (Winfield, 2020). One of the common myths about AI is that the technology eventually leads to a post-apocalyptic society where robots and cyborgs wreak havoc, similar to the Skynet system in the popular Hollywood franchise Terminator (Mach, 2019).…”

Transparency in artificial intelligence

“…On the one hand, AI indeed is a contested concept that lacks clear consensus, both in computer science (Monett, Lewis & Thórisson, 2020), law (Martinez, 2019) and the public perception (Fast & Horvitz, 2017). This is linked to the fact that intelligence alone has been defined in at least 70 different ways (Legg & Hutter, 2007).…”

Discrete and continuous representations and processing in deep learning: Looking forward

“…Wang further explains this definition: intelligent systems should hence be able to adapt and learn new skills and acquire new knowledge, they should be able to do so efficiently (insufficient resources) and under uncertainty (insufficient knowledge), they have embodied experience (they experience their environment), are open-ended and should operate in real-time. Other authors add ideas or emphasize different aspects, like social intelligence, which subsumes communication and hence language (Monett et al, 2020). A system can be said to have human-level AI if it has this capacity at least to the same extent that humans have it.…”