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.

There is much to be gained from interdisciplinary efforts to tackle complex psychological notions such as 'theory of mind' by combining the rich history of study and debates in cognitive science and recent findings from AI research. However, careful and consistent communication is essential when comparing artificial and biological intelligence, say Henry Shevlin and Marta Halina. Many of the most promising approaches in cognitive science seek to explicate notions like perception, belief, and motivation in information processing terms [1]. A similar move from information processing to psychology is occurring in artificial intelligence (AI) research and machine learning. Currently, few people working in AI would literally attribute beliefs, thoughts, or feelings to machines. However, as new techniques extend the capabilities of artificial systems, it has become increasingly common to use psychological terms to describe processing architectures [2-6]. Such use of psychological terms in AI research may be in many cases justified and unproblematic. Some terms with both narrow computational and psychological meanings, such as "memory" and "reinforcement", have clearly distinct histories in psychology and artificial intelligence and their senses are unlikely to be confused. Others such as "learning" and "behavior" straddle psychology and artificial intelligence, but are broad enough that there is little reason to quibble with their use to describe machine capacities. However, we argue here that there is a third kind of more robustly psychological concepts-including notions like awareness, perception, agency, and theory of mind-that have rich and complex histories in cognitive science. We suggest that these terms-which we call rich psychological concepts-require greater caution when employed to describe the capabilities of machine intelligence.

Most scientific theories of consciousness are challenging to apply outside the human case insofar as non‐human systems (both biological and artificial) are unlikely to implement human architecture precisely, an issue I call the specificity problem. After providing some background on the theories of consciousness debate, I survey the prospects of four approaches to this problem. I then consider a fifth solution, namely the theory‐light approach proposed by Jonathan Birch. I defend a modified version of this that I term the modest theoretical approach, arguing that it may provide insights into challenging cases that would otherwise be intractable.