Yehoshua Rubin
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Initially conceived as a way to explain memory sharing in romantic couples, the concept of transactive memory systems (TMS) has been adopted by organizational psychology, information management, and other fields of study to examine team performance in corporate settings. While findings highlight a clear advantage for humans teams with TMS, it's not evident if AI-human teams could also develop such a psychological dynamic. This paper considers AI-human interaction through the lens of TMS and identifies potential opportunities for improvement in this area.
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Interruptions in digital services are a common occurrence for users. These disruptions, however, exact a cost in terms of attention, task completion rate, and, most importantly, emotional state. While several methods currently employed by service providers attempt to address this, the paper will argue that browser games or similar interactive interfaces should become a standard mechanism to ease the aforementioned effects.
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Artificial intelligence interactivity on a mass scale often revolves around conversational consumer applications, such as chatbots and virtual assistants. Video games, which contain non-playable characters controlled by AI, can offer valuable insight into the patterns and nuances of the aforementioned discussion. Specifically, this paper investigates player-game interaction in Fallout 76, a video game that implemented NPCs in a controversial manner, and suggests concomitant implications for modern AI products.
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“What’s with the eggplant?”: The use of emoji in content design through the lens of gesture studies
Google (2022)
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Within the last ten years, emojis have become a universal phenomenon. Various frameworks attempt to explain the pragmatics of emoji use in language. In particular, a recent theory that applies gesture studies to emojis enjoys much popularity. This paper engages with the theory to develop insight into the proper and improper use of emojis in content design.
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A New Paradigm: Chess AI In Game Analysis
Google (2022)
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In 1997, a chess artificial intelligence, IBM’s DeepBlue, defeated the chess world champion, Garry Kasparov. Since then, chess artificial intelligence has only increased in skill, achieving chess ratings (measures that evaluate player skill) that beat those of top skill by several standard deviations. Seemingly, chess has been “solved.” Yet, the chess community continues to grow in terms of population, interest, and theory. Instead of retreating into existential defeat, chess players embrace chess AI as a critical part of their chess experience. One of the manifestations of this change is game analysis boards. This paper examines the on-going interaction between chess AI and chess players, including the utilization of AI in game analysis, game preparation, and cheating.
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