Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment ("AI spring") and periods of disappointment, loss of confidence, and reduced funding ("AI winter"). Even with today's seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.
Exploring the AI Obedience: Why is Generating a Pure Color Image Harder than CyberPunk?
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；City University of Hong Kong（城市大学）；Chinese University of Hong Kong（香港中文大学）；McGill University（麦吉尔大学）
The illusion of a perfect metric: Why evaluating AI's words is harder than it looks
Iris AI Borgen, Norway（Iris AI 布尔根，挪威）；Iris AI Neurobiology BAS Sofia, Bulgaria（Iris AI 神经生物学 BAS 萨福ia，保加利亚）；Iris AI Sofia, Bulgaria（Iris AI 萨福ia，保加利亚）
