Innateness, AlphaZero, and Artificial Intelligence
This addresses a foundational debate in AI about the role of innate structures, potentially influencing how researchers design and evaluate learning systems.
The paper critiques claims that AlphaGo and its successors achieve superhuman performance 'starting tabula rasa,' arguing these are overstated due to overlooked innate elements, and advocates for greater attention to innateness in AI.
The concept of innateness is rarely discussed in the context of artificial intelligence. When it is discussed, or hinted at, it is often the context of trying to reduce the amount of innate machinery in a given system. In this paper, I consider as a test case a recent series of papers by Silver et al (Silver et al., 2017a) on AlphaGo and its successors that have been presented as an argument that a "even in the most challenging of domains: it is possible to train to superhuman level, without human examples or guidance", "starting tabula rasa." I argue that these claims are overstated, for multiple reasons. I close by arguing that artificial intelligence needs greater attention to innateness, and I point to some proposals about what that innateness might look like.