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. Gary Marcus considers 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.”
Gary Marcus argues that these claims are overstated, for multiple reasons. Gary Marcus close by arguing that artificial intelligence needs greater attention to innateness, and I point to some proposals about what that innateness might look like.
One of the oldest debates in intellectual history revolves around the somewhat nebulous concept of innateness. How much of the human mind is built-in, and how much of it is constructed by experience?
Virtually all modern observers would concede that genes and experience work together; it is “nature and nurture”, not “nature versus nurture”. No nativist, for instance, would doubt that we are also born with specific biological machinery that allows us to learn. Chomsky’s Language Acquisition Device should be viewed precisely as an innate learning mechanism, and nativists such as Pinker, Peter Marler (Marler, 2004) and myself (Marcus, 2004) have frequently argued for a view in which a significant part of a creature’s innate armamentarium consists not of specific knowledge but of learning mechanisms, a form of innateness that enables learning.
There is a lot of reason to believe that humans and many other creatures are born with significant amounts of innate machinery. The guiding question for the current paper is
whether artificially intelligent systems ought similarly to be endowed with significant amounts of innate machinery, or whether, in virtue of the powerful learning systems that have recently been developed, it might suffice for such systems to work in a more bottom up, tabula rasa fashion.
List of Innate Machinery candidates by Gary Marcus
• Representations of objects
• Structured, algebraic representations
• Operations over variables
• A type-token distinction
• A capacity to represent sets, locations, paths, trajectories, obstacles and enduring
• A way of representing the affordances of objects
• Spatiotemporal contiguity
• Translational invariance
• Capacity for cost-benefit analysis
• a representation of time
• Intentionality (in the sense of inferring the intentions of others)