Cognitive Development: Informing the Design of Architectures for Natural Intelligence.

A particularly stinging criticism of the entire artificial intelligence enterprise has been its’ ability to produce systems that are far more capable than a typical human on domain-specific tasks but its’ striking inability to produce systems that perform some of the simplest tasks that toddlers excel at. While various proposals have been made regarding the construction of a “mechanical child,” going as far back as Turing (Turing 1950) and more recently in the realm of robotics, it has been rare for developers of architectures for general intelligence to inform their work with results from child development in the abstract. We describe a cognitive architecture in terms of a set of domaingeneral functional primitives capable of succeeding on a variety of well-known tasks from the child development literature spanning the so-called “core” cognitive domains of infant physics and folk psychology. Finally, we discuss the advantages of this approach in moving forward toward modeling other sorts of higher-order cognition, including the understanding and use of natural language. The Paradox of Today’s Intelligent Machines Former World Chess Champion Garry Kasparov was quoted in the March 1996 Time Magazine as saying: “I got my first glimpse of Artificial Intelligence on Feb. 10, 1996, at 4:45 p.m. EST, when in the first game of my match with Deep Blue, the computer nudged a pawn forward to a square where it could easily be captured. It was a wonderful and extremely human move. If I had been playing White, I might have offered this pawn sacrifice. It fractured Black's pawn structure and opened up the board.” Copyright © 2008, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. * I’d like to acknowledge the many hours of productive conversation with Nick Cassimatis and members of the Human-Level Intelligence Laboratory at Rensselaer Polytechnic Institute, as well as conversations with Mr. Derek Brock at the Naval Research Laboratory, all of which helped to shape this document. Later in the article, Kasparov stated that it was at this moment that he sensed “a new kind of intelligence” sitting across from him at the table. In a completely different vein, the following excerpt from (Anderson et. al. 2005) we have the following: “A recent entry in the DARPA Grand Challenge—a robotic maze-running/road-race contest—ran into a fence that it was unable to see. As it could not see the fence, it did not register it as an obstacle, and it continued to try to move forward. Eventually, the system had to be shut down lest it burn out its motors.” Herein lays the paradox. On the one hand, greater computational resources in terms of processing speed and power are providing us with machines that are overwhelmingly more competent than an average human on specific tasks. However, I’ve personally never seen an infant foolishly crawl in the same direction after bumping its’ head on the wall – not to say that this isn’t possible, but it seems an unlikely phenomenon. Similarly, I’ve never seen chess software play even marginal chess when rows, columns, and pieces are removed from play. This disparity between brilliant chess-players, scheduling systems, GPS navigators, and intelligence displayed by even the most cognitively unsophisticated humans is plain as day, and ought to at least stimulate our interest in how these extraordinarily unsophisticated infants manage to navigate such a complex world. Perhaps it is by studying the core capacities exhibited by infants, we will arrive at a fuller understanding of how intelligence arises from fairly simple parts. AI and Child Development The concept of “child machines” hasn’t been completely lost on the AI community. In fact, Alan Turing in his classic (Turing 1950) anticipates the connection: “In the process of trying to imitate an adult human mind we are bound to think a good deal about the process which has brought it to the state that it is in. We may notice three components. (a) The initial state of the mind, say at birth, (b) The education to which it has been subjected, (c) Other experience, not to be described as education, to which it has been subjected. Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain.” To his considerable credit, Turing foresees issues with trying to hand-code all of the knowledge and experiences required to behave in a generally intelligent fashion, suggesting a bit of nature to bootstrap the process, some nurture, and a learning algorithm to aid in organizing the relevant information. All of Turing’s insights seem agreeable in general, but we are still left with any number of important questions to answer. What mechanisms comprise the initial state of the mind at birth? What sorts of learning algorithms possess the necessary characteristics to organize knowledge, experiences, action routines and their consequences? How do we define “education?” Is the environment a teacher? What about other humans/nonhumans? Can we use our past accumulated experience to learn arbitrary new knowledge that isn’t motivated by supervisory signals? At the end of it all, we have a set of questions that may take lifetimes to answer adequately, but this shouldn’t bring progress to a screeching halt. We do, after all, know quite a bit about the sorts of mechanisms that might be hardwired into our human cognitive architecture. We also have access to the findings of the entire field of developmental psychology that is attempting to characterize infant knowledge across a number of “core” cognitive domains. These domains roughly partial themselves out into (possibly non-disjoint) knowledge about objects, space, numerosity, persons/animals, causality, time, and identity (Baillargeon 2004, Spelke et. al 1996). While the debates rage among the cognitive scientists as to the status of individual cognitive mechanisms in regards to their membership in our innate endowment or as a product of developmental change, we researchers in the computational modeling community have an opportunity to seize upon what is already known in the construction and extension of new cognitive architectures capable of generally intelligent behavior.