16.2 Elements of Preschool Design 291
BDS03, TSO07, RZDKO5]. This is one of the capabilities an AI agent will need to simultaneously learn different types of tasks as proposed in the Preschool scenario given here. And there is a literature on “shaping,” where the idea is to build up the capability of an AI by training it on progressively more difficult versions of the same tasks [LD03]. Again, this is one sort of capability an AI will need to possess if it is to move up some type of curriculum, such as a school curriculum.
While we applaud the work done on multitask learning and shaping, we feel that explor- ing these processes using mathematical abstractions, or in the domain of various narrowly- proscribed machine-learning or robotics test problems, may not adequately address the prob- lem of AGI. The problem is that generalization among tasks, or from simpler to more difficult versions of the same task, is a process whose nature may depend strongly on the overall nature of the set of tasks and task-versions involved. Real-world tasks have a subtlety of intercon- nectedness and developmental course that is not captured in current mathematical learning frameworks nor standard AI test problems.
To put it mathematically, we suggest that the universe of real-world human tasks has a host of “special statistical properties” that have implications regarding what sorts of AI programs will be most suitable; and that, while exploring and formalizing the nature of these statistical properties is important, an easier and more reliable approach to AGI testing is to create a testing environment that embodies these properties implicitly, via its being an emulation of the cognitively meaningful aspects of the real-world human learning environment.
One way to see this point vividly is to contrast the current proposal with the “General Game Player” AI competition, in which Als seek to learn to play games based on formal descriptions of the rules.!. Clearly doing GGP well requires powerful AGI; and doing GGP even mediocrely probably requires robust multitask learning and shaping. But we suspect GGP is far inferior to AGI Preschool as an approach to testing early-stage AI programs aimed at roughly humanlike intelligence. This is because, unlike the tasks involved in AI Preschool, the tasks involved in doing simple instances of GGP seem to have little relationship to humanlike intelligence or real-world human tasks.
16.2 Elements of Preschool Design
What we mean by an “AGI Preschool” is simply a porting to the AGI domain of the essential aspects of human preschools. While there is significant variance among preschools there are also strong commonalities, grounded in educational theory and experience. We will briefly discuss both the physical design and educational curriculum of the typical human preschool, and which aspects transfer effectively to the AGI context.
On the physical side, the key notion in modern preschool design is the “learning center,” an area designed and outfitted with appropriate materials for teaching a specific skill. Learning centers are designed to encourage learning by doing, which greatly facilitates learning processes based on reinforcement, imitation and correction (see Chapter 31 of Part 2 for a detailed dis- cussion of the value of this combination); and also to provide multiple techniques for teaching the same skills, to accommodate different learning styles and prevent over-fitting and overspe- cialization in the learning of new skills.
1 http://gqames.stanford.edu/
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