16.2 Elements of Preschool Design 291 BDS03, TS07, 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