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Chapter 16
AGI Preschool

Co-authored with Stephan Vladimir Bugaj

16.1 Introduction

In conversations with government funding sources or narrow AI researchers about AGI work, one
of the topics that comes up most often is that of “evaluation and metrics” – i.e., AGI intelligence
testing. We actually prefer to separate this into two topics: environments and methods for careful
qualitative evaluation of AGI systems, versus metrics for precise measurement of AGI systems.
The difficulty of formulating bulletproof metrics for partial progress toward advanced AGI
has become evident throughout the field, and in Chapter 8 we have elaborated one plausible
explanation for this phenomenon, the "trickiness" of cognitive synergy. [LWML09], summarizing
a workshop on “Evaluation and Metrics for Human-Level AI” held in 2008, discusses some of
the general difficulties involved in this type of assessment, and some requirements that any
viable approach must fulfill. On the other hand, the lack of appropriate methods for careful
qualitative evaluation of AGI systems has been much less discussed, but we consider it actually
a more important issue – as well as an easier (though not easy) one to solve.

We haven’t actually found the lack of quantitative intelligence metrics to be a major obstacle
in our practical AGI work so far. Our OpenCogPrime implementation lags far behind the
CogPrime design as articulated in Part 2 of this book, and according to the theory underlying
CogPrime, the more interesting behaviors and dynamics of the system will occur only when all
the parts of the system have been engineered to a reasonable level of completion and integrated
together. So, the lack of a great set of metrics for evaluating the intelligence of our partially-
built system hasn’t impaired too much. Testing the intelligence of the current OpenCogPrime
system is a bit like testing the flight capability of a partly-built airplane that only has stubs
for wings, lacks tail-fins, has a much less efficient engine than the one that’s been designed for
use in the first "real" version of the airplane, etc. There may be something to be learned from
such preliminary tests, but making them highly rigorous isn’t a great use of effort, compared
to working on finishing implementing the design according to the underlying theory.

On the other hand, the problem of what environments and methods to use to qualitatively
evaluate and study AGI progress, has been considerably more vexing to us in practice, as
we’ve proceeded in our work on implementing and testing OpenCogPrime and developing the
CogPrime theory. When developing a complex system, it’s nearly always valuable to see what
this system does in some fairly rich, complex situations, in order to gain a better intuitive
understanding of the parts and how they work together. In the context of human-level AGI, the
theoretically best way to do this would be to embody one’s AGI system in a humanlike body

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