How intelligible is intelligence

Abstract

If human-level AI is eventually created, it may have unprecedented positive or negative consequences for humanity. It is therefore worth constructing the best possible forecasts of policy-relevant aspects of AI development trajectories—even though, at this early stage, the unknowns must remain very large.

We propose that one factor that can usefully constrain models of AI development is the “intelligibility of intelligence”—the extent to which efficient algorithms for general intelligence follow from simple general principles, as in physics, as opposed to being necessarily a conglomerate of special case solutions. Specifically, we argue that estimates of the “intelligibility of intelligence” bear on:

• Whether human-level AI will come about through a conceptual breakthrough, rather than through either the gradual addition of hardware, or the gradual accumulation of special-case hacks;

• Whether the invention of human-level AI will, therefore, come without much warning;

• Whether, if AI progress comes through neuroscience, neuroscientific knowledge will enable brain-inspired human-level intelligences (as researchers “see why the brain works”) before it enables whole brain emulation;

• Whether superhuman AIs, once created, will have a large advantage over humans in designing still more powerful AI algorithms;

• Whether AI intelligence growth may therefore be rather sudden past the human level; and

• Whether it may be humanly possible to understand intelligence well enough, and to design powerful AI architectures that are sufficiently transparent, to create demonstrably safe AIs far above the human level.

The intelligibility of intelligence thus provides a means of constraining long-term AI forecasting by suggesting relationships between several unknowns in AI development trajectories. Also, we can improve our estimates of the intelligibility of intelligence, e.g. by examining the evolution of animal intelligence, and the track record of AI research to date.