If you want to measure the intelligence of a system, I would suggest measuring its optimization power as before, but then dividing by the resources used. Or you might measure the degree of prior cognitive optimization required to achieve the same result using equal or fewer resources.
This really won’t do. I think what you want is think in terms of a production function, which describe a system’s output on a particular task as a function of its various inputs and features. Then we can talk about partial derivatives; rates at which output increases as a function of changes in inputs or features. The hard thing here is how to abstract well, how to collapse diverse tasks into similar task aggregates, and how to collapse diverse inputs and features into related input and feature aggregates. In particular, there is the challenge of how to identify which of those inputs or features count as its “intelligence.”
IQ is a feature of human brains, and many have studied how the output of humans vary with IQ and task, both when other inputs are in some standard “reasonable” range, and when other inputs vary substantially. Even this is pretty hard; it is not at all clear to me how to broaden these abstractions even further, to talk about the “intelligence” of arbitrary systems on very wide ranges of tasks with wide ranges of other inputs and features.
If you want to measure the intelligence of a system, I would suggest measuring its optimization power as before, but then dividing by the resources used. Or you might measure the degree of prior cognitive optimization required to achieve the same result using equal or fewer resources.
This really won’t do. I think what you want is think in terms of a production function, which describe a system’s output on a particular task as a function of its various inputs and features. Then we can talk about partial derivatives; rates at which output increases as a function of changes in inputs or features. The hard thing here is how to abstract well, how to collapse diverse tasks into similar task aggregates, and how to collapse diverse inputs and features into related input and feature aggregates. In particular, there is the challenge of how to identify which of those inputs or features count as its “intelligence.”
IQ is a feature of human brains, and many have studied how the output of humans vary with IQ and task, both when other inputs are in some standard “reasonable” range, and when other inputs vary substantially. Even this is pretty hard; it is not at all clear to me how to broaden these abstractions even further, to talk about the “intelligence” of arbitrary systems on very wide ranges of tasks with wide ranges of other inputs and features.