4 probability estimates seem essential to me.
- A projection of the growth of computing power. This is of course extensively discussed, so it is at least somewhat reasonable to use standard predictions.
- Estimation of probable AI intelligence levels given different hardware levels. There is a lot of hidden complication here. First, how do we want to measure intelligence? How should we define intelligence level? The most relevant way of doing this is to estimate real-world problem solving capabilities (in each important area). Second, what we want isn't just a fixed maximum intelligence level, but rather a curve over time; this captures information about intelligence explosions due to fast learning or recursive self-improvement. Third, ideally we don't just want one probable curve for each hardware level but rather a probability distribution over such curves.
- Estimation of probability of unfriendly behavior, given various classes of probable goal functions that AI programmers might come up with. Of course we'll also want to assign a probability to each of these goal-classes. The probability of unfriendly behavior depends mainly on the intelligence level reached (which depends in turn on the hardware level). An AI that is only somewhat smarter than humans will more probably have incentive to be nice to humans for the same reasons that humans have incentive to be nice to eachother.
- Estimation of human ability to to respond to various intelligence levels if an AI of that level turns out to be unfriendly.