Accidental risks prediction for advanced AI

This project attempts to formulate how we can predict the risks of deploying advanced AI to specific problems. E.g. controlling the electricity grid, making breakfast, or handling all accounting for a company.

Here are some of the things to consider in your model:

• Speed of resolving an error (e.g. cars crash instantly)
• Failure amplitude (e.g. nuclear weapons sent off <> dropping an egg)…
• …multiplied by number of actions taken in a problem (e.g. number of times deciding on/off for nuclear controls)…
• …multiplied by probability of failure in each action (e.g. fine motor control breaking the egg).

if you are able to put economic value to these, it would be able to inform an economic taxation based on risks and show this for specific areas in a scientifically precise way. E.g for electricity grid control:

Problem failure: Partial destruction of grid reliability due to current mismanagement in upper lines of Platform 4.

• 1.3 months
• Failure amplitude: $130,000,000 costs for a control failure that causes destruction
• Number of times this failure might happen: 230,000 / second / grid module
• 0.0001% expected failure probability (controlled for chaotic divergence in quantum energy fields (or something) causing similar errors)

(PS: this example might be unrealistic since you'd have pretty good controls already in place for electricity grids)