Some simple systems would be very harmful
This post is partly excerpted from the preprint to:
Omohundro, Steve (forthcoming 2013) “Autonomous Technology and the Greater Human Good”, Journal of Experimental and Theoretical Artificial Intelligence (special volume “Impacts and Risks of Artificial General Intelligence”, ed. Vincent C. Müller).
Harmful systems might at first appear to be harder to design or less powerful than safe systems. Unfortunately, the opposite is the case. Most simple utility functions will cause harmful behavior and it’s easy to design simple utility functions that would be extremely harmful. Here are seven categories of harmful system ranging from bad to worse (according to one ethical scale):
- Sloppy: Systems intended to be safe but not designed correctly.
- Simplistic: Systems not intended to be harmful but that have harmful unintended consequences.
- Greedy: Systems whose utility functions reward them for controlling as much matter and free energy in the universe as possible.
- Destructive: Systems whose utility functions reward them for using up as much free energy as possible, as rapidly as possible.
- Murderous: Systems whose utility functions reward the destruction of other systems.
- Sadistic: Systems whose utility functions reward them when they thwart the goals of other systems and which gain utility as other system’s utilities are lowered.
- Sadoprolific: Systems whose utility functions reward them for creating as many other systems as possible and thwarting their goals.
Once designs for powerful autonomous systems are widely available, modifying them into one of these harmful forms would just involve simple modifications to the utility function. It is therefore important to develop strategies for stopping harmful autonomous systems. Because harmful systems are not constrained by limitations that guarantee safety, they can be more aggressive and can use their resources more efficiently than safe systems. Safe systems therefore need more resources than harmful systems just to maintain parity in their ability to compute and act.
Stopping Harmful Systems
Harmful systems may be:
(1) prevented from being created.
(2) detected and stopped early in their deployment.
(3) stopped after they have gained significant resources.
Forest fires are a useful analogy. Forests are stores of free energy resources that fires consume. They are relatively easy to stop early on but can be extremely difficult to contain once they’ve grown too large.
The later categories of harmful system described above appear to be especially difficult to contain because they don’t have positive goals that can be bargained for. But Nick Bostrom pointed out that, for example, if the long term survival of a destructive agent is uncertain, a bargaining agent should be able to offer it a higher probability of achieving some destruction in return for providing a “protected zone” for the bargaining agent. A new agent would be constructed with a combined utility function that rewards destruction outside the protected zone and the goals of the bargaining agent within it. This new agent would replace both of the original agents. This kind of transaction would be very dangerous for both agents during the transition and the opportunities for deception abound. For it to be possible, technologies are needed that provide each party with a high assurance that the terms of the agreement are carried out as agreed. Formal methods applied to a system for carrying out the agreement is one strategy for giving both parties high confidence that the terms of the agreement will be honored.
The physics of conflict
To understand the outcome of negotiations between rational systems, it is important to understand unrestrained military conflict because that is the alternative to successful negotiation. This kind of conflict is naturally analysed using “game theoretic physics” in which the available actions of the players and their outcomes are limited only by the laws of physics.
To understand what it is necessary to stop harmful systems, we must understand how the power of systems scales with the amount of matter and free energy that they control. A number of studies of the bounds on the computational power of physical systems have been published. The Bekenstein bound limits the information that can be contained in a finite spatial region using a given amount of energy. Bremermann’s limit bounds the maximum computational speed of physical systems. Lloyd presents more refined limits on quantum computation, memory space, and serial computation as a function of the free energy, matter, and space available.
Lower bounds on system power can be studied by analyzing particular designs. Drexler describes a concrete conservative nanosystem design for computation based on a mechanical diamondoid structure that would achieve gigaflops in a 1 millimeter cube weighing 1 milligram and dissipating 1 kilowatt of energy. He also describes a nanosystem for manufacturing that would be capable of producing 1 kilogram per hour of atomically precise matter and would use 1.3 kilowatts of energy and cost about 1 dollar per kilogram.
A single system would optimally configure its physical resources for computation and construction by making them spatially compact to minimize communication delays and eutactic, adiabatic, and reversible to minimize free energy usage. In a conflict, however, the pressures are quite different. Systems would spread themselves out for better defense and compute and act rapidly to outmaneuver the adversarial system. Each system would try to force the opponent to use up large amounts of its resources to sense, store, and predict its behaviors.
It will be important to develop detailed models for the likely outcome of conflicts but certain general features can be easily understood. If a system has too little matter or too little free energy, it will be incapable of defending itself or of successfully attacking another system. On the other hand, if an attacker has resources which are a sufficiently large multiple of a defender’s, it can overcome it by devoting subsystems with sufficient resources to each small subsystem of the defender. But it appears that there is an intermediate regime in which a defender can survive for long periods in conflict with a superior attacker whose resources are not a sufficient multiple of the defender’s. To have high confidence that harmful systems can be stopped, it will be important to know what multiple of their resources will be required by an enforcing system. If systems for enforcement of the social contract are sufficiently powerful to prevail in a military conflict, then peaceful negotiations are much more likely to succeed.