Bostrom and Habermas: technical and political moralities, and the God’s eye view

An intriguing chapter that follows naturally from Nick Bostrom’s core argument is his discussion of machine ethics writ large. He asks: suppose one could install into an omnipotent machine ethical principles, trusting it with the future of humanity. What principles should we install?

What Bostrom accomplishes by positing his Superintelligence (which begins with something simply smarter than humans, and evolves over the course of the book into something that takes over the galaxy) is a return to what has been called “the God’s eye view”. Philosophers once attempted to define truth and morality according to perspective of an omnipotent–often both transcendent and immanent–god. Through the scope of his work, Bostrom has recovered some of these old themes. He does this not only through his discussion of Superintelligence (and positing its existence in other solar systems already) but also through his simulation arguments.

The way I see it, one thing I am doing by challenging the idea of an intelligence explosion and its resulting in a superintelligent singleton is problematizing this recovery of the God’s Eye view. If your future world is governed by many sovereign intelligent systems instead of just one, then ethics are something that have to emerge from political reality. There is something irreducibly difficult about interacting with other intelligences and it’s from this difficulty that we get values, not the other way around. This sort of thinking is much more like Habermas’s mature ethical philosophy.

I’ve written about how to apply Habermas to the design of networked publics that mediate political interactions between citizens. What I built and offer as toy example in that paper, @TheTweetserve, is simplistic but intended just as a proof of concept.

As I continue to read Bostrom, I expect a convergence on principles. “Coherent extrapolated volition” sounds a lot like a democratic governance structure with elected experts at first pass. The question of how to design a governance structure or institution that leverages artificial intelligence appropriately while legitimately serving its users motivates my dissertation research. My research so far has only scratched the surface of this problem.

Recalcitrance examined: an analysis of the potential for superintelligence explosion

To recap:

  • We have examined the core argument from Nick Bostrom’s Superintelligence: Paths, Dangers, Strategies regarding the possibility of a decisively strategic superintelligent singleton–or, more glibly, an artificial intelligence that takes over the world.
  • With an eye to evaluating whether this outcome is particularly likely relative to other futurist outcomes, we have distilled the argument and in so doing have reduced it to a simpler problem.
  • That problem is to identify bounds on the recalcitrance of the capacities that are critical for instrumental reasoning. Recalcitrance is defined as the inverse of the rate of increase to intelligence per time per unit of effort put into increasing that intelligence. It is meant to capture how hard it is to make an intelligent system smarter, and in particular how hard it is for an intelligent system to make itself smarter. Bostrom’s argument is that if an intelligent system’s recalcitrance is constant or lower, then it is possible for the system to undergo an “intelligence explosion” and take over the world.
  • By analyzing how Bostrom’s argument depends only on the recalcitrance of instrumentality, and not of the recalcitrance of intelligence in general, we can get a firmer grip on the problem. In particular, we can focus on such tasks as prediction and planning. If we discover that these tasks are in fact significantly recalcitrant that should reduce our expected probability of an AI singleton and consequently cause us to divert research funds to problems that anticipate other outcomes.

In this section I will look in further depth at the parts of Bostrom’s intelligence explosion argument about optimization power and recalcitrance. How recalcitrant must a system be for it to not be susceptible to an intelligence explosion?

This section contains some formalism. For readers uncomfortable with that, trust me: if the system’s recalcitrance is roughly proportional to the amount that the system is able to invest in its own intelligence, then the system’s intelligence will not explode. Rather, it will climb linearly. If the system’s recalcitrance is significantly greater than the amount that the system can invest in its own intelligence, then the system’s intelligence won’t even climb steadily. Rather, it will plateau.

To see why, recall from our core argument and definitions that:

Rate of change in intelligence = Optimization power / Recalcitrance.

Optimization power is the amount of effort that is put into improving the intelligence of system. Recalcitrance is the resistance of that system to improvement. Bostrom presents this as a qualitative formula then expands it more formally in subsequent analysis.

\frac{dI}{dt} = \frac{O(I)}{R}

Bostrom’s claim is that for instrumental reasons an intelligent system is likely to invest some portion of its intelligence back into improving its intelligence. So, by assumption we can model O(I) = \alpha I + \beta for some parameters \alpha and \beta, where 0 < \alpha < 1 and \beta represents the contribution of optimization power by external forces (such as a team of researchers). If recalcitrance is constant, e.g R = k, then we can compute:

\Large \frac{dI}{dt} = \frac{\alpha I + \beta}{k}

Under these conditions, I will be exponentially increasing in time t. This is the “intelligence explosion” that gives Bostrom’s argument so much momentum. The explosion only gets worse if recalcitrance is below a constant.

In order to illustrate how quickly the “superintelligence takeoff” occurs under this model, I’ve plotted the above function plugging in a number of values for the parameters \alpha, \beta and k. Keep in mind that the y-axis is plotted on a log scale, which means that a roughly linear increase indicates exponential growth.

Plot of exponential takeoff rates

Modeled superintelligence takeoff where rate of intelligence gain is linear in current intelligence and recalcitrance is constant. Slope in the log scale is determine by alpha and k values.

It is true that in all the above cases, the intelligence function is exponentially increasing over time. The astute reader will notice that by my earlier claim \alpha cannot be greater than 1, and so one of the modeled functions is invalid. It’s a good point, but one that doesn’t matter. We are fundamentally just modeling intelligence expansion as something that is linear on the log scale here.

However, it’s important to remember that recalcitrance may also be a function of intelligence. Bostrom does not mention the possibility of recalcitrance being increasing in intelligence. How sensitive to intelligence would recalcitrance need to be in order to prevent exponential growth in intelligence?

Consider the following model where recalcitrance is, like optimization power, linearly increasing in intelligence.

\frac{dI}{dt} = \frac{\alpha_o I + \beta_o}{\alpha_r I + \beta_r}

Now there are four parameters instead of three. Note this model is identical to the one above it when \alpha_r = 0. Plugging in several values for these parameters and plotting again with the y-scale on the log axis, we get:

Plot of takeoff when both optimization power and recalcitrance are linearly increasing in intelligence. Only when recalcitrance is unaffected by intelligence level is there an exponential takeoff. In the other cases, intelligence quickly plateaus on the log scale. No matter how much the system can invest in its own optimization power as a proportion of its total intelligence, it still only takes off at a linear rate.

Plot of takeoff when both optimization power and recalcitrance are linearly increasing in intelligence. Only when recalcitrance is unaffected by intelligence level is there an exponential takeoff. In the other cases, intelligence quickly plateaus on the log scale. No matter how much the system can invest in its own optimization power as a proportion of its total intelligence, it still only takes off at a linear rate.

The point of this plot is to illustrate how easily exponential superintelligence takeoff might be stymied by a dependence of recalcitrance on intelligence. Even in the absurd case where the system is able to invest a thousand times as much intelligence that it already has back into its own advancement, and a large team steadily commits a million “units” of optimization power (whatever that means–Bostrom is never particularly clear on the definition of this), a minute linear dependence of recalcitrance on optimization power limits the takeoff to linear speed.

Are the reasons to think that recalcitrance might increase as intelligence increases? Prima facie, yes. Here’s a simple thought experiment: What if there is some distribution of intelligence algorithm advances that are available in nature and that some of them are harder to achieve than others. A system that dedicates itself to advancing its own intelligence, knowing that it gets more optimization power as it gets more intelligent, might start by finding the “low hanging fruit” of cognitive enhancement. But as it picks the low hanging fruit, it is left with only the harder discoveries. Therefore, recalcitrance increases as the system grows more intelligent.

This is not a decisive argument against fast superintelligence takeoff and the possibility of a decisively strategic superintelligent singleton. Above is just an argument about why it is important to consider recalcitrance carefully when making claims about takeoff speed, and to counter what I believe is a bias in Bostrom’s work towards considering unrealistically low recalcitrance levels.

In future work, I will analyze the kinds of instrumental intelligence tasks, like prediction and planning, that we have identified as being at the core of Bostrom’s superintelligence argument. The question we need to ask is: does the recalcitrance of prediction tasks increase as the agent performing them becomes better at prediction? And likewise for planning. If prediction and planning are the two fundamental components of means-ends reasoning, and both have recalcitrance that increases significantly with the intelligence of the agent performing them, then we have reason to reject Bostrom’s core argument and assign a very low probability to the doomsday scenario that occupies much of Bostrom’s imagination in Superintelligence. If this is the case, that suggests we should be devoting resources to anticipating what he calls multipolar scenarios, where no intelligent system has a decisive strategic advantage, instead.