Arguments for risks from general AI are sometimes criticized on the grounds that they rely on a series of linear events, each of which has to occur for the proposed scenario to go through. For example, that a sufficiently intelligent AI could escape from containment, that it could then go on to become powerful enough to take over the world, that it could do this quickly enough without being detected, etc.
The intent of my following series of posts is to briefly demonstrate that AI risk scenarios are in fact disjunctive: composed of multiple possible pathways, each of which could be sufficient by itself. To successfully control the AI systems, it is not enough to simply block one of the pathways: they all need to be dealt with.
In this post, I will be drawing on arguments discussed in my and Roman Yampolskiy’s paper, Responses to Catastrophic AGI Risk (section 2), and focusing on one particular component of AI risk scenarios: AIs gaining a decisive advantage over humanity. Follow-up posts will discuss other disjunctive scenarios discussed in Responses, as well as in other places.
AIs gaining a decisive advantage
Suppose that we built a general AI. How could it become powerful enough to end up threatening humanity?
1. Discontinuity in AI power
The classic scenario is one in which the AI ends up rapidly gaining power, so fast that humans are unable to react. We can say that this is a discontinuous scenario, in that the AI’s power grows gradually until it suddenly leaps to an entirely new level. Responses describes three different ways for this to happen:
1a. Hardware overhang. In a hardware overhang scenario, hardware develops faster than software, so that we’ll have computers with more computing power than the human brain does, but no way of making effective use of all that power. If someone then developed an algorithm for general intelligence that could make effective use of that hardware, we might suddenly have an abundance of cheap hardware that could be used for running thousands or millions of AIs, possibly with a speed of thought much faster than that of humans.
1b. Speed explosion. In a speed explosion scenario, intelligent machines design increasingly faster machines. A hardware overhang might contribute to a speed explosion, but is not required for it. An AI running at the pace of a human could develop a second generation of hardware on which it could run at a rate faster than human thought. It would then require a shorter time to develop a third generation of hardware, allowing it to run faster than on the previous generation, and so on. At some point, the process would hit physical limits and stop, but by that time AIs might come to accomplish most tasks at far faster rates than humans, thereby achieving dominance. In principle, the same process could also be achieved via improved software.
The extent to which the AI needs humans in order to produce better hardware will limit the pace of the speed explosion, so a rapid speed explosion requires the ability to automate a large proportion of the hardware manufacturing process. However, this kind of automation may already be achieved by the time that AI is developed.
1c. Intelligence explosion. In an intelligence explosion, an AI figures out how to create a qualitatively smarter AI and that smarter AI uses its increased intelligence to create still more intelligent AIs, and so on. such that the intelligence of humankind is quickly left far behind and the machines achieve dominance.
One should note that the three scenarios depicted above are by no means mutually exclusive! A hardware overhang could contribute to a speed explosion which could contribute to an intelligence explosion which could further the speed explosion, and so on. So we are dealing with three basic events, which could then be combined in different ways.
2. Power gradually shifting to AIs
While the traditional AI risk scenario involves a single AI rapidly acquiring power (a “hard takeoff”), society is also gradually becoming more and more automated, with machines running an increasing share of things. There is a risk that AI systems that were initially simple and of limited intelligence would gradually gain increasing power and responsibilities as they learned and were upgraded, until large parts of society were under the AI’s control – and it might not remain docile forever.
Labor is automated for reasons of cost, efficiency and quality. Once a machine becomes capable of performing a task as well as (or almost as well as) a human, the cost of purchasing and maintaining it may be less than the cost of having a salaried human perform the same task. In many cases, machines are also capable of doing the same job faster, for longer periods and with fewer errors.
If workers can be affordably replaced by developing more sophisticated AI, there is a strong economic incentive to do so. This is already happening with narrow AI, which often requires major modifications or even a complete redesign in order to be adapted for new tasks. To the extent that an AI could learn to do many kinds of tasks—or even any kind of task—without needing an extensive re-engineering effort, the AI could make the replacement of humans by machines much cheaper and more profitable. As more tasks become automated, the bottlenecks for further automation will require adaptability and flexibility that narrow-AI systems are incapable of. These will then make up an increasing portion of the economy, further strengthening the incentive to develop AI – as well as to turn over control to it.
Conclusion. This gives a total of four different scenarios by which AIs could gain a decisive advantage over humans. And note that, just as scenarios 1a-1c were not mutually exclusive, neither is scenario 2 mutually exclusive with scenarios 1a-1c! An AI that had gradually acquired a great deal of power could at some point also find a way to make itself far more powerful than before – and it could already have been very powerful.
This blog post was written as part of research funded by the Foundational Research Institute.