No one (to my knowledge?) highlighted that the future might well go as follows: “There’ll be gradual progress on increasingly helpful AI tools. Companies will roll these out for profit and connect them to the internet. There’ll be discussions about how these systems will eventually become dangerous, and safety-concerned groups might even set up testing protocols (“safety evals”). Still, it’ll be challenging to build regulatory or political mechanisms around these safety protocols so that, when they sound the alarm at a specific lab that the systems are becoming seriously dangerous, this will successfully trigger a slowdown and change the model release culture from ‘release by default’ to one where new models are air-gapped and where
Hmm, I feel like I always had something like this as one of my default scenarios. Though it would of course have been missing some key details such as the bit about model release culture, since that requires the concept of widely applicable pre-trained models that are released the way they are today.
E.g. Sotala & Yampolskiy 2015 and Sotala 2018 both discussed there being financial incentives to deploy increasingly sophisticated narrow-AI systems until they finally crossed the point of becoming AGI.
S&Y 2015:
Ever since the Industrial Revolution, society has become increasingly automated. Brynjolfsson [60] argue that the current high unemployment rate in the United States is partially due to rapid advances in information technology, which has made it possible to replace human workers with computers faster than human workers can be trained in jobs that computers cannot yet perform. Vending machines are replacing shop attendants, automated discovery programs which locate relevant legal documents are replacing lawyers and legal aides, and automated virtual assistants are replacing customer service representatives.
Labor is becoming 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. In addition to replacing workers entirely, machines may also take over aspects of jobs that were once the sole domain of highly trained professionals, making the job easier to perform by less-skilled employees [298].
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. ‘A roadmap for US robotics’ [154] calls for major investments into automation, citing the potential for considerable improvements in the fields of manufacturing, logistics, health care and services.
Similarly, the US Air Force Chief Scientistʼs [78] ‘Technology horizons’ report mentions ‘increased use of autonomy and autonomous systems’ as a key area of research to focus on in the next decade, and also notes that reducing the need for manpower provides the greatest potential for cutting costs. In 2000, the US Congress instructed the armed forces to have one third of their deep strike force aircraft be unmanned by 2010, and one third of their ground combat vehicles be unmanned by 2015 [4].
To the extent that an AGI could learn to do many kinds of tasks—or even any kind of task—without needing an extensive re-engineering effort, the AGI 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 AGI. Increasingly sophisticated AI may eventually lead to AGI, possibly within the next several decades [39, 200].
Eventually it will make economic sense to automate all or nearly all jobs [130, 136, 289].
And with regard to the difficulty of regulating them, S&Y 2015 mentioned that:
… there is no clear way to define what counts as dangerous AGI. Goertzel [115] point out that there is no clear division between narrow AI and AGI and attempts to establish such criteria have failed. They argue that since AGI has a nebulous definition, obvious wide-ranging economic benefits and potentially significant penetration into multiple industry sectors, it is unlikely to be regulated due to speculative long-term risks.
and in the context of discussing AI boxing and oracles, argued that both AI boxing and Oracle AI are likely to be of limited (though possibly still some) value, since there’s an incentive to just keep deploying all AI in the real world as soon as it’s developed:
Oracles are likely to be released. As with a boxed AGI, there are many factors that would tempt the owners of an Oracle AI to transform it to an autonomously acting agent. Such an AGI would be far more effective in furthering its goals, but also far more dangerous.
Current narrow-AI technology includes HFT algorithms, which make trading decisions within fractions of a second, far too fast to keep humans in the loop. HFT seeks to make a very short-term profit, but even traders looking for a longer-term investment benefit from being faster than their competitors. Market prices are also very effective at incorporating various sources of knowledge [135]. As a consequence, a trading algorithmʼs performance might be improved both by making it faster and by making it more capable of integrating various sources of knowledge. Most advances toward general AGI will likely be quickly taken advantage of in the financial markets, with little opportunity for a human to vet all the decisions. Oracle AIs are unlikely to remain as pure oracles for long.
Similarly, Wallach [283] discuss the topic of autonomous robotic weaponry and note that the US military is seeking to eventually transition to a state where the human operators of robot weapons are ‘on the loop’ rather than ‘in the loop’. In other words, whereas a human was previously required to explicitly give the order before a robot was allowed to initiate possibly lethal activity, in the future humans are meant to merely supervise the robotʼs actions and interfere if something goes wrong.
Human Rights Watch [90] reports on a number of military systems which are becoming increasingly autonomous, with the human oversight for automatic weapons defense systems—designed to detect and shoot down incoming missiles and rockets—already being limited to accepting or overriding the computerʼs plan of action in a matter of seconds. Although these systems are better described as automatic, carrying out pre-programmed sequences of actions in a structured environment, than autonomous, they are a good demonstration of a situation where rapid decisions are needed and the extent of human oversight is limited. A number of militaries are considering the future use of more autonomous weapons.
In general, any broad domain involving high stakes, adversarial decision making and a need to act rapidly is likely to become increasingly dominated by autonomous systems. The extent to which the systems will need general intelligence will depend on the domain, but domains such as corporate management, fraud detection and warfare could plausibly make use of all the intelligence they can get. If oneʼs opponents in the domain are also using increasingly autonomous AI/AGI, there will be an arms race where one might have little choice but to give increasing amounts of control to AI/AGI systems.
I also have a distinct memory of writing comments saying something “why does anyone bother with ‘the AI could escape the box’ type arguments, when the fact that financial incentives would make the release of those AIs inevitable anyway makes the whole argument irrelevant”, but I don’t remember whether it was on LW, FB or Twitter and none of those platforms has a good way of searching my old comments. But at least Sotala 2018 had an explicit graph showing the whole AI boxing thing as just one way by which the AI could escape, that was irrelevant if it was released otherwise:
Okay now I know why I got this one wrong. It’s your fault. You hid it in chapter 22 of a book! Not even a clickbait title for the chapter! I even bought that book when it came out and read a good portion of it but never saw the chapter :(
That title!! I was even fan of you and yam specifically and had even gone through a number of your old works looking for nuggets! Figure 22.3 makes up for it all though haha. Diagrams are so far superior to words...
Hmm, I feel like I always had something like this as one of my default scenarios. Though it would of course have been missing some key details such as the bit about model release culture, since that requires the concept of widely applicable pre-trained models that are released the way they are today.
E.g. Sotala & Yampolskiy 2015 and Sotala 2018 both discussed there being financial incentives to deploy increasingly sophisticated narrow-AI systems until they finally crossed the point of becoming AGI.
S&Y 2015:
And with regard to the difficulty of regulating them, S&Y 2015 mentioned that:
and in the context of discussing AI boxing and oracles, argued that both AI boxing and Oracle AI are likely to be of limited (though possibly still some) value, since there’s an incentive to just keep deploying all AI in the real world as soon as it’s developed:
I also have a distinct memory of writing comments saying something “why does anyone bother with ‘the AI could escape the box’ type arguments, when the fact that financial incentives would make the release of those AIs inevitable anyway makes the whole argument irrelevant”, but I don’t remember whether it was on LW, FB or Twitter and none of those platforms has a good way of searching my old comments. But at least Sotala 2018 had an explicit graph showing the whole AI boxing thing as just one way by which the AI could escape, that was irrelevant if it was released otherwise:
Okay now I know why I got this one wrong. It’s your fault. You hid it in chapter 22 of a book! Not even a clickbait title for the chapter! I even bought that book when it came out and read a good portion of it but never saw the chapter :(
Dang. I did post the chapter online, and linked to it from e.g. this post summarizing it.
That title!! I was even fan of you and yam specifically and had even gone through a number of your old works looking for nuggets! Figure 22.3 makes up for it all though haha. Diagrams are so far superior to words...
Btw, why didn’t we have vending machines for everything 50 years ago?