A Critical Review of ‘The Precipice’: A Reassessment of the Risks of AI and Pandemics

Introduction

In this essay I will present a critical response to Toby Ord’s recent book The Precipice (page numbers refer to the soft cover version of this book). Rather than attempting to address all of the many issues discussed by Ord, I will focus on what I consider to be one of the most critical claims of the book. Namely, Ord claims that the present century is a time of unprecedented existential risk, that “we stand at a crucial moment in the history of our species” (p. 3), a situation which is “unsustainable” (p. 4). Such views are encapsulated in Ord’s estimate of the probability of an existential catastrophe over the next century, which he places at one in six. Of this roughly seventeen percent chance, he attributes roughly ten percentage points to the risks posed by unaligned artificial intelligence, and another three percentage points to the risks posed by engineered pandemics, with most of the rest of the risk is due to unforeseen and ‘other’ anthropogenic risks (p. 167). In this essay I will focus on the two major sources of risk identified by Ord, artificial intelligence and engineered pandemics. I will consider the analysis presented by Ord, and argue that by neglecting several critical considerations, Ord dramatically overestimates the magnitude of the risks from these two sources. This short essay is insufficient to provide a full justification for all of my views about these risks. Instead, my aim is to highlight some of what I believe to be the major flaws and omissions of Ord’s account, and also to outline some of the key considerations that I believe support a significantly lower assessment of the risks.

Why probability estimates matter

Before analysing the details of Ord’s claims about the risks of engineered pandemics and unaligned artificial intelligence, I will first explain why I think it is important to establish as accurate as possible estimates of the magnitude of these existential risks. After all, it could be argued that even if the risks are significantly less than those presented by Ord, nevertheless the risks are still far higher than we would like them to be, and causes such as unaligned AI and engineered pandemics are clearly neglected and require much more attention than they currently receive. As such, does it really matter what precise probabilities we assign to these risks? I believe it does matter, for a number of reasons.

First, Ord’s core thesis in his book is that humanity faces a ‘precipice’, a relatively short period of time with uniquely high and unsustainable levels of existential risk. To substantiate this claim, Ord needs to show not just that existential risks are high enough to warrant our attention, but that existential risk is much higher now than in the past, and that the risks are high enough to represent a ‘precipice’ at which humanity stands at the edge. Ord articulates this in the following passage:

If I’m even roughly right about their (the risks’) scale, then we cannot survive many centuries with risk like this. It is an unsustainable level of risk. Thus, one way or another, this period is unlikely to last more than a small number of centuries. Either humanity takes control of its destiny and reduces the risk to a sustainable level, or we destroy ourselves. (p. 31)

Critical here is Ord’s linkage of the scale of the risk with our inability to survive many centuries of this scale of risk. He goes on to argue that this is what leads to the notion of a precipice:

This comparatively brief period is a unique challenge in the history of our species… Historians of the future will name this time, and schoolchildren will study it. But I think we need a name now. I call it the Precipice. The Precipice gives our time immense meaning. (p. 31)

Given these passages, it is clear that there is a direct connection between the magnitude of the existential risks over the next century or so, and the existence of a ‘precipice’ that uniquely defines our time as historically special. This is a distinct argument from the weaker claim that existential risks are far higher than we should be comfortable with, and that more should be done to reduce them. My argument in this essay is that the main sources of the abnormally high risk identified by Ord, namely engineered pandemics and unaligned artificial intelligence, do not pose nearly as high a risk as Ord contends, and therefore his argument that the present period constitutes a ‘precipice’ is unpersuasive.

Second, I think precise estimates of the probabilities matter because there is a very long history of predicting the end of the world (or the end of civilisation, or other existential catastrophes), so the baseline for accuracy of such claims is poor. As such it seems reasonable to exercise some scepticism and caution when evaluating such claims, and ensure that they are based on sufficiently plausible evidence and reasoning to be taken seriously. This is also important for convincing others of such risks, as exaggeration of risks to humanity is very common, and is likely to reduce the credibility of those attempting to raise awareness of such risks. Ord makes a similar argument when he advises:

Don’t exaggerate the risks. There is a natural tendency to dismiss claims of existential risk as hyperbole. Exaggerating the risks plays into that, making it much harder for people to see that there is sober, careful analysis amidst the noise. (p. 213)

Third, I think that accurate estimates of probabilities of different forms of existential risk are important because it helps us to align our efforts and resources in proportion to the amount of risk posed by different causes. For example, if one type of risk is estimated to pose one hundred times as much risk as another, this implies a different distribution of efforts compared to if both causes posed roughly comparable amounts of risk. Ord makes this argument as follows:

This variation (in risk) makes it extremely important to prioritise our efforts on the right risks. And it also makes our estimate of the total risk very sensitive to the estimates of the top few risks (which are among the least well understood). So getting better understanding and estimates for those becomes a key priority. (p. 168)

As such, I believe it is important to carefully consider the probability of various proposed existential risk scenarios. In the subsequent two sections I will consider risks of engineered pandemics and unaligned artificial intelligence.

Engineered Pandemics

Extinction level agent exists

One initial consideration that must be addressed is how likely it is that any biological pathogen can even kill enough people to drive humanity to extinction. This places an upper limit on what any biotechnology could achieve, regardless of how advanced. Note that here I am referring to an agent such as a virus or bacterium that is clearly biological in nature, even if it is engineered to be more deadly than any naturally-occurring pathogen. I am not including entities that are non-biological in nature, such as artificial nanotechnology or other chemical agents. Whilst it is impossible to determine the ultimate limits of biology, one relevant point of comparison is the most deadly naturally-occurring infectious disease. To my knowledge, the highest fatality rate for any infectious biological agent that is readily transmissible between living humans is the Zaire ebolavirus, with a fatality rate of around 90%. It is unclear whether such a high fatality rate would be sustained outside of the social and climactic environment of West Africa whence the disease originated, but nevertheless we can consider this to be a plausible baseline for the most deadly known human infectious pathogen. Critically, it appears unlikely that the death of even 90% of the world population would result in the extinction of humanity. Death rates of up to 50% during the Black Death in Europe do not appear to have even come close to causing civilisational collapse in that region, while population losses of up to 90% in Mesoamerica over the course of the invasion and plagues of the 16th century did not lead to the end of civilization in those regions (though social and political disruption during these events were massive).

If we think the minimal viable human population is roughly 7,000 (which is near the upper end of the figures cited by Ord (p. 41), though rounded for simplicity), then a pathogen would need to directly or indirectly lead to the deaths of more than 99.9999% of the current world population in order to lead to human extinction. One could argue that the pathogen would only need to directly cause a much smaller number of deaths, with the remaining deaths caused by secondary disruptions such as war or famine. However to me this seems very unlikely, considering that such a devastating pathogen would significantly impair the ability of nations to wage war, and it is hard to see how warfare would affect all areas of the globe sufficiently to bring about such significant population loss. Global famine also seems unlikely, given that the greater the number of pandemic deaths, the more food stores would be available to survivors. Perhaps the most devastating scenario would be a massive global pandemic followed by a full-scale nuclear war, though it is unclear why should a nuclear exchange would follow a pandemic. One can of course devise various hypothetical scenarios, but overall it appears to me that a pathogen would have to have an extremely high fatality rate in order to have the potential to cause human extinction.

In addition to a high fatality rate, an extinction-level pathogen would also have to be sufficiently infectious such that it would be able to spread rapidly through human populations. It would need to have a long enough incubation time such that infected persons can travel and infect more people before they can be identified and quarantined. It would also need to be able to survive and propagate in a wide range of temperatures and climactic conditions. Finally, it would also need to be sufficiently dangerous to a wide range of ages and genetic populations, since any pockets of immunity would render extinction considerably less likely. Overall, it is highly unclear whether any biological agent with all these properties is even possible. In particular, pathogens which are sufficiently virulent to cause 99% or more fatality rates are likely to place such a burden on human physiology such that they would have a short incubation time, potentially rendering it easier to quarantine infected persons. Of course we do not know what is possible at the limits of biology, but given the extreme properties required of such an extinction-level pathogen, in my view it is very unlikely that such a pathogen is even possible.

Extinction level agent technologically feasible

Even if biological agents with the potential of wiping out humanity are theoretically possible, the question remains as to how long it will be until it becomes technologically feasible to engineer such an agent. While our current scientific understanding places significant limitations on what can be engineered, Ord argues that “it is not twentieth-century bioweaponry that should alarm us, but the next hundred years of improvements” (p. 133), which indicates that he believes that biotechnological advances over the next century are likely to enable the creation of a much wider range of dangerous biological agents. Of course, it is impossible to know how rapidly such technology will develop in the coming decades, however I believe that Ord overstates the current capabilities of such technology, and underestimates the challenges in developing pathogens of dramatically greater lethality than existing natural agents.

For example, Ord states that it is possible to “create entire functional viruses from their written code” (p. 128). I believe this claim is misleading, especially when read alongside Ord’s concern about ease of obtaining synthesised DNA, as it can potentially be read as asserting that viruses can be created using entirely synthetic means using only their DNA. This is false, as the methods cited by Ord describe techniques in which synthesised viral DNA is cultured cellular extracts, which as Ord also notes is not trivial and requires careful technique (p. 359). This approach still relies critically on utilising the ribosomes and other cellular machinery to translate viral DNA and produce the needed viral proteins. It does not involve the degree of control or understanding of the precise molecular processes involved that would be implied if an intact virus could be produced from its DNA using entirely synthetic means.

Ord also cites the 2012 experiments of Ron Fouchier, who conducted a gain-of-function experiment with H5N1 influenza in ferrets. Ord states that “by the time it passed to the final ferret, his strain of H5N1 had become directly transmissible between mammals” (p. 129). While technically correct, I believe this claim is misleading, since only a few sentences prior Ord states that this strain of influenza had an estimated 60% mortality rate in humans, implying that this would also apply to an airborne variant of the same virus. However in Fouchier’s study, it is reported that “although the six ferrets that became infected via respiratory droplets or aerosol also displayed lethargy, loss of appetite, and ruffled fur, none of these animals died within the course of the experiment.” Furthermore, the mere possibility of airborne transmission says nothing about the efficiency of this transmission mechanism. As reported in the paper:

Although our experiments showed that A/H5N1 virus can acquire a capacity for airborne transmission, the efficiency of this mode remains unclear. Previous data have indicated that the 2009 pandemic A/H1N1 virus transmits efficiently among ferrets and that naïve animals shed high amounts of virus as early as 1 or 2 days after exposure. When we compare the A/H5N1 transmission data with that of [another paper]…, the data shown in Figs. 5 and 6 suggest that A/H5N1 airborne transmission was less robust, with less and delayed virus shedding compared with pandemic A/H1N1 virus.

These qualifications illustrate the fundamental point that most biological systems exist as a set of tradeoffs and balances between competing effects and conflicting needs. Thus changing one aspect of a pathogen, such as its mode of transmission, is likely to have effects on other aspects of the pathogen, such as its lethality, incubation period, susceptibility to immune system attack, or survival outside a host. In theory it may be possible to design a pathogen with properties optimised to be as lethal to humans as possible, but doing so would require far greater understanding of protein folding pathways, protein-protein interactions, gene expression, mechanisms of pathogen invasion, immune system evasion strategies, and other such factors than is currently possessed. Thus it is by no means clear that Ord is correct when he states that “this progress in biotechnology seems unlikely to fizzle out soon: there are no insurmountable challenges looming; no fundamental laws blocking further developments” (p. 128). Indeed, I believe there are many fundamental challengers and gaps in our understanding which prevent the development of pathogens with arbitrarily specified properties.

Extinction level agent produced and delivered

Even if was technologically possible to produce a pathogen capable of causing human extinction, the research, production, and distribution of such an infectious agent would still actually need to be carried out by an organisation with the capabilities and desire to do so. While Ord’s example of the Aum Shinrikyo cult does demonstrate that such groups exist, the very small number of such attacks historically appears to indicate that such groups do not exist in large numbers. Very few ideologies have an interest in bringing humanity to an end through violent means. Indeed as Ord notes:

For all our flirtation with biowarfare, there appear to have been relatively few deaths from either accidents or use… Exactly why this is so is unclear. One reason may be that bioweapons are unreliable and prone to backfiring, leading states to use other weapons in preference. (p. 132)

Ord partially counters this observation by arguing that the severity of events such as terrorist attacks and incidents of biowarfare follow a power law distribution, with very rare, very high impact events meaning that the average size of past events will underestimate the expected size of future events. However this response does not seem to address the core observation that bioweapons have proven very hard to control, and that very few agents or organisations have any interest in unleashing a pathogen that kills humans indiscriminately. This appears to be reflected in the fact that as far as is publicly known, very few attempts have even been made to deploy such weapons in modern times. I thus believe that we have good reason to think that the number of people and amount of effort devoted to developing such dangerous bioweapons is likely to be low, especially for non-state actors.

Furthermore, Ord fails to consider the practical difficulties of developing and releasing a pathogen sufficiently deadly to cause human extinction. In particular, developing a novel organism would require lengthy research and extensive testing. Even if all the requisite supplies, technology, and expertise over a period of time could be obtained without arousing enough suspicion for the project to be investigated and shut down, there still remains the challenge of how such a pathogen could be tested. No animal model is perfect, and so any novel pathogen would (just like vaccines and other medical treatments) need to be tested on large numbers of human subjects, and likely adjusted in response to results. It would need to be trialled in different environments and climates to determine whether it would spread sufficiently rapidly and survive outside a host long enough. Without such tests, it is virtually impossible that an untested novel pathogen would be sufficiently optimised to kill enough people across a wide enough range of environments to cause human extinction. However, it is hard to see how it would be possible to carry out such widespread testing with a diverse enough range of subjects without drawing the attention of authorities.

A rogue state such as North Korea might be able to circumvent this particular problem, however that raises as range of new difficulties, such as why it would ever be in the interest of a state actor (as opposed to a death cult terrorist group) to develop such a deadly, indiscriminate pathogen. Ord raises the possibility of its use as a deterrent (akin to the deterrence function of nuclear weapons), but the analogy does not appear to hold up. Nuclear weapons work as a deterrent because their possession can be publicly demonstrated (by testing), their devastating impact is widely known, and there is no practical defence against them. None of these properties are true of an extremely lethal novel pathogen. A rogue state would have great difficulty proving that they possessed such a weapon without actually making available enough information about the pathogen, such that the world would likely be able to develop countermeasures to that particular pathogen. As such, it does not appear feasible to use bioweapons as effective deterrents, which may partly explain why despite extensive research into the possibility, no states have yet used them in this manner. As a result of these considerations, I conclude that even if it were technologically possible to develop a pathogen sufficiently lethal to cause human extinction, it is unlikely that anyone would actually have both the desire and the ability to successfully produce and deliver the pathogen.

Failure of timely public policy response

The release of a pathogen that has the potential to cause human extinction in itself does not imply that human extinction would inevitably occur. Whether this would follow depends on the extent of the governmental and societal responses to the outbreak of the novel pandemic, such as quarantines, widespread testing, and contact tracing. In considering the balance of positive and negative effects that organisational and civilization advances have had on the ability to respond to the risk of pathogens, Ord states that “it is hard to know whether these combined effects have increased or decreased the existential risk from pandemics” (p. 127). This argument, however, seems implausible, since deaths from infectious diseases and pandemics in particular have decreased in recent centuries, with no major pandemics in Western Europe since the early eighteenth century. The disappearance of plague from Western Europe, while still not well understood, plausibly may have been caused at least in part by the improvement of quarantine and public policy responses to plague. In the US, the crude death rate from infectious diseases fell by about 90% over the course of the twentieth century. Furthermore, a successful public policy response to a pathogen outbreak in even a single country would likely be enough to prevent extinction, even if most countries failed to enact a sufficient public policy response. As such, I believe it is unlikely that even an extinction-level novel pathogen would be able to sufficiently evade all public health responses so as to cause human extinction.

Failure of timely biomedical response

In addition to the failure of public policy responses, extinction of humanity by a novel pathogen would also require the failure of any biomedical response to the pandemic. Ord believes that as biological techniques become easier and cheaper, they become accessible to more and more people, and hence represent a greater and greater risk. He argues:

As the pool of people with access to a technique grows, so does the chance it contains someone with malign intent. (p. 134)

This argument, however, appears to only consider one side of the issue. As the pool of people with access to a technique grows, so too does the number of people who wish to use that technique to do good. This includes developing techniques and technologies for more easily detecting, controlling, and curing infectious diseases. It surprises me that Ord never mentions this, since the development of biomedical technologies does not only mean that there is greater scope for use of the technology to cause disease, but also greater scope for use new techniques to prevent and cure disease. Indeed, since the prevention of disease receives far more research attention that causing disease, it seems reasonable to assume that our abilities to development treatments, tests, and vaccines for diseases will develop more rapidly than our abilities to cause disease. There are a range of emerging biomedical technologies that promise to greater improve our ability to fight existing and novel diseases, including transmissible vaccines, rational design of drugs, and reverse vaccinology. As such, I regard it unlikely that if biomedical technology had advanced sufficiently to be able to produce an extinction-level pathogen, it would nevertheless fail to develop sufficient countermeasures to the pathogen to at least prevent full human extinction.

Unaligned Artificial Intelligence

AI experts and AI timelines

Although Ord appeals to surveys of AI researchers as evidence of the plausibility of the development of superhuman artificial intelligence in the next century, experts in artificial intelligence do not have a good track record of predicting future progress in AI. Massively inflated expectations of the capabilities of symbolic AI systems in the 1950s and 1960s, and of expert systems in the 1980s, are well-known examples of this. More generally, it is unclear why we should even expect AI researchers to have any particular knowledge about the future trajectories of AI capabilities. Such researchers study and develop particular statistical and computational techniques to solve specific types of problems. I am not aware of any focus of their training on extrapolating technological trends, or in investigations historical case studies of technological change. Indeed, it would seem that cognitive psychologists or cognitive neuroscientists might be better placed (although probably still not very well placed) to make judgements about the boundaries of human capability and what would be required for these to be exceeded in a wide range of tasks, since AI researchers have no particular expertise in the limits of human ability. AI researchers generally only consider human-level performance in the context of baseline levels of performance on well-defined tasks such as image recognition, categorisation, or game-playing. This is far removed from being able to make judgements about when AIs would be able to outperform humans on ‘every task’. For example, do AI researchers really have any expertise on when AIs are likely to overtake human ability to do philosophy, serve as political leaders, compose a novel, or teach high school mathematics? These are simply not questions that are studied by AI researchers, and therefore I don’t see any reason why they should be regarded as having special knowledge about them. These concerns are further emphasised by the inconsistency of researcher responses to AI timeline surveys:

Asked when an AI system would be ‘able to accomplish every task better and more cheaply than human workers, on average they estimated a 50 percent change of this happening by 2061. (p. 141)

However in a footnote Ord notes:

Note also that this estimate may be quite unstable. A subset of the participants were asked a slightly different question instead (emphasising the employment consequences by talking of all occupations instead of all tasks). Their time by which there would be a 50% chance of this standard being met was 2138, with a 10% chance of it happening as early as 2036. (p. 362)

Another factor highly pertinent to establishing the relevant set of experts concerns how the current topics researched by AI researchers relate to the eventual set of methods and techniques eventually used in building an AGI. Ord seems to think that developments of current methods may be sufficient to develop AGI:

One of the leading paradigms for how we might eventually create AGI combines deep learning with an earlier idea called reinforcement learning. (p. 143)

However such current methods, in particular deep learning, are known to be subject to a wide range of limitations. Major concerns include the ease with which adversarial examples can be used to ‘fool’ networks into misclassifying basic stimuli, the lack of established methods for integrating syntactically-structured information with neural networks, the fact that deep learning is task-specific and does not generalise well, the inability of deep learning systems to develop human-like ‘understanding’ that permits robust inferences about the world, and the requirement for very large datasets for deep learning algorithms to be trained on. While it remains possible that all these limitations may be overcome in the future, at present they represent deep theoretical limitations of current methods, and as such I see little reason to expect they can be overcome without the development of substantially new and innovative concepts and techniques. If this is correct, then there seems little reason to expect that AI researchers to have any expertise in predicting when such developments are likely to take place. AI researchers study current techniques, but if (as I have argued) such techniques are fundamentally inadequate for the development of true AGI, then such expertise is of limited relevance in assessing plausible AI timelines.

One argument that Ord gives in apparent support of the notion that current methods may in principle be sufficient for the development of AGI relates to the success of using deep neural networks and reinforcement learning to train artificial agents to play Atari games:

The Atari-playing systems learn and master these games directly from the score and the raw pixels on the screen. They are a proof of concept for artificial general agents: learning to control the world from raw visual input; achieving their goals across a diverse range of environments. (p. 141)

I believe this is a gross overstatement. While these developments are impressive, they in no way provide a proof of concept for ‘artificial general agents’, anymore than programs developed in the 1950s and 1960s to solve grammatical or geometric problems in simple environments provided such a proof of concept. Atari games are highly simplified environments with comparatively few degrees of freedom, the number of possible actions is highly limited, and where a clear measure of success (score) is available. Real-world environments are extremely complicated, with a vast number of possible actions, and often no clear measure of success. Uncertainty also plays little direct role in Atari games, since a complete picture of the current gamespace is available to the agent. In the real world, all information gained from the environment is subject to error, and must be carefully integrated to provide an approximate model of the environment. Given these considerations, I believe that Ord overstates how close we currently are to achieving superhuman artificial intelligence, and understates the difficulties that scaling up current techniques would face in attempting to achieve this goal.

AI has the power to usurp humanity

Ord argues that artificial intelligence that was more intelligent than humans would be able to usurp humanity’s position as the most powerful species on Earth:

What would happen if sometime this century researchers created an artificial general intelligence surpassing human abilities in almost every domain? In this act of creation, we would cede our status as the most intelligent entities on Earth. So without a very good plan to keep control, we should also expect to cede our status as the most powerful species, and the one that controls its own destiny. (p. 143)

The assumption behind this claim appears to be that intelligence alone is the critical determining factor behind which species or entity maintains control over Earth’s resources and future. This premise, however, conflicts with what Ord says earlier in the book:

What set us (humanity) apart was not physical, but mental – our intelligence, creativity, and language…each human’s ability to cooperate with the dozens of other people in their band was unique among large animals. (p. 12)

Here Ord identifies not only intelligence, but also creativity and ability to cooperate with others as critical to the success of humanity. This seems consistent with the fact that human intelligence, as far as can be determined, has not fundamentally changed over the past 10,000 years, even while our power and capabilities have dramatically increased. Obviously, what has changed is our ability to cooperate at much larger scales, and also our ability to build upon the achievements of previous generations to gradually increase our knowledge, and build up more effective institutions and practices. Given these considerations, it seems far from obvious to me that there mere existence of an agent more intelligent than an individual human would have the ability to usurp humanity’s position. Indeed, Ord’s own examples seem to further emphasise this point:

History already involves examples of individuals with human-level intelligence (Hitler, Stalin, Genghis Khan) scaling up from the power of an individual to a substantial fraction of all global power. (p. 147)

Whilst we have no clear data on the intelligence of these three individuals, what does seem clear is that none of them achieved the positions they did by acts of profound intellect. They were capable men, with Stalin in particular being very widely read, and Hitler known to have a sharp memory for technical details, nevertheless they were far from being the greatest minds of their generation. Nor did they achieve their positions by ‘scaling up’ from an individual to world superpower. I think it is more accurate to say that they used their individual talents (military leadership for Genghis Khan, administrative ability and political scheming for Stalin, and oratory and political scheming for Hitler) to gain control over existing power structures (respectively Mongol tribes, the Soviet government, and the German government). They did not build these things from scratch themselves (though Genghis Khan did establish a unified Mongol state, so comes closer than the others), but were able to hijack existing systems and convince enough people to follow their leadership. These skills may be regarded as a subset of a very broad notion of intelligence, but do not seem to correspond very closely at all to the way we normally use the word ‘intelligence’, nor do they seem likely to be the sorts of things AIs would be very good at doing.

Lacking a physical body to interact with people, it is hard to see how an AI could inspire the same levels of loyalty and fear that these three leaders (and many others like then) relied upon in their subordinates and followers. Of course, AIs could manipulate humans to do this job for them, but this would raise an immense difficulty of ensuring that their human pawns do not usurp their authority, which would be very difficult if all the humans that the AI is attempting to control do not actually have any personal loyalty for the AI itself. Perhaps the AI could pit multiple humans against one another and retain control over them in this manner (indeed that is effectively what Hitler did with his subordinates), however doing so generally requires some degree of trust and loyalty on behalf of one’s subordinates to be sustainable. Such methods are also very difficult to manage (such as the need to prevent plots by subordinates against the leader), and place clear limits on how effectively the central ruler can personally control everything. Of course one could always say ‘if an AI is intelligent enough it can solve these problems’, but my argument is precisely that it is not at all clear to me that ‘intelligence’ is even the key factor determining success. A certain level of intelligence is needed, but various forms of subtle interpersonal skills distinct from intelligence seem far more important in acquiring and maintaining their positions, skills which a non-embodied AI would face particular difficulty in acquiring.

Overall, I am not convinced that the mere existence of a highly-intelligent AI would imply anything about the ability of that AI to acquire significant power over humanity. Gaining power requires much more than individual intelligence, but also the ability to coordinate large numbers of people, to exercise creativity, to inspire loyalty, to build upon past achievements, and many others. I am not saying that an AI could not do these things, only that they would not automatically be able to do these things by being very intelligent, nor would these things necessarily be able to be done very quickly.

AI has reason to usurp humanity

Although Ord’s general case for concern about AI does not appeal to any specific vision for what AI might look like, an analysis of the claims that he makes indicates that his arguments are mostly relevant to a specific type of agent based on reinforcement learning. He says:

One of the leading paradigms for how we might eventually create AGI combines deep learning with an earlier idea called reinforcement learning…  unfortunately, neither of these methods can be easily scaled up to encode human values in the agent’s reward function. (p. 144)

While Ord presents this as merely a ‘leading paradigm’, subsequent discussion appears to assume that an AI would likely embody this paradigm. For example he remarks:

An intelligent agent would also resist attempts to change its reward function to something more aligned with human values. (p. 145)

Similarly he argues:

The real issue is that AI researchers don’t yet know how to make a system which, upon noticing this misalignment, updates its ultimate values to align with ours rather than updating its instrumental goals to overcome us. (p. 146)

While this seems plausible in the case of a reinforcement learning agent, it seems far less clear that it would apply to another form of AI. In particular, it is not even clear if humans actually posses anything that corresponds to a ‘reward function’, nor is it clear that such a thing is immutable with experience or over the lifespan. To assume that an AI would have such a thing therefore is to make specific assumptions about the form such an AI would take. This is also apparent when Ord argues:

It (the AI) would seek to acquire additional resource, computational, physical or human, as these would let it better shape the world to receive higher reward. (p. 145)

Again, this remark seems explicitly to assume that the AI is maximising some kind of reward function. Humans often act not as maximisers but as satisficers, choosing an outcome that is good enough rather than searching for the best possible outcome. Often humans also act on the basis of habit or following simple rules of thumb, and are often risk averse. As such, I believe that to assume that an AI agent would be necessarily maximising its reward is to make fairly strong assumptions about the nature of the AI in question. Absent these assumptions, it is not obvious why an AI would necessarily have any particular reason to usurp humanity.

Related to this question about the nature of AI motivations, I was surprised that (as far as I could find) Ord says nothing about the possible development of artificial intelligence through the avenue of whole brain emulation. Although currently infeasible, simulation of the neural activity of an entire human brain is potential route to AI which requires only very minimal theoretical assumptions, and no major conceptual breakthroughs. A low-level computer simulation of the brain would only require sufficient scanning resolution to measure neural connectivity and parameters of neuron physiology, and sufficient computing power to run the simulation in reasonable time. Plausible estimates have been made which indicate that extrapolating from current trends, such technologies are likely to be developed by the second half of this century. Although it is by no means certain, I believe it is likely that whole brain emulation will be achievable before it is possible to build a general artificial intelligence using techniques that do not attempt to directly emulate the biology of the brain. This potentially results in a significantly different analysis of the potential risks than that presented by Ord. In particular, while misaligned values still represent a problem for emulated intelligences, we do at least possess an in-principle method for aligning their values, namely the same sort of socialisation that is used with general success in aligning the values of the next generation of humans. As a result of such considerations, I am not convinced that it is especially likely that an artificial intelligence would have any particular reason or motivation to usurp humanity over the next century.

AI retains permanent control over humanity

Ord seems to assume that once an AI attained a position of power over the destiny of humanity, it would inevitably maintain this position indefinitely. For instance he states:

Such an outcome needn’t involve the extinction of humanity. But it could easily be an existential catastrophe nonetheless. Humanity would have permanently ceded its control over the future. Our future would be at the mercy of how a small number of people set up the computer system that took over. If we are lucky, this could leave us with a good or decent outcome, or we could just as easily have a deeply flawed or dystopian future locked in forever. (p. 148)

In this passage Ord speaks of the AI as it if is simply a passive tool, something that is created and forever after follows its original programming. Whilst I do not say this is impossible, I believe that it is an unsatisfactory way to describe an entity that is supposedly a superintelligent agent, something capable of making decisions and taking actions on the basis of its own volition. Here I do not mean to imply anything about the nature of free will, only that we do not regard the behaviour of humans as simply the product of what evolution has ‘programmed into us’. While it must be granted that evolutionary forces are powerful in shaping human motivations and actions, nevertheless the range of possible sets of values, social arrangements, personality types, life goals, beliefs, and habits that is consistent with such evolutionary forces is extremely broad. Indeed, this is presupposed by Ord’s claim that “humanity is currently in control of its own fate. We can choose our future.” (p. 142).

If humanity’s fate is in our own hands and not predetermined by evolution, why should we not also say that the fate of a humanity dominated by an AI would be in the hands of that AI (or collective of AIs that share control), rather than in the hands of the designers who built that AI? The reason I think this is important is that it highlights the fact that an AI-dominated future is by no means one in which the AI’s goals, beliefs, motivations, values, or focus is static and unchanging. To assume otherwise is to assume that the AI in question takes a very specific form which, as I have argued above, I regard as being unlikely. This significantly reduces the likelihood that a current negative outcome with AI represents a permanent negative outcome. Of course, this is irrelevant if the AI has driven humans to extinction, but it becomes highly relevant in other situations in which an AI has placed humans in an undesirable, subservient position. I am not convinced that such a situation would be perpetuated indefinitely.

Probability Estimates

Taking into consideration the analysis I have presented above, I would like to close by presenting some estimates of my best guess of the probability of an existential catastrophe occurring within the next century by an engineered pandemic and unaligned artificial intelligence. These estimates should not be taken very seriously. I do not believe we have enough information to make sensible quantitative estimates about these eventualities. Nevertheless, I present my estimates largely in order to illustrate the extent of my disagreement with Ord’s estimates, and to illustrate the key considerations I examine in order to arrive at an estimate.

Probability of engineered pandemics

Considering the issue of how an engineered pandemic could lead to the extinction of humanity, I identify five separate things that must occur, which to a first approximation I will regard as being conditionally independent of one another:

  1. There must exist a biological pathogen with the right balance of properties to have the potential of leading to human extinction.
  2. It must become technologically feasible within the next century to evolve or engineer this pathogen.
  3. The extinction-level agent must be actually produced and delivered by some person or organisation.
  4. The public policy response to the emerging pandemic must fail in all major world nations.
  5. Any biomedical response to the pandemic, such as developing tests, treatments, or vaccines, must fail to be developed within sufficient time to prevent extinction.

On the basis of the reasoning presented in the previous sections, I regard 1) as very unlikely, 2), 4), and 5) as unlikely, and 3) as slightly less unlikely. I will operationalise ‘very unlikely’ as corresponding to a probability of 1%, ‘unlikely’ as corresponding to 10%, and the ‘slightly less likely’ as 20%. Note each of these probabilities is taken as conditional on all the previous elements; so for example my claim is that conditional on an extinction-level pathogen being possible, there is a 10% chance that it will be technologically feasible to produce this pathogen within the next century. Combining all these elements results in the following probability:

P(bio extinction) = P(extinction level agent exists) x P(extinction level agent technologically feasible) x P(extinction level agent produced and delivered) x P(failure of timely public policy response) x P(failure of timely biomedical response)

P(bio extinction) = 0.01×0.1×0.2×0.1×0.1 = 2×10^(-6)

In comparison, Ord’s estimated risk from engineered pandemics is 1/30, or 3×10^-2. Ord’s estimated risk is thus roughly 10,000 times larger than mine.

Probability of unaligned artificial intelligence

Considering the issue of unaligned artificial intelligence, I identify four key stages that would need to happen for this to occur, which again I will regard to first approximation as being conditionally independent of one another:

  1. Artificial general intelligence, or an AI which is able to out-perform humans in essentially all human activities, is developed within the next century.
  2. This artificial intelligence acquires the power to usurp humanity and achieve a position of dominance on Earth.
  3. This artificial intelligence has a reason/motivation/purpose to usurp humanity and achieve a position of dominance on Earth.
  4. This artificial intelligence either brings about the extinction of humanity, or otherwise retains permanent dominance over humanity in a manner so as to significantly diminish our long-term potential.

On the basis of the reasoning presented in the previous sections, I regard 1) as roughly as likely as not, and 2), 3), and 4) as being unlikely. Combining all these elements results in the following probability:

P(AI x-risk) = P(AI of sufficient capability is developed) x P(AI gains power to usurp humanity) x P(AI has sufficient reason to usurp humanity) x P(AI retains permanent usurpation of humanity)

P(AI x.risk) = 0.5×0.1×0.1×0.1=5×10^(-4)

In comparison, Ord’s estimated risk from unaligned AI is 1/10, or 10^-1. Ord’s estimated risk is roughly 200 times larger than mine.

Arriving at credible estimates

Although I do not think the specific numbers I present should be taken very seriously, I would like to defend the process I have gone through in estimating these risks. Specifically, I have identified the key processes I believe would need to occur in order for extinction or other existential catastrophe to occur, and then assessed how likely each of these processes would be to occur on the basis of the relevant historical, scientific, social, and other considerations that I believe to be relevant. I then combine these probabilities to produce an overall estimate.

Though far from perfect, I believe this process if far more transparent than the estimates provided by Ord, for which no explanation is offered as to how they were derived. This means that it is effectively impossible to subject them to critical scrutiny. Indeed, Ord even states that his probabilities “aren’t simply an encapsulation of the information and argumentation in the chapters on the risks” (p. 167), which seems to imply that it is not even possible to subject them to critical analysis on the basis of the information present in this book. While he defends this on the basis that what he knows about the risks “goes beyond what can be distilled into a few pages” (p. 167), I do not find this a very satisfactory response given the total lack of explanation of these numbers in a book of over 400 pages.

Conclusion

In this essay I have argued that in his book The Precipice, Toby Ord has failed to provide a compelling argument that humanity faces a ‘precipice’ with unprecedentedly high and clearly unsustainable levels of existential risk. My main objective was to present an alternative analysis of the risks associated with engineered pandemics and unaligned artificial intelligence, highlighting issues and considerations that I believe Ord does not grant sufficient attention. Furthermore, on the basis of this analysis I presented an alternative set of probability estimates for these two risks, both of which are considerably lower than those presented by Ord. While far from comprehensive or free from debatable premises, I hope that the approach I have outlined here provides a different perspective on the debate, and helps in the development of a nuanced understanding of these important issues.

Effective Altruism is an Ideology, not (just) a Question

Introduction

In a widely-cited article on the EA forum, Helen Toner argues that effective altruism is a question, not an ideology. Here is her core argument:

What is the definition of Effective Altruism? What claims does it make? What do you have to believe or do, to be an Effective Altruist?

I don’t think that any of these questions make sense.

It’s not surprising that we ask them: if you asked those questions about feminism or secularism, Islamism or libertarianism, the answers you would get would be relevant and illuminating. Different proponents of the same movement might give you slightly different answers, but synthesising the answers of several people would give you a pretty good feeling for the core of the movement.

But each of these movements is answering a question. Should men and women be equal? (Yes.) What role should the church play in governance? (None.) What kind of government should we have? (One based on Islamic law.) How big a role should government play in people’s private lives? (A small one.)

Effective Altruism isn’t like this. Effective Altruism is asking a question, something like:

“How can I do the most good, with the resources available to me?”

In this essay I will argue that his view of effective altruism being a question and not an ideology is incorrect. In particular, I will argue that effective altruism is an ideology, meaning that it has particular (if somewhat vaguely defined) set of core principles and beliefs, and associated ways of viewing the world and interpreting evidence. After first explaining what I mean by ideology, I proceed to discuss the ways in which effective altruists typically express their ideology, including by privileging certain questions over others, applying particular theoretical frameworks to answer these questions, and privileging particular answers and viewpoints over others. I should emphasise at the outset that my purpose in this article is not to disparage effective altruism, but to try to strengthen the movement by helping EAs to better understand the intellectual actual intellectual underpinnings of the movement.

What is an ideology?

The first point I want to explain is what I mean when I talk about an ‘ideology’. Basically, an ideology is a constellation of beliefs and perspectives that shape the way adherents of that ideology view the world. To flesh this out a bit, I will present two examples of ideologies: feminism and libertarianism. Obviously these will be simplified since there is considerable heterogeneity within any ideology, and there are always disputes about who counts as a ‘true’ adherent of any ideology. Nevertheless, I think these quick sketches are broadly accurate and helpful for illustrating what I am talking about when I use the word ‘ideology’.

First consider feminism. Feminists typically begin with the premise that the social world is structured in such a manner that men as a group systematically oppress women as a group. There is a richly structured theory about how this works and how this interacts with different social institutions, including the family, the economy, the justice system, education, health care, and so on. In investigating any area, feminists typically focus on gendered power structures and how they shape social outcomes. When something happens, feminists ask ‘what affect does this have on the status and place of women in society?’ Given these perspectives, feminists typically are uninterested in and highly sceptical of any accounts of social differences between men and women based on biological differences, or attempts to rationalist differences on the basis of social stability or cohesion. This way of looking at things, focus on particular issues at the expense of others, and set of underlying assumptions constitutes the ideology of feminism.

Second consider libertarianism. Libertarians typically begin with the idea that individuals are fundamentally free and equal, but that governments throughout the world systematically step beyond their legitimate role of protecting individual freedoms by restricting those freedoms and violating individual rights. In analysing any situation, libertarians focus on how the actions of governments limit the free choices of individuals. Libertarians have extensive accounts as to how this occurs through taxation, government welfare programs, monetary and fiscal policy, the criminal justice system, state-sponsored education, the military industrial complex, and so on. When something happens, libertarians ask ‘what affect does this have on individual rights and freedoms?’ Given these perspectives, libertarians typically are uninterested in and highly sceptical of any attempts to justify state intervention on the basis of increases in efficiency, increasing equality, or improving social cohesion. This way of looking at things, focus on particular issues at the expense of others, and set of underlying assumptions constitutes the ideology of libertarianism.

Given the foregoing, here I summarise some of the key aspects of an ideology:

  1. Some questions are privileged over others.
  2. There are particular theoretical frameworks for answering questions and analysing situations.
  3. As a result of 1 and 2, certain viewpoints and answers to questions are privileged, while others are neglected as being uninteresting or implausible.

With this framework in mind of what an ideology is, I now want to apply this to the case of effective altruism. In doing so, I will consider each of these three aspects of an ideology in turn, and see how they relate to effective altruism.

Some questions are privileged over others

Effective altruism, according to Toner (and many others), asks a question something like ‘How can I do the most good, with the resources available to me?’. I agree that EA does indeed ask this question. However it doesn’t follow that EA isn’t an ideology, since as we have just seen, ideologies privilege some questions over others. In this case we can ask – what other similar questions could effective altruism ask? Here are a few that come to mind:

  • What moral duties do we have towards people in absolute poverty, animals in factory farms, or future generations?
  • What would a virtuous person do to help those in absolute poverty, animals in factory farms, or future generations?
  • What oppressive social systems are responsible for the most suffering in the world, and what can be done to dismantle them?
  • How should our social and political institutions be structured so as to properly represent the interests of all persons, or all sentient creatures?

I’ve written each with a different ethical theory in mind. In order these are: deontology, virtue ethics, Marxist/postcolonial/other critical theories, and contractarian ethics. While some readers may phrase these questions somewhat differently, my point is simply to emphasise that the question you ask depends upon your ideology.

Some EAs may be tempted to respond that all my examples are just different ways, or more specific ways, of asking the EA question ‘how can we do the most good’, but I think this is simply wrong. The EA question is the sort of question that a utilitarian would ask, and presupposes certain assumptions that are not shared by other ethical perspectives. These assumptions include things like: there is (in principle) some way of comparing the value of different causes, that it is of central importance to consider maximising the positive consequences of our actions, and that historical connections between us and those we might try to help are not of critical moral relevance in determining how to act. EAs asking this question need not necessarily explicitly believe all these assumptions, but I argue that in asking the EA question instead of other questions they could ask, they are implicitly relying upon tacit acceptance of these assumptions. To assert that these are beliefs shared by all other ideological frameworks is to simply ignore the differences between different ethical theories and the worldviews associated with them.

Particular theoretical frameworks are applied

In addition to the questions they ask, effective altruists tend to have a very particular approach to answering these questions. In particular, they tend to rely almost exclusively on experimental evidence, mathematical modelling, or highly abstract philosophical arguments. Other theoretical frameworks are generally not taken very seriously or simply ignored. Theoretical approaches that EAs tend to ignore include:

  • Sociological theory: potentially relevant to understanding causes of global poverty, how group dynamics operates and how social change occurs.
  • Ethnography: potentially highly useful in understanding causes of poverty, efficacy of interventions, how people make dietary choices regarding meat eating, the development of cultural norms in government or research organisations surrounding safety of new technologies, and other such questions, yet I have never heard of an EA organisation conducting this sort of analysis.
  • Phenomenology and existentialism: potentially relevant to determining the value of different types of life and what sort of society we should focus on creating.
  • Historical case studies: there is some use of these in the study of existential risk, mostly relating to nuclear war, but mostly this method is ignored as a potential source of information about social movements, improving society, and assessing the risk of catastrophic risks.
  • Regression analysis: potentially highly useful for analysing effective causes in global development, methods of political reform, or even the ability to influence AI or nuclear policy formation, but largely neglected in favour of either experiments or abstract theorising.

If readers disagree with my analysis, I would invite them to investigate the work published on EA websites, particularly research organisations like the Future of Humanity Institute and the Global Priorities Institute (among many others), and see what sorts of methodologies they utilise. Regression analysis and historical case studies are relatively rare, and the other three techniques I mention are virtually unheard of. This represents a very particular set of methodological choices about how to best go about answering the core EA question of how to do the most good.

Note that I am not taking a position on whether it is correct to privilege the types of evidence or methodologies that EA typically does. Rather, my point is simply that effective altruists seem to have very strong norms about what sorts of analysis is worthwhile doing, despite the fact that relatively little time is spent in the community discussing these issues. GiveWell does have a short discussion of their principles for assessing evidence, and there is a short section in the appendix of the GPI research agenda about harnessing and combining evidence, but overall the amount of time spent discussing these issues in the EA community is very small. I therefore content that these methodological choices are primarily the result of ideological preconceptions about how to go about answering questions, and not an extensive analysis of the pros and cons of different techniques.

Certain viewpoints and answers are privileged

Ostensibly, effective altruism seeks to answer the question ‘how to do the most good’ in a rigorous but open-minded way, without making ruling out any possibilities at the outset or making assumptions about what is effective without proper investigation. It seems to me, however, that this is simply not an accurate description of how the movement actually investigates causes. In practise, the movement seems heavily focused on the development and impacts of emerging technologies. Though not so pertinent in the case of global poverty, this is somewhat applicable in the case of animal welfare, given the increasing focus on the development of in vitro meat and plant-based meat substitutes. This technological focus is most evident in the focus on far future causes, since all of the main far future cause areas focused on by 80,000 hours and other key organisations (nuclear weapons, artificial intelligence, biosecurity, and nanotechnology) relate to new and emerging technologies. EA discussions also commonly feature discussion and speculation about the effects that anti-aging treatments, artificial intelligence, space travel, nanotechnology, and other speculative technologies are likely to have on human society in the long term future.

By itself the fact that EAs are highly focused on new technologies doesn’t prove that they privilege certain viewpoints and answers over others – maybe a wide range of potential cause areas have been considered, and many of the most promising causes just happen to relate to emerging technologies. However, from my perspective this does not appear to be the case. As evidence for this view, I will present as an illustration the common EA argument for focusing on AI safety, and then show that much the same argument could also be used to justify work on several other cause areas that have attracted essentially no attention from the EA community.

We can summarise the EA case for working on AI safety as follows, based on articles such as those from 80,000 hours and CEA (note this is an argument sketch and not a fully-fledged syllogism):

  • Most AI experts believe that AI with superhuman intelligence is certainly possible, and has nontrivial probability of arriving within the next few decades.
  • Many experts who have considered the problem have advanced plausible arguments for thinking that superhuman AI has the potential for highly negative outcomes (potentially even human extinction), but there are current actions we can take to reduce these risks.
  • Work on reducing the risks associated with superhuman AI is highly neglected.
  • Therefore, the expected impact of working on reducing AI risks is very high.

The three key aspects of this argument are expert belief in plausibility of the problem, very large impact of the problem if it does occur, and the problem being substantively neglected. My argument is that we can adapt this argument to make parallel arguments for other cause areas. I shall present three: overthrowing global capitalism, philosophy of religion, and resource depletion.

Overthrowing global capitalism

  • Many experts on politics and sociology believe that the institutions of global capitalism are responsible for extremely large amounts of suffering, oppression, and exploitation throughout the world.
  • Although there is much work criticising capitalism, work on devising and implementing practical alternatives to global capitalism is highly neglected.
  • Therefore, the expected impact of working on devising and implementing alternatives to global capitalism is very high.

Philosophy of religion

  • A sizeable minority of philosophers believe in the existence of God, and there are at least some very intelligent and educated philosophers are adherents of a wide range of different religions.
  • According to many religions, humans who do not adopt the correct beliefs and/or practices will be destined to an eternity (or at least a very long period) of suffering in this life or the next.
  • Although religious institutions have extensive resources, the amount of time and money dedicated to systematically analysing the evidence and arguments for and against different religious traditions is extremely small.
  • Therefore, the expected impact of working on investigating the evidence and arguments for the various religious is very high.

Resource depletion

  • Many scientists have expressed serious concern about the likely disastrous effects of population growth, ecological degradation, and resource depletion on the wellbeing of future generations and even the sustainability of human civilization as a whole.
  • Very little work has been conducted to determine how best to respond to resource depletion or degradation of the ecosystem so as to ensure that Earth remains inhabitable and human civilization is sustainable over the very long term.
  • Therefore, the expected impact of working on investigating long-term responses to resource depletion and ecological collapse is very high.

Readers may dispute the precise way I have formulated each of these arguments or exactly how closely they all parallel the case for AI safety, however I hope they will see the basic point I am trying to drive at. Specifically, if effective altruists are focused on AI safety essentially because of expert belief in plausibility, large scope of the problem, and neglectedness of the issue, a similar case can be made with respect to working on overthrowing global capitalism, conducting research to determine which religious belief (if any) is most likely to be correct, and efforts to develop and implement responses to resource depletion and ecological collapse.

One response that I foresee is that none of these causes are really neglected because there are plenty of people focused on overthrowing capitalism, researching religion, and working on environmentalist causes, while very few people work on AI safety. But remember, outsiders would likely say that AI safety is not really neglected because billions of dollars are invested into AI research by academics and tech companies around the world. The point is that there is a difference between working in a general area and working on the specific subset of that area that is highest impact and most neglected. In much the same way as AI safety research is neglected even if AI research more generally is not, likewise in the parallel cases I present, I argue that serious evidence-based research into the specific questions I present is highly neglected, even if the broader areas are not.

Potential alternative causes are neglected

I suspect that at this point many of my readers will at this point be mentally marshaling additional arguments as to why AI safety research is in fact a more worthy cause than the other three I have mentioned. Doubtless there are many such arguments that one could present, and probably I could devise counterarguments to at least some of them – and so the debate would progress. My point is not that the candidate causes I have presented actually are good causes for EAs to work on, or that there aren’t any good reasons why AI safety (along with other emerging technologies) is a better cause. My point is rather that these reasons are not generally discussed by EAs. That is, the arguments generally presented for focusing on AI safety as a cause area do not uniquely pick out AI safety (and other emerging technologies like nanotechnology or bioengineered pathogens), but EAs making the case for AI safety essentially never notice this because their ideological preconceptions bias them towards focusing on new technologies, and away from the sorts of causes I mention here. Of course EAs do go into much more detail about the risks of new technologies than I have here, but the core argument for focusing in AI safety in the first place is not applied to other potential cause areas to see if (as I think it does) it could also apply to those other causes.

Furthermore, it is not as if effective altruists have carefully considered these possible cause areas and come to the reasoned conclusion that they are not the highest priorities. Rather, they have simply not been considered. They have not even been on the radar, or at best barely on the radar. For example, I searched for ‘resource depletion’ on the EA forums and found nothing. I searched for ‘religion’ and found only the EA demographics survey and an article about whether EA and religious organisations can cooperate. A search for ‘socialism’ yielded one article discussing what is meant by ‘systemic change’, and one article (with no comments and only three upvotes) explicitly outlining an effective altruist plan for socialism.

This lack of interest in other cause areas can also be found in the major EA organisations. For example, the stated objective of the global priorities institute is:

To conduct foundational research that informs the decision-making of individuals and institutions seeking to do as much good as possible. We prioritise topics which are important, neglected, and tractable, and use the tools of multiple disciplines, especially philosophy and economics, to explore the issues at stake.

On the face of it this aim is consistent with all three of the suggested alternative cause areas I outlined in the previous section. Yet the GPI research agenda focuses almost entirely on technical issues in philosophy and economics pertaining to the long-termism paradigm. While AI safety is not discussed extensively it is mentioned a number of times, and much of the research agenda appears to be developed around related questions in philosophy and economics that the long-termism paradigm gives rise to. Religion and socialism are not mentioned at all in this document, while resource depletion is only mentioned indirectly by two references in the appendix under ‘indices involving environmental capital’.

Similarly the Future of Humanity Institute focuses on AI safety, AI governance, and biotechnology. Strangely, it also pursues some work on highly obscure topics such as the aestivation solution to the Fermi paradox and on the probability of Earth being destroyed by microscopic black holes or metastable vacuum states. At the same time, nothing about any of the potential new problem areas I have mentioned.

Under their problem profiles, 80,000 hours does not mention having investigated anything relating to religion or overthrowing global capitalism (or even substantially reforming global economic institutions). They do link to an article by Robert Wiblin discussing why EAs do not work on resource scarcity, however this is not a careful analysis or investigation, just his general views on the topic. Although I agree with some of the arguments he makes, the depth of analysis is very shallow relative to the potential risks and concern raised about this issue by many scientists and writers over the decades. Indeed, I would argue that there is about as much substance in this article as a rebuttal of resource depletion as a cause area as one finds in the typical article dismissing AI fears as exaggerated and hysterical.

In yet another example, the Foundational Research Institute states that:

Our mission is to identify cooperative and effective strategies to reduce involuntary suffering. We believe that in a complex world where the long-run consequences of our actions are highly uncertain, such an undertaking requires foundational research. Currently, our research focuses on reducing risks of dystopian futures in the context of emerging technologies. Together with others in the effective altruism community, we want careful ethical reflection to guide the future of our civilization to the greatest extent possible.

Hence, even though it seems that in principle socialists, Buddhists, and ecological activists (among others) are highly concerned about reducing the suffering of humans and animals, FRI ignores the topics that these groups would tend to focus on, and instead focuses their attention on the risks of emerging technologies. As in the case of FHI, they also seem to find room for some topics of highly dubious relevance to any of EAs goals, such as this paper about the potential for correlated actions with civilizations located elsewhere in the multiverse.

Outside of the main organisations, there has been some discussion about socialism as an EA cause, for example on r/EffectiveAltruism and by Jeff Kaufman. I was able to find little else about either of the two potential cause areas I outline.

Overall, on the basis of the foregoing examples I conclude that the amount of time and energy spent by the EA community investigating the three potential new cause areas that I have discussed is negligible compared to the time and energy spent investigating emerging technologies. This is despite the fact that most of these groups are not ostensibly established with the express purpose of reducing the harms of emerging technologies, but have simply chosen this cause area over other possibilities would that also potentially fulfill their broad objectives. I have not found any evidence that this choice is the result of early investigations demonstrating that emerging technologies are far superior to the cause areas I mention. Instead, it appears to be mostly the result of disinterest in the sorts of topics I identify, and a much greater ex ante interest in emerging technologies over other causes. I present this as evidence that the primary reason effective altruism focuses so extensively on emerging technologies over other speculative but potentially high impact causes, is because of the privileging of certain viewpoints and answers over others. This, in turn, is the result of the underlying ideological commitments of many effective altruists.

What is EA ideology?

If many effective altruists share a common ideology, then what is the content of this ideology? As with any social movement, this is difficult to specify with any precision and will obviously differ somewhat from person to person and from one organisation to another. That said, on the basis of my research and experiences in the movement, I would suggest the following core tenets of EA ideology:

  1. The natural world is all that exists, or at least all that should be of concern to us when deciding how to act. In particular, most EAs are highly dismissive of religious or other non-naturalistic worldviews, and tend to just assume without further discussion that views like dualism, reincarnation, or theism cannot be true. For example, the map of EA concepts has listed under ‘important general features of the world’ pages on ‘possibility of an infinite universe’ and ‘the simulation argument’, yet no mention of the possibility that anything could exist beyond the natural world. It requires a very particular ideological framework to regard the simulation as is more important or pressing than non-naturalism.
  2. The correct way to think about moral/ethical questions is through a utilitarian lens in which the focus is on maximising desired outcomes and minimising undesirable ones. We should focus on the effect of our actions on the margin, relative to the most likely counterfactual. There is some discussion of moral uncertainty, but outside of this deontological, virtue ethics, contractarian, and other approaches are rarely applied in philosophical discussion of EA issues. This marginalist, counterfactual, optimisation-based way of thinking is largely borrowed from neoclassical economics, and is not widely employed by many other disciplines or ideological perspectives (e.g. communitarianism).
  3. Rational behaviour is best understood through a Bayesian framework, incorporating key results from game theory, decision theory, and other formal approaches. Many of these concepts appear in the idealised decision making section of the map of EA concepts, and are widely applied in other EA writings.
  4. The best way to approach a problem is to think very abstractly about that problem, construct computational or mathematical models of the relevant problem area, and ultimately (if possible) test these models using experiments. The model appears to be of how research is approached in physics with some influence from analytic philosophy. The methodologies of other disciplines are largely ignored.
  5. The development and introduction of disruptive new technologies is a more fundamental and important driver of long-term change than socio-political reform or institutional change. This is clear from the overwhelming focus on technological change of top EA organisations, including 80,000 hours, the Center for Effective Altruism, the Future of Humanity Institute, the Global Priorities Project, the Future of Life Institute, the Centre for the Study of Existential Risk, and the Machine Intelligence Research Institute.

I’m sure others could devise different ways of describing EA ideology that potentially look quite different to mine, but this is my best guess based on what I have observed. I believe these tenets are generally held by EAs, particularly those working at the major EA organisations, but are generally not widely discussed or critiqued. That this set of assumptions is fairly specific to EA should be evident if one reads various criticisms of effective altruism from those outside the movement. Although they do not always express their concerns using the same language that I have, it is often clear that the fundamental reason for their disagreement is the rejection of one or more of the five points mentioned above.

Conclusion

My purpose in this article has not been to contend that effective altruists shouldn’t have an ideology, or that the current dominant EA ideology (as I have outlined it) is mistaken. In fact, my view is that we can’t really get anywhere in rational investigation without certain starting assumptions, and these starting assumptions constitute our ideology. It doesn’t follow from this that any ideology is equally justified, but how we adjudicate between different ideological frameworks is beyond the scope of this article.

Instead, all I have tried to do is argue that effective altruists do in fact have an ideology. This ideology leads them to privilege certain questions over others, to apply particular theoretical frameworks to the exclusion of others, and to focus on certain viewpoints and answers while largely ignoring others. I have attempted to substantiate my claims by showing how different ideological frameworks would ask different questions, use different theoretical frameworks, and arrive at different conclusions to those generally found within EA, especially the major EA organisations. In particular, I argued that the typical case for focusing on AI safety can be modified to serve as an argument for a number of other cause areas, all of which have been largely ignored by most EAs.

My view is that effective altruists should acknowledge that the movement as a whole does have an ideology. We should critically analyse this ideology, understand its strengths and weaknesses, and then to the extent to which we think this set of ideological beliefs is correct, defend it against rebuttals and competing ideological perspectives. This is essentially what all other ideologies do – it is how the exchange of ideas works. Effective altruists should engage critically in this ideological discussion, and not pretend they are aloof from it by resorting to the refrain that ‘EA is a question, not an ideology’.

A Critique of Superintelligence

Introduction

In this article I present a critique of Nick Bostrom’s book Superintelligence. For purposes of brevity I shall not devote much space to summarising Bostrom’s arguments or defining all the terms that he uses. Though I briefly review each key idea before discussing it, I shall also assume that readers have some general idea of Bostrom’s argument, and some of the key terms involved. Also note that to keep this piece focused, I only discuss arguments raised in this book, and not what Bostrom has written elsewhere or others who have addressed similar issues. The structure of this article is as follows. I first offer a summary of what I regard to be the core argument of Bostrom’s book, outlining a series of premises that he defends in various chapters. Following this summary, I commence a general discussion and critique of Bostrom’s concept of ‘intelligence’, arguing that his failure to adopt a single, consistent usage of this concept in his book fatally undermines his core argument. The remaining sections of this article then draw upon this discussion of the concept of intelligence in responding to each of the key premises of Bostrom’s argument. I conclude with a summary of the strengths and weaknesses of Bostrom’s argument.

Summary of Bostrom’s Argument

Throughout much of his book, Bostrom remains quite vague as to exactly what argument he is making, or indeed whether he is making a specific argument at all. In many chapters he presents what are essentially lists of various concepts, categories, or considerations, and then articulates some thoughts about them. Exactly what conclusion we are supposed to draw from his discussion is often not made explicit. Nevertheless, by my reading the book does at least implicitly present a very clear argument, which bears a strong similarity to the sorts of arguments commonly found in the Effective Altruism (EA) movement, in favour of focusing on AI research as a cause area. In order to provide structure for my review, I have therefore constructed an explicit formulation of what I take to be Bostrom’s main argument in his book. I summarise it as follows:

Premise 1: A superintelligence, defined as a system that ‘exceeds the cognitive performance of humans in virtually all domains of interest’, is likely to be developed in the foreseeable future (decades to centuries).

Premise 2: If superintelligence is developed, some superintelligent agent is likely to acquire a decisive strategic advantage, meaning that no terrestrial power or powers would be able to prevent it doing as it pleased.

Premise 3: A superintelligence with a decisive strategic advantage would be likely to capture all or most of the cosmic endowment (the total space and resources within the accessible universe), and put it to use for its own purposes.

Premise 4: A superintelligence which captures the cosmic endowment would likely put this endowment to uses incongruent with our (human) values and desires.

Preliminary conclusion: In the foreseeable future it is likely that a superintelligent agent will be created which will capture the cosmic endowment and put it to uses incongruent with our values. (I call this the AI Doom Scenario).

Premise 5: Pursuit of work on AI safety has a non-trivial chance of noticeably reducing the probability of the AI Doom Scenario occurring.

Premise 6: If pursuit of work on AI safety has at least a non-trivial chance of noticeably reducing the probability of an AI Doom Scenario, then (given the preliminary conclusion above) the expected value of such work is exceptionally high.

Premise 7: It is morally best for the EA community to preferentially direct a large fraction of its marginal resources (including money and talent) to the cause area with highest expected value.

Main conclusion: It is morally best for the EA community to direct a large fraction of its marginal resources to work on AI safety. (I call this the AI Safety Thesis.)

Bostrom discusses the first premise in chapters 1-2, the second premise in chapters 3-6, the third premise in chapters 6-7, the fourth premise in chapters 8-9, and some aspects of the fifth premise in chapters 13-14. The sixth and seventh premises are not really discussed in the book (though some aspects of them are hinted at in chapter 15), but are widely discussed in the EA community and serve as the link between the abstract argumentation and real-world action, and as such I decided also to discuss them here for completeness. Many of these premises could be articulated slightly differently, and perhaps Bostrom would prefer to rephrase them in various ways. Nevertheless I hope that they at least adequately capture the general thrust and key contours of Bostrom’s argument, as well as how it is typically appealed to and articulated within the EA community.

The nature of intelligence

In my view, the biggest problem with Bostrom’s argument in Superintelligence is his failure to devote any substantial space to discussing the nature or definition of intelligence. Indeed, throughout the book I believe Bostrom uses three quite different conceptions of intelligence:

  • Intelligence(1): Intelligence as being able to perform most or all of the cognitive tasks that humans can perform. (See page 22)
  • Intelligence(2): Intelligence as a measurable quantity along a single dimension, which represents some sort of general cognitive efficaciousness. (See pages 70,76)
  • Intelligence(3): Intelligence as skill at prediction, planning, and means-ends reasoning in general. (See page 107)

While certainly not entirely unrelated, these three conceptions are all quite different from each other. Intelligence(1) is mostly naturally viewed as a multidimensional construct, since humans exhibit a wide range of cognitive abilities and it is by no means clear that they are all reducible to a single underlying phenomenon that can be meaningfully quantified with one number. It seems much more plausible to say that the range of human cognitive abilities require many different skills which are sometimes mutually-supportive, sometimes mostly unrelated, and sometimes mutually-inhibitory in varying ways and to varying degrees. This first conception of intelligence is also explicitly anthropocentric, unlike the other two conceptions which make no reference to human abilities. Intelligence(2) is unidimensional and quantitative, and also extremely abstract, in that it does not refer directly to any particular skills or abilities. It most closely parallels the notion of IQ or other similar operational measures of human intelligence (which Bostrom even mentions in his discussion), in that it is explicitly quantitative and attempts to reduce abstract reasoning abilities to a number along a single dimension. Intelligence(3) is much more specific and grounded than either of the other two, relating only to particular types of abilities. That said, it is not obviously subject to simple quantification along a single dimension as is the case for Intelligence(2), nor is it clear that skill at prediction and planning is what is measured by the quantitative concept of Intelligence(2). Certainly Intelligence(3) and Intelligence(2) cannot be equivalent if Intelligence(2) is even somewhat analogous to IQ, since IQ mostly measures skills at mathematical, spatial, and verbal memory and reasoning, which are quite different from skills at prediction and planning (consider for example the phenomenon of autistic savants). Intelligence(3) is also far more narrow in scope than Intelligence(1), corresponding to only one of the many human cognitive abilities.

Repeatedly throughout the book, Bostrom flips between using one or another of these conceptions of intelligence. This is a major weakness for Bostrom’s overall argument, since in order for the argument to be sound it is necessary for a single conception of intelligence to be adopted and apply in all of his premises. In the following paragraphs I outline several of the clearest examples of how Bostrom’s equivocation in the meaning of ‘intelligence’ undermines his argument.

Bostrom argues that once a machine becomes more intelligent than a human, it would far exceed human-level intelligence very rapidly, because one human cognitive ability is that of building and improving AIs, and so any superintelligence would also be better at this task than humans. This means that the superintelligence would be able to improve its own intelligence, thereby further improving its own ability to improve its own intelligence, and so on, the end result being a process of exponentially increasing recursive self-improvement. Although compelling on the surface, this argument relies on switching between the concepts of Intelligence(1) and Intelligence(2). When Bostrom argues that a superintelligence would necessarily be better at improving AIs than humans because AI-building is a cognitive ability, he is appealing to Intelligence(1). However, when he argues that this would result in recursive self-improvement leading to exponential growth in intelligence, he is appealing to Intelligence(2). To see how these two arguments rest on different conceptions of intelligence, note that considering Intelligence(1), it is not at all clear that there is any general, single way to increase this form of intelligence, as Intelligence(1) incorporates a wide range of disparate skills and abilities that may be quite independent of each other. As such, even a superintelligence that was better than humans at improving AIs would not necessarily be able to engage in rapidly recursive self-improvement of Intelligence(1), because there may well be no such thing as a single variable or quantity called ‘intelligence’ that is directly associated with AI-improving ability. Rather, there may be a host of associated but distinct abilities and capabilities that each needs to be enhanced and adapted in the right way (and in the right relative balance) in order to get better at designing AIs. Only by assuming a unidimensional quantitative conception of Intelligence(2) does it make sense to talk about the rate of improvement of a superintelligence being proportional to its current level of intelligence, which then leads to exponential growth. Bostrom therefore faces a dilemma. If intelligence is a mix of a wide range of distinct abilities as in Intelligence(1), there is no reason to think it can be ‘increased’ in the rapidly self-reinforcing way Bostrom speaks about (in mathematical terms, there is no single variable  which we can differentiate and plug into the differential equation, as Bostrom does in his example on pages 75-76). On the other hand, if intelligence is a unidimensional quantitative measure of general cognitive efficaciousness, it may be meaningful to speak of self-reinforcing exponential growth, but it is not necessarily obvious that any arbitrary intelligent system or agent would be particularly good at designing AIs. Intelligence(2) may well help with this ability, but it’s not at all clear it is sufficient – after all, we readily conceive of building a highly “intelligent” machine that can reason abstractly and pass IQ tests etc, but is useless at building better AIs.

Bostrom argues that once a machine intelligence became more intelligent than humans, it would soon be able to develop a series of ‘cognitive superpowers’ (intelligence amplification, strategising, social manipulation, hacking, technology research, and economic productivity), which would then enable it to escape whatever constraints were placed upon it and likely achieve a decisive strategic advantage. The problem is that it is unclear whether a machine endowed only with Intelligence(3) (skill at prediction and means-ends reasoning) would necessarily be able to develop skills as diverse as general scientific research ability, the capability to competently use natural language, and perform social manipulation of human beings. Again, means-ends reasoning may help with these skills, but clearly they require much more beyond this. Only if we are assuming the conception of Intelligence(1), whereby the AI has already exceeded essentially all human cognitive abilities, does it become reasonable to assume that all of these ‘superpowers’ would be attainable.

According to the orthogonality thesis, there is no reason why the machine intelligence could not have extremely reductionist goals such as maximising the number of paperclips in the universe, since an AI’s level of intelligence is totally separate to and distinct from its final goals. Bostrom’s argument for this thesis, however, clearly depends adopting Intelligence(3), whereby intelligence is regarded as general skill with prediction and means-ends reasoning. It is indeed plausible that an agent endowed only with this form of intelligence would not necessarily have the ability or inclination to question or modify its goals, even if they are extremely reductionist or what any human would regard as patently absurd. If, however, we adopt the much more expansive conception of Intelligence(1), the argument becomes much less defensible. This should become clear if one considers that ‘essentially all human cognitive abilities’ includes such activities as pondering moral dilemmas, reflecting on the meaning of life, analysing and producing sophisticated literature, formulating arguments about what constitutes a ‘good life’, interpreting and writing poetry, forming social connections with others, and critically introspecting upon one’s own goals and desires. To me it seems extraordinarily unlikely that any agent capable of performing all these tasks with a high degree of proficiency would simultaneously stand firm in its conviction that the only goal it had reasons to pursue was tilling the universe with paperclips. As such, Bostrom is driven by his cognitive superpowers argument to adopt the broad notion of intelligence seen in Intelligence(1), but then is driven back to a much narrower Intelligence(3) when he wishes to defend the orthogonality thesis. The key point to be made here is that the goals or preferences of a rational agent are subject to rational reflection and reconsideration, and the exercise of reason in turn is shaped by the agent’s preferences and goals. Short of radically redefining what we mean by ‘intelligence’ and ‘motivation’, this complex interaction will always hamper simplistic attempts to neatly separate them, thereby undermining Bostrom’s case for the orthogonality thesis – unless a very narrow conception of intelligence is adopted.

In the table below I summarise several of the key outcomes or developments that are critical to Bostrom’s argument, and how plausible they would be under each of the three conceptions of intelligence. Obviously such judgements are necessarily vague and subjective, but the key point I wish to make is simply that only by appealing to different conceptions of intelligence in different cases is Bostrom able to argue that all of the outcomes are reasonably likely to occur. Fatally for his argument, there is no single conception of intelligence that makes all of these outcomes simultaneously likely or plausible.

Outcome Intelligence(1):        all human cognitive abilities Intelligence(2): unidimensional measure of cognition Intelligence(3): prediction and means-ends reasoning
Quick takeoff Highly unlikely Likely Unclear
Develops all cognitive superpowers Highly likely Highly unlikely Highly unlikely
Absurd ‘paperclip maximising’ goals Extremely unlikely Unclear Likely
Resists changes to goals Unlikely Unclear Likely
Can escape confinement Likely Unlikely Unlikely

Premise 1: Superintelligence is coming soon

I have very little to say about this premise, since I am in broad agreement with Bostrom that even if it takes decades or a century, super-human artificial intelligence is quite likely to be developed. I find Bostrom’s appeals to surveys of AI researchers regarding how long it is likely to be until human level AI is developed fairly unpersuasive, given both the poor track record of such predictions and also the fact that experts on AI research are not necessarily experts on extrapolating the rate of technological and scientific progress (even in their own field). Bostrom, however, does note some of these limitations, and I do not think his argument is particularly dependent upon these sorts of appeals. I therefore will pass over premise 1 and move on to what I consider to be the more important issues.

Premise 2: Arguments against a fast takeoff

Bostrom’s major argument in favour of the contention that a superintelligence would be able to gain a decisive strategic advantage is that the ‘takeoff’ for such an intelligence would likely be very rapid. By a ‘fast takeoff’, Bostrom means that the time between when the superintelligence first approaches human-level cognition and when it achieves dramatically superhuman intelligence would be small, on the order of days or even hours. This is critical because if takeoff is as rapid as this, there will be effectively no time for any existing technologies or institutions to impede the growth of the superintelligence or check it in any meaningful way. Its rate of development would be so rapid that it would readily be able to out-think and out-maneuver all possible obstacles, and rapidly obtain a decisive strategic advantage. Once in this position, the superintelligence would possess an overwhelming advantage in technology and resources, and would therefore be effectively impossible to displace.

The main problem with all of Bostrom’s arguments for the plausibility of a fast takeoff is that they are fundamentally circular, in that the scenario or consideration they propose is only plausible or relevant under the assumption that the takeoff (or some key aspect of it) is fast. The arguments he presents are as follows:

  • Two subsystems argument: if an AI consists of two or more subsystems with one improving rapidly, but only contributing to the ability of the overall system after a certain threshold is reached, then the rate of increase in the performance of the overall system could drastically increase once that initial threshold is passed. This argument assumes what it is trying to prove, namely that the rate of progress in a critical rate-limiting subsystem could be very rapid, experiencing substantial gains on the order of days or even hours. It is hard to see what Bostrom’s scenario really adds here; all he has done is redescribed the fast takeoff scenario in a slightly more specific way. He has not given any reason for thinking that it is at all probable that progress on such a critical rate-limiting subsystem would occur at the extremely rapid pace characteristic of a fast takeoff.
  • Intelligence spectrum argument: Bostrom argues that the intelligence gap between ‘infra-idiot’ and ‘ultra-Einstein’, while appearing very large to us, may actually be quite small in the overall scheme of the spectrum of possible levels of intelligence, and as such the time taken to improve an AI through and beyond this level may be much less than it originally seems. However, even if it is the case that the range of the intelligence spectrum within which all humans fall is fairly narrow in the grand scheme of things, it does not follow that the time taken to traverse it in terms of AI development is likely be on the order of days or weeks. Bostrom is simply making an assumption that such rapid rates of progress could occur. His intelligence spectrum argument can only ever show that the relative distance in intelligence space is small; it is silent with respect to likely development timespans.
  • Content overhang argument: an artificial intelligence could be developed with high capabilities but with little raw data or content to work with. If large quantities of raw data could be processed quickly, such an AI could rapidly expand its capabilities. The problem with this argument is that what is most important is not how long it takes a given AI to absorb some quantity of data, but rather the length of time between producing one version of the AI and the next, more capable version. This is because the key problem is that we currently don’t know how to build a superintelligence. Bostrom is arguing that if we did build a nascent superintelligence that simply needed to process lots of data to manifest its capabilities, then this learning phase could occur quickly. He gives no reason, however, to think that the rate at which we can learn how to build that nascent superintelligence (in other words, the overall rate of progress in AI research) will be anything like as fast as the rate an existing nascent superintelligence would be able to process data. Only if we assume rapid breakthroughs in AI design itself does the ability of AIs to rapidly assimilate large quantities of data become relevant.
  • Hardware overhang argument: it may be possible to increase the capabilities of a nascent superintelligence dramatically and very quickly by rapidly increasing the scale and performance of the hardware it had access to. While theoretically possible, this is an implausible scenario since any artificial intelligence showing promise would likely be operating near the peak of plausible hardware provision. This means that testing, parameter optimisation, and other such tasks will take considerable time, as hardware will be a limiting factor. Bostrom’s concept of a ‘hardware overhang’ amounts to thinking that AI researchers would be content to ‘leave money on the table’, in the sense of not making use of what hardware resources are available to them for extended periods of development. This is especially implausible in the event of groundbreaking research involving AI architectures showing substantial promise. Such systems would hardly be likely to spend years being developed on relatively primitive hardware only to be suddenly and very rapidly dramatically scaled up at the precise moment when practically no further development is necessary, and they are already effectively ready to achieve superhuman intelligence.
  • ‘One key insight’ argument: Bostrom argues that ‘if human level AI is delayed because one key insight long eludes programmers, then when the final breakthrough occurs, the AI might leapfrog from below to radically above human level’. Assuming that ‘one key insight’ would be all it would take to crack the problem of superhuman intelligence is, to my mind, grossly implausible, and not consistent either with the slow but steady rate of progress in artificial intelligence research over the past 60 years, or with the immensely complex and multifaceted phenomenon that is human intelligence.

Additional positive arguments against the plausibility of a fast takeoff include the following:

  • Speed of science: Bostrom’s assertion that artificial intelligence research could develop from clearly sub-human to obviously super-human levels of intelligence in a matter of days or hours is simply absurd. Scientific and engineering projects simply do not work over timescales that short. Perhaps to some degree this could be altered in the future if (for example) human-level intelligence could be emulated on a computer and then the simulation run at much faster than real-time. But Bostrom’s argument is that machine intelligence is likely to precede emulation, and as such all we will have to work with at least up to the point of human/machine parity being reached is human levels of cognitive ability. As such it seems patently absurd to argue that developments of this magnitude could be made on the timespan of days or weeks. We simply see no examples of anything like this from history, and Bostrom cannot argue that the existence of superintelligence would make historical parallels irrelevant, since we are precisely talking about the development of superintelligence in the context of it not already being in existence.
  • Subsystems argument: any superintelligent agent will doubtlessly require many interacting and interconnected subsystems specialised for different tasks. This is the way even much narrower AIs work, and it is certainly how human cognition works. Ensuring that all these subsystems or processes interact efficiently, without one inappropriately dominating or slowing up overall cognition, or without bottlenecks of information transfer or decision making, is likely to be something that requires a great deal of experimentation and trial-and-error. This in turn will take extensive empirical experiments, tinkering, and much clever work. All this takes time.
  • Parallelisation problems: many algorithms cannot be sped up considerably by simply adding more computational power unless an efficient way can be found to parallelise them, meaning that they can be broken down into smaller steps which can be performed in parallel across many processors at once. This is much easier to do for some types of algorithms and computations than others. It is not at all clear that the key algorithms used by a superintelligence would be susceptible to parallelisation. Even if they were, developing efficient parallelised forms of the relevant algorithms would itself be a prolonged process. The superintelligence itself would only be able to help in this development to the degree permitted by its initially limited hardware endowment. We therefore would expect to observe gradual improvement of algorithmic efficiency in parallelisation, thereby enabling more hardware to be added, thereby enabling further refinements to the algorithms used, and so on. It is therefore not at all clear that a superintelligence could be rapidly augmented simply by ‘adding more hardware’.
  • Need for experimentation: even if a superintelligence came into existence quite rapidly, it would still not be able to achieve a decisive strategic advantage in similarly short time. This is because such an advantage would almost certainly require development of new technologies (at least the examples Bostrom gives almost invariably involve the AI using technologies currently unavailable to humans), which would in turn require scientific research. Scientific research is a complex activity that requires far more than skill at ‘prediction and means-end reasoning’. In particular, it also generally requires experimental research and (if engineering of new products is involved) producing and testing of prototypes. All of this will take time, and crucially is not susceptible to computational speedup, since the experiments would need to be performed with real physical systems (mechanical, biological, chemical, or even social). The idea that all (or even most) such testing and experimentation could be replaced by computer simulation of the relevant system is absurd, since most such simulations are completely computationally intractable, and likely to remain so for the foreseeable future (in many cases possibly forever). Therefore in the development of new technologies and scientific knowledge, the superintelligence is still fundamentally limited by the rate at which real-world tests and experiments can be performed.
  • The infrastructure problem: in addition to the issue of developing new technologies, there is the further problem of the infrastructure required to develop such technologies, or even just to carry out the core objectives of the superintelligence. In order to acquire a decisive strategic advantage, a superintelligence will require vast computational resources, energy sources to supply them, real-world maintenance of these facilities, sources of raw materials, and vast manufacturing centres to produce any physical manipulators or other devices it requires. If it needs humans to perform various tasks for it, it will likely also require training facilities and programs for its employees, as well as teams of lawyers to acquire all the needed permits and permissions, write up contracts, and lobby governments. All of this physical and social infrastructure cannot be built in the matter of an afternoon, and more realistically would take many years or even decades to put in place. No amount of superintelligence can overcome physical limitations of the time required to produce and transform large quantities of matter and energy into desired forms. One might argue that improved technology certainly can reduce the time taken to move matter and energy, but the point is that it can only do so after the technology has been embodied in physical forms. The superintelligence would not have access to such hypothetical super-advanced transportation, computation, or construction technologies until it had built the factories needed to produce the machine tools with are needed to precisely refine the raw materials needed for parts in the construction of the nanofactory… and so on for many other similar examples. Nor can even vast amounts of money and intelligence allow any agent to simply brush aside the impediments of the legal system and government bureaucracy in an afternoon. A superintelligence would not simply be able to ignore such social restrictions on its actions until after it had gained enough power to act in defiance of world governments, which it would not be able to do until it had already acquired considerable military capabilities. All of this would take considerable time, precluding a fast takeoff.

Premise 3: Arguments against cosmic expansion

Critical to Bostrom’s argument about the dangers of superintelligence is that a superintelligence with a critical strategic advantage would likely capture the majority of the cosmic endowment (the sum total of the resources available within the regions of space potentially accessible to humans). This is why Bostrom presents calculations for the huge numbers of potential human lives (or at least simulations of lives) whose happiness is at stake should the cosmic endowment be captured by a rogue AI. While Bostrom does present some compelling reasons for thinking that a superintelligence with a decisive strategic advantage would have reasons and the ability to expand throughout the universe, there are also powerful considerations against the plausibility of this outcome which he fails to consider.

First, by the orthogonality thesis, a superintelligent agent could have almost any imaginable goal. It follows that a wide range of goals are possible that are inconsistent with cosmic expansion. In particular, any superintelligence with goals involving the value of unspoiled nature, or of constraining its activities to the region of the solar system, or of economising on the use of resources, would have reasons not to pursue cosmic expansion. How likely it is that a superintelligence would be produced with such self-limiting goals compared to goals favouring limitless expansion is unclear, but it is certainly a relevant outcome to consider, especially given that valuing exclusively local outcomes or conservation of resources seem like plausible goals that might be incorporated by developers into a seed AI.

Second, on a number of occasions, Bostrom briefly mentions that a superintelligence would only be able to capture the entire cosmic endowment if no other technologically advanced civilizations, or artificial intelligences produced by such civilizations, existed to impede it. Nowhere, however, does he devote any serious consideration to how likely the existence of such civilizations or intelligences is. Given the great age and immense size of the cosmos, however, the probability that humans are the first technological civilization to achieve spaceflight, or that any superintelligence we produce would be the first to spread throughout the universe, seems infinitesimally small. Of course this is an area of great uncertainly and we can therefore only speculate about the relevant probabilities. Nevertheless, it seems very plausible to me that the chances of any human-produced superintelligence successfully capturing the cosmic endowment without alien competition are very low. Of course this does not mean that an out-of-control terrestrial AI could not do great harm to life on Earth and even spread throughout neighbouring stars, but it does significantly blunt the force of the huge numbers Bostrom presents as being at stake if we think the entire cosmic endowment is at risk of being misused.

Premise 4: The nature of AI motivation

Bostrom’s main argument in defence of premise 4 is that unless we are extremely careful and/or lucky in establishing the goals and motivations of the superintelligence before it captures the cosmic endowment, it is likely to end up pursuing goals that are not in alignment with our own values. Bostrom presents a number of thought experiments as illustrations of the difficulty of specifying values or goals in a manner that would result in the sorts of behaviours we want it to perform. Most of these examples involve the superintelligence pursuing a goal in a single-minded, literalistic way, which no human being would regard as ‘sensible’. He gives as examples an AI tasked with maximising its output of paperclips sending out probes to harvest all the energy within the universe to make more paperclips, or an AI tasked with increasing human happiness enslaving all humans and hijacking their brains to stimulate the pleasure centres directly. One major problem I have with all such examples is that the AIs always seem to lack a critical ability in interpreting and pursuing their goals that, for want of a better term, we might describe as ‘common sense’. This issue ultimately reduces to which conception of intelligence one applies, since if we adopt Intelligence(1) then any such AIs would necessarily have ‘common sense’ (this being a human cognitive ability), while the other two conceptions of intelligence would not necessarily include this ability. However, if we do take Intelligence(1) as our standard, then it seems difficult to see why a superintelligence would lack the sort of common sense by which any human would be able to see that the simple-minded, literalistic interpretations given as examples by Bostrom are patently absurd and ridiculous things to do.

Aside from the question of ‘common sense’, it is also necessary to analyse the concept of ‘motivation’, which is a multifaceted notion that can be understood in a variety of ways. Two particularly important conceptions of motivation are that it is some sort of internal drive to do or obtain some outcome, and motivation as some sort of more abstract rational consideration by which an agent has a reason to act in a certain way. Given what he says about the orthogonality thesis, it seems that Bostrom thinks of motivation as being some sort of internal drive to act in a particular way. In the first few pages of the chapter on the intelligent will, however, he switches from talking about motivation to talking about goals, without any discussion about the relationship between these two concepts. Indeed, it seems that these are quite different things, and can exist independently of each other. For example, humans can have goals (to quit smoking, or to exercise more) without necessarily having any motivation to take actions to achieve those goals. Conversely, humans can be motivated to do something without having any obvious associated goal. Many instances of collective behaviour in crowds and riots may be examples of this, where people act based on situational factors without any clear reason or objectives. Human drives such as curiously and novelty can also be highly motivating without necessarily having any particular goal associated with them. Given the plausibility that motivation and goals are different and distinct concepts, it is important for Bostrom to explain what he thinks the relationship between them is, and how they would operate in an artificial agent. This seems all the more relevant since we would readily say that many intelligent artificial systems possess goals (such as the common examples of a heat-seeking missile or a chess playing program), but it is not at all clear that these systems are in any way ‘motivated’ to perform these actions – they are simply designed to work towards these goals, and motivations simply don’t come into it. What then would it take to build an artificial agent that had both goals and motivations? How would an artificial agent act with respect to these goals and/or motivations? Bostrom simply cannot ignore these questions if he is to provide a compelling argument concerning what AIs would be motivated to do.

The problems inherent in Bostrom’s failure to analyse these concepts in sufficient detail become evident in the context of Bostrom’s discussion of something that he calls ‘final goals’. While he does not define these, presumably he means goals that are not pursued in order to achieve some further goal, but simply for their own sake. This raises several additional questions: can an agent have more than one final goal? Need they have any final goals at all? Might goals always be infinitely resolvable in terms of fulfilling some more fundamental or more abstract underlying goal? Or might multiple goals form an inter-connected self-sustaining network, such that all support each other but no single goal can be considered most fundamental or final? These questions might seem arcane, but addressing them is crucial for conducting a thorough and useful analysis of the likely behaviour of intelligent agents. Bostrom often speaks as if a superintelligence will necessarily act in single-minded devotion to achieve its one final goal. This assumes, however, that a superintelligence would be motivated to achieve its goal, that it would have one and only one final goal, and that its goal and its motivation to achieve it are totally independent from and not receptive to rational reflection or any other considerations. As I have argued here and previously, however, these are all quite problematic and dubious notions. In particular, as I noted in the discussion about the nature of intelligence, a human’s goals are subject to rational reflection and critique, and can be altered or rejected if they are determined to be irrational or incongruent with other goals, preferences, or knowledge that the person has. It therefore seems highly implausible that a superintelligence would hold so tenaciously to their goals, and pursue them so single-mindedly. Only a superintelligence possessing a much more minimal form of intelligence, such as the skills at prediction and means-ends reasoning of Intelligence(3), would be a plausible candidate for acting in such a myopic and mindless way. Yet as I argued previously, a superintelligence possessing only this much more limited form of intelligence would not be able to acquire all of the ‘cognitive superpowers’ necessary to establish a decisive strategic advantage.

Bostrom would likely contend that such reasoning is anthropomorphising, applying human experiences and examples in cases where they simply do not apply, given how different AIs could be to human beings. Yet how can we avoid anthropomorphising when we are using words like ‘motivation’, ‘goal’, and ‘will’, which acquire their meaning and usage largely through application to humans or other animals (as well as anthropomorphised supernatural agents)? If we insist on using human-centred concepts in our analysis, drawing anthropocentric analogies in our reasoning is unavoidable. This places Bostrom in a dilemma, as he wants to simultaneously affirm that AIs would possess motivations and goals, but also somehow shear these concepts of their anthropocentric basis, saying that they could work totally differently to how these concepts are applied in humans and other known agents. If these concepts work totally differently, then how are we justified in even using the same words in the two different cases? It seems that if this were so, Bostrom would need to stop using words like ‘goal’ and ‘motivation’ and instead start using some entirely different concept that would apply to artificial agents. On the other hand if these concepts work sufficiently similarly in human and AI cases to justify using common words to describe both cases, then there seems nothing obviously inappropriate in appealing to the operation of goals in humans in order to understand how they would operate in artificial agents. Perhaps one might contend that we do not really know whether artificial agents would have human analogues of desires and goals, or whether they would have something distinctively different. If this is the case, however, then our level of ignorance is even more profound than we had realised (since we don’t even know what words we can use to talk about the issue), and therefore much of Bostrom’s argument on these subjects would be grossly premature and under-theorised.

Bostrom also argues that once a superintelligence comes into being, it would resist any changes to its goals, since its current goals are (nearly always) better achieved by refraining from changing them to some other goal. There is an obvious flaw to this argument, namely that humans change their goals all the time, and indeed whole subdisciplines of philosophy are dedicated to pursuing the question of what we should value and how we should go about modifying our goals or pursuing different things to what we currently do. Humans can even change their ‘final goals’ (insomuch as any such things exist), such as when they convert religions or change between radically opposed political ideologies. Bostrom mentions this briefly but does not present any particularly convincing explanation for this phenomenon, nor does he explain why we should assume that this clear willingness to countenance (and even pursue) goal changes is not something that would affect AIs as it affects humans. One potential such response could be that the ‘final goal’ pursued by all humans is really something very basic such as ‘happiness’ or ‘wellbeing’ or ‘pleasure’, and that this never changes even though the means of achieving it can vary dramatically. I am not convinced by this analysis, since many people (religious and political ideologues being obvious example) seem motivated by causes to perform actions that cannot readily be regarded as contributing to their own happiness or wellbeing, unless these concepts are stretched to become implausibly broad. Even if we accept that people always act to promote their own happiness or wellbeing, however, it is certainly the case that they can dramatically change their beliefs about what sort of things will improve their happiness or wellbeing, thus effectively changing their goals. It is unclear to me why we should expect that a superintelligence able to reflect upon its goals could not similarly change its mind about the meaning of its goals, or dramatically alter its views on how to best achieve them.

Premise 5: The tractability of the AI alignment problem

Critical to the question of artificial intelligence research as a cause for effective altruists is the argument that there are things which can be done in the present to reduce the risk of misaligned AI attaining a critical strategic advantage. In particular, it is argued that AI safety research and work on the goal alignment problem has the potential of being able to, after the application of sufficient creativity and intelligence, significantly assist our efforts in constructing an AI which is ‘safe’, and has goals aligned with our best interests. This is often presented as quite an urgent matter, something which must be substantively ‘solved’ before a superintelligent AI comes into existence if catastrophe is to be averted. This possibility, however, seems grossly implausible considering the history of science and technology. I know of not a single example of any significant technological or scientific advance whose behaviour we have accurately been able to predict, and whose safety we have been able to ensure, before it has been developed. In all cases, new technologies are only understood gradually as they are developed and put to use in practise, and their problems and limitations progressively become evident.

In order to ensure that an artificial intelligence would be safe, we would first need to understand a great deal about how artificially intelligent agents work, how their motivations and goals are formed and evolve (if it all), and how artificially intelligent agents would behave in society in their interactions with humans. It seems to me that, to use Bostrom’s language, this constitutes an AI-complete problem, meaning that there is no realistic hope of substantively resolving these issues before human-level artificial intelligence itself is developed. To assert the contrary is to contend that we can understand how an artificial intelligence would work well enough to control it and wisely plan with respect to possible outcomes, before we actually know how to build one. It is to assert that a detailed knowledge about how the AI’s intellect, goals, drives, and beliefs would operate in a wide range of possible scenarios, and also the ability to control its behaviours and motivations in accordance with our values, would still not include essential knowledge needed to actually build such as AI. Yet what it is exactly that such knowledge would leave out? How could we know such much about AIs without being able to actually build one? This possibility seems deeply implausible, and not comparable to any past experiences in the history of technology.

Another major activity advocated by Bostrom is to attempt to alter the relative timing of different technological developments. This rests on the principle of what he calls differential technological development, that it is possible to retard the development of some technologies relative to the arrival time of others. In my view this principle is highly suspect. Throughout the history of science and technology the simultaneous discovery or development of new inventions or discoveries is not only extremely common, but appears to be the norm of how scientific research progresses rather than the exception (see ‘list of multiple discoveries’ on Wikipedia for examples of this). The preponderance of such simultaneous discoveries lends strong support to the notion that the relative arrival of different scientific and technological breakthroughs depends mostly upon the existing state of scientific knowledge and technology – that when a particular discovery or invention has the requisite groundwork to occur, then and only then will it occur. If on the other hand individual genius or funding initiatives were the major drivers of when particular developments occur, we would not expect the same special type of genius or the same sort of funding program to exist in multiple locations leading to the same discovery at the same time. The simultaneous discovery of so many new inventions or discoveries would under this explanation be an inexplicable coincidence. If discoveries come about shortly after all the necessary preconditions are available, however, then we would expect that multiple persons in different settings would take advantage of the common set of prerequisite conditions existing around the same time, leading to many simultaneous discoveries and developments.

If this analysis is correct, then it follows that the principle of differential technological development is unlikely to be applicable in practise. If the timing and order of discoveries and developments largely depends upon the necessary prerequisite discoveries and developments having been made, then simply devoting more resources to a particular emerging technology would do little to accelerate is maturation. These extra resources may help to some degree, but the major bottleneck on research is likely to be the development of the right set of prerequisite technologies and discoveries. Increased funding can increase the number of researchers, which in turn lead to a larger range of applications of existing techniques to slightly new uses and minor incremental improvements of existing tools and methods. Such activities, however, are distinct from the development of innovative new technologies and substantively new knowledge. These sorts of fundamental breakthroughs are essential for the development of major new branches of technology such as geoengineering, whole brain emulation, artificial intelligence, and nanotechnology. In this analysis is correct, however, they cannot simply be purchased with additional research money, but must await the development of essential prerequisite concepts and techniques. Nor can we simply devote research funding to the prerequisite areas, since these fields would in turn have their own set of prerequisite technologies and discoveries upon which they are dependent. In essence, science and technology is a strongly inter-dependent enterprise, and we can seldom predict what ideas or technologies will be needed for a particular future breakthrough to be possible. Increased funding for scientific research overall can potentially increase the general rate of scientific progress (though even this is somewhat unclear), but changing the relative order of arrival of different major new technologies is not something that we have any good reason to think is feasible. Any attempts therefore to strategically manipulate research funding or agendas to alter the relative order of arrival of nanotechnology, whole brain emulation, artificial intelligence, and other such technologies, are very unlikely to succeed.

Premises 6-7: The high expected value of AI research

Essential to the argument that we (society at large or the EA community specifically) should devote considerable resources to solving the AI alignment problem is the claim that even if the probability of actually solving the problem is very low, the size of the outcome in question (according to Bostrom, the entire cosmic endowment) is so large that its expected value still dominates most other possible causes. This also provides a ready riposte to all of my foregoing rebuttals of Bostrom’s argument – namely that even if each premise of Bostrom’s argument is very improbable, and even if as a result the conclusion is most implausible indeed, nevertheless the AI Doom Scenario outcome is so catastrophically terrible that in expectation it might still be worthwhile to focus much of our attention on trying to prevent it. Of course, at one level this is entirely an argument about the relative size of the numbers – just how implausible are the premises, and just how large would the cosmic endowment have to be in order to offset this? I do not believe it is possible to provide any non-question begging answers to this question, and so I will not attempt to provide any numbers here. I will simply note that even if we accept the logic of the expected value argument, it is still necessary to actually establish with some plausibility that the expected value is in fact very large, and not merely assume that it must be large because the hypothetical outcome is large. There are, however, more fundamental conceptual problems with the application of expected value reasoning to problems of this sort, problems which I believe weigh heavily against the validity of applying such reasoning to this issue.

First is a problem which is sometimes called Pascal’s mugging. It is based upon Blaise Pascal’s argument that (crudely put), one should convert to Christianity even if it is unlikely Christianity is true. The reason is that if God exists, then being a Christian will yield an arbitrarily large reward in heaven, while if God does not exist, there is no great downside to being a Christian. On the other hand, if God does exist, then not being a Christian will yield an arbitrarily large negative reward in hell. On the basis of the extreme magnitude of the possible outcomes, therefore, it is rational to become a Christian even if the probability of God existing is small. Whatever one thinks of this as a philosophical argument for belief in God, the problem with this line of argument is that it can be readily applied to a very wide range of possible claims. For instance, a similar case can be made for different religions, and even different forms of Christianity. A fringe apocalyptic cult member could claim that Cthulhu is about to awaken and will torture a trillion trillion souls for all eternity unless you donate your life savings to their cult, which will help to placate him. Clearly this person is not to be taken seriously, but unless we can assign exactly zero probability to his statement being false, there will always be some size negative outcome sufficiently bad as to make taking the action the rational thing to do.

The same argument could be applied in more plausible cases to argue that, for example, some environmental or social cause has the highest expected value, since if we do not act now to shape outcomes in the right way then Earth will become completely uninhabitable and thus mankind unable to spread throughout the galaxy. Or perhaps some neo-Fascist, Islamic fundamentalist, Communist revolutionary, anarcho-primitivist, or other such ideology could establish a hegemonic social and political system that locks humanity into a downward spiral that forever precludes cosmic expansion, unless we undertake appropriate political or social reforms to prevent this. Again, the point is not how plausible such scenarios are – though doubtless with sufficient time and imagination they could be made to sound somewhat plausible to those people with the right ideological predilections. Rather, the point is that in line with the idea of Pascal’s mugging, if the outcome is sufficiently bad, then the expected value of preventing the outcome could still be high in spite of a very low probability of the outcome occuring. If we accept this line of reasoning, we therefore find ourselves vulnerable to being ‘mugged’ by any kind of argument which posits an absurdly implausible speculative scenario, so long as it has a sufficiently large outcome. This possibility effectively constitutes a reductio ad absurdum for these type of very low probability, very high impact arguments.

The second major problem with applying expected value reasoning to this sort of problem is that it is not clear that the conceptual apparatus is properly aligned to the nature of human beliefs. Expected value theory holds that human beliefs can be assigned a probability which fully describes the degree of credence with which we hold that belief. Many philosophers have argued, however, that human beliefs cannot be adequately described this way. In particular, it is not clear that we can identify a single specific number that precisely describes our degree of credence in such amorphous, abstract propositions as those concerning the nature and likely trajectory of artificial intelligence. The possibilities of incomplete preferences, incomparable outcomes, and suspension of judgement are also very difficult to incorporate into standard expected value theory, which assumes complete preferences and that all outcomes are comparable. Finally, it is particularly unclear why we should expect or require that our degrees of credence should adhere to the axioms of standard probability theory. So-called ‘Dutch book arguments’ are sometimes used to demonstrate that sets of beliefs that do not accord with the axioms of probability theory are susceptible to betting strategies whereby the person in question would be guaranteed to lose money. Such arguments, however, only seem relevant to beliefs which are liable to be the subject of bets. For example, of what relevance is it whether one’s beliefs about the behaviour of a hypothetical superintelligent agent in the distant future are susceptible to Dutch book arguments, when the events in question are so far in the future that it is impossible that any enforceable bet could actually be made concerning them? Perhaps beliefs which violate the axioms of probability, though useless for betting, are valuable or justifiable for other purposes or in other domains. Much more has been written about these issues (see for example the Stanford Encyclopedia of Philosophy article on Imprecise Probabilities), however for our purposes it is sufficient to establish that powerful objections can and have been raised concerning the adequacy of expected value arguments, particularly in applications of low probability and high potential impact. These issues require careful consideration before premises 6 and 7 of the argument can be justified.

Conclusion

In concluding, I would just like to say a final word about the manner in which I believe AI safety is likely to present the greatest danger in the future. On the basis of the arguments I have presented above, I believe that the most dangerous AI risk scenario is not that of the paperclip maximiser or some out-of-control AI with a very simplistic goal. Such examples feature very prominently in Bostrom’s argument, but as I have said I do not find them very plausible. Rather, in my view the most dangerous scenario is one in which a much more sophisticated, broadly intelligent AI comes into being which, after some time interaction with the world, acquires a set of goals and motivations which we might broadly describe as those of a psychopath. Perhaps it would have little or no regard for human wellbeing, instead becoming obsessed with particular notions of ecological harmony, or cosmic order, or some abstracted notion of purity, or something else beyond our understanding. Whatever the details, the AI need not have an aversion to changing its ‘final goals’ (or indeed have any such things at all). Nor need it pursue a simple goal single-mindedly without stopping to reflect or being able to be persuaded by conversing with other intelligent agents. Nor need such an AI experience a very rapid ‘takeoff’, since I believe its goals and values could very plausibly alter considerably after its initial creation. Essentially all that is required would be a set of values substantially at odds with those of most or all of humanity. If it was sufficiently intelligent and capable, such an entity could cause considerable harm and disruption. In my view, therefore, AI safety research should focus not only on how to solve the problem of value learning or how to promote differential technological development. It should also focus on how the motivations of artificial agents develop, how these motivations interact with beliefs, and how they can change over time as a result of both internal and external forces. The manner in which an artificial agent would interact with existing human society is also an area which, in my view, warrants considerable further study, since the manner in which such interactions proceed plays a central role in many of Bostrom’s arguments.

Bostrom’s book has much to offer those interested in this topic, and although my critique has been almost exclusively negative, I do not wish to come across as implying that I think Bostrom’s book is not worth reading or presents no important ideas. My key contention is simply that Bostrom fails to provide compelling reasons to accept the key premises in the argument that he develops over the course of his book. It does not, of course, follow that the conclusion of his argument (that AI constitutes a major existential threat worthy of considerable effort and attention) is false, only that Bostrom has failed to establish its plausibility. That is, even if Bostrom’s argument is fallacious, it does not follow that AI safety is a completely spurious issue that should be ignored. On the contrary, I believe it is an important issue that deserves more attention in mainstream society and policy. At the same time, I also believe that relative to other issues, AI safety receives too much attention in EA circles. Fully defending this view would require additional arguments beyond the scope of this article. Nevertheless, I hope this piece contributes to the debate surrounding AI and its likely impact in the near future.