Nick Bostrom

There is, in some quarters, concern about high–level machine intelligence and superintelligent AI coming up in a few decades, bringing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development, and how fast they see these developing. We thus designed a brief questionnaire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high-level machine intelligence will be developed around 2040–2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity.

The minds of biological creatures occupy a small corner of a much larger space of possible minds that could be created once we master the technology of artificial intelligence. Yet many of our moral intuitions and practices are based on assumptions about human nature that need not hold for digital minds. This points to the need for moral reflection as we approach the era of advanced machine intelligence. This chapter focuses on one set of issues, which arise from the prospect of digital minds with superhumanly strong claims to resources and influence. These could arise from the vast collective benefits that mass-produced digital minds could derive from relatively small amounts of resources. Alternatively, they could arise from individual digital minds with superhuman moral status or ability to benefit from resources. Such beings could contribute immense value to the world, and failing to respect their interests could produce a moral catastrophe, while a naive way of respecting them could be disastrous for humanity. A sensible approach requires reforms of our moral norms and institutions along with advance planning regarding what kinds of digital minds we bring into existence.

This chapter considers the speculative prospect of superintelligent AI and its normative implications for governance and global policy. Machine superintelligence would be a transformative development that would present a host of political challenges and opportunities. This chapter identifies a set of distinctive features of this hypothetical policy context, from which it derives a correlative set of policy desiderata (efficiency, allocation, population, and process)—considerations that should be given extra weight in long-term AI policy compared to in other policy contexts. This chapter describes a desiderata “vector field” showing the directional change from a variety of possible normative baselines or policy positions. The focus on directional normative change should make the findings in this chapter relevant to a wide range of actors, although the development of concrete policy options that meet these abstractly formulated desiderata will require further work.

_Anthropic Bias_ explores how to reason when you suspect that your evidence is biased by "observation selection effects"--that is, evidence that has been filtered by the precondition that there be some suitably positioned observer to "have" the evidence. This conundrum--sometimes alluded to as "the anthropic principle," "self-locating belief," or "indexical information"--turns out to be a surprisingly perplexing and intellectually stimulating challenge, one abounding with important implications for many areas in science and philosophy. There are the philosophical thought experiments and paradoxes: the Doomsday Argument; Sleeping Beauty; the Presumptuous Philosopher; Adam & Eve; the Absent-Minded Driver; the Shooting Room. And there are the applications in contemporary science: cosmology ("How many universes are there?", "Why does the universe appear fine-tuned for life?"); evolutionary theory ("How improbable was the evolution of intelligent life on our planet?"); the problem of time's arrow ("Can it be given a thermodynamic explanation?"); quantum physics ("How can the many-worlds theory be tested?"); game-theory problems with imperfect recall ("How to model them?"); even traffic analysis ("Why is the 'next lane' faster?"). _Anthropic Bias_ argues that the same principles are at work across all these domains. And it offers a synthesis: a mathematically explicit theory of observation selection effects that attempts to meet scientific needs while steering clear of philosophical paradox.

There is no strong reason to believe that human-level intelligence represents an upper limit of the capacity of artificial intelligence, should it be realized. This poses serious safety issues, since a superintelligent system would have great power to direct the future according to its possibly flawed motivation system. Solving this issue in general has proven to be considerably harder than expected. This paper looks at one particular approach, Oracle AI. An Oracle AI is an AI that does not act in the world except by answering questions. Even this narrow approach presents considerable challenges. In this paper, we analyse and critique various methods of controlling the AI. In general an Oracle AI might be safer than unrestricted AI, but still remains potentially dangerous.

We describe a significant practical consequence of taking anthropic biases into account in deriving predictions for rare stochastic catastrophic events. The risks associated with catastrophes such as asteroidal/cometary impacts, supervolcanic episodes, and explosions of supernovae/gamma-ray bursts are based on their observed frequencies. As a result, the frequencies of catastrophes that destroy or are otherwise incompatible with the existence of observers are systematically underestimated. We describe the consequences of the anthropic bias for estimation of catastrophic risks, and suggest some directions for future work.

Human beings are a marvel of evolved complexity. When we try to enhance poorly-understood complex evolved systems, our interventions often fail or backfire. It can appear as if there is a “wisdom of nature” which we ignore at our peril. A recognition of this reality can manifest as a vaguely normative intuition, to the effect that it is “hubristic” to try to improve on nature, or that biomedical therapy is ok while enhancement is morally suspect. We suggest that one root of these moral intuitions may be fundamentally prudential rather than ethical. More importantly, we develop a practical heuristic, the “evolutionary optimality challenge”, for evaluating the plausibility that specific candidate biomedical interventions would be safe and effective. This heuristic recognizes the grain of truth contained in “nature knows best” attitudes while providing criteria for identifying the special cases where it may be feasible, with present or near-future technology, to enhance human nature.

Human beings are a marvel of evolved complexity. Such systems can be difficult to enhance. When we manipulate complex evolved systems, which are poorly understood, our interventions often fail or backfire. It can appear as if there is a “wisdom of nature” which we ignore at our peril. Sometimes the belief in nature’s wisdom—and corresponding doubts about the prudence of tampering with nature, especially human nature—manifests as diffusely moral objections against enhancement. Such objections may be expressed as intuitions about the superiority of the natural or the troublesomeness of hubris or as an evaluative bias in favor of the status quo. This chapter explores the extent to which such prudence-derived anti-enhancement sentiments are justified. We develop a heuristic, inspired by the field of evolutionary medicine, for identifying promising human enhancement interventions. The heuristic incorporates the grains of truth contained in “nature knows best” attitudes while providing criteria for the special cases where we have reason to believe that it is feasible for us to improve on nature.

In some situations a number of agents each have the ability to undertake an initiative that would have significant effects on the others. Suppose that each of these agents is purely motivated by an altruistic concern for the common good. We show that if each agent acts on her own personal judgment as to whether the initiative should be undertaken, then the initiative will be undertaken more often than is optimal. We suggest that this phenomenon, which we call the unilateralist’s curse, arises in many contexts, including some that are important for public policy. To lift the curse, we propose a principle of conformity, which would discourage unilateralist action. We consider three different models for how this principle could be implemented, and respond to an objection that could be raised against it.

This chapter develops the issue of external world skepticism in a stunning new direction, suggesting that virtual reality science fiction thought experiments depict science fact. The author has authored an influential argument that we are, in fact, in a computer simulation. He observes that assuming that a civilization survives long enough to be technologically sophisticated, it would likely be very interested in running simulations of entire worlds. The core of the simulation argument shows that we should accept as true at least one of the following three propositions: the chances that a species at our current level of development can avoid going extinct before becoming technologically mature is negligibly small; almost no technologically mature civilisations are interested in running computer simulations of minds like ours; and a person is almost certainly in a simulation.

This chapter analyzes the control problem, the unique principal‐agent problem that arises with the creation of an artificial superintelligent agent. It distinguishes two broad classes of potential methods for addressing this problem, capability control and motivation selection, and examines several specific techniques within each class. It also alludes to the esoteric possibility of “anthropic capture”. Capability control methods seek to prevent undesirable outcomes by limiting what the superintelligence can do. This might involve boxing methods or incentive methods, stunting method. In addition, capability control methods might involve tripwires method. Motivation selection can involve explicitly formulating a goal or set of rules to be followed (direct specification) or setting up the system so that it can discover an appropriate set of values for itself by reference to some implicitly or indirectly formulated criterion (indirect normativity).

_Anthropic Bias_ explores how to reason when you suspect that your evidence is biased by "observation selection effects"--that is, evidence that has been filtered by the precondition that there be some suitably positioned observer to "have" the evidence. This conundrum--sometimes alluded to as "the anthropic principle," "self-locating belief," or "indexical information"--turns out to be a surprisingly perplexing and intellectually stimulating challenge, one abounding with important implications for many areas in science and philosophy. There are the philosophical thought experiments and paradoxes: the Doomsday Argument; Sleeping Beauty; the Presumptuous Philosopher; Adam & Eve; the Absent-Minded Driver; the Shooting Room. And there are the applications in contemporary science: cosmology ("How many universes are there?", "Why does the universe appear fine-tuned for life?"); evolutionary theory ("How improbable was the evolution of intelligent life on our planet?"); the problem of time's arrow ("Can it be given a thermodynamic explanation?"); quantum physics ("How can the many-worlds theory be tested?"); game-theory problems with imperfect recall ("How to model them?"); even traffic analysis ("Why is the 'next lane' faster?"). _Anthropic Bias_ argues that the same principles are at work across all these domains. And it offers a synthesis: a mathematically explicit theory of observation selection effects that attempts to meet scientific needs while steering clear of philosophical paradox.

_Anthropic Bias_ explores how to reason when you suspect that your evidence is biased by "observation selection effects"--that is, evidence that has been filtered by the precondition that there be some suitably positioned observer to "have" the evidence. This conundrum--sometimes alluded to as "the anthropic principle," "self-locating belief," or "indexical information"--turns out to be a surprisingly perplexing and intellectually stimulating challenge, one abounding with important implications for many areas in science and philosophy. There are the philosophical thought experiments and paradoxes: the Doomsday Argument; Sleeping Beauty; the Presumptuous Philosopher; Adam & Eve; the Absent-Minded Driver; the Shooting Room. And there are the applications in contemporary science: cosmology ("How many universes are there?", "Why does the universe appear fine-tuned for life?"); evolutionary theory ("How improbable was the evolution of intelligent life on our planet?"); the problem of time's arrow ("Can it be given a thermodynamic explanation?"); quantum physics ("How can the many-worlds theory be tested?"); game-theory problems with imperfect recall ("How to model them?"); even traffic analysis ("Why is the 'next lane' faster?"). _Anthropic Bias_ argues that the same principles are at work across all these domains. And it offers a synthesis: a mathematically explicit theory of observation selection effects that attempts to meet scientific needs while steering clear of philosophical paradox.

In a recent paper in this journal, Ken Olum attempts to refute the doomsday argument by appealing to the self–indication assumption (SIA) that your very existence gives you reason to think that there are many observers. Unlike earlier users of this strategy, Olum tries to counter objections that have been made against (SIA). We argue that his defence of (SIA) is unsuccessful. This does not, however, mean that one has to accept the doomsday argument (or the other counter–intuitive results that flow from related thought–experiments). A developed theory of observation selection effects shows why the doomsday argument is inconclusive, and how one can consistently reject both it and (SIA).

Once upon a time, the planet was tyrannized by a giant dragon. The dragon stood taller than the largest cathedral, and it was covered with thick black scales. Its red eyes glowed with hate, and from its terrible jaws flowed an incessant stream of evil-smelling yellowishgreen slime. It demanded from humankind a blood-curdling tribute: to satisfy its enormous appetite, ten thousand men and women had to be delivered every evening at the onset of dark to the foot of the mountain where the dragontyrant lived. Sometimes the dragon would devour these unfortunate souls upon arrival; sometimes again it would lock them up in the mountain where they would wither away for months or years before eventually being consumed

Several authors have made the argument that because blind evolutionary processes produced human intelligence on Earth, it should be feasible for clever human engineers to create human-level artificial intelligence in the not-too-distant future. This evolutionary argument, however, has ignored the observation selection effect that guarantees that observers will see intelligent life having arisen on their planet no matter how hard it is for intelligent life to evolve on any given Earth-like planet. We explore how the evolutionary argument might be salvaged from this objection, using a variety of considerations from observation selection theory and analysis of specific timing features and instances of convergent evolution in the terrestrial evolutionary record. We find that, depending on the resolution of disputed questions in observation selection theory, the objection can either be wholly or moderately defused, although other challenges for the evolutionary argument remain

[This is the short version of: Müller, Vincent C. and Bostrom, Nick (forthcoming 2016), ‘Future progress in artificial intelligence: A survey of expert opinion’, in Vincent C. Müller (ed.), Fundamental Issues of Artificial Intelligence (Synthese Library 377; Berlin: Springer).] - - - In some quarters, there is intense concern about high–level machine intelligence and superintelligent AI coming up in a few dec- ades, bringing with it significant risks for human- ity; in other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development and how fast they see these developing. We thus designed a brief questionnaire and distributed it to four groups of experts. Overall, the results show an agreement among experts that AI systems will probably reach overall human ability around 2040-2050 and move on to superintelligence in less than 30 years thereafter. The experts say the probability is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity.