Global Solutions vs. Local Solutions for the AI Safety Problem

There are two types of artificial general intelligence (AGI) safety solutions: global and local. Most previously suggested solutions are local: they explain how to align or “box” a specific AI (Artificial Intelligence), but do not explain how to prevent the creation of dangerous AI in other places. Global solutions are those that ensure any AI on Earth is not dangerous. The number of suggested global solutions is much smaller than the number of proposed local solutions. Global solutions can be divided into four groups: 1. No AI: AGI technology is banned or its use is otherwise prevented; 2. One AI: the first superintelligent AI is used to prevent the creation of any others; 3. Net of AIs as AI police: a balance is created between many AIs, so they evolve as a net and can prevent any rogue AI from taking over the world; 4. Humans inside AI: humans are augmented or part of AI. We explore many ideas, both old and new, regarding global solutions for AI safety. They include changing the number of AI teams, different forms of “AI Nanny” (non-self-improving global control AI system able to prevent creation of dangerous AIs), selling AI safety solutions, and sending messages to future AI. Not every local solution scales to a global solution or does it ethically and safely. The choice of the best local solution should include understanding of the ways in which it will be scaled up. Human-AI teams or a superintelligent AI Service as suggested by Drexler may be examples of such ethically scalable local solutions, but the final choice depends on some unknown variables such as the speed of AI progres

Aquatic refuges for surviving a global catastrophe

Recently many methods for reducing the risk of human extinction have been suggested, including building refuges underground and in space. Here we will discuss the perspective of using military nuclear submarines or their derivatives to ensure the survival of a small portion of humanity who will be able to rebuild human civilization after a large catastrophe. We will show that it is a very cost-effective way to build refuges, and viable solutions exist for various budgets and timeframes. Nuclear submarines are surface independent, and could provide energy, oxygen, fresh water and perhaps even food for their inhabitants for years. They are able to withstand close nuclear explosions and radiation. They are able to maintain isolation from biological attacks and most known weapons. They already exist and need only small adaptation to be used as refuges. But building refuges is only “Plan B” of existential risk preparation; it is better to eliminate such risks than try to survive them

Classification of Approaches to Technological Resurrection

Abstract. Death seems to be a permanent event, but there is no actual proof of its irreversibility. Here we list all known ways to resurrect the dead that do not contradict our current scientific understanding of the world. While no method is currently possible, many of those listed here may become feasible with future technological development, and it may even be possible to act now to increase their probability. The most well-known such approach to technological resurrection is cryonics. Another method is indirect mind uploading, or digital immortality, namely the preservation of data about a person to allow for future reconstruction by powerful AI. More speculative ways to immortality include combinations of future superintelligence on a galactic scale, which could use simulation to resurrect all possible people, and new physical laws, which may include time-travel or obtaining information from the past. Acausal trade with parallel worlds could help combine random resurrection and reconstruction based on known data without loss of share of worlds where I exist (known as existence measure). Quantum immortality could help to increase the probability of success for cryonics and digital immortality. There many possible approaches to technological resurrection and thus if large-scale future technological development occurs, some form of resurrection is inevitable

Catching Treacherous Turn: A Model of the Multilevel AI Boxing

With the fast pace of AI development, the problem of preventing its global catastrophic risks arises. However, no satisfactory solution has been found. From several possibilities, the confinement of AI in a box is considered as a low-quality possible solution for AI safety. However, some treacherous AIs can be stopped by effective confinement if it is used as an additional measure. Here, we proposed an idealized model of the best possible confinement by aggregating all known ideas in the field of AI boxing. We model the confinement based on the principles used in the safety engineering of nuclear power plants. We show that AI confinement should be implemented in several levels of defense. These levels include 1) AI design in fail-safe manner 2) limiting its capabilities, preventing self-improving and circuit breakers on treacherous turn 3) isolation from the outside world and, lastly, as the final hope 4) outside measures oriented on stopping AI in the wild. We demonstrate that the substantial number (more than 50 ideas listed in the article) of mutually independent measures could provide a relatively high probability of the containment of a human-level AI but may be not sufficient to preserve runaway of superintelligent AI. Thus, these measures will work only if they are used to prevent superintelligent AI creation, but not for containing superintelligence. We suggest that there could be a safe operation threshold, on which AI is useful, but is not able to hack containment system from the inside, the same way as a safe level of chain reaction inside nuclear power plants is maintained. However, overall, a failure of the confinement is inevitable, so we need to use the full AGI limited number of times (AI-ticks)

No Theory for Old Man. Evolution led to an Equal Contribution of Various Aging Mechanisms

Does a single mechanism of aging exit? Most scientists have their own pet theories about what is aging, but the lack of generally accepted theory is mind-blowing. Here we suggest an explanation: evolution works against unitary mechanism of aging because it equalizes ‘warranty period’ of different resilience systems. Therefore, we need life-extension methods that go beyond fighting specific aging mechanisms: such as using a combination of geroprotectors or repair-fixing bionanorobots controlled by AI

Presumptuous Philosopher Proves Panspermia

Abstract. The presumptuous philosopher (PP) thought experiment lends more credence to the hypothesis which postulates the existence of a larger number of observers than other hypothesis. The PP was suggested as a purely speculative endeavor. However, there is a class of real world observer-selection effects where it could be applied, and one of them is the possibility of interstellar panspermia (IP). There are two types of anthropic reasoning: SIA and SSA. SIA implies that my existence is an argument that larger total number of observers exists; SSA implies that I should find myself in a region with larger number of observers. However, as S. Armstrong showed, SIA can’t distinguish between different ways how larger number of observers appeared, so it can’t favor IP compared with other ways to get many observers. SSA application here is less controversial as it tells only about relation between regions size: e.g. I am more likely to live in a larger country than in a small one, conditioning that the total number of small countries is also small. Anthropic considerations suggest that the universes in the multiverse with interstellar panspermia will have orders of magnitude more civilizations than universes without IP, and thus we are likely to be in such a universe. This is a strong counterargument against a variant of the Rare Earth hypothesis based on the difficulty of abiogenesis: even if abiogenesis is difficult, IP will disseminate life over billions of planets, meaning we are likely to find ourselves in a universe where IP has happened. This implies that there should be many planets with life in our galaxy, and renders the Fermi paradox even sharper. Either the Great Filter is ahead of us and there are high risks of anthropogenic extinction, or there are many alien civilizations nearby and of the same age as ours—which is itself a global catastrophic risk, as in that case the wave of alien colonization could arrive between 25-500 ky from now

Long-Term Trajectories of Human Civilization

Purpose This paper aims to formalize long-term trajectories of human civilization as a scientific and ethical field of study. The long-term trajectory of human civilization can be defined as the path that human civilization takes during the entire future time period in which human civilization could continue to exist. Design/methodology/approach This paper focuses on four types of trajectories: status quo trajectories, in which human civilization persists in a state broadly similar to its current state into the distant future; catastrophe trajectories, in which one or more events cause significant harm to human civilization; technological transformation trajectories, in which radical technological breakthroughs put human civilization on a fundamentally different course; and astronomical trajectories, in which human civilization expands beyond its home planet and into the accessible portions of the cosmos. Findings Status quo trajectories appear unlikely to persist into the distant future, especially in light of long-term astronomical processes. Several catastrophe, technological transformation and astronomical trajectories appear possible. Originality/value Some current actions may be able to affect the long-term trajectory. Whether these actions should be pursued depends on a mix of empirical and ethical factors. For some ethical frameworks, these actions may be especially important to pursue

Approaches to the Prevention of Global Catastrophic Risks

Many global catastrophic and existential risks (X-risks) threaten the existence of humankind. There are also many ideas for their prevention, but the meta-problem is that these ideas are not structured. This lack of structure means it is not easy to choose the right plan(s) or to implement them in the correct order. I suggest using a “Plan A, Plan B” model, which has shown its effectiveness in planning actions in unpredictable environments. In this approach, Plan B is a backup option, implemented if Plan A fails. In the case of global risks, Plan A is intended to prevent a catastrophe and Plan B to survive it, if it is not avoided. Each plan has similar stages: analysis, planning, funding, low-level realization and high-level realization. Two variables—plans and stages—provide an effective basis for classification of all possible X-risks prevention methods in the form of a two-dimensional map, allowing the choice of optimal paths for human survival. I have created a framework for estimating the utility of various prevention methods based on their probability of success, the chances that they will be realized in time, their opportunity cost, and their risk. I also distinguish between top-down and bottom-up approaches

Assessing the future plausibility of catastrophically dangerous AI

In AI safety research, the median timing of AGI creation is often taken as a reference point, which various polls predict will happen in second half of the 21 century, but for maximum safety, we should determine the earliest possible time of dangerous AI arrival and define a minimum acceptable level of AI risk. Such dangerous AI could be either narrow AI facilitating research into potentially dangerous technology like biotech, or AGI, capable of acting completely independently in the real world or an AI capable of starting unlimited self-improvement. In this article, I present arguments that place the earliest timing of dangerous AI in the coming 10–20 years, using several partly independent sources of information: 1. Polls, which show around a 10 percent of the probability of an early creation of artificial general intelligence in the next 10-15 years. 2. The fact that artificial neural network (ANN) performance and other characteristics, like number of “neurons”, are doubling every year, and extrapolating this tendency suggests that roughly human-level performance will be reached in less than a decade. 3. The acceleration of the hardware performance available for AI research, which outperforms Moore’s law thanks to advances in specialized AI hardware, better integration of such hardware in larger computers, cloud computing and larger budgets. 4. Hyperbolic growth extrapolations of big history models