Чөлөөт нэвтэрхий толь — Википедиагаас
Харайх: Удирдах, Хайлт

Bulagaa (talk) 13:03, 27 Нэгдүгээр сар 2015 (UTC)1. Introduction By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it. Of course this problem is not limited to the field of AI. Jacques Monod wrote: “A curious aspect of the theory of evolution is that everybody thinks he understands it” (Monod 1975). My father, a physicist, complained about people making up their own theories of physics; he wanted to know why people did not make up their own theories of chemistry. (Answer: They do.) Nonetheless the problem seems to be unusually acute in Artificial Intelligence. The field of AI has a reputation for making huge promises and then failing to deliver on them. Most observers conclude that AI is hard; as indeed it is. But the embarrassment does not stem from the difficulty. It is difficult to build a star from hydrogen, but the field of stellar astronomy does not have a terrible reputation for promising to build stars and then failing. The critical inference is not that AI is hard, but that, for some reason, it is very easy for people to think they know far more about Artificial Intelligence than they actually do. In my other chapter for Global Catastrophic Risks, “Cognitive Biases Potentially Affecting Judgment of Global Risks” (Yudkowsky 2008), I opened by remarking that few people would deliberately choose to destroy the world; a scenario in which the Earth is destroyed by mistake is therefore very worrisome. Few people would push a button that they clearly knew would cause a global catastrophe. But if people are liable to confidently believe that the button does something quite different from its actual consequence, that is cause indeed for alarm. It is far more difficult to write about global risks of Artificial Intelligence than about cognitive biases. Cognitive biases are settled science; one need simply quote the literature. Artificial Intelligence is not settled science; it belongs to the frontier, not to the textbook. And, for reasons discussed in a later section, on the topic of global catastrophic risks of Artificial Intelligence, there is virtually no discussion in the existing technical literature. I have perforce analyzed the matter from my own perspective; given my own conclusions and done my best to support them in limited space. It is not that I have neglected to cite the existing major works on this topic, but that, to the best of my ability to discern, there are no existing major works to cite (as of January 2006). It may be tempting to ignore Artificial Intelligence because, of all the global risks discussed in this book, AI is hardest to discuss. We cannot consult actuarial statistics to assign small annual probabilities of catastrophe, as with asteroid strikes. We cannot use calculations from a precise, precisely confirmed model to rule out events or place infinitesimal upper bounds on their probability, as with proposed physics disasters. But this makes AI catastrophes more worrisome, not less. The effect of many cognitive biases has been found to increase with time pressure, cognitive busyness, or sparse information. Which is to say that the more difficult the analytic challenge, the more important it is to avoid or reduce bias. Therefore I strongly recommend reading “Cognitive Biases Potentially Affecting Judgment of Global Risks” before continuing with this paper. 2. Anthropomorphic Bias When something is universal enough in our everyday lives, we take it for granted to the point of forgetting it exists. Imagine a complex biological adaptation with ten necessary parts. If each of ten genes are independently at 50% frequency in the gene pool—each gene possessed by only half the organisms in that species—then, on average, only 1 in 1024 organisms will possess the full, functioning adaptation. A fur coat is not a significant evolutionary advantage unless the environment reliably challenges organisms with cold. Similarly, if gene B depends on gene A, then gene B has no significant advantage unless gene A forms a reliable part of the genetic environment. Complex, interdependent machinery is necessarily universal within a sexually reproducing species; it cannot evolve otherwise (Tooby and Cosmides 1992). One robin may have smoother feathers than another, but they will both have wings. Natural selection, while feeding on variation, uses it up (Sober 1984). In every known culture, humans experience joy, sadness, disgust, anger, fear, and surprise (Brown 1991), and indicate these emotions using the same facial expressions (Ekman and Keltner 1997). We all run the same engine under our hoods, though we may be painted different colors; a principle which evolutionary psychologists call the psychic unity of humankind (Tooby and Cosmides 1992). This observation is both explained and required by the mechanics of evolutionary biology. An anthropologist will not excitedly report of a newly discovered tribe: “They eat food! They breathe air! They use tools! They tell each other stories!” We humans forget how alike we are, living in a world that only reminds us of our differences. Humans evolved to model other humans—to compete against and cooperate with our own conspecifics. It was a reliable property of the ancestral environment that every powerful intelligence you met would be a fellow human. We evolved to understand our fellow humans empathically, by placing ourselves in their shoes; for that which needed to be modeled was similar to the modeler. Not surprisingly, human beings often “anthropomorphize”— expect humanlike properties of that which is not human. In The Matrix (Wachowski and Wachowski 1999), the supposed “artificial intelligence” Agent Smith initially appears utterly cool and collected, his face passive and unemotional. But later, while interrogating the human Morpheus, Agent Smith gives vent to his disgust with humanity—and his face shows the human-universal facial expression for disgust. Querying your own human brain works fine, as an adaptive instinct, if you need to predict other humans. If you deal with any other kind of optimization process—if, for example, you are the eighteenth-century theologian William Paley, looking at the complex order of life and wondering how it came to be—then anthropomorphism is flypaper for unwary scientists, a trap so sticky that it takes a Darwin to escape. Experiments on anthropomorphism show that subjects anthropomorphize unconsciously, often flying in the face of their deliberate beliefs. In a study by Barrett and Keil (1996), subjects strongly professed belief in non-anthropomorphic properties of God: that God could be in more than one place at a time, or pay attention to multiple events simultaneously. Barrett and Keil presented the same subjects with stories in which, for example, God saves people from drowning. The subjects answered questions about the stories, or retold the stories in their own words, in such ways as to suggest that God was in only one place at a time and performed tasks sequentially rather than in parallel. Serendipitously for our purposes, Barrett and Keil also tested an additional group using otherwise identical stories about a superintelligent computer named “Uncomp.” For example, to simulate the property of omnipresence, subjects were told that Uncomp’s sensors and effectors“cover every square centimeter of the earth and so no information escapes processing.” Subjects in this condition also exhibited strong anthropomorphism, though significantly less than the God group. From our perspective, the key result is that even when people consciously believe an AI is unlike a human, they still visualize scenarios as if the AI were anthropomorphic (but not quite as anthropomorphic as God). Anthropomorphic bias can be classed as insidious: it takes place with no deliberate intent, without conscious realization, and in the face of apparent knowledge. Back in the era of pulp science fiction, magazine covers occasionally depicted a sentient monstrous alien—colloquially known as a bug-eyed monster or BEM—carrying off an attractive human female in a torn dress. It would seem the artist believed that a non-humanoid alien, with a wholly different evolutionary history, would sexually desire human females. People don’t make mistakes like that by explicitly reasoning: “All minds are likely to be wired pretty much the same way, so presumably a BEM will find human females sexually attractive.” Probably the artist did not ask whether a giant bug perceives human females as attractive. Rather, a human female in a torn dress is sexy—inherently so, as an intrinsic property. They who made this mistake did not think about the insectoid’s mind; they focused on the woman’s torn dress. If the dress were not torn, the woman would be less sexy; the BEM doesn’t enter into it.1 People need not realize they are anthropomorphizing (or even realize they are engaging in a questionable act of predicting other minds) in order for anthropomorphism to supervene on cognition. When we try to reason about other minds, each step in the reasoning process may be contaminated by assumptions so ordinary in human experience that we take no more notice of them than air or gravity. You object to the magazine illustrator: “Isn’t it more likely that a giant male bug would sexually desire giant female bugs?” The illustrator thinks for a moment and then says to you: “Well, even if an insectoid alien starts out liking hard exoskeletons, after the insectoid encounters human females it will soon realize that human females have much nicer, softer skins. If the aliens have sufficiently advanced technology, they’ll genetically engineer themselves to like soft skins instead of hard exoskeletons.” This is a fallacy-at-one-remove. After the alien’s anthropomorphic thinking is pointed out, the magazine illustrator takes a step back and tries to justify the alien’s conclusion as a neutral product of the alien’s reasoning process. Perhaps advanced aliens could reengineer themselves (genetically or otherwise) to like soft skins, but would they want to? An insectoid alien who likes hard skeletons will not wish to change itself to like soft skins instead—not unless natural selection has somehow produced in it a distinctly human sense of meta-sexiness. When using long, complex chains of reasoning to argue in favor of an anthropomorphic conclusion, each and every step of the reasoning is another opportunity to sneak in the error. And it is also a serious error to begin from the conclusion and search for a neutralseeming line of reasoning leading there; this is rationalization. If it is self-brain-query which produced that first fleeting mental image of an insectoid chasing a human female, then anthropomorphism is the underlying cause of that belief, and no amount of rationalization will change that. Anyone seeking to reduce anthropomorphic bias in themselves would be well-advised to study evolutionary biology for practice, preferably evolutionary biology with math. Early biologists often anthropomorphized natural selection—they believed that evolution would do the same thing they would do; they tried to predict the effects of evolution by putting themselves “in evolution’s shoes.” The result was a great deal of nonsense, 1. This is a case of a deep, confusing, and extraordinarily common mistake which E. T. Jaynes named the “mind projection fallacy” (Jaynes 2003). Jaynes, a theorist of Bayesian probability, coined “mind projection fallacy” to refer to the error of confusing states of knowledge with properties of objects. For example, the phrase “mysterious phenomenon” implies that mysteriousness is a property of the phenomenon itself. If I am ignorant about a phenomenon, then this is a fact about my state of mind, not a fact about the phenomenon. which first began to be systematically exterminated from biology in the late 1960s, e.g. by Williams (1966). Evolutionary biology offers both mathematics and case studies to help hammer out anthropomorphic bias. 2.1. The Width of Mind Design Space Evolution strongly conserves some structures. Once other genes evolve which depend on a previously existing gene, that early gene is set in concrete; it cannot mutate without breaking multiple adaptations. Homeotic genes—genes controlling the development of the body plan in embryos—tell many other genes when to activate. Mutating a homeotic gene can result in a fruit fly embryo that develops normally except for not having a head. As a result, homeotic genes are so strongly conserved that many of them are the same in humans and fruit flies—they have not changed since the last common ancestor of humans and bugs. The molecular machinery of ATP synthase is essentially the same in animal mitochondria, plant chloroplasts, and bacteria; ATP synthase has not changed significantly since the rise of eukaryotic life two billion years ago. Any two AI designs might be less similar to one another than you are to a petunia. The term “Artificial Intelligence” refers to a vastly greater space of possibilities than does the term “Homo sapiens.” When we talk about “AIs” we are really talking about minds-in-general, or optimization processes in general. Imagine a map of mind design space. In one corner, a tiny little circle contains all humans; within a larger tiny circle containing all biological life; and all the rest of the huge map is the space of minds-ingeneral. The entire map floats in a still vaster space, the space of optimization processes. Natural selection creates complex functional machinery without mindfulness; evolution lies inside the space of optimization processes but outside the circle of minds. It is this enormous space of possibilities which outlaws anthropomorphism as legitimate reasoning.

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