This book represents a considerable revision and expansion of Public Choice II. Six new chapters have been added, and several chapters from the previous edition have been extensively revised. The discussion of empirical work in public choice has been greatly expanded. As in the previous editions, all of the major topics of public choice are covered. These include: why the state exists, voting rules, federalism, the theory of clubs, two-party and multiparty electoral systems, rent seeking, bureaucracy, interest groups, dictatorship, the (...) size of government, voter participation, and political business cycles. Normative issues in public choice are also examined including a normative analysis of the simple majority rule, Bergson–Samuelson social welfare functions, the Arrow and Sen impossibility theorems, Rawls's social contract theory and the constitutional political economy of Buchanan and Tullock. (shrink)

Because of accelerating technological progress, humankind may be rapidly approaching a critical phase in its career. In addition to well-known threats such as nuclear holocaust, the propects of radically transforming technologies like nanotech systems and machine intelligence present us with unprecedented opportunities and risks. Our future, and whether we will have a future at all, may well be determined by how we deal with these challenges. In the case of radically transforming technologies, a better understanding of the transition dynamics from (...) a human to a "posthuman" society is needed. Of particular importance is to know where the pitfalls are: the ways in which things could go terminally wrong. While we have had long exposure to various personal, local, and endurable global hazards, this paper analyzes a recently emerging category: that of existential risks. These are threats that could case our extinction or destroy the potential of Earth - originating intelligent life. Some of these threats are relatively well known while others, including some of the gravest, have gone almost unrecognized. Existential risks have a cluster of features that make ordinary risk management ineffective. A final section of this paper discusses several ethical and policy implications. A clearer understanding of the threat picture will enable us to formulate better strategies. (shrink)

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: the discovery of less-is-more effects; the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; an advancement from vague labels to computational models of heuristics; the (...) development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an “adaptive toolbox;” and the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies. (shrink)

I would like to thank the authors of the 26 contributions to this symposium on my article “The Singularity: A Philosophical Analysis”. I learned a great deal from the reading their commentaries. Some of the commentaries engaged my article in detail, while others developed ideas about the singularity in other directions. In this reply I will concentrate mainly on those in the first group, with occasional comments on those in the second. A singularity (or an intelligence explosion) is a rapid (...) increase in intelligence to superintelligence (intelligence of far greater than human levels), as each generation of intelligent systems creates more intelligent systems in turn. The target article argues that we should take the possibility of a singularity seriously, and argues that there will be superintelligent systems within centuries unless certain specific defeating conditions obtain. (shrink)

What if we someday learn how to model small brain units, and so can "upload" ourselves into new computer brains? What if this happens before we learn how to make human-level artificial intelligences? The result could be a sharp transition to an upload-dominated world, with many dramatic consequences. In particular, fast and cheap replication may once again make Darwinian evolution of human values a powerful force in human history. With evolved values, most uploads would value life even when life is (...) hard or short, uploads would reproduce quickly, and wages would fall. But total wealth should rise, so we could all do better by accepting uploads, or at worse taxing them, rather than trying to delay or segregate them. (shrink)

What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to ever-greater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singularity”. The basic argument here was set out by the statistician I.J. Good in his 1965 article “Speculations Concerning the First Ultraintelligent Machine”: Let an ultraintelligent machine be defined as a machine that can far (...) surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion”, and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make. The key idea is that a machine that is more intelligent than humans will be better than humans at designing machines. So it will be capable of designing a machine more intelligent than the most intelligent machine that humans can design. So if it is itself designed by humans, it will be capable of designing a machine more intelligent than itself. By similar reasoning, this next machine will also be capable of designing a machine more intelligent than itself. If every machine in turn does what it is capable of, we should expect a sequence of ever more intelligent machines. This intelligence explosion is sometimes combined with another idea, which we might call the “speed explosion”. The argument for a speed explosion starts from the familiar observation that computer processing speed doubles at regular intervals. Suppose that speed doubles every two years and will do so indefinitely. Now suppose that we have human-level artificial intelligence 1 designing new processors. Then faster processing will lead to faster designers and an ever-faster design cycle, leading to a limit point soon afterwards. The argument for a speed explosion was set out by the artificial intelligence researcher Ray Solomonoff in his 1985 article “The Time Scale of Artificial Intelligence”.1 Eliezer Yudkowsky gives a succinct version of the argument in his 1996 article “Staring at the Singularity”: “Computing speed doubles every two subjective years of work.. (shrink)

1: The End of History 2: The Beyondness of the Singularity 2.1: The Definition of Smartness 2.2: Perceptual Transcends 2.3: Great Big Numbers 2.4: Smarter Than We Are 3: Sooner Than You Think 4: Uploading 5: The Interim Meaning of Life 6: Getting to the Singularity.

Modularity has been the subject of intense debate in the cognitive sciences for more than 2 decades. In some cases, misunderstandings have impeded conceptual progress. Here the authors identify arguments about modularity that either have been abandoned or were never held by proponents of modular views of the mind. The authors review arguments that purport to undermine modularity, with particular attention on cognitive architecture, development, genetics, and evolution. The authors propose that modularity, cleanly defined, provides a useful framework for directing (...) research and resolving debates about individual cognitive systems and the nature of human evolved cognition. Modularity is a fundamental property of living things at every level of organization; it might prove indispensable for understanding the structure of the mind as well. (shrink)

No wonder, then, that I wanted to share this book with my friends--but I also wanted to keep it for myself! If you don't read this book, you'll be left wondering what everyone (else) is talking about.

A simple exogenous growth model gives conservative estimates of the economic implications of machine intelligence. Machines complement human labor when they become more productive at the jobs they perform, but machines also substitute for human labor by taking over human jobs. At first, expensive hardware and software does only the few jobs where computers have the strongest advantage over humans. Eventually, computers do most jobs. At first, complementary effects dominate, and human wages rise with computer productivity. But eventually substitution can (...) dominate, making wages fall as fast as computer prices now do. An intelligence population explosion makes per-intelligence consumption fall this fast, while economic growth rates rise by an order of magnitude or more. These results are robust to automating incrementally, and to distinguishing hardware, software, and human capital from other forms of capital. (shrink)

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. (...) We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines. (shrink)

The purpose of this paper, boldly stated, is to propose a new type of philosophy, a philosophy whose aim is prediction. The pace of technological progress is increasing very rapidly: it looks as if we are witnessing an exponential growth, the growth-rate being proportional to the size already obtained, with scientific knowledge doubling every 10 to 20 years since the second world war, and with computer processor speed doubling every 18 months or so. It is argued that this technological development (...) makes urgent many empirical questions which a philosopher could be well-suited to help answering. I try to cover a broad range of interesting problems and approaches, which means that I won't go at all deeply into any of them; I only try to say enough to show what some of the problems are, how one can begin to work with them, and why philosophy is relevant. My hope is that this will whet your appetite to deal with these questions, or at least increase general awareness that they worthy tasks for first-class intellects, including ones which might belong to philosophers. (shrink)

Computers have far to go to match human strengths, and our estimates will depend on analogy and extrapolation. Fortunately, these are grounded in the first bit of the journey, now behind us. Thirty years of computer vision reveals that 1 MIPS can extract simple features from real-time imagery--tracking a white line or a white spot on a mottled background. 10 MIPS can follow complex gray-scale patches--as smart bombs, cruise missiles and early self-driving vans attest. 100 MIPS can follow moderately unpredictable (...) features like roads--as recent long NAVLAB trips demonstrate. 1,000 MIPS will be adequate for coarse-grained three-dimensional spatial awareness--illustrated by several mid-resolution stereoscopic vision programs, including my own. 10,000 MIPS can find three-dimensional objects in clutter--suggested by several "bin-picking" and high-resolution stereo-vision demonstrations, which accomplish the task in an hour or so at 10 MIPS. The data fades there--research careers are too short, and computer memories too small, for significantly more elaborate experiments. (shrink)

We investigate the problem of how nonnatural entities are represented by examining university students’ concepts of God, both professed theological beliefs and concepts used in comprehension of narratives. In three story processing tasks, subjects often used an anthropomorphic God concept that is inconsistent with stated theological beliefs; and drastically distorted the narratives without any awareness of doing so. By heightening subjects’ awareness of their theological beliefs, we were able to manipulate the degree of anthropomorphization. This tendency to anthropomorphize may be (...) generalizable to other agents. God (and possibly other agents) is unintentionally anthropomorphized in some contexts, perhaps as a means of representing poorly understood.. (shrink)

We're all hypocrites. Why? Hypocrisy is the natural state of the human mind. Robert Kurzban shows us that the key to understanding our behavioral inconsistencies lies in understanding the mind's design. The human mind consists of many specialized units designed by the process of evolution by natural selection. While these modules sometimes work together seamlessly, they don't always, resulting in impossibly contradictory beliefs, vacillations between patience and impulsiveness, violations of our supposed moral principles, and overinflated views of ourselves. This modular, (...) evolutionary psychological view of the mind undermines deeply held intuitions about ourselves, as well as a range of scientific theories that require a "self" with consistent beliefs and preferences. Modularity suggests that there is no "I." Instead, each of us is a contentious "we"--a collection of discrete but interacting systems whose constant conflicts shape our interactions with one another and our experience of the world. In clear language, full of wit and rich in examples, Kurzban explains the roots and implications of our inconsistent minds, and why it is perfectly natural to believe that everyone else is a hypocrite. (shrink)