An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system that relies on generative models to predict the structure of sensory information. Such a view resonates with a body of work in machine learning that has explored the problem-solving capabilities of hierarchically-organized, multi-layer neural networks, many of which acquire and deploy generative models of their training data. The present chapter explores the extent to which the ostensible convergence on a common neurocomputational architecture might provide (...) inroads into the problem of digital immortality. In contrast to approaches that seek to recapitulate the physical structure of the human brain, the present chapter advocates an approach that is rooted in the use of machine learning algorithms. The claim is that a future form of deep learning system could be used to acquire generative models of a given individual or the sensory data that is processed by the brain of a given individual during the course of their biological life. The differences between these two forms of digital immortality are explored, as are some of the options for digital resurrection. (shrink)

This chapter sets aside the question of whether a Singularity will occur, to focus on the closely related issue of MU, specifically as presented by one of its most articulate proponents, David Chalmers. The fundamental premise of Chalmers' arguments about MU is some strong version of the Computational Theory of Mind (CTM). The chapter proceeds in the following fashion: first, it recalls Chalmers' main arguments; second, it argues that the ideas of MU and CTM do not take seriously enough the (...) fact that consciousness is a biological phenomenon, as pointed out in different contexts by both John Searle and Jerry Fodor, among others; third, it stipulates for the sake of argument that MU is somehow possible, and show that it would, at best, amount to a sort of mind cloning, but most certainly not to a preservation of anyone's consciousness in a medium different from one's own brain. (shrink)

Philosophy and Phenomenological Research, EarlyView.

Whole Brain Emulation has been championed as the most promising, well-defined route to achieving both human-level artificial intelligence and superintelligence. It has even been touted as a viable route to achieving immortality through brain uploading. WBE is not a fringe theory: the doctrine of Computationalism in philosophy of mind lends credence to the in-principle feasibility of the idea, and the standing of the Human Connectome Project makes it appear to be feasible in practice. Computationalism is a popular, independently plausible theory, (...) and Connectomics a well-funded empirical research program, so optimism about WBE is understandable. However, this optimism may be misplaced. This article argues that WBE is, at best, no more compelling than any of the other far-flung routes to achieving superintelligence. Similarly skeptical conclusions are found regarding immortality. The essay concludes with some positive considerations in favor of the Biological Theory of consciousness, as well as morals about the limits of Computationalism in both artificial intelligence and the philosophy of mind more generally. (shrink)

In this article, Lucas maintains the falseness of Mechanism - the attempt to explain minds as machines - by means of Incompleteness Theorem of Gödel. Gödel’s theorem shows that in any system consistent and adequate for simple arithmetic there are formulae which cannot be proved in the system but that human minds can recognize as true; Lucas points out in his turn that Gödel’s theorem applies to machines because a machine is the concrete instantiation of a formal system: therefore, for (...) every machine consistent and able of doing simple arithmetic, there is a formula that it can’t produce as true but that we can see to be true, and so human minds and machines have to be different. Lucas considers as well in this article some possible objections to his argument: for any Gödelian formula we could, for instance, construct a machine able to produce it or we could put the Gödelian formulae that we had proved as axioms of a further machine. However - as Lucas underlines - for every of such machines we could again formulate another Gödelian formula, the Gödelian formula of these machines, that they are not able to proof but that we can recognize as true. More general arguments, such as the possibility to escape Gödelian argument by suggesting that Gödel’s theorem applies to consistent systems while we could be inconsistent ones, are moreover refuted by Lucas by maintaining that our inconsistency corresponds to occasional malfunctioning of a machine and not to his normal inconsistency; indeed, a inconsistent machine is characterized by producing any statement, on the contrary human being are selective and not disposed to assert anything. (shrink)

Data‐driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so‐called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm‐based decision‐making. This paper aims to facilitate the application of philosophical analysis to these contested issues by providing an overview of three (...) key topics: What is algorithmic bias? Why and how can it occur? What can and should be done about it? Throughout, we highlight connections—both actual and potential—with philosophical ideas and concerns. (shrink)

In the 1960s, without realizing it, AI researchers were hard at work finding the features, rules, and representations needed for turning rationalist philosophy into a research program, and by so doing AI researchers condemned their enterprise to failure. About the same time, a logician, Yehoshua Bar-Hillel, pointed out that AI optimism was based on what he called the “first step fallacy”. First step thinking has the idea of a successful last step built in. Limited early success, however, is not a (...) valid basis for predicting the ultimate success of one’s project. Climbing a hill should not give one any assurance that if he keeps going he will reach the sky. Perhaps one may have overlooked some serious problem lying ahead. There is, in fact, no reason to think that we are making progress towards AI or, indeed, that AI is even possible, in which case claiming incremental progress towards it would make no sense. In current excited waiting for the singularity, religion and technology converge. Hard headed materialists desperately yearn for a world where our bodies no longer have to grow old and die. They will be transformed into information, like Google digitizes old books, and we will achieve the promise of eternal life. As an existential philosopher, however, I suggest that we may have to overcome the desperate desire to digitalize our bodies so as to achieve immortality, and, instead, face up to and maybe even enjoy our embodied finitude. (Paper from PT-AI 2011 conference. Volume ed. Vincent C. Müller). (shrink)

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...

If a brain is uploaded into a computer, will consciousness continue in digital form or will it end forever when the brain is destroyed? Philosophers have long debated such dilemmas and classify them as questions about personal identity. There are currently three main theories of personal identity: biological, psychological, and closest continuer theories. None of these theories can successfully address the questions posed by the possibility of uploading. I will argue that uploading requires us to adopt a new theory of (...) identity, psychological branching identity. Psychological branching identity states that consciousness will continue as long as there is continuity in psychological structure. What differentiates this from psychological identity is that it allows identity to continue in multiple selves. According to branching identity, continuity of consciousness will continue in both the original brain and the upload after nondestructive uploading. Branching identity can also resolve long standing questions about split-brain syndrome and can provide clear predictions about identity in even the most difficult cases imagined by philosophers. (shrink)

This paper discusses the relation between intelligence and motivation in artificial agents, developing and briefly arguing for two theses. The first, the orthogonality thesis, holds (with some caveats) that intelligence and final goals (purposes) are orthogonal axes along which possible artificial intellects can freely vary—more or less any level of intelligence could be combined with more or less any final goal. The second, the instrumental convergence thesis, holds that as long as they possess a sufficient level of intelligence, agents having (...) any of a wide range of final goals will pursue similar intermediary goals because they have instrumental reasons to do so. In combination, the two theses help us understand the possible range of behavior of superintelligent agents, and they point to some potential dangers in building such an agent. (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)

This volume contains a selection of authoritative essays exploring the central questions raised by the conjectured technological singularity. In informed yet jargon-free contributions written by active research scientists, philosophers and sociologists, it goes beyond philosophical discussion to provide a detailed account of the risks that the singularity poses to human society and, perhaps most usefully, the possible actions that society and technologists can take to manage the journey to any singularity in a way that ensures a positive rather than a (...) negative impact on society. The discussions provide perspectives that cover technological, political and business issues. The aim is to bring clarity and rigor to the debate in a way that will inform and stimulate both experts and interested general readers. (shrink)

The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...) from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank’s computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI’s potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society. (shrink)

Humanity stands at a precipice. -/- Our species could survive for millions of generations — enough time to end disease, poverty, and injustice; to reach new heights of flourishing. But this vast future is at risk. With the advent of nuclear weapons, humanity entered a new age, gaining the power to destroy ourselves, without the wisdom to ensure we won’t. Since then, these dangers have only multiplied, from climate change to engineered pandemics and unaligned artificial intelligence. If we do not (...) act fast to reach a place of safety, it may soon be too late. -/- The Precipice explores the science behind the risks we face. It puts them in the context of the greater story of humanity: showing how ending these risks is among the most pressing moral issues of our time. And it points the way forward, to the actions and strategies we can take today to safeguard humanity’s future. (shrink)

A controversial scientific vision predicts a time in which humans and machines will merge and create a new form of non-biological intelligence, explaining how the occurrence will solve such issues as pollution, hunger, and aging.

Singularity Hypotheses: A Scientific and Philosophical Assessment offers authoritative, jargon-free essays and critical commentaries on accelerating technological progress and the notion of technological singularity. It focuses on conjectures about the intelligence explosion, transhumanism, and whole brain emulation. Recent years have seen a plethora of forecasts about the profound, disruptive impact that is likely to result from further progress in these areas. Many commentators however doubt the scientific rigor of these forecasts, rejecting them as speculative and unfounded. We therefore invited prominent (...) computer scientists, physicists, philosophers, biologists, economists and other thinkers to assess the singularity hypotheses. Their contributions go beyond speculation, providing deep insights into the main issues and a balanced picture of the debate. (shrink)

Enclosed is a guidebook for philanthropists, advocates, and policymakers who want to do the most good possible. This book introduces the philosophy of “longtermism,” the idea that it is particularly important that we act now to safeguard future generations. -/- The future is vast in scale: depending on our choices in the coming centuries, the future could stretch for eons or it could dwindle into oblivion, and be inordinately good or inordinately bad. And yet future generations are utterly disenfranchised in (...) the world today: they cannot participate in our markets, our movements, or our civil society. This presents 21st Century philanthropists with a historically unprecedented opportunity to do good by protecting the long-term future. -/- The essays within are from the pioneers who have developed the intellectual foundations of longtermism, as well as the experts and advocates who are now putting it into practice. Readers will hear the case for longtermism from professors at the University of Oxford and Longview’s own founders; longtermist policy proposals from political philosophers, members of the House of Lords, and the All-party Parliamentary Group for Future Generations; the case for work on biosecurity, artificial intelligence, and climate change from leading experts; and the first ever essay on longtermist cultural change from the former Chief of Cabinet to the Finnish President. -/- Together, these essays chart the path forward toward maximising the good that we do for our grandchildren, our grandchildren’s grandchildren, and every generation beyond. (shrink)

We normally take it for granted that other people will live on after we ourselves have died. Even if we do not believe in a personal afterlife in which we survive our own deaths, we assume that there will be a "collective afterlife" in which humanity survives long after we are gone. Samuel Scheffler maintains that this assumption plays a surprising - indeed astonishing - role in our lives.

To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, (...) while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans. (shrink)

Chalmers has articulated a compellingly simple argument for inevitability of the singularity—an explosion of increasingly intelligent machines, eventuating in super forms of intelligence. Chalmers then goes on to explore the implications of this outcome, and suggests ways in which we might prepare for the eventuality. I think Chalmers' argument proves both too much and too little. If the reasoning were right, it would follow inductively that the singularity already exists, in which case Chalmers would have proven more than he set (...) out to. Moreover, I will suggest that, if the singularity already exists, we are doomed. Fortunately, Chalmers' reasoning is problematic. I will consider several objections. Unfortunately, the most serious problem is that human life may end long before the singularity is created. In that case, we are doomed either way. Should we care? Perhaps not. (shrink)