Online research in philosophy

Scientists and engineers who extensively use the term “nanomachine” are not always aware of the philosophical implications of this term. The purpose of this paper is to clarify the concept of nanomachine through a distinction between three major paradigms of machine. After a brief presentation of two well-known paradigms - Cartesian mechanistic machines and Von Neumann’s complex and uncontrolled machines – we will argue that Drexler’s model was mainly Cartesian. But what about the model of his critics? We propose a third model - Gilbert Simondon’s notion of concrete machines – which seems more appropriate to understand nanomachines than the notion of “soft machines”. Finally we review a few strategies currently used to design nanomachines, in an effort to determine which paradigm they belong to.

In Darwin's Black Box: The BiochemicalChallenge to Evolution I argued thatpurposeful intelligent design, rather thanDarwinian natural selection, better explainssome aspects of the complexity that modernscience has discovered at the molecularfoundation of life. In the five years since itspublication the book has been widely discussedand has received considerable criticism. Here Irespond to what I deem to be the mostfundamental objections. In the first part ofthe article I address empirical criticismsbased on experimental studies alleging eitherthat biochemical systems I discussed are notirreducibly complex or that similar systemshave been demonstrated to be able to evolve byDarwinian processes. In the remainder of thearticle I address methodological concerns,including whether a claim of intelligent designis falsifiable and whether intelligent designis a permissible scientific conclusion.

For a long time, emotions have been ignored in the attempt to model intelligent behavior. However, within the last years, evidence has come from neuroscience that emotions are an important facet of intelligent behavior being involved into cognitive problem solving, decision making, the establishment of social behavior, and even conscious experience. Also in research communities like software agents and robotics, an increasing number of researchers start to believe that computational models of emotions will be needed to design intelligent systems. Nevertheless, modeling emotions in technical terms poses many difficulties and has often been accounted as just not feasible. In this article, there are identified the main problems, which occur when attempting to implement emotions into machines. By pointing out these problems, it is aimed to avoid repeating mistakes committed when modeling computational models of emotions in order to speed up future development in this area. The identified issues are not derived from abstract reflections about this topic but from the actual attempt to implement emotions into a technical system based on neuroscientific research findings. It is argued that besides focusing on the cognitive aspects of emotions, a consideration of the bodily aspects of emotions—their grounding into a visceral body—is of crucial importance, especially when a system shall be able to learn correlations between environmental objects and events and their “emotional meaning”.

While “Intelligent Design” has garnered increasing support in America, its critics have been hesitant to address it publicly. In this paper I argue that it is important for defenders of evolution to take the supporters of intelligent design head-on. I refute the notion that the best way of addressing the threat posed by intelligent design is by ignoring it. I point out how academics’ unwillingness to speak publicly on the issue of intelligent design is symptomatic of a general reticence towards communicating with the public. Finally, I argue that this reticence is detrimental both to science and the general welfare.

Nature exhibits a rich variety of adaptations. Cells contain complex biomolecular structures, such as proteins, that are exquisitely adapted to perform specific biological functions. Evolutionary biology explains how biomolecular structures evolve. Intelligent design creationists reject evolutionary explanations. They want to believe that all adaptations in nature are the handiwork of God. Their critics aver that “it ain't necessarily so.” The anthology under review is an excellent display of the issues between intelligent design creationists and their critics. I agree with the critics.

In my paper “Intelligent Design Theory and the Supernatural—the ‘God or Extra-Terrestrial’ Reply,” I argued that Intelligent Design (ID) Theory, when coupled with independently plausible further assumptions, leads to the conclusion that a supernatural intelligent designer exists. ID theory is therefore not neutral on the question of whether there are supernatural agents. In this respect, it differs from the Darwinian theory of evolution. John Beaudoin replies to my paper in his “Sober on Intelligent Design Theory and the Intelligent Designer,” arguing that my paper faces two challenges. In the present paper, I try to address Beaudoin’s challenges.

Improvements in computational hardware enabled by nanotechnology promise a dual revolution in coming decades: machines which are both more intelligent and more numerous than human beings. This possibility raises substantial concern over the moral nature of such intelligent machines. An analysis of the prospects involves at least two key philosophical issues. The first, intentionality in formal systems, turns on whether a “mere machine” can be a mind whose thoughts have true meaning and understanding. Second, what is the moral nature of a machine vis-a-vis a human: can a machine be a true moral agent, capable of real responsibility, possessed of rights and duties? If so, might a machine be a better moral agent than a human?

This paper tackles the main changes that have taken place in the mechanical worldview of simple, self-regulating and intelligent machines, and studies their repercussions at the ethical and organisational level. These views of machines agree with the scientific, human-relations and postmodern proposals in organisation theory, in that they are in fact reflections on human nature which depend on metaphorical devices within which the machine metaphor is central.

According to the conventional wisdom, Turing (1950) said that computing machines can be intelligent. I don''t believe it. I think that what Turing really said was that computing machines –- computers limited to computing –- can only fake intelligence. If we want computers to become genuinelyintelligent, we will have to give them enough initiative (Turing, 1948, p. 21) to do more than compute. In this paper, I want to try to develop this idea. I want to explain how giving computers more ``initiative'''' can allow them to do more than compute. And I want to say why I believe (and believe that Turing believed) that they will have to go beyond computation before they can become genuinely intelligent.

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..