In this paper I start from a definition of “culture of the artificial” which might be stated by referring to the background of philosophical, methodological, pragmatical assumptions which characterizes the development of the information processing analysis of mental processes and of some trends in contemporary cognitive science: in a word, the development of AI as a candidate science of mind. The aim of this paper is to show how (with which plausibility and limitations) the discovery of the mentioned background might (...) be dated back to a period preceding the cybernetic era, the decade 1930–1940 at least. Therefore a somewhat detailed analysis of Hull's “robot approach” is given, as well as of some of its independent and future developments. -/- Reprinted in R.L. Chrisley (ed.), Artificial Intelligence: Critical Concepts in Cognitive Science, vol. 1, Routledge, London and New York, 2000, pp. 301-326. (shrink)

The early examples of self-directing robots attracted the interest of both scientific and military communities. Biologists regarded these devices as material models of animal tropisms. Engineers envisaged the possibility of turning self-directing robots into new “intelligent” torpedoes during World War I. Starting from World War II, more extensive interactions developed between theoretical inquiry and applied military research on the subject of adaptive and intelligent machinery. Pioneers of Cybernetics were involved in the development of goal-seeking warfare devices. But collaboration occasionally turned (...) into open dissent. Founder of Cybernetics Norbert Wiener, in the aftermath of World War II, argued against military applications of learning machines, by drawing on epistemological appraisals of machine learning techniques. This connection between philosophy of science and techno-ethics is both strengthened and extended here. It is strengthened by an epistemological analysis of contemporary machine learning from examples; it is extended by a reflection on ceteris paribus conditions for models of adaptive behaviours. (shrink)

Since the early eighties, computationalism in the study of the mind has been “under attack” by several critics of the so-called “classic” or “symbolic” approaches in AI and cognitive science. Computationalism was generically identified with such approaches. For example, it was identified with both Allen Newell and Herbert Simon’s Physical Symbol System Hypothesis and Jerry Fodor’s theory of Language of Thought, usually without taking into account the fact ,that such approaches are very different as to their methods and aims. Zenon (...) Pylyshyn, in his influential book Computation and Cognition, claimed that both Newell and Fodor deeply influenced his ideas on cognition as computation. This probably added to the confusion, as many people still consider Pylyshyn’s book as paradigmatic of the computational approach in the study of the mind. Since then, cognitive scientists, AI researchers and also philosophers of the mind have been asked to take sides on different “paradigms” that have from time to time been proposed as opponents of (classic or symbolic) computationalism. Examples of such oppositions are: -/- computationalism vs. connectionism, computationalism vs. dynamical systems, computationalism vs. situated and embodied cognition, computationalism vs. behavioural and evolutionary robotics. -/- Our preliminary claim in section 1 is that computationalism should not be identified with what we would call the “paradigm (based on the metaphor) of the computer” (in the following, PoC). PoC is the (rather vague) statement that the mind functions “as a digital computer”. Actually, PoC is a restrictive version of computationalism, and nobody ever seriously upheld it, except in some rough versions of the computational approach and in some popular discussions about it. Usually, PoC is used as a straw man in many arguments against computationalism. In section 1 we look in some detail at PoC’s claims and argue that computationalism cannot be identified with PoC. In section 2 we point out that certain anticomputationalist arguments are based on this misleading identification. In section 3 we suggest that the view of the levels of explanation proposed by David Marr could clarify certain points of the debate on computationalism. In section 4 we touch on a controversial issue, namely the possibility of developing a notion of analog computation, similar to the notion of digital computation. A short conclusion follows in section 5. (shrink)

This Research Topic aimed at deepening our understanding of the levels and explanations that are of interest for cognitive sci- entists, neuroscientists, psychologists, behavioral scientists, and philosophers of science. Indeed, contemporary developments in neuroscience and psy- chology suggest that scientists are likely to deal with a multiplicity of levels, where each of the different levels entails laws of behavior appropriate to that level (Berntson et al., 2012). Also, gathering and modeling data at the different levels of analysis is not suffi- (...) cient: the integration of information across levels of analysis is a crucial issue. Given such state of affairs, a number of interesting questions arise. How can the autonomy of explanatory levels be properly understood in behavioral explanation? Is reductionism a satis- factory strategy? How can high-level and low-level models be constrained in order to be actually explanatory of both behav- ioral and neurological or molecular evidence? What is the kind of relationship between those models? (shrink)

In this paper I put forward a reconstruction of the evolution of certain explanatory hypotheses on the neural basis of association and learning that are the premises of connectionism in the cybernetic age and of present-day connectionism. The main point of my reconstruction is based on two little-known case studies. The first is the project, published in 1913, of a hydraulic machine through which its author believed it was possible to simulate certain essential elements of the plasticity of nervous connections. (...) The author, S. Bent Russell, was an engineer deeply influenced by the neurological hypotheses on nervous conduction of Herbert Spencer, Max Meyer and Edward L. Thorndike. The second is the project, published in 1929, of an electromechanical machine in which the author, the psychologist J.M. Stephens, believed it was possible to embody Thorndike's law of effect. Thus both Bent Russell and Stephens referred to the principles of learning that Thorndike defined as connectionist . Their attempt was that of simulating by machines at least certain simple aspects of inhibition, association and habit formation that are typical of living organisms. I propose to situate their projects within the frame of thediscovery of a simulative (modelling) methodology which I believe might be considered an important topic of the Culture of the Artificial . Certain more recent steps toward such a methodology made by both connectionism of the 1950s and present-day connectionism are briefly pointed out in the paper. (shrink)

The expression ‘‘artificial intelligence’’ (AI) was introduced by John McCarthy, and the official birth of AI is unanimously considered to be the 1956 Dartmouth Conference. Thus, AI turned fifty in 2006. How did AI begin? Several differently motivated analyses have been proposed as to its origins. In this paper a brief look at those that might be considered steps towards Dartmouth is attempted, with the aim of showing how a number of research topics and controversies that marked the short history (...) of AI were touched on, or fairly well stated, during the year immediately preceding Dartmouth. The framework within which those steps were taken was the development of digital computers. Earlier computer applications in areas such as complex decision making and management, at that time dealt with by operations research techniques, were important in this story. The time was ripe for AI’s intriguingly tumultuous development, marked as it has been by hopes and defeats, successes and difficulties. (shrink)

Heuristic programming was the first area in which AI methods were tested. The favourite case-studies were fairly simple toy- problems, such as cryptarithmetic, games, such as checker or chess, and formal problems, such as logic or geometry theorem-proving. These problems are well-defined, roughly speaking, at least in comparison to real-life problems, and as such have played the role of Drosophila in early AI. In this chapter I will investigate the origins of heuristic programming and the shift to more knowledge-based and (...) real-life problem solving. (shrink)

In this article, I shall examine some of the issues and questions involved in the technology of autonomous robots, a technology that has developed greatly and is advancing rapidly. I shall do so with reference to a particularly critical field: autonomous military robotic systems. In recent times, various issues concerning the ethical implications of these systems have been the object of increasing attention from roboticists, philosophers and legal experts. The purpose of this paper is not to deal with these issues, (...) but to show how the autonomy of those robotic systems, by which I mean the full automation of their decision processes, raises difficulties and also paradoxes that are not easy to solve. This is especially so when considering the autonomy of those robotic systems in their decision processes alongside their reliability. Finally, I would like to show how difficult it is to respond to these difficulties and paradoxes by calling into play a strong formulation of the precautionary principle. (shrink)

My aim here is to raise a few questions concerning the problem of representation in scientific discovery computer programs. Representation, as Simon says in his paper, "imposes constraints upon the phenomena that allow the mechanisms to be inferred from the data". The issue is obviously barely outlined by Simon in his paper, while it is addressed in detail in the book by Langley, Simon, Bradshaw and Zytkow (1987), to which I shall refer in this note. Nevertheless, their analysis would appear (...) to leave open certain issues related to the nature of the "constraints" imposed by representation on problem solving strategies. (shrink)

The notion of loop seems to be ubiquitous in the study of organisms, the human mind and symbolic systems. With the possible exception of quantum-mechanical approaches, the treatments of consciousness we are acquainted with crucially appeal to the concept of loop. The uses of loops in this context fall within two broad classes. In the first one, loops are used to express the control of the organism’s interaction with the environment; in the second one, they are used to express self-reference. (...) Both classes are tied with investigations which aim at accounting for symbolic capabilities, and ultimately for subjectivity. Neurophysiological research detects loops in the animal nervous system since its very inception (e.g., all the work on the reflex arc). Recently, Gray proposed a model purporting to explain the mechanism supporting the contents of consciousness in the human CNS, in ways that are practically indistinguishable from models formulated within the cybernetical point of view. Both types of models apply loops in strikingly similar ways. While there is no conclusive evidence that loops are necessary to support consciousness, they are nonetheless as good a candidate as today can be found for inclusion in the list of essential ingredients for subjectivity to arise. In the first two sections we discuss the above mentioned uses by means of significant examples. In the third section, we compare Gray’s recent model to MacKay’s early cybernetical model of a selfobserving system, in the setting of a broader discussion on loops for consciousness. (shrink)

Aaron Sloman remarks that a lot of present disputes on consciousness are usually based, on the one hand, on re-inventing “ideas that have been previously discussed at lenght by others”, on the other hand, on debating “unresolvable” issues, such as that about which animals have phenomenal consciousness. For what it’s worth I would make a couple of examples, which are related to certain topics that Sloman deals with in his paper, and that might be useful for introducing some comments in (...) the following of this brief note. (shrink)