Machine Mentality, Misc

Scientific study of dreams requires the most objective methods to reliably analyze dream content. In this context, artificial intelligence should prove useful for an automatic and non subjective scoring technique. Past research has utilized word search and emotional affiliation methods, to model and automatically match human judges’ scoring of dream report’s negative emotional tone. The current study added word associations to improve the model’s accuracy. Word associations were established using words’ frequency of co-occurrence with their defining words as found in (...) a dictionary and an encyclopedia. It was hypothesized that this addition would facilitate the machine learning model and improve its predictability beyond those of previous models. With a sample of 458 dreams, this model demonstrated an improvement in accuracy from 59% to 63% on the negative emotional tone scale, and for the first time reached an accuracy of 77% on the positive scale. (shrink)

The essays in this volume represent the first steps by philosophers and artificial intelligence researchers toward explaining why it is necessary to add an ...

Fifty years before the present volume appeared, artificial intelligence (AI) and cognitive science (Cogsci) emerged from a couple of small-scale academic encounters on the East Coast of the United States. Wedded together like Siamese twins, these nascent research programs appeared to rest on some general assumptions regarding the human mind, and closely connected methodological principles, which set them at such a distance from phenomenology that no contact between the two approaches seemed conceivable. Soon however contact was made, in the form (...) of a head-on critique of the AI/Cogsci project mostly inspired by arguments from phenomenology. For a while, it seemed like nothing would come of it: AI/Cogsci bloomed while the small troop of critical phenomenologists kept objecting. Then AI and Cogsci went their separate ways. AI underwent a deep transformation and all but surrendered to the phenomenological critique. Cogsci meanwhile pursued the initial program with a far richer collection of problems, concepts and methods, and was for a long time quite unconcerned by suggestions and objections from phenomenology. The last decade and half has seen a remarkable reversal: on the one hand, a few cognitive scientists have been actively pursuing the goal of reconciliating Cogsci, whether empirically or foundationally, with some of the insights procured by phenomenology; on the other, many cognitive scientists and philosophers of mind who think of themselves as, respectively, mainstream and analytic, and have no or little acquaintance with, and often little sympathy for, phenomenology, have been actively pursuing research programs geared toward some of the key issues identified by phenomenological critics of early AI/Cogsci. It might seem then as if those critics were now vindicated. But while these new directions are undoubtedly promising, it is not yet clear that phenomenology and Cogsci can be truly reconciled. Some suspect that Cogsci must distort beyond recognition those phenomenological themes it means to weave into its fabric, while phenomenology may be losing touch with its roots by tuning onto the logic of Cogsci which is, after all, an empirical science. To the present writer, it is far too early for anything like a verdict, as the task of clarifying the issues and gaining a much deeper understanding of the issues on both sides has barely begun. But whatever emerges from this exploration will probably have deep consequences for both Cogsci and philosophy.. (shrink)

In its forty years of existence, Artificial Intelligence has suffered both from the exaggerated claims of those who saw it as the definitive solution of an ancestral dream — that of constructing an intelligent machine-and from its detractors, who described it as the latest fad worthy of quacks. Yet AI is still alive, well and blossoming, and has left a legacy of tools and applications almost unequalled by any other field-probably because, as the heir of Renaissance thought, it represents a (...) possible bridge between the humanities and the natural sciences, philosophy and neurophysiology, psychology and integrated circuits-including systems that today are taken for granted, such as the computer interface with mouse pointer and windows. This writing describes a few results of AI that have modified the scientific world, as well as the way a layman sees computers: thetechnology of programming languages, such asLISP-witness the unique excellence of academic departments that have contributed to them-thecomputing workstations-of which our modern PC is but a vulgarised descendant-theapplications to the educational field-e.g., the realisation of some ideas of genetic epistemology-and tointerdisciplinary philosophy-such as Hofstadter's associations between the arts and mathematics-and the use ofAI techniques in music and musicology. All this has led to a generalisation of AI towards Negrotti's overallTheory of the Artificial, which encompasses further specialisation such asartificial reality, artificial life, and applications ofneural networks among others. (shrink)

To be an agent, one must be able to formulate intentions. To be able to formulate intentions, one must have a first-person perspective. Computers lack a first-person perspective. So, computers are not agents.

As opposed to the computer, the human being can intentionally mislead in many different ways, can behave chaotically, and whenever he has the motivation can choose also by improvisation, non‐consequent misleading, and spontaneous manners of reasoning and articulation. Human perception and the elaboration of the experience are existentially interest‐related, and distorted if found necessary. The arbitrariness is unlimited; human beings can initiate and produce absurd combinations, contextual failures and deceptive expressions, and do so also by intonation and body‐language. These are (...) the sources of social behaviour, of literature, arts, jurisprudence, politics, etc., all representing systems of meaning constructed by human beings, present in their ‘Lebenswelt’. Human beings are flexible, sensible of emotions and well‐trained to code and decode hidden fallacies, to produce wild associations, capricious‐temporary conclusions, tasks that the rigid computer cannot fulfil. They can choose incorrect and dysfunctional answers; the exactness of their reasoning can be logically invalid. The computer has only inner horizons; human beings have also outer ones, in which they are able to produce multidimensional relationships. They are not truth‐conditioned.The reduction of organic human thinking to the computer’s mechanisms can end up in humankind’s dehumanisation. (shrink)

No philosopher has worked harder than Dan Dennett to set the possibility of machine mentality on firm philosophical footing. Dennett’s defense of this possibility has both a positive and a negative thrust. On the positive side, he has developed an account of mental activity that is tailor-made for the attribution of intentional states to purely mechanical contrivances, while on the negative side, he pillories as mystery mongering and skyhook grasping any attempts to erect barriers to the conception of machine mentality (...) by excavating gulfs to keep us “bona fide” thinkers apart from the rest of creation. While I think he’s “won” the rhetorical tilts with his philosophical adversaries, I worry that Dennett’s negative side sometimes gets the better of him, and that this obscures advances that can be made on the positive side of his program. In this paper, I show that Dennett is much too dismissive of original intentionality in particular, and that this notion can be put to good theoretical use after all. Though deployed to distinguish different grades of mentality, it can (and should) be incorporated into a philosophical account of the mind that is recognizably Dennettian in spirit. (shrink)

A fun piece discussing the challenges to and prospects of building machines that are able to produce and understand natural language.

The dominant assumptions -- throughout contemporary philosophy, psychology, cognitive science, and artificial intelligence -- about the ontology underlying intentionality, and its core of representationality, is that of encodings -- some sort of informational or correspondence or covariation relationship between the represented and its representation that constitutes that representational relationship. There are many disagreements concerning details and implementations, and even some suggestions about claimed alternative ontologies, such as connectionism (though none that escape what I argue is the fundamental flaw in these (...) dominant approaches). One assumption that seems to be held by all, however, usually without explication or defense, is that there is _one_ singular underlying ontology to representationality. In this paper, I argue that there are in fact quite a number of ontologies that manifest representationality -- levels of representationality -- and that _none_ of them are the standard "manipulations of encoded symbols" ontology, nor any other variation on the informational approach to representation. Collectively, these multiple representational ontologies constitute a framework for cognition, whether natural or artificial. (shrink)

In 1949, the Department of Philosophy at the University of Manchester organized a symposium “Mind and Machine” with Michael Polanyi, the mathematicians Alan Turing and Max Newman, the neurologists Geoff rey Jeff erson and J. Z. Young, and others as participants. Th is event is known among Turing scholars, because it laid the seed for Turing’s famous paper on “Computing Machinery and Intelligence”, but it is scarcely documented. Here, the transcript of this event, together with Polanyi’s original statement and his (...) notes taken at a lecture by Jeff erson, are edited and commented for the fi rst time. Th e originals are in the Regenstein Library of the University of Chicago. Th e introduction highlights elements of the debate that included neurophysiology, mathematics, the mind-body-machine problem, and consciousness and shows that Turing’s approach, as documented here, does not lend itself to reductionism. (shrink)

This paper tells the story of a recent laboratory medicine controversy in the Canadian province of Newfoundland and Labrador. During the controversy, a DAKOAutostainer machine was blamed for inaccurate breast cancer test results that led to the suboptimal treatment of many patients. In truth, the machine was not at fault. Using concepts developed by Bruno Latour and Pierre Bourdieu, we document the changing nature of the DAKO machine’s agency before, during, and after the controversy, and we make the ethical argument (...) that treating the machine as a scapegoat was harmful to patients. The mistreatment of patients was directly tied to a misrepresentation of the DAKO machine. The way to avoid both forms of mistreatment would have been to include all humans and nonhumans affected by the controversy in the network of decision-making. (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)

In an earlier paper in this journal[1], I sought to defend the claims that (1) substantial probability should be assigned to the hypothesis that machines will outsmart humans within 50 years, (2) such an event would have immense ramifications for many important areas of human concern, and that consequently (3) serious attention should be given to this scenario. Here, I will address a number of points made by several commentators.

Abstract: In the course of seeking an answer to the question "How do you know you are not a zombie?" Floridi (2005) issues an ingenious, philosophically rich challenge to artificial intelligence (AI) in the form of an extremely demanding version of the so-called knowledge game (or "wise-man puzzle," or "muddy-children puzzle")—one that purportedly ensures that those who pass it are self-conscious. In this article, on behalf of (at least the logic-based variety of) AI, I take up the challenge—which is to (...) say, I try to show that this challenge can in fact be met by AI in the foreseeable future. (shrink)

Andrew Boucher (1997) argues that ``parallel computation is fundamentally different from sequential computation'' (p. 543), and that this fact provides reason to be skeptical about whether AI can produce a genuinely intelligent machine. But parallelism, as I prove herein, is irrelevant. What Boucher has inadvertently glimpsed is one small part of a mathematical tapestry portraying the simple but undeniable fact that physical computation can be fundamentally different from ordinary, ``textbook'' computation (whether parallel or sequential). This tapestry does indeed immediately imply (...) that human cognition may be uncomputable. (shrink)

The dominant scientific and philosophical view of the mind – according to which, put starkly, cognition is computation – is refuted herein, via specification and defense of the following new argument: Computation is reversible; cognition isn't; ergo, cognition isn't computation. After presenting a sustained dialectic arising from this defense, we conclude with a brief preview of the view we would put in place of the cognition-is-computation doctrine.

This book argues that (1) AI will continue to produce machines with the capacity to pass stronger and stronger versions of the Turing Test but that (2) the "Person Building Project" (the attempt by AI and Cognitive Science to build a machine which is a person) will inevitably fail. The defense of (2) rests in large part on a refutation of the proposition that persons are automata -- a refutation involving an array of issues, from free will to Godel to (...) introspection to Searle and beyond. The defense of. (shrink)

Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...) producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporateeverything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments. (shrink)