Online research in philosophy

Artificial Intelligence has become big business in the military and in many industries. In spite of this growth there still remains no consensus about what AI really is. The major factor which seems to be responsible for this is the lack of agreement about the relationship between behavior and intelligence. In part certain ethical concerns generated from saying who, what and how intelligence is determined may be facilitating this lack of agreement.

The field of artificial intelligence and law is remarkably diverse not just because it encompasses many areas of academic study but also because it attracts the interest of both the research and commercial worlds. While much of the research is no doubt too exploratory and tentative to be of direct relevance to practising lawyers, in other projects there is but a short step from the research laboratory to the marketplace.Given that most readers of this journal tend to be involved with, or interested in, research findings in the field, it might well be asked to what extent there should also be coverage here of commercial projects in artificial intelligence and law.

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.

In this article I develop a case for a theory of intelligence incorporating transpersonal dimensions, namely integrated intelligence. Some recent expanded theories of intelligence move into concepts like creativity, wisdom, and emotional intelligence. Yet they remain embedded within mainstream intelligence theory and its reductionist and materialist presuppositions. Although various theorists in consciousness theory have developed transpersonal models that are beginning to be discussed in some mainstream circles, mainstream intelligence theory is yet to address the broader implications of this. Recent changes in the global economy and the needs of populations have created a need for an expanded theory of intelligence, and more intuitive thinking.

Modern skepticism can be usefully divided into two camps: the Cartesian and the Humean.1 Cartesian skepticism is a matter of a theoretical doubt that has little or no practical import in our everyday lives. Its employment concerns whether or not we can achieve a special kind of certain knowledge – something Descartes calls “scientia” 2—that is far removed from our everyday aims or standards of epistemic appraisal. Alternatively, Humean skepticism engages the ancient skeptical concern with whether we have good reason, or any reason at all, for our beliefs, including the common or garden beliefs that are presupposed in our ordinary practical affairs. On this traditional conception, philosophical doubt is a projection of everyday doubt and the lessons of the study are potentially lessons for the street. In this paper I shall focus on the Humean strain of skepticism whose focus concerns whether we have adequate reasons for our beliefs. Henceforth when I speak of skepticism it is this variety of skepticism that I am primarily referring to.3 I want to relate skepticism, so understood, to two kinds of self-knowledge. I shall argue that the failure of past solutions and dissolutions of skepticism to provide a satisfying response to the skeptic can be accounted for in terms of two stances that we can take towards our own..

Intuitive conceptions guide practice, but practice reciprocally reshapes intuition. The intuitive conception of intelligence in AI was originally highly anthropocentric. However, the internal dynamics of AI research have resulted in a divergence from anthropocentric concerns. In particular, the increasing emphasis on commonsense knowledge and peripheral intelligence (perception and movement) in effect constitutes an incipient reorientation of intuitions about the nature of intelligence in a non-anthropocentric direction. I argue that this conceptual shift undermines Joseph Weizenbaum's claim that the project of artificial intelligence is inherently dehumanizing.

The Turing Test (TT) is criticised for various reasons, one being that it is limited to testing only human-like intelligence. We can read, for example, that ‘TT is testing humanity, not intelligence,’ (Fostel, 1993), that TT is ‘a test for human intelligence, not intelligence in general,’ (French, 1990), or that a perspective assumed by TT is parochial, arrogant and, generally, ‘massively anthropocentric’ (Hayes and Ford, 1996). This limitation presumably causes a basic inadequacy of TT, namely that it misses a wide range of intelligence by focusing on one possibility only, namely on human intelligence. The spirit of TT enforces making explanations of possible machine intelligence in terms of what is known about intelligence in humans, thus possible specificity of the computer intelligence is ruled out from the oælset.

We human beings may not be the most admirable species on the planet, or the most likely to survive for another millennium, but we are without any doubt at all the most intelligent. We are also the only species with language. What is the relation between these two obvious facts?