Pietro Perconti

The Slowdown Hypothesis

The so-called singularity hypothesis embraces the most ambitious goal of Artificial Intelligence: the possibility of constructing human-like intelligent systems. The intriguing addition is that once this goal is achieved, it would not be too difficult to surpass human intelligence. While we believe that none of the philosophical objections against strong AI are really compelling, we are skeptical about a singularity scenario associated with the achievement of human-like systems. Several reflections on the recent history of neuroscience and AI, in fact, seem to suggest that the trend is going in the opposite direction.

TWO KINDS OF COMMON SENSE KNOWLEDGE (AND A CONSTRAINT FOR MACHINE CONSCIOUSNESS DESIGN)

In this paper, it will be argued that common sense knowledge has not a unitary structure. It is rather articulated at two different levels: a deep and a superficial level of common sense. The deep level is based on know-how procedures, on metaphorical frames built on imaginative bodily representations, and on a set of adaptive behaviors. Superficial level includes beliefs and judgments. They can be true or false and are culture dependent. Deep common sense is unavailable for any fast change, because it depends more on human biology than on cultural conventions. The deep level of common sense is characterized by a sensorimotor representational format, while the superficial level is largely made by propositional entities. This difference can be considered as a constraint for machine consciousness design, insofar this latter should be based on a reliable model of common sense knowledge.

A Biologically-Inspired Perspective on Commonsense Knowledge

Since the seminal papers by John McCarthy [1,2], the problem to design intelligent systems able to handle common sense knowledge has become a real puzzle [3,6,7]. According to the McCarthy and Hayes suggestion, “The first task [to construct a general intelligent computer program] is to define even a naive, common-sense view of the world precisely enough to program a computer to act accordingly. This is a very difficult task in itself” [5]: 6. Perhaps the frame problem, i.e., how can a representational system deal with the enormous amount of knowledge that is necessary to everyday behaviour, needs nowadays a new account. The BICA challenge, that is, the challenge to make a general purpose and computational equivalent of the human intelligence by means of an approach based on biologically inspired cognitive architectures, can be considered as an example of this kind of new perspective [1,8].

Language in Complexity

This contributed volume explores the achievements gained and the remaining puzzling questions by applying dynamical systems theory to the linguistic inquiry. In particular, the book is divided into three parts, each one addressing one of the following topics: 1) Facing complexity in the right way: mathematics and complexity 2) Complexity and theory of language 3) From empirical observation to formal models: investigation of specific linguistic phenomena, like enunciation, deixis, or the meaning of the metaphorical phrases The application of complexity theory to describe cognitive phenomena is a recent and very promising trend in cognitive science. At the time when dynamical approaches triggered a paradigm shift in cognitive science some decade ago, the major topic of research were the challenges imposed by classical computational approaches dealing with the explanation of cognitive phenomena like consciousness, decision making and language. The target audience primarily comprises researchers and experts in the field but the book may also be beneficial for graduate and post-graduate students who want to enter the field.

The Case for Cognitive Plausibility

This chapter is aimed at stressing a condition, i.e., cognitive plausibility, which complex computational psychology should satisfy in order to be compatible with the evidence of neuroscience and biology in general. The required condition says that the psychological (and hence linguistic) computations have to be (1) tractable and (2) fit to the ordinary situations that they are encoding. Brains have to do with concrete contexts, that is, boundaries in space and time and the influence of the associative networks embodied in the individual’s past. They are not idealized machines in front of an idealized world, but flesh and blood in search for life and survival.

Language as complex system: an introduction

This contributed volume explores the achievements gained and the remaining puzzling questions by applying dynamical systems theory to the linguistic inquiry. In particular, the book is divided into three parts, each one addressing one of the following topics: 1) Facing complexity in the right way: mathematics and complexity 2) Complexity and theory of language 3) From empirical observation to formal models: investigation of specific linguistic phenomena, like enunciation, deixis, or the meaning of the metaphorical phrases The application of complexity theory to describe cognitive phenomena is a recent and very promising trend in cognitive science. At the time when dynamical approaches triggered a paradigm shift in cognitive science some decade ago, the major topic of research were the challenges imposed by classical computational approaches dealing with the explanation of cognitive phenomena like consciousness, decision making and language. The target audience primarily comprises researchers and experts in the field but the book may also be beneficial for graduate and post-graduate students who want to enter the field.

Language in Complexity: The Emerging Meaning

This contributed volume explores the achievements gained and the remaining puzzling questions by applying dynamical systems theory to the linguistic inquiry. In particular, the book is divided into three parts, each one addressing one of the following topics: 1) Facing complexity in the right way: mathematics and complexity 2) Complexity and theory of language 3) From empirical observation to formal models: investigation of specific linguistic phenomena, like enunciation, deixis, or the meaning of the metaphorical phrases The application of complexity theory to describe cognitive phenomena is a recent and very promising trend in cognitive science. At the time when dynamical approaches triggered a paradigm shift in cognitive science some decade ago, the major topic of research were the challenges imposed by classical computational approaches dealing with the explanation of cognitive phenomena like consciousness, decision making and language. The target audience primarily comprises researchers and experts in the field but the book may also be beneficial for graduate and post-graduate students who want to enter the field.

An Epistemic Commitment in the Very Idea of “Speaker’s Intention”

In the tradition inspired by the ideas of Paul Grice, communication essentially consists in recognizing the intentions of the speaker and in the ability to provide enough clues for the hearer to guess his/her intentions. As it is well-known, this way of thinking is based on the difference between “sentence meaning”, that is, the semantic properties ascribed to a sentence by linguistic conventions, and the “speaker meaning”, that is, what the speaker actually intend to communicate by uttering a given sentence. This implies that linguistic communication cannot be reduced to a mere encoding-decoding process. Namely, it involves the attribution of mental states to the speaker. We are, therefore, committed to the idea that in the world there are things such as communicative intentions.