Narrow Results

Semantics is the essence of human communication. It concerns the manufacture and use of symbols as representations to exchange meanings. Information technology is faced with the problem of using intelligent machines as intermediaries for interpersonal communication. The problem of designing such semantic machines has been intractable because brains and machines work on very different principles. Machines process information that is fed to them. Brains construct hypotheses and test them by acting and sensing. Brains do not process information because the intake through the senses is infinite. Brains sample information, hold it briefly, construct meaning, and then discard the information. A solution to the problem of communication with machines is to simulate how brains create meaning and express it as information by making a symbol to represent the meaning to another brain in pairwise communication. An understanding of the neurodynamics by which brains create meaning and represent it may enable engineers to build devices with which they can communicate pairwise, as they do now with colleagues.

Systems composed of multiple interacting problem solvers are becoming increasingly pervasive and have been championed in some quarters as the basis of the next generation of intelligent information systems. If this technology is to fulfill its true potential then it is important that the systems which are developed have a sound theoretical grounding. One aspect of this foundation, namely the model of collaborative problem solving, is examined in this paper. A synergistic review of existing models of cooperation is presented, their weaknesses are highlighted and a new model (called joint responsibility) is introduced. Joint responsibility is then used to specify a novel high-level agent architecture for cooperative problem solving in which the mentalistic notions of belief, desire, intention and joint intention play a central role in guiding an individual’s and the group’s problem solving behaviour. An implementation of this high-level architecture is then discussed and its utility is illustrated for the real-world domain of electricity transportation management.

Theoretical models of entrepreneurship suggest that an individual's intention to start an enterprise is a strong predictor of eventual entrepreneurial action. Less understood are factors that influence the likelihood of entrepreneurial intentions and nascent behavior. In this study, we develop and test several hypotheses about how social network ties and self-efficacy affect entrepreneurial intentions and nascent behavior. We found that a personal network of supportive strong ties coupled with high entrepreneurial self-efficacy increases the likelihood of entrepreneurial intentions and nascent behavior. A personal network of weak ties with practical business knowledge and experience also increases the likelihood of entrepreneurial nascent behavior but not entrepreneurial intentions. In contrast, a personal network of strong ties with practical business knowledge and experience has little effect on either intentions or nascent behavior and may, in fact, suppress both. The contribution of this study to nascent entrepreneurship research and implications for future research are discussed.

This paper proposes a new interpretive stance for interpreting artistic works and performances that is relevant to artificial intelligence research but also has broader implications. Termed the expressive stance, this stance makes intelligible a critical distinction between present-day machine art and human art, but allows for the possibility that future machine art could find a place alongside our own. The expressive stance is elaborated as a response to Daniel Dennett's notion of the intentional stance, which is critically examined with respect to his specialized concept of rationality. The paper also shows that temporal scale implicitly serves to select between different modes of explanation in prominent theories of intentionality. It also considers the implications of the phenomenological background for systems that produce art.

The adventure of artificial intelligence (AI) is based on a revolutionary idea, namely, that machines are able to understand and produce linguistic acts endowed with meaning. Over the past decades, this idea has gained acceptance in the scientific community, but its consequences for human nature were not really appreciated. Recent developments in AI, due especially to Deep Learning (DL), have changed things dramatically by creating computer architectures capable of performing previously impossible tasks in areas such as image recognition and language understanding. Criticisms that were raised decades ago against this possibility have thus been revived. These criticisms are no different in argument from those made in the first place. The reason they are being raised again is because of the social impact that the new machine performances have been able to achieve, and is not driven by truly scientific reasons, which indeed prove to be inadequate when compared to the more recent artificial semantics based on deep neural networks.

In this work we start from the idea that intentionality is the chief characteristic of intelligent behavior, both cognitive and deliberative. Investigating the "originality of intelligent life" from this standpoint means investigating "intentional behavior" in living organisms. In this work, we ask epistemological questions involved in making the intentional behavior the object of physical and mathematical inquiry. We show that the subjective component of intentionality can never become object of scientific inquiry, as related to self–consciousness. On the other hand, the inquiry on objective physical and logical components of intentional acts is central to scientific inquiry. Such inquiry concerns logical and semantic questions, like reference and truth of logical symbols constituted as such, as well as their relationship to the "complexity" of brain networking. These suggestions concern cognitive neuroscience and computability theory, so to constitute one of the most intriguing intellectual challenges of our age. Such metalogical inquiry suggests indeed some hypotheses about the amazing "parallelism", "plasticity" and "storing capacity" that mammalian and ever human brains might exhibit. Such properties, despite neurons are over five orders of magnitude slower than microchips, make biological neural nets much more efficient than artificial ones even in execution of simple cognitive and behavioral tasks.