Noise-induced artificial intelligence

We show that unavoidable stochastic fluctuations are not only affecting information processing in a destructive or constructive way, but may even induce conditions necessary for the artificial intelligence itself. In this proof-of-principle paper we consider a model of a neuron-astrocyte network under the influence of multiplicative noise and show that information encoding (loading, storage, and retrieval of information patterns), one of the paradigmatic signatures of intelligent systems, can be induced by stochastic influence and astrocytes. Hence, astrocytes, recently proved to play an important role in memory and cognitive processing in mammalian brains, may play also an important role in the generation of a system's features providing artificial intelligence functions. Hence, one could conclude that intrinsic stochasticity is probably positively utilized by brains, not only to optimize the signal response but also to induce intelligence itself, and one of the key roles, played by astrocytes in information processing, could be dealing with noises

The semantics of similarity in geographic information retrieval

Similarity measures have a long tradition in fields such as information retrieval artificial intelligence and cognitive science. Within the last years these measures have been extended and reused to measure semantic similarity; i.e. for comparing meanings rather than syntactic differences. Various measures for spatial applications have been developed but a solid foundation for answering what they measure; how they are best applied in information retrieval; which role contextual information plays; and how similarity values or rankings should be interpreted is still missing. It is therefore difficult to decide which measure should be used for a particular application or to compare results from different similarity theories. Based on a review of existing similarity measures we introduce a framework to specify the semantics of similarity. We discuss similarity-based information retrieval paradigms as well as their implementation in web-based user interfaces for geographic information retrieval to demonstrate the applicability of the framework. Finally we formulate open challenges for similarity research

Basic Taxonomic Structures and Levels of Abstraction

Taxonomic knowledge structures are often used to organize information. We compare basic taxonomic structures in four areas: thesaurus construction in information retrieval, semantic data models in database management systems, semantic networks in artificial intelligence, and mental structures in cognitive psychology. We then discuss levels of abstraction, in panicular the importance of intermediate levels. In mental structures these turn out to be basic levels that are more important cognitively than higher or lower levels. We explore the role of abstraction levels in other taxonomic structures and suggest possible future research in this area

An Associative Semantic Network for Machine-Aided Indexing, Classification and Searching

Capturing and exploiting textual database associations has played a pivotal role in the evolution of automated information systems. A variety of statistical, linguistic and artificial intelligence approaches have been described in the literature.Many of these R and D concepts and techniques are now being incorporated into commercially available search systems and services. This paper discusses prior work and reports on research in progress aimed at creating and utilizing a global semantic associative database, AURA (Associative User Retrieval Aid) to facilitate machine-assisted indexing, classification and searching in the large-scale information processing environment of NLM's core bibliographic databases, MEDLINE and CATLINE. AURA is a semantic network of over two million natural language phrases derived from more than a million MEDLINE titles. These natural language phrases are associatively linked to NLM's MeSH (Medical Subject Headings) and UMLS Metathesaurus (Unified Medical Language System) controlled vocabulary and classification resources

Is anybody in there?: Towards a model of affect and trust in human – AI information interactions

Advancements in search engines that utilize machine learning increase the likelihood that users will perceive these systems as worthy of trust. The nature and implications of trust in the context of algorithmic systems that utilize machine learning is examined and the resulting conception of trust is modelled. While current artificial intelligence does not meet the requirements of moral autonomy necessary to be considered trustworthy, people may still engage in misplaced trust based on the perception of moral autonomy. Users who place their trust in algorithmic systems limit their critical engagement with, and assessment of, the information interaction. A preliminary high-level model of trust’s role in information interactions adapting Ingwersen and Jarvelin’s Integrative Model for Interactive Information Seeking and Retrieval is proposed using the Google search engine as an example. We need to recognize that is it possible for users to react to information systems in a social manner that may lead to the formation of trust attitudes. As information professionals we want to develop interventions that will encourage users to stay critically engaged with their interactions with information systems, even when they perceive them to be autonomous.Peer Reviewe