Tacit Representations and Artificial Intelligence: Hidden Lessons from an Embodied Perspective on Cognition

In this paper, I explore how an embodied perspective on cognition might inform research on artificial intelligence. Many embodied cognition theorists object to the central role that representations play on the traditional view of cognition. Based on these objections, it may seem that the lesson from embodied cognition is that AI should abandon representation as a central component of intelligence. However, I argue that the lesson from embodied cognition is actually that AI research should shift its focus from how to utilize explicit representations to how to create and use tacit representations. To develop this suggestion, I provide an overview of the commitments of the classical view and distinguish three critiques of the role that representations play in that view. I provide further exploration and defense of Daniel Dennett’s distinction between explicit and tacit representations. I argue that we should understand the embodied cognition approach using a framework that includes tacit representations. Given this perspective, I will explore some AI research areas that may be recommended by an embodied perspective on cognition

Knowledge Representation with Ontologies: The Present and Future

Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent

Wittgenstein and the memory debate

Original article can be found at: http://www.sciencedirect.com/science/journal/0732118X Copyright Elsevier Ltd. DOI: 10.1016/j.newideapsych.2008.04.015In this paper, I survey the impact on neuropsychology of Wittgenstein’s elucidations of memory. Wittgenstein discredited the storage and imprint models of memory, dissolved the conceptual link between memory and mental images or representations and, upholding the context-sensitivity of memory, made room for a family resemblance concept of memory, where remembering can also amount to doing or saying something. While neuropsychology is still generally under the spell of archival and physiological notions of memory, Wittgenstein's reconceptions can be seen at work in its leading-edge practitioners. However, neuroscientists, generally, are finding memory difficult to demarcate from other cognitive and noncognitive processes, and I suggest this is largely due to their considering automatic responses as part of memory, termed nondeclarative or implicit memory. Taking my lead from Wittgenstein's On Certainty, I argue that there is only remembering where there is also some kind of mnemonic effort or attention, and therefore that so-called implicit memory is not memory at all, but a basic, noncognitive certainty.Peer reviewe

Neurally Implementable Semantic Networks

We propose general principles for semantic networks allowing them to be implemented as dynamical neural networks. Major features of our scheme include: (a) the interpretation that each node in a network stands for a bound integration of the meanings of all nodes and external events the node links with; (b) the systematic use of nodes that stand for categories or types, with separate nodes for instances of these types; (c) an implementation of relationships that does not use intrinsically typed links between nodes.Comment: 32 pages, 12 figure

Philosophical Commitments, Empirical Evidence, and Theoretical Psychology

The philosophical or theoretical commitments informing psychological research are sometimes characterized, even by theoretical psychologists themselves, as nonempirical, outside the bounds of methodological consideration, and/or nonrational. We argue that this characterization is incoherent. We illustrate our concern by analogy with problematic appeals to Kuhn’s work that have been influential in theoretical psychology. Following the contemporary pragmatist tradition, we argue that our philosophical/theoretical commitments are part of our larger webs of belief, and that for any of these beliefs to have meaning their content must be informed by our practical engagement with the world, i.e., they are based on empirical evidence, broadly construed. It is this empirical basis that allows us to recognize our commitments at all and rationally to assess and criticize them when necessary. We conclude by demonstrating a rational assessment of the philosophical/theoretical commitments underlying a recent study in the social psychology of religion

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue. In an effort to model this kind of generative process, we propose a neural network-based generative architecture, with latent stochastic variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with recent neural network architectures. We evaluate the model performance through automatic evaluation metrics and by carrying out a human evaluation. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate the generation of long outputs and maintain the context.Comment: 15 pages, 5 tables, 4 figure

Horizon Report 2009

El informe anual Horizon investiga, identifica y clasifica las tecnologías emergentes que los expertos que lo elaboran prevén tendrán un impacto en la enseñanza aprendizaje, la investigación y la producción creativa en el contexto educativo de la enseñanza superior. También estudia las tendencias clave que permiten prever el uso que se hará de las mismas y los retos que ellos suponen para las aulas. Cada edición identifica seis tecnologías o prácticas. Dos cuyo uso se prevé emergerá en un futuro inmediato (un año o menos) dos que emergerán a medio plazo (en dos o tres años) y dos previstas a más largo plazo (5 años)