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Weekly QuEST Discussion Topics and News, 12 Sept

QuEST 12 Sept 2014:

1.) We want to start by addressing a comment made at the end of last week by our colleague Qing W from Rome on Semantic Web efforts and specifically how they relate to ‘big-data’ and how they both relate to QuEST. I’ve spent some time this week updating our Big data and QuEST slides to include capturing up front many of the walk-aways. We have previously (several years ago) gone down this semantic web path but it is worth revisiting where semantic web work fits. Our Rome colleagues have been interacting with James Hendler of RPI. We want to hit some of his presentations and discuss – this also led us to an article from our Google colleagues ‘The unreasonable effectiveness of data’ by Halevy, Norvig, and Pereira. The best way to capture where all this fits versus what we are seeking in QuEST is a section in that article that draws the distinction between Semantic Web and Semantic Interpretation (if you will meaning – thanx Laurie F for keeping us focused on this key). Semantic web is a convention for formal representation languages that lets software services interact with each other without needing AI (or any of the meaning making we’ve discussed in QuEST). Services interact because they use the same standard OR known translations into a chosen standard – it is for ‘comprehending’ appropriately constructed semantic documents / data NOT understanding human speech / writings that haven’t been so constructed – that is the semantic interpretation problem which requires imprecise, ambiguous natural language. ** I clearly have issues with their use of the term ‘comprehending’ in that it is a form of rigidly defining pieces of documents and/or data so they can be combined in a rigorously defined manner and I don’t consider that ‘comprehension’ by the software that code embodies the comprehension of a predefined set of activities that should be allowed with these entries** The semantics in Semantic web is in the code that implements the services in accordance with the pre-wired specifications expressed by accepted ontologies and documentation on appropriate / acceptable manipulation of entries. The semantics in semantic interpretation is associated with meaning to a human as embodied in human cognitive and cultural processes…the goal of QuEST is to engineer computer agents that capture some of the ‘comprehension’ characteristics of human agents to include both intuitive (Type 1) and conscious (Type 2) aspects. We do NOT restrict the semantic aspects to the type 2 and our discussions on big data has captured what we can expect it to provide along the Type 1 axes.

2.) The second topic we want to hit maybe this week is the generation of symbolic representations – for QuEST we are talking the vocabulary of working memory, Qualia. We want to review an article provided to our colleague Sandy V by Prof Ron Sun. Autonomous generation of symbolic representations through subsymbolic activities Ron Sun Version of record first published: 04 Sep 2012. …This paper explores an approach for autonomous generation of symbolic representations from an agent’s subsymbolic activities within the agent-environment interaction. The paper describes a psychologically plausible general framework and its various methods for autonomously creating symbolic representations. The symbol generation is accomplished within, and is intrinsic to, a generic and comprehensive cognitive architecture for capturing a wide variety of psychological processes (namely, CLARION). This work points to ways of obtaining more psychologically/cognitively realistic symbolic and subsymbolic representations within the framework of a cognitive architecture, and accentuates the relevance of such an approach to cognitive science and psychology.

3.) Also I would like to point out an article we reviewed this week associated with the use of Google Glass for physiological parameter estimation – BioGlass: Physiological Parameter Estimation Using a Head-mounted Wearable Device – by Hernandez et al.

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