Home > Uncategorized > Weekly QuEST Discussion Topics and News, 11 Dec

Weekly QuEST Discussion Topics and News, 11 Dec

QuEST 11 Dec 2015

See below for a link to Capt Amerika’s GEOINT talk from June 2015 (starts around 15 min mark)

https://www.bing.com/videos/search?q=geoint+2015+rogers&view=detail&&&mid=2591ED38A64CAFE96AC52591ED38A64CAFE96AC5&rvsmid=2591ED38A64CAFE96AC52591ED38A64CAFE96AC5#view=detail&mid=2591ED38A64CAFE96AC52591ED38A64CAFE96AC5

In our continuing effort to tie together topics from the year to include in the Jan ‘Kabrisky memorial Lecture’ – ‘What is QuEST?’ – we will return to the topic of the unexpected query and specifically attempt to resolve the relationship between the Pan article on Transfer Learning – our use of the phrase ‘unexpected query’ – our quadrant diagram on automation versus autonomy and the unexpected query and the word Sandy V used last week ‘context’.

This topic then leads to a discussion on when we don’t have the right model for the agent to acceptably respond – conceptual combination –

  • Conceptual combination is the process of creating and understanding new meanings from old referents. Our ability to understand novel word compounds, such as octopus apartment or fame advantage, is predicated upon the inherently constructive nature of cognition that allows us to represent new concepts by mentally manipulating old ones.
  • Central to research into how people process such combinations is an understanding of what constitutes the representations of these concepts.

how do we form new models out of previously developed representations – we’ve covered this topic a couple of times this year – in fact this week I was asked to comment on a new article:

Human-level concept learning

through probabilistic

program induction

Brenden M. Lake,1* Ruslan Salakhutdinov,2 Joshua B. Tenenbaum3

11 DECEMBER 2015 • VOL 350 ISSUE 6266  Science

People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms—for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world’s alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches.We also present several “visual Turing tests” probing the model’s creative generalization abilities, which in many cases are indistinguishable from human behavior.

 

We have previously discussed related works:

 

Interpretation and Representation: Testing the
Embodied Conceptual Combination (ECCo) Theory V2
Louise Connell (louise.connell@manchester.ac.uk)
School of Psychological Sciences, University of Manchester
Oxford Road, Manchester M13 9PL, UK
Dermot Lynott (dermot.lynott@manchester.ac.uk)
Decision and Cognitive Sciences Research Centre, Manchester Business School, University of Manchester
Booth Street West, Manchester M15 6PB, UK

  • The Embodied Conceptual Combination (ECCo) theory differs from previous theories of conceptual combination in two key respects.

–     First, ECCo proposes two basic interpretation types: destructive and nondestructive.

–     Second, ECCo assumes complementary roles for linguistic distributional information and perceptual simulation information. Here, we empirically test these assumptions using a noun-noun compound interpretation task.

  • We show that ECCo’s destructive/nondestructive interpretation distinction is a significant predictor of people’s successful interpretation times, while the traditional property/relation based distinction is not.

We also demonstrate that both linguistic and simulation systems make complementary contributions to the time course of successful and unsuccessful interpretation. Results support the ECCo theory’s account of conceptual combination

There was also the work:

Embodied conceptual combination
Dermot Lynott1* and Louise Connell2
1 Manchester Business School, University of Manchester, Manchester, UK
2 School of Psychological Sciences, University of Manchester, Manchester, U

Frontiers in psychology – November 2010 | Volume 1 | Article 212

  • Conceptual combination research investigates the processes involved in creating new meaning from old referents.

–     It is therefore essential that embodied theories of cognition are able to explain this constructive ability and predict the resultant behavior.

  • However, by failing to take an embodied or grounded view of the conceptual system,existing theories of conceptual combination cannot account for the role of perceptual, motor, and affective information in conceptual combination.
  • In the present paper, we propose the embodied conceptual combination (ECCo) model to address this oversight.

–     In ECCo, conceptual combination is the result of the interaction of the linguistic and simulation systems,

–     such that linguistic distributional information guides or facilitates the combination process,

–     but the new concept is fundamentally a situated, simulated entity.

  • So, for example, a cactus beetle is represented as a

–     multimodal simulation that includes

  • visual (e.g., the shiny appearance of a beetle)
  • and haptic (e.g., the prickliness of the cactus) information,

–     all situated in the broader location of a desert environment under a hot sun,

–     and with (at least for some people) an element of creepy-crawly revulsion** Affective aspects **

  • The ECCo theory differentiates interpretations according to whether theconstituent concepts are destructively, or non-destructively, combined in the situated simulation.
  • We compare ECCo to other theories of conceptual combination, and discuss how it accounts for classic effects in the literature.

news summary (35)

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