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# introduction
Training Common Sense ( proposed by Manetta Berends, Femke Snelting )
Where and how can we find difference, ambiguity and dissent in pattern-recognition?
"Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves." Chris Anderson (2008) http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory
-> This track is co-organised in close collaboration with the Text generation project, http://pad.constantvzw.org/p/text_generation , and will partially overlap.
What kind of assumptions do we encounter when valuating information from the point of view of an algorithm? In what way does the introduction of pattern-recognition allow (or makes impossible) difference, ambiguity and dissent? Through exploring the actual math and processes of pattern-recognition together, and by studying and experimenting with software packages (Pattern, ...), methods and reference-libraries (WordNet. ...) we would like to understand better what agency human and computational actors might have in the co-production of 'common sense'.
Pattern-recognition is a method applied in all kinds of data-mining applications. Data mining is an industry aimed at producing predictable, conventional and plausible patterns within a dataset. In other words it is about avoiding exceptions, uncertainties and surprises. It promises to have overcome ideology and the need for models by letting the data 'speak' for itself, but it relies on the extrapolation of the common sense of human actors (eg. mining-software developers, designers of mining methods, dataset annotators, ...). In order to start recognizing patterns in a set of data, normalization is applied on many interconnected levels. While arranging categories, annotating a training set, and in comparing to a so called (preset) Golden Standard, mining-algorithms are being trained. All these steps contain acts of normalization. Is the information in such process valuated on its regularity or rather on its average?
Training Common Sense is inspired by discoveries we did during Cqrrelaties (January 2015) but we'll focus on pattern-recognition, not just for text but also for images, 3D-objects etc.
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# Knowdledge Discovery in Data (KDD) steps
http://pad.constantvzw.org/p/commonsense_kdd_step-1 --> data collection
http://pad.constantvzw.org/p/commonsense_kdd_step-2 --> data preperation
http://pad.constantvzw.org/p/commonsense_kdd_step-3 --> data mining
http://pad.constantvzw.org/p/commonsense_kdd_step-4 --> interpretation
http://pad.constantvzw.org/p/commonsense_kdd_step-5 --> determine actions
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# Text mining application examples
"Online social media such as Facebook are a particularly promising resource for the study of people, as “status” updates are self-descriptive, personal, and have emotional content [7]. Language use is objective and quantifiable behavioral data [96], and unlike surveys and questionnaires, Facebook language allows researchers to observe individuals as they freely present themselves in their own words. Differential language analysis (DLA) in social media is an unobtrusive and non-reactive window into the social and psychological characteristics of people's everyday concerns."
"This method can complement traditional assessments, and can quickly and cheaply assess many people with minimal burden."
"Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions."
---
* Facebook messages Gender/Age profiles
*Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) --> http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783449/
*Word Well-Being Project --> http://www.wwbp.org/data.html#refinement
*- TEDx video, Using twitter to predict heart disease --> individual modeling from Facebook --> https://www.youtube.com/watch?v=FjibavNwOUI
*
*The Big Five Personality test --> http://www.outofservice.com/bigfive/
*- I'm a O80-C74-E18-A32-N14 Big Five!! --> http://www.outofservice.com/bigfive/results/?oR=0.85&cR=0.722&eR=0.375&aR=0.583&nR=0.312
*- http://www.thebigfiveproject.com/results/?ocean=775,555,718,694,25,7c&dem=1970,f,dd
*the Big Five (wiki) --> https://nl.wikipedia.org/wiki/Big_Five_%28persoonlijkheidsdimensies%29
*"De Big Five is oorspronkelijk gebaseerd op een Amerikaans onderzoek naar het gebruik van alle bijvoeglijk naamwoorden waarmee proefpersonen het karakter van een hun bekende persoon beschreven."
*Lexical hypothesis (wiki) --> https://en.wikipedia.org/wiki/Lexical_hypothesis
*"Many traits of psychological importance are too complex to be encoded into single terms or used in everyday language."
*"The Lexical Hypothesis relies on terms that were not developed by experts."
*https://upload.wikimedia.org/wikipedia/commons/b/b2/Galton%27s_correlation_diagram_1875.jpg
*
* Hedonometer --> http://hedonometer.org/api.html
*Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter --> http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0026752
* CLiPS --> AMiCA
*AMiCA, Automatic Monitoring for Cyberspace Applications -->http://www.clips.ua.ac.be/category/projects/amica , http://www.amicaproject.be/
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# notes:
common sense = "?"
*= "something we take for granted easily, as we don't feel the need of reconsidering anymore" (?)
*= "the data that 'speaks' for itself (as commonly is stated by data-mining corporations)" (?)
*= "opposite to context specific 'sense' (?): a specific moment, location, spoken by a specific author, and read by a specific reader"
"In machine learning, one aims to construct algorithms that are able to learn to predict a certain target output." (Mitchell, 1980; desJardins and Gordon, 1995). — http://en.wikipedia.org/wiki/Inductive_bias
A trained algorithm has 'learned to predict', which already contains a speculative act within it. What if we use the fact that our predictions doesn't nescessarely need to find a truth in a near future? We could stretch and scale the type of training-elements we would like to work with. This fiction element could help us to show the absurdity of annotating a certain 'truth'/term/concept with 0's or 1's.
* machine-training elements we could replace:
(that could touch the problem of common sense)
*- machine-readable data or sources to train the algorithm on
*- parsing (pre-process text for more efficient analysis)
*- deciding on outcome algorithm on forhand (where to train algorithm on?)
*- fixed classification-'settings' to train the algorithm on
*- questions/problems of classification
*- callibration (editing the results of the algorithm)
*- trained algorithm (to apply on other sources)
*- a corpus of pre-analysed and pre-parsed data, for testing and training purposes
*- (golden) standards
*--> can we define a set of 'Golden Standards' according to specific context/situation?
*--> example: when looking to used training-datasets (for example WordNet), could we think about types of output we would like to create (speculative outputs), that would reach our own 'Liquid Standards'?
*- alternative ways of training
*--> example: when annotating a set of images as either positive or negative for example, can we then re-assemble them into a form, like collages for instance, by thinking of a 'fantastical' algorithm that would determine their location? (trying to think within a practise-based worksetting)
*--> 'ways of training' (after: ways of seeing, John Berger)
* problems related to the common-sense results:
*- "it is only possible to categorise, after you have defined the categories" — Solon Barocas at Cqrrelations, 2015
*- how can we escape the vicious circle (of training by showing examples of current 'status quo')?
*- when an algorithm is made to predict a certain 'truth', how does it find the unpredictable / margins?
* data mining methods:
"Several data mining methods are particularly suitable for profiling. For instance, classification and clustering may be used to identify groups. Regression is more useful for making predictions about a known individual or group." Discrimination and Privacy in the Information Society (2013), Bart Custers, Toon Calders, Bart Schermer, Tal Zarsky, (eds.) — page 13
* "Supervised learning is the machine learning task of inferring a function from labeled training data." The term 'supervised learning' does quite nicely higlight the position of the human in an machine training process. (http://en.wikipedia.org/wiki/Supervised_learning)
Occam's razor, simplification
Galileo Galilei lampooned the misuse of Occam's razor in his Dialogue. The principle is represented in the dialogue by Simplicio. The telling point that Galileo presented ironically was that if one really wanted to start from a small number of entities, one could always consider the letters of the alphabet as the fundamental entities, since one could construct the whole of human knowledge out of them.
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# Links
text-mining software
*Pattern: http://www.clips.ua.ac.be/pages/pattern
*
data-mining training-sets
*WordNet: http://wordnet.princeton.edu/
*SUN group dataset of scenes & objects: http://groups.csail.mit.edu/vision/SUN/
*ModelNet: http://modelnet.cs.princeton.edu/#
*ImageNet: http://www.image-net.org/search?q=cup
*Senti WordNet: http://sentiwordnet.isti.cnr.it/
*
data-mining model types
*https://en.wikipedia.org/wiki/Bag-of-words_model
*https://en.wikipedia.org/wiki/Cluster_analysis
*https://en.wikipedia.org/wiki/Statistical_classification
other
*the Annotator, read-me file report derived from Cqrrelations (jan 2015): http://www.cqrrelations.constantvzw.org/1x0/the-annotator/
*ontology model: http://www.opencyc.org/ --> "the world’s largest and most complete general knowledge base and commonsense reasoning engine"
*http://test.manettaberends.nl/machine-training/plot_multioutput_face_completion_001.png
*http://ooteoote.nl/2015/03/de-dichter-als-informatiemanager/
*https://en.wikipedia.org/wiki/Wikipedia:Manual_of_Style/Words_to_watch#Unsupported_attributions
*http://sicv.activearchives.org/logbook/template-after-the-fact/
*notes from an Annotator: http://people.csail.mit.edu/torralba/publications/memories.pdf
*LabelMe, the open annotation tool: http://labelme.csail.mit.edu/Release3.0/
*"open vocabulary research" http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0073791
*MALLET: MAchine Learning for LanguagE Toolkit http://mallet.cs.umass.edu/
*Heteromation and its discontents http://firstmonday.org/ojs/index.php/fm/article/view/5331/4090
*Touching correlations, workshopnotes on Machine Learning http://etherdump.constantvzw.org/public/notesTouchingCorrelationsWorkshopSeda.html
texts
*- Household words, Stephanie A. Smith (U. of Minnesota Press, 2005)
*- Bernhard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age (U. of Chicago Press, 2007) http://libgen.org/book/index.php?md5=cc10cea0de40bfd17dc6dbc202f80cc3
*- Gerald Moore, Stuart Elden, Henri Lefebvre. Rhythmanalysis: Space, Time and Everyday Life (Continuum, 2004) http://libgen.org/book/index.php?md5=4D8E81ABDF0AF9055887C40ED0DFEB39
*- Matteo Pasquinelli, Anomaly Detection: The Mathematization of the Abnormal in the Metadata Society (2015) http://matteopasquinelli.com/anomaly-detection/
*- Nathan Jurgenson, View From Nowhere: On the Cultural Ideology of Big Data, Oct 2014, http://thenewinquiry.com/essays/view-from-nowhere/
datasets
*http://www.springeropen.com/about/datamining/
*
*
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Subject: [Cqrrelations] The Annotator, report and afterthoughts.
Date: Thu, 07 May 2015 10:44:42 +0200
From: Roel Roscam Abbing <roel@roelroscamabbing.nl>
To: cqrrelations@lists.constantvzw.org
Dear Cqrrelators,
Femke and me finished the report on The Annotator, which together with a
group of Annotators we worked on during Cqrrelations: http://pad.constantvzw.org/p/the_annotator
A few months after Cqrrelations we had digested some impressions and
intuitions and wrote these into the report. The focus of this was the
idea of how 'common sense' is being produced by the self-referential
system of data-selection, the Gold Standard, parsing, desired outcomes
and training. Text-mining requires normalization on all levels of the
process, which for us was exemplified by 'The Removal of Pascal'.
Although the report is a way to round this project up, it is not the
end. Rather we would see it as a beginning to look deeper into these
technological processes of normalization. Perhaps Relearn 2015 is a good
opportunity to continue thinking along these lines. So if you are
interested in collaborating on that please don't hesitate get in touch!
all the best,
R
-------------------
http://www.imprint.co.uk/data-driven-narcissism-how-will-big-data-feed-back-on-us/
https://en.wikipedia.org/wiki/Abductive_reasoning