Tag Archives: Statistics

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<< The Ambiguating Languages of Stat, Status, Statistic >>


To love automation is to love statistics; unwavering, unquestioned, unambiguously and as purely wholesome?

In 1749 the “Summarisk Tabell” or the first  “systematic collection of statistics” was architectured by the Swedish government and its “Tabellverket” which means ‘tabular work.’ In this context  it became to mean their office for tabulation and was entitled ‘Statistiska centralbyrån’ or the Swedish Central Bureau of Statistics (The Joy of Stats 2010: 12:10)

Spiegelhalter rationally reminds us that statistics used to be called “political arithmetic”. (Ibid: 14:24).

Statistics is etymologically related to the Latin word “status.” In turn, this directly links to the concept of “political state”. The statista, or statesmen, were/are probably more skilled in affairs of state, unveiling and organizing resources for they who were controlling and running the state, than skilled in the measurement or probability via numerical accuracy. They were skilled toward the industrialization of the resources of the state. “In what way is the status ‘a’ changin’?,” might here then be a concern in favor of status rather than too much in favor of change and too many outliers. Is then (social) innovation at all times looked upon with eagerness?

This historical awareness is allowing one possible dimension in continuing processes of (mis)understanding of what was then a drive for increased control and perceived decrease of misunderstanding of (their) populations.

It is however not history alone. Similar centers of power are at play today. They might be nation states. They might be transnational. They might be known as corporate entities or (private) financial institutions. Please note, one does not need to loose track into any conspiracy theorizing to identify these. By the way, the latter I sense as a conspiracy-of-the-self against the self, by using hyperambiguating narratives (aka conspiracies) as a blindfold of what is (is as “realities”) versus what is-imagined. The real(s) is(are) “fantastical” enough (to me).

Returning back to the above referenced video —hosted by the delightful, energetic and sadly late Professor Rosling— it continues in unveiling the 19th century popular excitement for statistical (visualized) facts. Today, with a popular engrossment with distrust as a proverbial spoon, excitement is stirring up and thinning down statistical fact. We could note that by questioning our present-day versions of feudal masters we might also be deconstructing our own tools to enable us to question the same (a “conspiracy of the “self” serving the “self”?). The false linear dichotomy is as disenfranchising as any side of this faux-2D plastic coin: “Ambiguate all and thy shall be ruled through your fog. Disambiguate all and they shall be hammered and tyrannized.“

As with statistics, automation too could be controlling and enabling, rational and mesmerizing. Logos and pathos. Enlightening and clouding. liberating and enshackling; …ad infinitum and gone immediately. While ethos might have been sulking in the corner.

In light of enablement and increasing both awareness and voice, W.E.B Du Bois’ work, for instance, is still an awe-inspiring and humbling exemplar, especially to the statistically-privileged and exnominated samples within the larger and diverse human population.


Automation could be interpreted as an applied extension of statistical control and narrowing of understanding by means of repurposing, appropriating and regurgitating the statistical styles of the most likely/ed (resources).

Automation, as statistics, was initially not invested into with the aim of democratization. It was a matter of control, understanding, and increase of efficiencies toward a more desired return for those who initiated and enabled the creation, architecturing and implementation.

The needed “ambiguation” (here meaning: pluralization, nuancing, modding and jailbreaking of meaning, relation, intent, application, usage, etc.) of initial intent by diversification and decentralization of intent(s), could best be seen as a process rather than an opposition against a more popular idea of a fixed denotation of language (this latter which I would prefer not subscribing to too rigidly either).

Riding yet another vector: statistics applications could be cannibalizing statistics. This could be seen as one type of ambiguation. Clear information through the lens of statistics is undone by automated diffusing statistical probabilities, possibly waging siege (with mal-, mis- and dis-information as arsenals) against initiatives aiming to unveil the incorrect and (almost) unconscious, biased “stats” we impose, as people, onto ourselves (and others). This latter too can be seen as yet another type of ambiguation. Herewith might come to mind such initiatives as Gapminder (see Rosling), Our World in Data, The Deep, etc. These are initiatives in counterattack against conspiracies, scaled bias, systemic mis-, mal- and dis-informing/conception (…and yet, brittle these aforementioned initiatives are as well).

Automation and statistics are not inherently, nor complacently, democratizing, freeing, nor enlightening. There is nothing inherently socio-historically linear nor monolithic about these. They can be and have been historically invented and applied as such though. They are/should neither (be) a fait accompli to defining your acts, relations nor realities. There must be vigilant, at times incessant, work and a labor of citizen love.

It might be felt as a real-time theater play with the actors Ambiguous and Disambiguous, in the starring roles portraying luscious eroticism between fact and fuzz, creating worlds as stages for realities re-re-formed.

References

animasuri’23. (2022). Data in, fear and euphoria out. (Blog). https://www.animasuri.com/iOi/?p=3480

animasuri’23. (2023). Learning is Relational Entertainment; … (blog).  https://www.animasuri.com/iOi/?p=4442

Aschenwall, Gottfried. (1748). Vorbereitung zur Staatswissenschaft der heutigen fürnehmsten europäischen Reiche und Staaten.

Battle-Baptiste, W., Du Bois, W.E. B., Rusert, B. (2018). W.E.B Du Bois’s data portraits. visualizing Black America. Princeton Architectural Press.

Dehbozorgi, Alireza. (2023). LinkedIn post: “”Language is an instrument of political and social domination. From ancient China to Europe, the number of words and languages one mastered were signs of belonging to an elite. Artificial intelligence is reshaping the linguistic landscape. An interview with linguist Stefanie Ullmann, machine learning specialist Omolabake Adenle, and philosopher Marc Crepon.” from: ARTE.tv Documentary. (2023). AI and Language

Gapminder  https://ourworldindata.org/

Rosling, H. (2010). IN: Hillman, D, et al. (2010). The Joy of Stats with Professor Hans Rosling.  (Video) BBC & Wingspan Production via Gapminder  last retrieved on May 8, 2023 from https://vimeo.com/18477762

Rosling, H., Rosling Ronnlund, A. (2018). Factfulness: Ten Reasons We’re Wrong About the World–and Why Things Are Better Than You Think. Flatiron Books; Later prt. editio

Our World in Data. https://ourworldindata.org/

Sustainable Development Goals Tracker (https://sdg-tracker.org/) 

The Deep: http://thedeep.io/

van Bergen, Emille. (20223). quoting Marc Crepon “…we basically need to maintain a relationship with language that resists anything aiming to format it, calculate it or program it…” via Dehbozorgi, Alireza. (2023). LinkedIn post

text | wondering, The field of AI

The Field of AI (Part 02-6): A “Pre-History” & a Foundational Context

post version: 2 (April 28, 2020)
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URLs for A “Pre-History” & a Foundational Context:

  • This post is the main post on a Pre-History & a Foundational context of the Field of AI. In this post a narrative is constructed surrounding the “Pre-History”. It links with the following posts:
  • The post here is a first and very short linking with on Literature, Mythology & Arts as one of the foundational contexts of the Field of AI
  • The second part in the contextualization is the post touching on a few attributes from Philosophy, Psychology and Linguistics
  • Following, one can read about very few attributes picked up on from Control Theory as contextualizing to the Field of AI
  • Cognitive Science is the fourth field that is mapped with the Field of AI.
  • Mathematics & Statistics is in this writing the sixth area associated as a context to the Field of AI
  • Other fields contextualizing the Field of AI are being considered (e.g. Data Science & Statistics, Economy, Engineering fields)


05 — The Field of AI: A Foundational Context: Mathematics & Statistics

Mathematics & Statistics

The word ‘mathematics’ comes from Ancient Greek and means as much as “fond of learning, study or knowledge”. Dr. Hardy, G.H. (1877 – 1947), a famous mathematician, defined mathematics as the study and the making of patterns[1]. At least intuitively, as seen from these different perspectives, this might make a link between the fields of Cognitive Science, AI and mathematics a bit more obvious or exciting to some.

Looking at these two simple identifiers of math, one might come to appreciate math in itself even more but, also one might think slightly differently of  “pattern recognition” in the field of “Artificial Intelligence” and its sub-study of  “Machine Learning.”[2] Following, one might wonder whether mathematics perhaps lies at the foundation of machine or other learning.

Mathematics[3] and its many areas are covering formal proof, algorithms, computation and computational thinking, abstraction, probability, decidability, and so on. Many introductory K-16 resources are freely accessible on various mathematical topics[4] such as statistics.[5]

Statistics, as a sub-field or branch of mathematics, is the academic area focused on data and their collection, analysis (e.g. preparation, interpretation, organization, comparison, etc.), and visualization (or other forms of presentation). The field studies models based on these processes imposed onto data. Some practitioners argue that Statistics stands separately from mathematics.

These following areas of study in mathematics (and more) lie at the foundation of Machine Learning (ML).[6] Yet, it should be noted, one never stops learning mathematics for specialized ML applications:

  • (Bayesian) Statistics[7]
    • Statistics.[8]
    • See a future post for more perceptions on probability
    • Probability[9] Theory[10] which, is applied to make assumptions of a likelihood in the given data (Bayes’ Theorem, distributions, MLE, regression, inference, …);[11]
    • Markov[12] Chains[13] which model probability[14] in processes that are possibly changing from one state into another (and back) based on the present state (and not past states).[15]
    • Linear Algebra[16] which, is used to describe parameters and build algorithm and Neural Network structures;
      • Algebra for K-16[17]. Again, over-simplified, algebra is a major part of mathematics studying the manipulation of mathematical symbols with the use of letters, such as to make equations and more.
      • Vectors[18]            
      • Matrix Algebras[19]
    •  (Multivariate or multivariable) Calculus[20] which, is used to develop and improve learning-related attributes in Machine Learning.
      • Pre-Calculus & Calculus[21]: oversimplified, one can state that this is the mathematical study of change and thus also motion.[22] Note, just perhaps it might be advisable to consider first laying some foundations of (linear) algebra, geometry and trigonometry before calculus.
      • Multivariate (Multivariable) Calculus: instead of only dealing with one variable, here one focuses on calculus with many variables. Note, this seems not commonly covered within high school settings, ignoring the relatively few exceptional high school students who do study it.[23]
        • Vector[24] Calculus (i.e. Gradient, Divergence, Curl) and vector algebra:[25] of use in understanding the mathematics behind the Backpropagation Algorithm, used in present-day artificial neural networks, as part of research in Machine Learning or Deep Learning and the supervised learning technique.
      • Mathematical Series and Convergence, numerical methods for Analysis
    • Set Theory[26] or Type Theory: the latter is similar to the former except that the latter eliminates some paradoxes found in Set Theory.
    • Basics of (Numerical) Optimization[27] (Linear / Quadratic)[28]
    • Other: discrete mathematics (e.g. proof, algorithms, set theory, graph theory), information theory, optimization, numerical and functional analysis, topology, combinatorics, computational geometry, complexity theory, mathematical modeling, …
    • Additional: Stochastic Models and Time Series Analysis; Differential Equations; Fourier’s and Wavelengths; Random Fields;
    • Even More advanced: PDEs; Stochastic Differential Equations and Solutions; PCA; Dirichlet Processes; Uncertainty Quantification (Polynomial Chaos, Projections on vector space)
Mini Project #___ : 
Markov Chains 
Can you rework this Python project by Ms. Linsey Bieda, to use Chinese or another language’s word list?
Project context: https://rarlindseysmash.com/posts/2009-11-21-making-sense-and-nonsense-of-markov-chains 
Code source: https://gist.github.com/3928224

[1] Hardy. H.R. & Snow, C.P. (1941).  A Mathematician’s Apology. London: Cambridge University Press

[2] More on “pattern recognition” in the field of “Artificial Intelligence” and its sub-study of  “Machine Learning” will follow elsewhere in future posts.

[3] Courant, R. et al. (1996). What Is Mathematics? An Elementary Approach to Ideas and Methods. USA: Oxford University Press  

[4] For instance (in alphabetical order):

[5] Meery, B. (2009). Probability and Statistics (Basic). FlexBook.  Online: CK-12 Foundation. Retrieved on March 31, 2020 from  http://cafreetextbooks.ck12.org/math/CK12_Prob_Stat_Basic.pdf

[6] a sub-field in the field of Artificial Intelligence research and development (more details later in a future post). A resource covering mathematics for Machine learning can be found here:

Deisenroth, M. P. et al. (2020). Mathematics for Machine Learning. Online: Cambridge University Press. Retrieved on April 28, 2020 from https://mml-book.github.io/book/mml-book.pdf AND https://github.com/mml-book/mml-book.github.io

Orland, P. (2020). Math for Programmers. Online: Manning Publications. Retrieved on April 28, 2020 from https://www.manning.com/books/math-for-programmers 

[7] Downey, A.B. (?).Think Stats. Exploratory Data Analysis in Python. Version 2.0.38 Online: Needham, MA: Green Tea Press. Retrieved on March 9, 2020 from http://greenteapress.com/thinkstats2/thinkstats2.pdf

[8] A basic High School introduction to Statistics (and on mathematics) can be freely found at Khan Academy. Retrieved on March 31, 2020 from https://www.khanacademy.org/math/probability

[9] Grinstead, C. M.; Snell, J. L. (1997). Introduction to Probability. USA: American Mathematical Society (AMS). Online: Dartmouth College. Retrieved on March 31, 2020 from https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf AND solutions to the exercises retrieved from http://mathsdemo.cf.ac.uk/maths/resources/Probability_Answers.pdf

[10] Such as: Distributions, Expectations, Variance, Covariance, Random Variables, …

[11] Doyle, P. G. (2006). Grinstead and Snell’s Introduction to Probability. The CHANCE Project. Online: Dartmouth retrieved on March 31, 2020 from https://math.dartmouth.edu/~prob/prob/prob.pdf

[12] Norris, J. (1997). Markov Chains (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge: Cambridge University Press. Information retrieved on March 31, 2020 from https://www.cambridge.org/core/books/markov-chains/A3F966B10633A32C8F06F37158031739  AND http://www.statslab.cam.ac.uk/~james/Markov/  AND  http://www.statslab.cam.ac.uk/~rrw1/markov/    http://www.statslab.cam.ac.uk/~rrw1/markov/M.pdf AND https://books.google.com.hk/books/about/Markov_Chains.html?id=qM65VRmOJZAC&redir_esc=y

[13] Markov, A. A. (January 23, 1913). An Example of Statistical Investigation of the Text Eugene Onegin Concerning the Connection of Samples in Chains. Lecture at the physical-mathematical faculty, Royal Academy of Sciences, St. Petersburg, Russia. In (2006, 2007). Science in Context 19(4), 591-600. UK: Cambridge University Press. Information retrieved on March 31, 2020 from https://www.cambridge.org/core/journals/science-in-context/article/an-example-of-statistical-investigation-of-the-text-eugene-onegin-concerning-the-connection-of-samples-in-chains/EA1E005FA0BC4522399A4E9DA0304862

[14] Doyle, P. G. (2006). Grinstead and Snell’s Introduction to Probability. Chapter 11, Markov Chains. Dartmouth retrieved on March 31, 2020 from https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf

[15] A fun and fantasy-rich introduction to Markov Chains: Bieda, L. (2009). Making Sense and Nonsense of Markov Chains. Online, retrieved on March 31, 2020 from https://rarlindseysmash.com/posts/2009-11-21-making-sense-and-nonsense-of-markov-chains AND https://gist.github.com/LindseyB/3928224

[16] Such as: Scalars, Vectors, Matrices, Tensors….

See:

Lang, S. (2002). Algebra. Springer AND

Strang, G. (2016). Introduction to Linear Algebra. (Fifth Edition). Cambridge MA, USA: Wellesley-Cambridge & The MIT Press. Information retrieved on April 24, 2020 from https://math.mit.edu/~gs/linearalgebra/ AND https://math.mit.edu/~gs/AND

Strang, G. (Fall 1999). Linear Algebra. Video Lectures (MIT OpenCourseWare). Online: MIT Center for Advanced Educational Services. Retrieved on March 9, 2020 from https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/ AND

Hefferon, J. Linear Algebra. http://joshua.smcvt.edu/linearalgebra/book.pdf  AND http://joshua.smcvt.edu/linearalgebra/#current_version  (teaching slides, answers to exercises, etc.)

[17] Algebra basics and beyond can be studied via these resources retrieved on March 31, 2020 from https://www.ck12.org/fbbrowse/list?Grade=All%20Grades&Language=All%20Languages&Subject=Algebra

[18] Roche, J. (2003). Introducing Vectors. Online Retrieved on April 9, 2020 from http://www.marco-learningsystems.com/pages/roche/introvectors.htm

[19] Petersen, K.B & Pedersen, M.S. (November 15, 2012). The Matrix Cookbook. Online Retrieved from http://matrixcookbook.com and https://www2.imm.dtu.dk/pubdb/views/edoc_download.php/3274/pdf/imm3274.pdf

[20] Such as: Derivatives, Integrals, limits, Gradients, Differential Operators, Optimization. …See a leading text book for more details: Goodfellow, I. et al. (2017). Deep Learning. Cambridge, MA: MIT Press + online via www.deeplearningbook.org and its https://www.deeplearningbook.org/contents/linear_algebra.html Retrieved on March 2, 2020.

[21] Spong, M. et al. (20-19). CK-12 Precalculus Concepts 2.0. Online: CK-12 Retrieved on March 31, 2020 from https://flexbooks.ck12.org/cbook/ck-12-precalculus-concepts-2.0/ and more at https://www.ck12.org/fbbrowse/list/?Subject=Calculus&Language=All%20Languages&Grade=All%20Grades

[22] Jerison, D. (2006, 2010). 18.01 SC Single Variable Calculus. Fall 2010. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA. Retrieved on March 31, 2020 from https://ocw.mit.edu/courses/mathematics/18-01sc-single-variable-calculus-fall-2010/#

[23] A couple of anecdotal examples can be browsed here: https://talk.collegeconfidential.com/high-school-life/1607668-how-many-people-actually-take-multivariable-calc-in-high-school-p2.html and https://www.forbes.com/sites/johnewing/2020/02/15/should-i-take-calculus-in-high-school/#7360ae8a7625 .  In this latter article references to formal studies are provided; it is suggested to be cautious about taking Calculus, let alone the multivariable type. An online course on Multivariable Calculus for High school students is offered at John Hopkins’s Center for Talented Youth: Retrieved on March 31, 2020 from https://cty.jhu.edu/online/courses/mathematics/multivariable_calculus.html Alternatively, the MIT Open Courseware option is also available: https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/Syllabus/

[24] Enjoy mesmerizing play with vectors here: https://anvaka.github.io/fieldplay  

[25] Hubbard, J. H. et al. (2009). Vector Calculus, Linear Algebra, and Differential Forms A Unified Approach. Matrix Editions

[26] The study of collections of distinct objects or elements. The elements can be any kind of object (number or other)

[27] Boyd, S & Vandenberghe, L. (2009). Convex Optimization. Online: Cambridge University Press. Retrieved on March 9, 2020 from https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf

[28] Luke, S. (October 2015). Essentials of Metaheuristics. Online Version 2.2. Online: George Mason University. Retrieved on March 9, 2020 from https://cs.gmu.edu/~sean/book/metaheuristics/Essentials.pdf