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AI’s Overfitting, Noise, Thresholds, and… are we alone in the universe?



Noise as beauty.

A tache de beauté on the model’s face, located on the top right, above the curvature of the lipping graph, unfitting the correlation yet, necessarily there.

If included in an artificial neural network, it is undesirably over-fitted. it is the outcast, the stain, confusing the learning. It is the anecdotal clouding of the generalization.

To fit is to overfit, as is ripe to over-ripe. Yet,…

…what about the cheese, the wine, the alcoholic fruit, touching the beauty of a calculated time? What of the fertility of the germ digested?

what of the wrinkles and ripples given substance to life and to relationship with experience unfitting the dances of the spheres?

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what of that ripple of life, as a tache of the universe; a noise of a germing of beginnings in galaxies otherwise void of it; as a baby’s skin void of experience?

Is its novelty worthwhile the read in ambiguity of meaning, without the noise of the intrigue surrounding its essential patterns to come?

is the novelty of life, the germ of patterns emerging from it, as noise onto the pattern of physics as an overtone onto cosmic springs, vibrating?

The universe’s babbling.

we are not alone: we are with life, beyond the learn’ed curvatures.

therein lies the pattern of the disorganized, the cast-out from the star’s dust, as a noise of an escaping parabolic sphere.

text | wondering, The field of AI

The Field of AI (Part 07): a bibliography with URLs

compared to the previous posts this post is very rough and messy. When time permits I will clean it up.


Here below are a few lists associated with my personal learning about the academic field of AI and its related fields.

I am not an expert in this field; rather the contrary. I am interested in considering questions related with bringing technology literacy into the thinking of educators and learners within the K-12 realm. The resources below and elsewhere here in this blog are mainly catered to high school (or those beyond). If some resources seem too advanced, they were added to show where one could aim to learn toward or where one could end up if one continued studies in this area. Therefore, some resources are technical while others are not.

This post includes a name list. It is a list of what I understand to be leading voices in the field. Secondly, there is a bibliography of works I found online or offline.

I superficially or more profoundly browsed these , in search of a better understanding and contextualization (in a historical and trans-disciplinary setting).

Where found and available URLs are offered. Some additional or the same URLs or references can be found in other posts (see the one on mathematics and the field of AI, to name but one).

Lastly, a list of URLs to data sets is included at the end of this post.

An Incomplete List of Leading Voices

As a young learner on your educational path and as a young participant in your community you might like to find some references that can give you an idea or that can ignite your imagination of how and where and with whom to walk your path towards growing into tomorrow’s AI expert.

We learn on our own but better even by the guidance of others and by observing and learning from what they have done or are doing.

Here below is a list of historical figures and present-day scholars. The list is surely not complete and needs continued updating. The list does not imply a preference imposed by the author. It tries to highlight a few scholars from around the world, scholars from within China and scholars that are in pure academic research as well as in innovation and entrepreneurial areas. More scholars and innovators, involved in important work in the field of AI, are missing than those listed. It also is incomplete as to what all the work is that these experts have been involved in up till now. The list does however give you a spark, a hint and points you in directions you can further explore by means of your own research and study.

Maybe you can add a few names to this list. Perhaps one day your work will be a beacon as well for another young learner.

Following The Master Part 1: Some Historical, Foundational key-Figures or Technological Pioneers in the Earliest Development of the field of AI.

·         BOOLE, George (1815 – 1864)
·         Dr. CHOMSKY, Noam (1928 – )
·         SHANNON, Claude
·         Dr. FEIGENBAUM, Edward (1936 –     )
·         Dr. FREGE, Gottlob (1848 – 1925)
·         Dr. GÖDEL, Kurt (1906 – 1978)
  • Mathematician and one of the most important logisticians.
    • His work lies at the mathematical and logical foundation of, for instance, Turing’s work.
·         Dr. GOOD, Irving John (1916 – 2009)
    • [2] on “intelligence explosion,”[3] that are claimed to have led to today’s hypothetical concept of the “technological singularity”.[4] Similar to this present-day hypothesis, Dr. Good envisioned a potential future creation, in the likes of what we now call the future-existence of an Artificial General Intelligence agent, (AGI), that would be able to solve human concerns and that would far outweigh human (intellectual) ability. He felt it stood to reason that it would be the last invention humans had to make.[5]
·         Dr. HOLLAND, John Henry (1929 – 2015)
  • 1929-2015
    • While suggested by Turing[6], he pioneered Genetic Algorithms with other scientists such as Dr. GOLDBERG, David Edward.
·         Dr. Licklider, J. C. R. (1915 – 1990)
·         Dr. McCARTHY, John (1927 – 2011)
  • Inventor of the LISP programming language, used in the early research and development of AI systems
    • Co-founder of the academic field of Artificial Intelligence
    • Invented the term “Artificial Intelligence”
·         Dr. MINSKY, Marvin (1927 – 2016)
  • Co-founder of The MIT Artificial Intelligence Lab
    • Leading voice on and pioneer in Artificial Intelligence
    • Built the first neural network learning machine in 1951
·         Dr. NEWELL, Allen (1927 – 1992)
·         Dr. PAPERT, Seymour (1928 – 2016)
·         Dr. ROBINSON, John Alan
·         ROCHESTER, Nathaniel
·         Dr. SAMUEL, Arthur (1901 – 1990)
  • A pioneer in computer gaming and AI
    • He put the term and the research surrounding “Machine Learning” on the map in the 1950s and with his paper: “Some Studies in Machine Learning Using the Game of Checkers“. IBM Journal of Research and Development. 44: 206–226
  • ·         STRACHERY, Christopher
·         Dr. TURING, Alan (1912 – 1954)
  • By many referred to as the father of Artificial Intelligence.
    • Created fundamental theories of Computation and in Computer Science; e.g. the idea of the binary digital language of ones and zeroes, the (theoretical) Turing Machines
    • Created the Turing Test allowing to see whether or not a machine showcased intelligent behavior[8]
    • Created the first electronic computer
·         Dr. von NEUMANN, John (1903 – 1957)
  • [blablabla]
    • Most computers, as we know them today, are based on his conceptualizations.
  • Dr. WÁNG Hào (王浩)(1921 – 1995)
    • Tsinghua University graduate
    • Mathematician, philosopher and logistician.
    • Proved hundreds of mathematical theories with a computer program written in 1959
    • Inventor of the Wang Tiles; proved that any Turing Machine can be turned into Wang Tiles.
    • Inventor of a number of computational models.
  • Dr. ZADEH, Lotfi Aliasker (1921 – 2017)
    • AI researcher, mathematician, computer scientist
    • Inventor[9] of Fuzzy Mathematics, Fuzzy Algorithms, Fuzzy Sets, Fuzzy Logic and so on.  Over-simplified, Fuzzy logic is a generalization of Boolean logic and a form of many-value logic, with values in-between 0 and 1 (whereas traditionally, logic operates with values that are either on or off; one or zero; right or wrong).
Mini Project #___ : Inspiration by Following the Old Masters
  • Which of these figures or their work are you most inspired by? Why?
  • Can you find some other historical figures associated with research & developments (R&D) in AI whom you are inspired by?

Following The Master Part 2: Some Present-day Leading Figures in the field of AI (in alphabetic order) with attention to Chinese Scholars

The following is an incomplete list composed as of January 2020. Please note, more experts or leading voices are mentioned across the text chapters (e.g. see footnotes and references; see section 05 of this text). The author apologizes to any expert or leading voice that might not yet be represented or that might be inaccurately represented here. Pease contact the author so that refinements can be added. The aim is to give young learners some sort of direction, motivation, hints and passion for the field of AI. Thank you for your support:

  • ·         Dr. BENGIO, Yoshua:  Research into Artificial Neural Networks, Unsupervised Machine Learning and Deep Learning
  • ·         Dr. BLEI, David M.: research in Machine Learning, Statistical Models (e.g. Topic Modeling), algorithms, and related topics.
  • ·         Dr. BOSTROM, Nick: Research in ethics surrounding Artificial Superintelligence.
  • ·         Dr. BREASEAL, Cynthia: Research in Human-Robot Interaction.
  • ·         Dr. BRYSON, Joanna: Research in AI ethics and in AI systems helping to understand biological intelligence
  • ·         Dr. Chén Dānqí 陈丹琦: a Tsinghua & Stanford Universities graduate. Assistant Professor at Princeton University. Main field of research in Natural Language Processing (NLP).
  • Graduate from and Professor at the Institute of Computing Technology, Chinese Academy of Sciences. Selected by MIT Technology Review as one of the 2015 top 35 innovators under 35 years old. Research and developments in large-scale Machine Learning solutions, Deep Learning, reduction of energy requirements or computational costs, brain-inspired processor chips, and related topics.
  • ·         Dr. DUAN Weiwen: AI Director of the Department of Philosophy of Science and Technology, The Institute of Philosophy, The Chinese Academy of Social Sciences (CASS). He specializes, among others, in ignorance in sciences, philosophy of IT, Big Data issues, and Artificial Intelligence. Dr. Duan is the deputy chairman of the Committee of Big Data Experts of China. His research is supported by the National Social Sciences Fund of China (NSSFC).
  • Research and developments in Emotional AI, subtle expression recognition, facial recognition.  Entrepreneur in related AI developments.
  • ·         Dr. ERKAN, Ayse Naz: Research in Content Understanding & Applied Deep Learning.
  • ·         Dr. FREUND, Yoav: Research and advances in algorithm design, Machine Learning and probability theory.
  • ·         Dr. FUNG, Pascale馮雁: leading researcher in Natural Language Processing.
  • Research on Human-centered AI, autonomous vehicles, Deep Learning, robotics
  • Research on algorithm design, Bayesian Machine Learning, Computational Neuroscience, Bioinformatics, Statistics and other areas.
  • ·         Dr. GOERTZEL, Ben Research on and developments in AGI and robotics.
  • Research and developments in Machine Learning and Deep Learning
  • Neuroscientist. Research and developments in Artificial General Intelligence (AGI), Machine Learning (AlphaGo) and related topics.
  • ·         Dr. HINTON, Geoffrey E:  Leading AI academic, computer scientist and cognitive psychologist with a focus on artificial neural networks. The great-great-grandson of the logician George Boole ( who’s work on, among others, algorithms for logic deduction, is still of computational importance). Dr. Hinton is considered one of the most prominent pioneers in Deep Learning.
  • ·         Dr. HUTTER, Marcus. researching the mathematical foundations of AI and Reinforcement Learning; leading authority on theoretical models of super intelligent machines.
  • Trained Dr. Ng and other leading scholars in the field of AI. Research in Machine Learning, recurrent neural networks, Bayesian networks in Machine Learning and other links between Machine Learning and statistics
  • ·         Dr. KARPATHY, Andrej: Research on Deep Learning in Computer Vision, Generative Modeling, Reinforcement Learning, Convolutional Neural Networks, Recurrent Neural Networks, Natural Language Processing
  • Research on Artificial Intelligence (e.g. Learning platforms for humans; Content recommendation, etc.)1
  • ·         Dr. KURZWEIL, Ray: a legendary authority on AI and thinker on the technological singularity
  • ·         Dr. LAFFERTY, John D.: Research in Language and graphic models, semi supervised learning, information retrieval, speech recognition.
  • ·         Dr. LECUN, Yann: Research in computational neuroscience, Machine Learning, mobile robotics, and computer vision. Developed the ‘Convolutional Neural Networks’ (a model of image recognition mimicking biological processes)
  • ·         Dr. LI Fei-fei: AI scientist & Machine Learning expert with a focus on computational neuroscience, image / visual recognition and Big Data. Pushed the collection and creation of large quality datasets and this towards the improvement of algorithm design. The result was ImageNet, containing more than 10million hierarchically organized images.
  • ·         Dr. LI Kāifù: research on Machine Learning and pattern recognition. The world’s first speaker-independent, continuous speech recognition system; investor in mainly China’s AI R&D; Chairman of the World Economic Forum’s Global AI Council; author on AI and a leading force in supporting the training of AI-related engineers in China.
  • ·         Dr. LI, Sheng李生 : leading research on Natural Language Processing (NLP) and one of China’s pioneers in this field. Graduate from and professor at the Harbin Institute of Technology (HIT). President of Chinese Information Processing Society of China (CIPSC).
  • Former President of Beihang University and Professor of Computer Science at the Beihang University. Co-lead China’s National Engineering Laboratory of Deep Learning. Research in AI and network computing.
  • ·         Dr. LIM, Angelica: robotic development & human-styled learning
  • ·         Dr. LIN Dekang: A Tsinghua University graduate. Senior research scientist in Machine Learning, Natural Language Processing and more at a major AI lab.
  • ·         Dr. LIN Yuanqing: A Tsinghua University graduate. Research and developments in AI, Big Data, Deep Learning,
  • ·         Dr. LIU, Ting (刘挺): research and development in the area of Natural Language Processing.
  • Fudan University graduate. Leading AI researcher and developer with a focus on autonomous driving and other aspects in the field of AI. Leadership in related industry.
  • ·         Dr. MARCUS, Gary: Research on natural and artificial intelligence in areas of psychology, genetics and neuroscience.
  • ·         Dr. McCALLUM, Andrew: Research in Machine Learning (i.e. Semi Supervised Learning, natural language processing, information extraction, information integration, and social network analysis)
  • ·         Dr. MIN Wanli: A University of Science & Technology of China graduate. Research and developments in AI applications, traffic pattern recognition, Machine Learning and related aspects.
  • AI researcher, famous AI educator, worked on autonomous helicopters, Artificial Intelligence for robots and created the Robot Operating System (ROS). Research in Machine Learning (Reinforcement Learning, Supervised Learning, …). IS also well-known for his online course material.[10]
  • ·         Dr. Oliphant, Travis: Scientific Computing developer. Created NumPy, SciPy and Numba. Founded Anaconda and more
  • Data Scientist. Research in Big Data, Data Mining and Machine Learning. Editor of KDnugget (a source for Data Science and Machine Learning).
  • Research in AI in self-driving vehicles
  • ·         Dr. RUS, Daniela L.: research in self-reconfiguring distributed and collaborative robots (e.g. autonomous swarms), autonomous environment-adaptable shape-shifting machines (this is of use where conditions cannot be foreseen and therefore cannot be hard-coded)
  • ·         Dr. RUSSELL, Stuart J.: author of the most cited book (together with Dr. NORVIG, Peter) on AI which is also used in about 1300 universities, across about 116 countries as AI course material. He did research on inductive and analogical reasoning. He founded the Center for Human-Compatible Artificial Intelligence
  • ·         Dr. SCHMIDHUBER, Jurgen: AI scientist with a focus on and self-improving AI and (recurrent) neural networks, used for speech recognition. He works on AI for finance and autonomous vehicles.
  • ·         Dr. SCHöLKOPF, Bernhard: Research in Machine Learning, Brain-computer interfaces, and other related areas,
  • ·         Dr. SHAPIRE, Robert: Research and Developments in Machine Learning, Decision Trees, Game Theory.
  • ·         Dr. SHEN Xiangyang; Industry leader in Research and Development in the field of Artificial Intelligence.
  • AI scientist with a focus on learning. One of the creators behind the AI method known as modern Computational Reinforcement Learning.
  • ·         Dr. SWEENEY, Latanya: Research in the area of biases in Machine Learning algorithms
  • ·         Dr. TANG Xiaoou: Research in computer vision, pattern recognition, and video processing. A leading entrepreneur.
  • ·         Dr. TEGMARK, Max: Investigates existential risk from advanced artificial intelligence
  • ·         Dr. WHYE, Teh Yee: Research in Machine Learning (Deep Learning), Statistical Machine Learning and Face Recognition.
  • ·         Dr. THRUN, Sebastian: Research in Machine Learning, autonomous vehicles, probabilistic algorithms, robotic mapping.
  • ·         Dr. VALIANT, Leslie: research and advances in computational theory, complexity theory, algorithms and machine learning.
  • ·         Dr. VAPNIK, Vladimir: Research in the area of Machine Learning and Statistical Learning. Co-invented the Support-Vector Machine Method
  • Research in the area of Machine Learning, Deep Learning and Reinforcement Learning
  • 王海峰: a Harbin Institute of Technology graduate. Leadership in AI developments with foci on Deep Learning, Big Data, computer vision, Natural Language Processing (NLP), machine translation, speech recognition, personalized recommendations, and so on.
  • ·         Dr. WU Hua: A graduate from the Chinese Academy of Sciences. Cutting-edge breakthroughs in Conversational AI & Natural Language Processing (NLP), dialog systems, Neural Machine Translation and related topics.
  • ·         Dr. XU Wei: A Tsinghua University graduate. Received the title of “Distinguished Scientist”. Research and developments in areas of Deep Learning, image classification, autonomous vehicles, translation processes, and so on,
  • ·         Dr. YANG Yiming: Research in Machine Learning
  • ·         Dr. YE Jieping: A Fudan University graduate. Research and developments in Big Data, Data Mining, Machine Learning, autonomous vehicles, and so on.
  • ·         Dr. YU Dong: A Zhejiang University graduate. Research and developments in Speech recognition, Natural Language Processes, natural language understanding, and related topics.
  • ·         Dr. YU Kai: a Nanjing University graduate. Research and developments in Deep Learning, pervasive AI hardware systems, facial recognition, automatic ordering, driver-assistant systems, and related areas.
  • ·         Dr. ZADEH, Reza: Research on Discrete Applied Mathematics, AI, Machine Learning
  • Research on technical models for Brain-inspired AI, AI Ethics and Governance. Professor and Deputy Director at Research Center for Brain-inspired Intelligence (RCBII), Institute of Automation, Chinese Academy of Sciences. He is Director for the Research Center on AI Ethics and Governance, Beijing Academy of Artificial Intelligence. Dr. Zeng is a board member for the National Governance Committee for the New Generation Artificial Intelligence, Ministry of Science and Technology China.
  • A Hefei University of Technology graduate. Professor of Machine Learning at the Peking University (aka Beida; 北京大学, PKU). Former Vice Dean of the School of EECS. Research in Machine Perception, computer vision and other related areas.
  • ·         Dr. ZHANG Bo: Co-leads China’s National Engineering Laboratory of Deep Learning. A member of the member of the Chinese Academy of Sciences. A graduate from and Professor at Tsinghua University. Research in Machine Learning, neural networks, task and motion planning, pattern recognition, image retrieval and classification, and other areas.
  • ·         Dr. ZHANG, Min张民: research and development in the area of Natural Language Processing at the Soochow University (in Sūzhōu, Jiāngsū Province, P.R. China; Sūzhōu Dàxué, 苏州大学).
  • 张潼). Research focus on Machine Learning algorithms and theory, statistical methods for big data and their applications, computer vision, speech recognition, Natural Language Processing, and so on.
  • ·         Dr ZHAO, Tiejun (赵铁军): research and development in the area of Natural Language Processing.
  • Graduated from 3 Chinese universities: Northeastern University, Beihang University and the Chinese Academy of Sciences, Institute of Automation. Conducts research on Machine Learning and Explainable AI at one of the front-running labs in AI development.
  • ·         Dr. ZHOU Jingren: A graduate from the University of Science and Technology of China. Research and Developments in AI, Big Data, large scale Machine Learning, Speech and Language Processing, image & video processing, and so on.
  • ·         Dr. ZHOU, Ming: cutting edge research and development in the area of Natural Language Processing.
  • Doctor of Science at the Chinese Academy of Sciences. Research and Developments in ChatBots, conversational interfaces, and related areas.
Mini Project #___: Inspiration from Following Today’s Masters
  • Which of these figures or their work are you most inspired by? Why?
  • Can you find some more details and up-to-date information about your chosen role model?
  • Can you find some other leading figures you are inspired by that are also working in the field of AI and that are not (yet) in this list?
  • Can you figure out how your choice of leading figures relates to AI and to other researchers by creating an Entity-Relationship Model (see example here)?

[1] Retrieved on March 27, 2020 from https://library.stanford.edu/collections/edward-feigenbaum-papers

[2] Good, I.J. (1965). Speculations Concerning the First Ultraintelligent Machine. in F. L. Alt and M. Rubinoff (eds.). (1966). Advances in Computers Vol.6: pp. 31–88.

[3] Bostrom. N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press

[4] Shanahan, M. (2015). The Technological Singularity. The MIT Essential Knowledge Series. Cambridge, MA: The MIT Press.

[5] Good, I.J. (1965). p.33

[6] Turing, A. M. (October, 1950). “Computing Machinery and Intelligence” in Mind Lix(236), 49:433-460. pp.459-460

[7] Robinson, John Alan (January 1965). A Machine-Oriented Logic Based on the Resolution Principle. J. ACM. 12 (1): 23–41 Retrieved on March 24, 2020 from https://web.stanford.edu/class/linguist289/robinson65.pdf

[8] Turing, A. (1948). Intelligent Machinery. http://www.turingarchive.org/viewer/?id=127&title=1 and https://weightagnostic.github.io/papers/turing1948.pdf see: Copeland, J. (2004). The Essential Turing. Oxford: Clarendon Press. pp. 411-432

[9] Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8(3),pp. 338–353. Online: Elsevier Inc. ScienceDirect; Retrieved on March 18, 2020 from https://www.sciencedirect.com/journal/information-and-control/vol/8/issue/3

[10] An example of Dr. Ng’s online course material: https://www.coursera.org/learn/machine-learning

A List of Leading Academic Voices, Bibliography, References, Examples & URLs

AI Magazine. Online: Association for the Advancement of Artificial Intelligence. Retrieved on April 21, 2020 from:

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

Al-Jazarī, Ismail al-Razzāz; Translated & annotated by Donald R. Hill. (1206). The Book of Knowledge of Ingenious Mechanical Devices. Dordrecht, The Netherlands: D. Reidel Publishing Company. Online Retrieved on March 25, 2020 from https://archive.org/details/TheBookOfKnowledgeOfIngeniousMechanicalDevices/mode/2up

Alpaydin, E. (2020). Introduction to Machine Learning. The MIT Press Essential Knowledge Series. Cambridge, MA: MIT Press. Retrieved an introduction on March 25, 2020 from https://mitpress.mit.edu/contributors/ethem-alpaydin Lecture notes to the 2014 print retrieved from https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/

Angwin, J., et al. (2016). Machine Bias. There’s software used across the country to predict future criminals. And it’s biased against blacks. In Pro Publica Retrieved on July 23rd, 2019 from https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

Anthony, S. (March 14, 2017). DeepMind in talks with the National Grid to reduce UK energy use by 10%. Online: ars technica. Retrieved February 14, 2020 from https://arstechnica.com/information-technology/2017/03/deepmind-national-grid-machine-learning/

Bacaër, N. (2011). Verhulst and the logistic equation (1838). A Short History of Mathematical Population Dynamics. London: Springer. pp. 35–39.  Information retrieved from https://link.springer.com/chapter/10.1007%2F978-0-85729-115-8_6#citeas

Barat, J. (2013). Our Final Invention: Artificial Intelligence and the End of the Human Era.  New York: Thomas Dunne Books

Baum, S. (2017). A Survey of Artificial General Intelligence Projects for Ethics, Risk and Policy. The Global Catastrophic Risk Institute Working Paper 17-1. Onine: The Global Catastrophic Risk Institute. Retrieved on February 25, 2020 from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3070741

Bayes, T. (1763). An Essay towards solving a Problem in the Doctrine of Chances.  Retrieved on March 13, 2020 from the University of California, Irvine, School of Social Sciences at https://www.socsci.uci.edu/~bskyrms/bio/readings/bayes_essay.pdf  and with some additional annotations from MIT OPenCourseWare at https://ocw.mit.edu/courses/literature/21l-017-the-art-of-the-probable-literature-and-probability-spring-2008/readings/bayes_notes.pdf

BBC Bitesize. What is an algorithm? Retrieved on February 12, 2020 from https://www.bbc.co.uk/bitesize/topics/z3tbwmn/articles/z3whpv4

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 

Berke, J.D. (2018).What does dopamine mean? Online: Nature Neuroscience 21, 787–793. Retrieved on March 27, 2020 from https://www.nature.com/articles/s41593-018-0152-y#citeas  

Berridge, K. C. et al (1998). What is the Role of Dopamine in Reward: Hedonic Impact, Reward Learning or Incentive Salience? In Brain Research Reviews 28 (1998) 309-369. Elsevier

Bermúdez, J. L., (2014). Cognitive Science: An Introduction to the Science of the Mind. Cambridge: Cambridge University Press. Retrieved on March 23, 2020 from https://www.cambridge.org/us/academic/textbooks/cognitivescience 

Bird, S. et al. (2010). Natural Language Processing with Python — Analyzing Text with the Natural Language Toolkit. Retrieved on April 29, 2020 from https://www.nltk.org/book/ AND https://www.nltk.org/book_1ed/ AND https://www.nltk.org/nltk_data/

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer Retrieved March 1, 2020 from https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/ AND https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf   This book is aimed at “advanced undergraduates or first-year PhD students, as well as researchers and practitioners.” Information retrieved on April 24, 2020 from https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/

Blum, A. et al. (2018). Foundations of Data Science. Online: Cornell University; Department of Computer Science. Retrieved on April 28, 2020 from https://www.cs.cornell.edu/jeh/book.pdf

Boole, G. (1847). The Mathematical Analysis of Logic. Cambridge: MacMillan, Barclay & Macmillan. Online: Internet Archive. Retrieved on March 25, 2020 from https://archive.org/details/mathematicalanal00booluoft . Alternatively from: https://history-computer.com/Library/boole1.pdf  and https://www.gutenberg.org/files/36884/36884-pdf.pdf . See Lifschitz, V. (2009) for lecture notes

Boole, G. (1853 1854). An Investigation of the Laws of Thought. Cork: Queens College. Online: Auburn University. Samuel Ginn College of Engineering.Retrieved on March 25, 2020 from http://www.eng.auburn.edu/~agrawvd/COURSE/READING/DIGITAL/15114-pdf.pdf . Alternatively: https://www.gutenberg.org/files/15114/15114-pdf.pdf

Bostrom, N. (2014). Superintelligence. Paths, Dangers, Strategies. Oxford: Oxford University Press. A book review: Brundage, M. (2015). Taking Superintelligence seriously. Superintelligence: Paths, dangers, strategies by Nick Bostrom. In Futures 72 (2015) 32 – 35. Online: University of Oxford; Future of Humanity Institute. Retrieved on March 25, 2020 from https://www.fhi.ox.ac.uk/wp-content/uploads/1-s2.0-S0016328715000932-main.pdf

Brownlee, J. (2016). Machine Learning Mastery with Python. Vermont Victoria, Australia: Machine Learning Mastery Pty. Ltd

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

Butz, M. V. et al. (2017). How the Mind Comes into Being: Introducing Cognitive Science from a Functional and Computational Perspective. Oxford, UK: Oxford University Press.

Calcott, B., et al. (2011). The Major Transitions in Evolution Revisited. The Vienna Series in Theoretical Biology. Cambridge, MA: The MIT Press.

Calculus 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=Calculus

Charniak, E. and McDermott, D. (1985). Introduction to Artificial Intelligence. Addison-Wesley

Charniak, E. (2018). Introduction to Deep Learning. Cambridge, MA: The MIT Press

Chen N. (2016).  China Brain Project to Launch Soon, Aiming to Develop Effective Tools for Early Diagnosis of Brain Diseases. Online: CAS. Retrieved on February 25, 2020 from The Chinese Academy of Sciences English site at http://english.cas.cn/newsroom/archive/news_archive/nu2016/201606/t20160617_164529.shtml  

Chollet, F. ( ). Deep Learning with R.

Chollet, F. (2018). Deep Learning with Python. USA: Manning Publications. Retrieved on April 21, 2020 from https://livebook.manning.com/book/deep-learning-with-python?origin=product-liveaudio-upsell   Information Retrieved from https://github.com/fchollet/deep-learning-with-python-notebooks

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Piatetsky-Shapiro, G. et al. (2020). KDnuggets. Knowledge Discovery Nuggets is a site on AI, Analytics, Big Data, Data Mining, Data Science, and Machine Learning. Here a selection for beginners: https://www.kdnuggets.com/tag/beginners

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Rasmussen, D. E. et al. (2006). Gaussian Processes for Machine Learning. Online: The MIT Press. Retrieved on April 21, 2020 from http://www.gaussianprocess.org/gpml/chapters/RW.pdf AND http://www.gaussianprocess.org/gpml/errata.html  

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Russell, S. (2019). Human Compatible. Artificial Intelligence and the Problem of Control. Viking

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Samuel, A.L. (1959, 1967, 2000). Some Studies in Machine Learning Using the Game of Checkers. Online: IBM Journal of Research and Development, 44(1.2), 206–226. doi:10.1147/rd.441.0206 Retrieved February 18, 2020 from https://dl.acm.org/doi/10.1147/rd.33.0210 and  https://www.sciencedirect.com/science/article/abs/pii/0066413869900044 and https://researcher.watson.ibm.com/researcher/files/us-beygel/samuel-checkers.pdf

Sanderson, G.  (? Post-2016).  3BLUE1BROWN SERIES. But what is a Neural Network? | Deep learning, chapter 1.  S3 • E1 (Video). Online. Retrieved on April 22, 2020 from https://www.bilibili.com/video/BV12t41157gx?from=search&seid=15254673027813667063  AND https://www.youtube.com/watch?v=aircAruvnKk Information Retrieved from https://www.3blue1brown.com/about

Sarkar, D. (2019). Text Analytics with Python. A Practitioner’s Guide to Natural Language Processing. Bangalore: Apress.

Shalev-Shwartz, S. et al. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge:  Cambridge University Press Information retrieved on April 24, 2020 from https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/ AND https://www.cse.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

Shashta,  et al. (2019). Application of Reinforcement Learning to a Robotic Drinking Assistant. In Robotics. Special Issue “Reinforcement Learning for Robotics Applications” Robotics 2020, 9(1), 1; Online: MDPI Publishing. Retrieved on April 29, 2020 from https://www.mdpi.com/journal/robotics  AND https://www.mdpi.com/2218-6581/9/1/1/htm  AND https://www.mdpi.com/2218-6581/9/1/1/pdf

Schrittwieser, J. et al. (2020). Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. Online: arXiv e-Print Archive; Cornell University; Retrieved on April 1, 2020 from  https://arxiv.org/abs/1911.08265

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Sexton, C. (2020). CK-12 Interactive Algebra 1 for CCSS. Online: CK-12 retrieved on March 31, 2020 from https://flexbooks.ck12.org/cbook/ck-12-interactive-algebra-1-for-ccss/

Shanahan, M. (2015). The Technological Singularity. The MIT Essential Knowledge Series. Cambridge, MA: The MIT Press.

Simeone, O. (2018). A Brief Introduction to Machine Learning for Engineers. Online: King’s College London, UK; Department of Informatics. Retrieved on April 21, 2020 from Online: arXiv e-Print Archive; Cornell University;   https://arxiv.org/pdf/1709.02840.pdf  

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Skanski, S. (2020). Guide to Deep Learning. Basic Logical, Historical and Philosophical Perspectives. Switzerland: Springer Nature.

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Spice, B. (April 11, 2017). Carnegie Mellon Artificial Intelligence Beats Chinese Poker Players. Online: Carnegie Mellon University. Retrieved January 7, 2020 from https://www.cmu.edu/news/stories/archives/2017/april/ai-beats-chinese.html   

Spielkamp, M. (June 12, 2017). “We need to shine more light on algorithms so they can help reduce bias, not perpetuate It.” MIT Technology Review. Retrieved on July 23, 2019 from https://www.technologyreview.com/s/607955/inspecting-algorithms-for-bias/

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Statistics 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=Statistics

Stevens (Stevenson), E. et al (2019). Deep Learning with PyTorch. Essential Excerpts. Online: Manning Publications. Retrieved on April 21, 2020 from https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf AND https://pytorch.org/deep-learning-with-pytorch

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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/  

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Additionally, an incomplete list of online data sets:

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Data sets

Data set location area
http://cricsheet.org/downloads/  
http://usfundamentals.com/download  
http://www.sports-reference.com/  
http://yann.lecun.com/exdb/mnist/  
  https://data.world/  
https://dev.twitter.com/streaming/overview  
https://stocktwits.com/developers/docs  
https://www.ehdp.com/vitalnet/datasets.htm  
https://www.quandl.com/data/FRED/documentation/documentation  
https://www.quandl.com/data/WIKI/documentation/bulk-download  
https://www.quandl.com/open-data  
https://www.quantopian.com/data  
https://github.com/awesomedata/awesome-public-datasets Agriculture, biology, climate, weather, earth sciences, economics, education, energy, finance, healthcare, …
http://datamarket.azure.com/browse/data?price=free Agriculture, weather and so on
https://openlibrary.org/developers/dumps Book library data sets
137.189.35.203/WebUI/CatDatabase/catData.html   Cat images
https://archive.org/details/2015_reddit_comments_corpus Chat data set
https://developer.twitter.com/en/docs/tweets/search/overview Chats-related content data
https://developers.facebook.com/products/instagram/ Chats-related content data
https://dataportals.org/search Civil, country, etc.
  http://gcmd.nasa.gov/ earth sciences and environmental sciences
https://nces.ed.gov/ Education data sets
https://nces.ed.gov/ education demographics in the USA and the world
http://www.mlopt.com/?p=6598 Electric Vehicle recharge points dataset from Northern Ireland and Republic of Ireland
http://www.cs.cmu.edu/~enron/      Email text as data set
http://open-data.europa.eu/en/data/ EU civil data sets
https://data.europa.eu/euodp/en/home EU civil, social
https://opendatamonitor.eu/frontend/web/index.php?r=dashboard%2Findex European data sets
http://vis-www.cs.umass.edu/lfw/ Faces data set
http://www.imdb.com/interfaces/ Film data
http://www.bfi.org.uk/education-research/film-industry-statistics-research Film data (UK)
https://markets.ft.com/data/ finance
https://www.imf.org/en/Data finances, debt rates, foreign exchange
https://cmu-perceptual-computing-lab.github.io/foot_keypoint_dataset/ Foot Keypoint Dataset (Carnegie Mellon University)
https://opencorporates.com/ Global database of companies
https://trends.google.com/trends/ Global internet searches
https://comtrade.un.org/ Global trade
  http://yann.lecun.com/exdb/mnist/ handwritten digits examples, and a test set of 10,000 examples
https://www.data.gov/health/ Health (USA)
http://data.nhm.ac.uk/ historical specimens in the London museum
https://www.glassdoor.com/developer/index.htm Human resource related data sets
https://archive.org/details/audio-covers image processing research data set
  https://archive.ics.uci.edu/ml/datasets/Iris Iris flowers data set. This is claimed to be one of the better freely available classification sets. It is used in the beginners project on Machine Learning and classification.
https://www.bjs.gov/index.cfm?ty=dca law enforcement in the USA
http://www.londonair.org.uk/london/asp/datadownload.asp London air quality data
http://archive.ics.uci.edu/ml/datasets.php Machine Learning
  http://archive.ics.uci.edu/ml/ Machine Learning data sets
http://mldata.org/ machine learning datasets for training systems
http://www.msmarco.org/ machine learning datasets for training systems in reading comprehension and question answering
https://aws.amazon.com/datasets/million-song-dataset/ Music data set
  https://opendata.cityofnewyork.us/ New York City data sets
https://data.worldbank.org/data-catalog/health-nutrition-and-population-statistics Nutrition, health, population
https://go.developer.ebay.com/ebay-marketplace-insights Online sales datasets (mainly USA)
http://opendata.cern.ch/ particle physics experiments data
https://exoplanetarchive.ipac.caltech.edu/ planets and stars
https://data.worldbank.org/ population demographics, economics
https://www.qlik.com/us/products/qlik-data-market population, currencies
https://deepmind.com/research/open-source/kinetics pose and action data sets
http://fivethirtyeight.com/ public opinion on sport and more
https://www.google.com/publicdata/directory public-interest data (USA)
https://scholar.google.com/ Scholarly text as data sets
https://science.mozilla.org/projects Sciences
https://cooldatasets.com/ Sciences, civil, entertainment, machine learning, etc.
https://www.ukdataservice.ac.uk/ social, economic population in the UK
http://www.databasesports.com/ Sports data
https://data.gov.uk/ UK civil, social
https://data.unicef.org/ UNICEF data sets, civil
http://data.un.org/ United Nations data sets
  http://data.humdata.org/ United Nations humanitarian data sets
https://archive.ics.uci.edu/ml/index.php University of California Machine Learning dataset
https://lodum.de/ University of Münster data sets
https://www.data.gov/ USA civil, social
https://ucr.fbi.gov/ USA crime statistics
http://www.census.gov/data.html USA population
https://www.yelp.com/dataset User data sets, (Yelp users)
http://opendataimpactmap.org/ Various
http://plenar.io/ Various
https://ckan.org/ Various
https://datahub.io/search Various
https://dataverse.org/ Various
https://github.com/datasets/ various
https://knoema.com/ various
https://opendatakit.org/ Various
https://opendatamonitor.eu/frontend/web/index.php?r=dashboard%2Findex Various
https://registry.opendata.aws/ Various
https://rs.io/100-interesting-data-sets-for-statistics/ various
https://www.columnfivemedia.com/100-best-free-data-sources-infographic Various
https://www.kaggle.com/datasets Various data sets
https://github.com/freeCodeCamp/open-data Various for coders
  http://datahub.io/ Various open data sets
https://www.kaggle.com/ Various, general data resource
https://www.reddit.com/r/datasets/comments/exnzrd/coronavirus_datasets/    https://github.com/CryptoKass/ncov-data Virus data set
https://wiki.dbpedia.org/ Wikipedia data sets
https://www.who.int/gho/database/en/ World Health Organization data sets
https://cdan.dot.gov/query   https://github.com/wgetsnaps/ftp.nhtsa.dot.gov–fars Traffic, USA, fatality data set
http://www.image-net.org/  Images as huge quality dataset
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.296.9477&rep=rep1&type=pdf  
http://xiaming.me/posts/2014/10/23/leveraging-open-data-to-understand-urban-lives/ New York (civil) data sets and more
The field of AI

AI application in social settings: Facial Recognition & Surgical Masks

In some Asian countries the deployment of AI applications for facial recognition in the public sphere has been well on its way. In these same countries, for other reasons, may people are used to wearing facial masks. Some wear them, respecting their fellow citizens, when going through the motions of a cold or other illness. Some wear them to protect themselves from pollutants in the air. At times, facial masks or coverings are used to protect oneself from the effects of sunlight or sandstorms.

In European countries, for instance, masks have been used during festivities such as carnival and during civil disobedience acts, such as demonstrations. Presently, yet reluctantly, a few more individuals use surgical masks or similar filtering masks to protect themselves from illness. In general, until recently, governments in the EU have not been promoting their usage.

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Now that surgical masks are used even more onto faces within the general population, will this have an effect on investments made in AI research and developments towards facial recognition in the public sphere? Will it effect the usability of these systems already in place?

text | wondering, The field of AI, Uncategorized

predicting pandoflux: a natural shift in Artificial sentiments on Emerging planetary patterns.

While browsing LinkedIn one can quickly sense the site is filled with professionalized visionaries professing the future.

This is the wonderful imagination we can expect from entrepreneurs, inventors, innovators, some makers, a few or more artists, a number of artisans, a whole lot of movers & shakers and policy makers’ think-tank spokespersons, who all frequent this social platform.

These days and months, since the end of 2019 into 2020, I have been noticing a shift into how that “just-a-flu” morphed into an emergency for a relatively few, into a pandemic for some more and into a fore-bearer of dramatic change to the human species, mapped without or with climate change (as an instigator of epidemics).

For some this has “suddenly” appeared the last two weeks or so. For others — that is, for those who are global nomads or global citizens, anyone from around the world living in China yet with loved-ones around the world; any Chinese citizen living in the world — this is now going into its 4th month and counting. For even fewer this was, now in retrospect, foreseeable; or so does the power of probability theory offer us.

Making a forecast, in the spirit of this biased opinion piece here, I foresee to be influenced in an emotionally heightened manner, as it has been, for another 3 months. That’s a very personal event of 6 to 7 months; for each one of those who fit the above category. That is, if I’m allowed being a bit too self-centered, not anticipating the passing of anyone close in these coming months because of virus-related complications.

Then there will be the echoes and reflections (and hopefully as little fall-out as possible) following this. Perhaps adding another 6 months? I’m just using a wild unsubstantiated version of prediction. I will call this my not so impressive “prediction” of the “Pandoflux”. Is a world of, and a world in, change a progressive world? Or, is progress what we do with change in relation to others and their context?

My predicting is not impressive to me since I also sense that flux seems simply inherent; even at a cellular or deeper level; even if we are imposingly-conserving. The latter too will pass, while its mechanism seems ever there?

Although I am very serious when I smile, is this attitude implied here too flippant or is it rather a watered-down version of a Taoist view on the world? At the least, I want you to think with me. Give it a moment.

“Things will never be the same again…. we will never go back to how it was.” Previously, in a pre-pandemic sense, such statements seemed to come with an undertone of optimism and progressive thinking. Now, peri-pandemic, it sounds as if driven by fear and loss. It does not have to be, though.

Again, without wanting to be callous nor frivolous, nothing ever is the same and one can never ever go back to how something was before. That is, unless the affect of the memory of a change can be wiped from any mind that has been zapped back into a previous state. You know, like a reset button and a factory preset as the one suffered by Buzz Lightyear, in one of the Toystory animation franchises. Buzz too could not forget his previous setting.

Humanity and its events, however, are not a cartoon. It might seem like one, at times, but this tends to smell of sarcasm, disdain or at least of irony at the awkward moment. Indeed, perhaps this writing runs that risk as well.

When is the right moment to speak of change? Where and by who? When can we observe markers of change? What is such marker but a trigger of a parameter in a probability calculation of an environment that has always been in flux and has thrived on change?

In that regard, and as a side note, is a Machine Learning application an agent of change? Is it rather an agent in a process of corroboration that change is inherently part of the human experience and nature, as formalized via the field of advanced Calculus? Is perhaps such an AI application a neurotic obsession with control and its implied hanging onto a veil of pseudo-fixed and comforting insight?

After all, is a pattern not a pattern because it does not change? Or does it? What shall we call a pattern that is not to be recognized as a fixed pattern; chaos or rather, life?

I choose the latter.

When some individuals reminisce over the obvious how-it-was and the yet unknown changes to come, which dynamic pattern do they envision? A Chaotic one or one of LIFE?

In the struggles we face, whichever type, form, degree or function, we humans do want a sense of meaning as to the changes or the continuity these struggles imply. We make choices. We choose and recognize patterns.

This choice is there even if it is the meaning-giving idea of letting-go, breathing-out, moving-on and not looking for or clinging-on meaning in one attribute of a struggle in question. That is meaning. It could specifically be concerning if the meaning-giving labeling turns out as a painfully meaning-less one; driving one to the brink of or into madness and despair. That too is meaning. Meaning-giving is geared towards giving a future to a past event or to an event imagined becoming a past.

It is equally so as it is with communication: there is no such thing as no communication . One can not not-communicate with one’s brain; that meaning-giving thing between our ears. Even if we are trying to delegate this meaning-creation to the artificial realm of Machine Learning . This meaning-giving is inescapable.

On the other multiple ends of this 4-dimensional spectrum (yes, try to imagine this in a 3D high fidelity manner with a variable changing attribute over time), we can either observe small-minded yet large-sounding conspiracies of contrasting flavors and we can also see analyses of large Geo-political potentials and paradigm shifts.

This morning I was presented with a snippet of just that; the latter that is. The former is too irritating to me, while I do care about its extreme dangers.

In the earliest hours of the morning, I wake up very early, I was listening to BBC News World Service and its Newshour show. In it the astronomic numbers of applications for unemployment benefits in the USA were discussed. The data indicated about 10 million individuals were “shed” from their previous employment . Yes, “shed,” a word used in reporting as if humans are prickly needles from an ever-green pine tree that surprisingly looses its convoluted leaves. They and those without health insurance in the USA were discussed and then this was followed by an interview with Noam Chomsky. He was introduced as the academic who has “a radical solution to the economic shock” yet, who himself has repeatedly, and in this interview, rebutted this by stating that neo-liberalism is the radical paradigm here.

The episode, Newshour-20200402-USJoblessClaimsHitNewRecord, was retrieved on April 3, 2020 from http://www.bbc.co.uk/programmes/w172x2ylvg5rx9l

I suppose there is a reason why this morning the BBC, of all newscasters, suddenly interviewed Professor Noam Chomsky… no longer only Ms. Amy Goodman does so…

Later that same morning, I was sent a second item. It was a audio-video recording of an interview given by the present-day governor of New York State.

The New York State governor gives an anecdotal description of how the various states and a national agency in the USA compete against each other for respiratorsDownload
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Yes, one competes in a free market construct. Is this “free,” though? Is the following forecast, here below, of not-so-much-change too esoteric? Could it, in the end, be the common USA citizen, with house loans and student loans, in the billions, and some of whom can not afford insurance, that shall pay for this? Is this an attribute of the so-called change we have to see happen (from our distance)?

This pandemic could very well be a massive shift in some human consciousness that previously did not see the issues we are facing. Now, that is without linking this to climate change which has been done, preceding the pandemic in that it was suggested that with climate alterations pandemics might become more frequent.

It might be that the idea of nothing ever being the same again, which some are talking about, is the re-delegation of education to the parents turned teacher, on top of their in-house distant working, their gig-economy project, their home-cooked meals and their in-house floor-mopping.

Isn’t human civilization (at least quantatitively) perceived as great because its members have invented the process of delegation? At least, one person is not looking forward to this change in delegational power:

mother about imposed home-schooling (as opposed to self-willed homeschooling which is at least intentionally and experientially different)Download

Or, the foreseen change might be that new EdTech APP we can innovate on with increased human-originated data collection and Machine Learning processes in support of the mother company and its marketing or advertising-placement strategies.

Will it be a never-seen-before change in child-like bickering, finger-pointing, belly-button staring and saddening forms of competing between (nation) states?

Or, is it a change in a form derived from that which people such as Noam Chomsky are speaking of ?

Humans are living proof of the possibility of a multitude of patterns in change and in changing patterns. Surely changing patterns of and in life are as well. Life and lives without meaning and meaning without life and lives, are not the changes a human needs.

Luckily for you, I cannot offer you any of these or other such changes, nor am I a forecaster.

I breathe out. At the least, I can offer one constant of hope: be well and do well, my fellow earthling.

Letters from Aliens to Aliens, text | poetry, text | wondering

The Universe as a Collective Memory; as a Collective Narrative

The perceived universe is a history. One’s perceived thoughts are a history. Each inevitably lie in a past; one slightly longer distanced in timespace then the other.

As a child, and still today, I imagine(d) that some attributes that make up interstellar space could function as memory banks for our collective (and individual) information (knowledge, anecdotes, personal stories, that evasive theory or elegant formula, that silly cat video and so on).

As a child, and still in my today’s naivete, I envision(ed) that the information could be encoded onto radiation, onto waves (of light or which ever), used as carrier waves, carried for eternity (or until the next Big Bang) at the speed of near-to-light, across the galaxies, as our echoes of who and what we once were with one another. …Yes, one might prefer to be more cautious, just in case there is that space-faring parasitic yet intelligent species out there. Though, this might be more telling of how we reflect upon ourselves as a species and how we have been perceiving how we (should) relate to other species around us (and vice versa) rather than being about that hypothetical non-terrestrian.

Some might pose that vision without action is delusional. However, I beg that nuances do exist. One can envision something and can, at the least, be exhilarated or rejuvenated when seeing that somehow similar visions are brought to execution by others. Then such delusional self shifts to becoming a meaning-full self. This occurs through a positive psychology of interconnection with that unknown other, by means of the unintended gift of meaning, or hope, through proxy. That is somewhat what stories do; both fictional or less fictional ones.

I have seen the nascence of DNA computing and information storage on DNA , allowing for enormous capacity and durability. I discovered this through the media I browsed.

I have seen the mentioning of 5D data storage through some kind of glass imprinting of information, of even more impressive proportions. This is achieved at a nano-scale, by means of a dense laser beam of sorts.

I have observed reports about initiatives of databases and storage-redundancies on- and off-planet; not only of stories and information but also of flora’s “story” in the form of seed banks. Who’s and what will (not) be stored?

I have heard of research towards brain implants aimed at increasing memory capacities within an individual

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I have seen initiatives where common individuals can record their common stories of a lifetime for themselves and with others to enjoy and be touched by: a mother about her son; a person about a lost love; that common story that makes us daringly, beautifully and yet frailly human in-between one another.

I also encountered questions of the “right to be forgotten”. In extension to that, it has been said that for instance on LinkedIn an increasing percentage of profiles are of deceased individuals and that in a not too long a near future that can easily reach up to 20% (I will want to verify this). I am equally thrilled about this in associated with the sensation of the echoes of humanity as portrayed in the opening paragraph. Why should one or more of one’s story (not) be stored?

This then in turn entangles with concepts of “privacy”, “freedom of speech”, “secrecy”, “intimacy”, “trust”, “ownership of information”, “the right to perform or distribute”, and many more. Does storage dismiss the story thought or the story hushedly stumbled and stuttered in private settings of the fleeing moment and among those who are trusted? Is there your poetry in the ephemeral, the passing?

I also hear about some who ponder whether or not more and faster (information) is unequivocally better.

Either way, I distill a tautology: we are story keepers, story tellers and maybe more so, story hackers; irrespective of whether we like, want or agree with their content or not. That brings us to meaning-giving, meaning-taking and meaning-maintaining or meaning-alteration.

I wonder, …

I wonder, besides one’s intentions with a story and those intentions being different from the perceptions of a story –which in turn can be seen as an example of the process of “meaning-alterations”– is wanting to be forgotten the ultimate alteration of meaning? Has intention the upper-hand to perception? Has one infinite ownership over one’s story? Could one, should one and how could one ensure the intended meaning of a story over time? Is this the human tragedy or playful dance between the individual and the collective as portrayed in stories over space and time and imagination (i.e. meaning-alteration; since perception is the processing of a story through filters such as one’s imagination)? [if you are wondering how I am making these statements and assumptions as to how someone might process a story: while anecdotal, last I checked I am human and I process stories through imagination and other cognitive mechanisms].

This post will function as a repository of these and associated initiatives or considerations by others. If I find them I will post URLs here.

text | poetry, text | wondering

In-Between Languages

Learning and using multiple languages enables one to play in-between the languages. Since I believe (and I am not alone) that languages exist intertwined with cultures, one is hence also playing in-between cultures; perhaps unwittingly so.

…our earliest pets, totems, talisman or mascots?

This in-between interaction enables (at least me and, as I observe, also some others) a form of playful language (usage and construction) that can only exist and be understood by those enabled to be moving in-between them.

At least metaphorically (but I sense this is very practical or pragmatic as well), this is allowing the player to stand on the proverbial door sill. This is in turn allowing the player (limited in this writing here by the highly constraining, linear nature of language constructs, such as sentences in paragraphs) to be looking, at least, at the one language usage on one side and at the other on the other side (if applying the play between two languages only, while multiple language usage is plausible as well). The player then can be “tasting” (and, simultaneously, be creating ) the linguistic mixture, as an observer and producer. The player can do so in-between two or more languages.

This awareness is not particularly new nor is it unique.

For instance, in China’s broadcasts, of its voice radio performance art, one can, at times, listen to wordsmiths playing in-between English and Chinese. For instance, they might use an English word or two that sound like a very different Chinese word. Though, the audience or creators might be “limited” to Mandarin and some basic English, nevertheless, it is just that: a creative fluidity in-between languages (for the moment ignoring the motivation or the perception thereof, in this particular reference).

An example between Dutch and Chinese could be this: “poesje“, which is Dutch for “small cat“. It sounds, via slight shifts in the Dutch pronunciation, as /bu-shi/ , which could, besides conjuring a rude English wording, also be shifted into the Chinese “bù shì” (不是). These two Chinese characters stand for “not” and “is“, or slightly more freely translated, as “not yes“. In turn this could be used to mean something as “not“, “no“, “it isn’t“…

If “bù shì poesje” then what is it?

I sense one can see this activity as an analogy of potential processes and actual evolution in any creation or (in-between) any framework. One might perceive these as experiments of shifts and “perversions” (depending on one’s “political” stance) into innovations or into new and different languages or into potentially new meaning-giving. This could occur, at least, at the level of the individual or in-between a few initiated individuals. This movement could transcode from the absurd into the formal and vice versa.

Is this a movement similar to that one person’s crazy idea that can only become accepted if a second person endorses it (preferably a second person otherwise unassociated with the first person) and then becomes a movement by the undefined masses following it? I now see a thought turned into a (set of meaning-imbued) word(s), turned into a culture.

As a sidenote: 

"Framework" here is meant as a collection of thought creations (e.g. a connection of associated concepts). 

For instance, I, as one individual, over my life span, have cognitively collected a number of frameworks. Such Frameworks, I sense, are semiotic and thus have linguistic or meaning-giving features. I perceive them as being cultural in nature. 

I feel these, to me, do not simply have to consist of isolated memorized words. I imagine these might consist of unclear networks of not well-defined emotions, blurry definitions, attached to opaque images, other words and fading experiences. In turn these interconnected meaning-giving items are vaguely set into complexes of intuitions.  

I feel, for me, these sets form an undefined number of frameworks in my mind. Some seem fluid and temporary while others seem more stubborn and fixated. While some frameworks feel as if overlapping, others are contradictory to one another, adjacent or seemingly entirely unrelated, except then by one attribute: they are my metaphorical constructs in my brain. 

I use these frameworks as references to make sense of the world around me; ever so transiently. I also explore the spaces in-between frameworks. 

One such framework is my vague and abstract conception of one language; let's say English. Another framework could be another language. 

Such a framework could also be my adoption and adaptation of a set of believes one, and one's community, holds or a set of habits, or attributes recognized as memes of one human collective (e.g. a community or a set of ideas held in one's brain), etc. For instance: the Flemish, the Beijingers, the Belgians, the Europeans, The Han, The Asians, The people on the subway, the people in the building I work or those where I live, The people in a news clip, etc.; a set of cultural frameworks.

As another example, a framework I hold could also be built around the concept of "data" or a specific set of data. For instance: the number of people who suffered fatal or other injuries, say, due to road vehicles, let's say in the USA from one specific year to another.

I imagine this in-between play as potentially being an example (with practical implications) of Deleuze’s territorialization, de-territorialization and re-territorialization. Therefor the in-between is always a becoming rather than a being. I also see it as a possible candidate example of fluidity, and of inherent changes that occur beyond one or two or more fixed frameworks one might hold on to (e.g. the use and learning of one language only).

I sense this in-between activity, its existence, the existence of the potential links, the existence of the potential shifts in meaning and usage, are a collection of human output (somewhere floating between being willingly or being serendipitously expressed) which are too often ignored, and I dare state, which might have non-party political consequences. 

As a second sidenote: 

"Political" here is meant as how we act as citizens among each other within the "polis"; i.e. the city of our daily activities and power-relations. 

I sense these in-between expressions might highlight or unveil or at least create imaginations about power-relations and the shift thereof across languages. 

I admit, they make me, rather then perhaps you, think about this. Granted,  possibly this tells me more about my own obsessions with power-relations rather than it stating anything substantial or corroborative about what I think to perceive. 

That stated, please let us continue to allow the process of potential discovery by means of initially unsubstantiated imagination and naive wonder.

Yes, for the moment I opt to sense that one can best achieve this exploration (either in daily personal experiences and poetics, or as a stepping stone towards rigorous analysis) with and in-between any number of languages and any number of other languages and dialects (yes, dialects, since some claim that “language” is a dialect “with an army”…) .

The experience of an (intangible) in-between space has been on my mind for as long as I remember. Especially the etymology as observable in-between two distinct official languages yet, with some degree of common ancestry.

For instance, the present-day English word ” mascot” or “mascotte” (in Dutch) compared to the Spanish word “mascota“. The latter means “pet” (English) or “huisdier” (Dutch), which again translated to English might make for a (to me) fun new word: “house-animal“…

In a moment of associated digression: Is a couch potato a species of “house-animal“? …

…” My favorite pet is a potato . It likes staying home, lie on the couch and watch a movie. It’s such a house-animal; I enjoy petting my potato.” …

–the pet owner (pulled from my imagination).


potato, “house-animal”

Coming back to the main storyline: one touches on the semantic realm of “talisman” (i.e. “mascot” & “mascotte“) while the other touches on the realm of companionship for a human and this of an animal, other than human (yes, imagine…), for instance, a dog or a tarantula (i.e. “mascota“) .

If we were to dig a bit deeper we could argue that both (“mascotte” and “mascota“) are about companionship yet the intuitively comparable power-relation might be different, or is it?

I am excitingly concerned about how one could achieve this comparison in a quantitative manner, besides my often-faulty yet beloved intuition, which I am presently applying. I also wonder, in a dance with an old polemic, whether we, as humans, should only value the quantitative (notice, please, my stress on ‘only’). For sure, this entire in-between language is not quantatative in nature; it’s pure nurture coming naturally to me. (I hope you can read the serious irony here).

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Coming back to the in-between language play: the word “mascot” can semantically and denotatively (i.e. as being,
“in accordance with fact or the primary meaning of a term“) be mapped with the word “talisman” which, in turn, can be mapped with words such as the nouns “charm” or “amulet“.

Some claim that a “mascota” has a “master” (…you still don’t see power-play at play? Think about the use of “pet” in relation to excessive loyalty of an employee to a superior); does a mascot have a master?

In some storytelling I have noticed that some iteration playing with the concept of the talisman also links the mascot to a master, as a pet is to one.

One can see the animation series, based on a game, entitled “Wakfu” for such narrative . In it the character named “Sir Percedal of Sadlygrove” is emboldened by his powerful luck-bringing sword …and as I notice how a charm or talisman is applied in narratives, these are not always charming nor offering good luck at all times. Yes, as could a cat, a mascot can scratch you the wrong way!

The offered mapping with the word “talisman” and with “Wakfu“, mentioned above, might be acceptable if one could allow for an imaginary and literary “good” demon-possessed item to be seen as a “talisman” or as a bringer-of-luck, does then my pet give me extra power?

Some teams do have, for instance, a living pet dog as a mascot. Moreover, and ever so slightly in dissonance, notice that etymologically, the word mascot is claimed to have associations with “witch”, “wizard”, “nightmare”, “mask” and “black”). Are my pets not what they seems to be?

While in “actual” life, I have heard of, someone carrying a plastic chain-restaurant’s spoon to a sports match, believing it allows their favorite team to win, in Wakfu it is, for instance, a consciously possessed sword.

This is obviously fantasy narrative –I mean, Wakfu. Yes, one might consider the above-mentioned spoon equally fantastical. Yet, this latter reference is a factual example. This is while perhaps one might feel more accepting towards a scarf or a never-washed t-shirt instead of a spoon.

By the way, in the spirit of this text, you might like to know that in Wakfu, these demons which posses linearly-practical objects, turning the items into charms of sorts, are called “shushu(s)”. Interestingly–talking about in-between languages– “Shūshu” ( 叔叔), in Chinese, means “uncle“. Besides the obvious family-relation, it is also used as a name of endearment–yes! that’s a “pet name” for ye– to refer to older male individuals who are not actually related by blood. For instance, my children refer to their Chinese school bus driver as Shūshu. Is this now a magic school bus? Perhaps, in a sense, in Wakfu, this is a sword, giving its adventurous user extra power. In effect, this Sir Percedal character, who wields such powerful sword, might have a relationship with this magical sword as if one has a relationship with a pet. The character is at times rather literally defined by the sword, as a sports team is unitingly defined by its mascot. Perhaps as this is as much as a master is defined by their pet and their pet by them (…it is said that the bacteria in one’s body are defined by the kind of pet one nurtures).

Is this where “mascotte” and “mascota” meet?

…maybe not, maybe the perceived link between “mascot” and “mascota” is entirely serendipitous. Or, maybe one can judge it as a negative form of cultural appropriation; but then, which culture is appropriating which (a topic that could use a posting of its own)? Maybe, in similarity with “salary” and “celery” which are sounding rather similar yet, one being healthier and the other being more or less edible (or something of the sort), such serendipity could be sufficient. In truth, I admit, the second meaning of the Spanish word “mascota” is indeed ” the animal that represents a team.” What then are the links between a pet and a mascot?

Cat-headed deity Bastet

Do I believe in mascots as being like a talisman;.. I personally do not; it’s too irrational for my taste. However, I know many out there (e.g. in sports or in brand loyalty) who do. In human (pre)history we can surely uncover this strong and deep-seated conviction (e.g. in Shamanism, in the wearing of a powerful animal’ skin or skeletal parts, etc.). Is it in Shamanism where we could unveil the cross-over between talisman, mascot and pet? One might have heard of animal spirits… Is this where the Pharaohs and their cats lived in-between the world of the “pet” and the world of the “mascota”? Is the trans-language activity allowing us to, more or less easily, shift in-between more than just a linear translation?

Egyptian mummified cats

The relationship and experiences I sense which I could have with a “mascotte” versus that of a “mascota“, versus that of a “pet“, are very different. While arguably “mascota” and “pet” are the “same”, I can guarantee you: I do not perceive them as the same; not at all (besides the rational yet reductionist knowledge they are “translatables” between English and Spanish). I could elaborate yet the feelings are still conflicting and chaotically intertwined as the yarn my cat-companions got their paws on during their not-so-quiet midnight hours.

As a third sidenote: 

I am learning Spanish. The arguments as to why I am can be covered in another posting. 

However, this exploration of the in-between aids me to stoke the fire of increased willingness to continue my studies. It also aids me to look deeper and see hints of associations between words, beyond one language alone (...there are links between pets and mascots). 

It allows me to slowly but surely unveil my blindness into other languages and areas:  Italian: mascotte; Portuguese: mascote‎;  Spanish: mascota‎; and to me excitingly surprising even  
Polish: maskotka‎. 

I imagine that the act of this inter-language play, functions as an object of my imaginary making. I imagine it as my personal talisman. As much as the meaning of "talisman" is that of being an object that completes another object, the linguistic inter-play completes a passion for learning via the ritual of the creative act. The in-between language play increases a sense of playful power, energy (rejuvenation of learning), and perhaps other learning benefits. 

Additional reasoning as to why this works for me could be yet another posting.

Another example is the Spanish word “negocio“, which seems to mean “business“. Following, I believe I can claim that “Su negocio” means “(their/her/…) your business” as in, for instance, “their shop“. In English a seemingly similar word exists, “negotiation“. Sure, for both we can follow the thread back to the common source in Latin: negotiari (“to carry on business”), from negotium (“business”).

Nevertheless, one word, the English word “business“, feels –that is, as in the initial moment of my sensation of perceiving some meaning– as it connotes (to me, at least) a fixed point, a done deal. The other, the Spanish word “negocio”, when overshadowed with the English word “negotiation”, superficially connotes (to me) a process; not a done deal. This is all the while, contradictory, the Spanish word in isolation away from the English, could feel to me as referring to someone’s shop, someone’s business; a fixed location. I am confident, as time and thinking passes by, that my sensations might change.

Consecutively and for now, I continue to wonder whether in one or versus a combinatorial language-usage, the business owner might experience to be more confronted with the constant uninterrupted negotiations it takes to maintain a business in relation to many an intrinsic and extrinsic force, support, constraint, potential or many a stakeholder. On the other hand, this is all the while in the other language one (me) might more easily go with an assumption where, following a negotiation, one is “in business“. This feels perhaps as if arrived at a specific point of an almost unquestioned doing and being “in business”. Is one more or less delusional / irrational then the other? Does one lead to more or less entrepreneurial dare and risk taking than the other? I imagine yet, I cannot (yet) know. I do question whether anyone has done any research on differences in perceptions and consequential (in)action compared between (multi-)language groups?

I am noticing some writing, in various media outlets, and in a number of fields (e.g. in topics covering psychology, business, well-being, ethics, leadership, etc) that do mention the effect and affect of language usage on the well-being of one’s self and in-between oneself and others. The co-creation of the poetic experience with real-life consequences is exciting to me, to say the least.

In any case, I have been using this in-between language learning and expression for many years now. I also use it with friends across cultures (e.g. my Chinese friends) . This play seems to be universally sensed. At the least, pragmatically, it has helped to strengthen social bonds through playfulness. 

Epilogue: My two cats are wonderful pets and this while they do scratch and destroy, as two little demons of the night. Look at their picture, heading this text! However cute, as far as them being charms or talismans, I am not yet convinced.  In retrospect, instead of having named them Luna and Molly I could have named one Charm and the other Mascota... oh well...
text | wondering, thoughts of the wise old woman

Quotes That Attracted My Attention.

 

Quotes That Attracted My Attention is not a list. Attention is not only attracted in a sequential manner, nor in a manner that is polarizing. At least this is not consciously done; not in that they are liked versus those that are not liked and not pasted or commented on here. It is not that one story narrative. So, it is possible as this posting grows over time that some content of some quotes seemingly contradicts. They might form the seeds for an imaginary forest to be grown. Who knows. That would be lovely.

 

“… it is data as therapy.” — Rosling, Hans

 

Hans Rosling’s work, continued by his children, will continue to feel as being globally essential. I have been intellectually enthralled with his work for years. It is liberating and truly unveiling and enlightening. He shows us without any doubt that we are each ignorant yet we each have the tools available to change this state. I like that, at least as a sentiment and secondly as something I want to work towards.

Visit the website. Take the test; it’s confrontational yet fun. Browse Dollar Street. Read his book entitled Factfulness. Check out visabi.

 
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“Anything that is in the world when you’re born is natural. Anything that’s invented between when you’re 15 and 35 is exciting. And anything invented after you’re 35 is against the natural order of things” — Douglas Adams

 

Supposedly, according to a posting of one of my LinkedIn contacts, the author of The Hitchhiker’s Guide to the Galaxy birthed this statement. I have yet to corroborate it. Either way, in and of itself it attracted my attention.

It made me imagine what is natural, invented and against the natural order of things. It reminds me of a book by Foucault. More so, what do these terms even mean, experientially?

Or rather, if I were to use them, how would they affect (the well-being of) others and myself? Also, these, to me, do not simply seem to be segregated by three age groups. Maybe this could be a stand-alone blog posting one day. Surely, why should I be taking it all seriously, out of fear of nihilism or the mundane? Then again, if not this then what, if not now then when, if not me then who?

Still, the statement makes me smile; if only because the association with the story of, and the characters in, The Guide do. Now, “42” pops up in my mind.

 

text | wondering

mutual exclusivity: coffee or tea; cat or dog; blue or pink; the Blues or Baroque; coffee or Baroque?

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…one must not think ill of the paradox, for the paradox is the passion of thought… — Kierkegaard

 

I am thinking, and firstly learning about, ‘mutual exclusivity’. It’s interesting to me. Here is some of my thinking that this learning process activated. Please note, there is some winking in my  wondering (hence, the title as it is).

I understand that ‘mutual exclusivity’ is a concept from mathematics, from statistics and more specifically from the study of probability.  I presently understand it as a mechanism where events that are mutual exclusive, are events that cannot happen at the same time.

Would I then be mistaken to continue thinking that these events are not possible to be combined and that therefor these events can not occupy the same space and time?

Yes, in my ignorance, questions pop up. I’m still learning about ‘mutual exclusivity’’s deeper meaning and about how to perform computations associated with it. So, my mind begins to wonder.

What if the fact that events cannot happen at the same time, are not happening at the same time, because of the manner with which we look at the events to begin with, rather than because of some intrinsic attribute of the events that are believed not to be possible (or that are considered to be irrational) to occur at the same time? OK, that’s a mouth full. What I think I am asking is:

  1. if there is a cup of coffee (‘A’) and there is a cup of tea (‘B’);
  2. if there is a group of humans that is asked by an other individual (yet not asked in person but rather by means of a questionnaire): “which do you prefer, ‘A’ or ‘B’?“
  3. if this questionnaire allows for only 3 answers: ‘coffee’, or ‘tea’ or ‘no preference’…

…is then a ‘mutual exclusivity’ not simply imposed, by the design of the questionnaire and this by ignoring dynamic attributes?

Concretely this seems, for the moment, to mean to me that the asked human(s) must express one and only one preference, or must express not to have any preference. I guess that some (to me) unknown amount of individuals might be clear on how to answer this. Though, would all and every individual have such a fixed clarity?

The answer of “no preference” could in turn be interpreted by the person indirectly asking about the preference (and who is only looking at the answers in a data set organized in a table, combined with the output from other individuals who were asked the same question) as: “…so, x, y and z individuals can be offered a random choice of these two offered drinks;” or as: “I would offer nothing at all to this individual, or that individual, claiming to have no preference possibly because they like neither coffee nor tea“.

Now, what if the individual presented with the obligation of choice is polyamorous about her or his relationship with both coffee and tea; then surely, “no preference” would be a betrayal as much as only choosing “tea” over “coffee” or vice versa; no?

So, it feels to me that the resulting data set seems to create ambiguity in the reader of the data set (I being one such yet-unclear reader). What could be the usefulness of such data set, I wonder. I truly wonder and do not yet judge. I also laugh, indeed, equally so, what is the use of all this writing here? The answer to that could be someone else’s blog post…

In support of such blog-post: someone, whom I care about (measurably more than tea or coffee), recently claimed that those who speak too much (this can be substituted with “write”) probably like espresso rather than an average cup of coffee.

Indeed, this unveils yet another issue with the questionnaire: what kind of coffee (or tea)? This is then compounded by a dilemma when the individual begins realizing how one could be judged by others if choosing one kind over another. Therefor, instead, the individual might be tempted choosing, perhaps falsely, one of the many types of tea categorized under the choice “tea” (…not to mention, the problem with “tea” and all those non-tea-based “teas” which, might be generally better labelled as infuses…). This, I sense, could thus lead to biased data and a false sense of mutual exclusivity.

Is then this ‘mutual exclusivity’ possibly a construct (sure, I assume not at all times), out of some (lazy) convenience? Does it serve a model rather than serve the complexity of (human) experience / of the realities of complex dynamic systems?

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Perhaps, less social or psychologically-motivated: does such exclusivity ignore going  beyond some possibilities outside of Newtonian physics or outside of binary logic?

…Or is the “construct” not that of ‘mutual exclusivity’ alone but also, or rather, that of an educator aiming to simplify the introductory teaching of ‘mutual exclusivity’? Do we, as learners, first have to unquestioningly submit and accept such over-simplifications to become enabled to understand the basics? Surely, I agree, I do need it… While the risk does exist that over time, and across learning as a habituation process, someone could forget one’s love for the other choice.

Nevertheless, it would be nice to be hinted, that exciting complexities need to be considered towards future learning, which could be leading to possible paradoxes, dissonances or fuzzinesses. It’s OK, the evaluator of the data will survive; it’s only coffee or is it tea?

This forecasting of future learning  would map out a learning path to me which I could look forward to while at the presently bland stage of looking at a simple (and perhaps somewhat acceptably flawed) data set towards learning about ‘mutual exclusivity’. Easily solved: I’m now hinting this to myself.

I imagine such intuitively felt potential complexity is implied in questions that bubble up in my thinking and this as a side-effect of my process of learning.  To continue, for the brave reader, here is another one:

What if the preference (i.e. ‘coffee’, tea’, ‘no preference’) is defined or influenced by an external factor (or a set of factors)?

For instance, one could assume the existence of a third influencing item, catalyzing or weighing the choice into a certain direction. Note, ironically, this assumption of there only being a third and not a fourth, etc., feels as a reduction as well to me, and one that could almost equally influence the questioned output (i.e. a data set of three possible answers collected from a set of individuals and organized in a table).

Anyway, such external influence does not yet feel too far-fetched to be a possibility. I feel it is more realistic than a choice created as being a static attribute (i.e. a clearly definable choice, at all times, in all environments, with or without any additional actors; e.g. “I am a coffee drinker; period.“). I think such external influencing attributes seem to feel to be more common than a clear inherent preference to the individual being asked as implied in the basic introduction of ‘mutual exclusivity’?

For instance, coffee is preferred over tea depending on the time of day. Tea is preferred depending on other people with whom the individual, who is asked about the preference, is meeting with.

Imagine then, in addition, if those people would be met at the time of day that would make the individual prefer coffee, this imaginary  individual here would still choose tea since the individual, at that time and in that space, who would otherwise decide for a preference of coffee versus tea, has now decided this as being  less weighted than her/his social interaction and experience of bonding as symbolized by the ritual of the tea drinking with others, who prefer tea (or so the individual assumes).

So, under these external attributes, the individual chooses to drink tea whereas otherwise, when no one else is there at that time, the individual would choose coffee. Unless, there is an article in her/his favorite newspaper questioning the health benefits of the one over the other.

At that same moment, yet on another day, the individual, as a biological system, simply is not thirsty. Then “preference” of any kind becomes temporarily irrelevant. If at that moment the individual were to answer to the inquiry, she/he would (have to) then dismiss this possibility of their reality as non-existent in the model.

Or, surely, such situation does not have to result in a one-time outlier on a scatter diagram. Contrary, as in a diplomatic ping-pong match of thinking where no one thought has to win, it might equally result in an outlier on such scatter diagram; if the individual were a consistent creature of unwavering habit and ritual. Mmmmmm,  come to think of it, could one actually plot this triangulation of choice onto a scatter diagram? I need to check this.

“Not thirsty” as a preference option is non existent in the given questionnaire.  By the way, as suggested previously, this is different from not having a preference, from liking either equally as much, and different from not liking coffee nor tea. Though, the individual does not realize that with sufficient explicit and subliminal messaging he or she will believe to be thirsty after all for that or the other beverage.

No, such playful consideration of complexity is not nihilism nor defeatism. I will return in vigor to my learning of these basics and that during and following this writing.

Options, options, options of recombinable outcomes that are being less-then-mutually exclusive,  if seen over time and space and thus in changing contexts.  Perhaps, I am trying too much to make a model as a 1:1 representation of the complexities of reality?

At least, I suppose that in some of the above scenarios, the data collected is hence dramatically influenced not by the preference of the individual as if a static fact (i.e. “it’s just a preference“). Rather, it is defined by other attributes that temporarily weigh in more. This statement too seems to be an abstraction of sorts and therefor could lead to a (useful) reductionist model. Nevertheless, it feels as less dismissive of experiential complexities and it feels as more sensible towards deviations, away from the idea of fixed preferences (over time and space), as the one I was presented with at first.

If my thinking is flawed, where is it flawed? (besides an obvious and dismissive and debasing: “you’re making it all too complex; who gives a ****, dude!“)

Gosh, indeed, suddenly ‘mutual exclusivity’ becomes excitingly complex and granted, who cares? Well,  …I do   :-p

How would one go about calculating such externally influencing factors? How do statistics that do not consider such externals actually provide sufficiently accurate information for interpretation? So much still to learn…

With this writing, I have just further carved out my passion and vigor to continue learning about this ‘mutual exclusivity’.  It’s rejuvenating to realize I know so little and could still explore so much.

I mean, I’m really just starting and the application of the “general multiplication rule” versus the “addition rule” alone is already intriguingly startling. That’s perhaps for another post.

For the moment I will continue my very basic study, accepting the choices between “cat or dog” and “coffee versus tea”.

text | wondering

AI, Impact Investment, Ethics & Deeply Human-Centered Innovation

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text | wondering

AI, Impact Investment, Ethics & Deeply Human-Centered Innovation: #8

Part #8: A First Suggested Methodological Approach Towards Solutions

 

I am imagining such a methodology that could be applied from an early age to a later age and that could be practiced as a life-long learning of one’s cognitive processes in inter-action with another person’s cognitive processes-to-action. As implied in my definition on human intelligence (HI), that what makes us intelligent humans is how we think about and act with the other (irrespective of which one technology we try to design, try to improve or try to use).

These two imaginary agents (i.e. the human and the other) are actors that would ideally act inter-subjectively and thus would be aware of each one’s preferences, biases and human frailties in terms of the encompassing “Human Intelligence” as suggested above. So too, such methodology to instill such abilities could be one that can consequentially be applied towards the ethos of AI. This brings me to Ethical Consciousness and Intersubjective Discernment.

I introduce here, Dr. Gilda Darlas’ two decades of research and fieldwork. Dr. Darlas has both an academic and professional background in Artificial Intelligence as well as in Ethics.

Dr. Darlas’ work aims at methodologically increasing Ethical Consciousness and Intersubjective Discernment in human beings. Her approach and applications in association to ethics and AI are, to me, dramatically different from the approach suggested in, for instance, the World Economic Forum’s white paper.

While Dr. Darlas does not dismiss these approaches as offered by these experts, she too (as do I) sees them as temporary and incomplete solutions towards a more transformational and paradigm shifting solution where the human being is considered and not only, for instance, the AI.

The terms, Ethical Consciousness and intersubjective Discernment, so states Dr. Darlas, are used to explain “the capacity to see and understand our human nature as a dynamic composition of conditioned and conditioning factors. Understanding this, is not only important for a proper thinking of a conscious human mind but is a key condition for the development of ethical consciousness as it was proven throughout our global research.”

The methodology towards achieving these, she continues, was coined as “Disciplinary Phenomenology for Ethical Development.”

Dr. Darlas states that this term defines a set of “processes and techniques that focus on developing mental discipline proper attention, vision and understanding of the subjective content of human experience.”

Dr. Darlas underlines that this was a fundamental development in order to bring about “systematic transformation of our ways of relating to ourselves and others. In other words, the methodology seeks to achieve intersubjective discernment from where Ethical Consciousness and Wisdom could fully flourish.”

Gilda heads a Foundation and a Center for Research in Ethical Development, presently based in Mexico. Through these her team and she support about 500 schools in a number of countries and via a number of operational languages. They do so with methodological programs for ethical development to pupils, students, parents and teachers.

Dr. Darlas’ work has been recognized by established organizations and has received a number of nominations and awards. She aims to bring more prototypes into more schools around the world. Also she wishes to invite others to “co-create new ways in which the principles and approach to ethical development can be put to service humanity.”

The programs enable humans to “learn how to think and not what to think. This is not a question of being intelligent or acquiring more data or knowledge, this is about being able to achieve deepest levels of comprehension and discernment by learning how to think”.

“The need for cognitive development in schools as the required platform for ethical development in teachers and students is urgent,” shares Dr. Darlas.

“Nonetheless, we realized,” she states, “that education was and still is more preoccupied with content and its methodological approaches for acquiring and playing with such content than on helping children to develop a proper thought process, let alone the intersubjective discernment required for ethical consciousness. This results in blinding the human mind… where self-centered assumptions rule our perceptions, thoughts and actions.”

Dr. Darlas is in the process of finding social impact investors to enable her to re-open her university campus in Mexico. Uethics,[17] is the name of this university. Uethics is to be a highly specialized university, aimed at advancing studies and R&D in the areas of ethics and discernment. It would be one of a kind in the LATAM region. It would also stand out compared to existing tertiary ethics or Computer Science programs from around the world.

In this tertiary educational institution, she aims to offer 3 Master Degrees, one of which has an AI focus: the “Master’s Degree in Ethical Development and Artificial Intelligence”. The program is described as follows:

“This program does not address issues arising from cyber-security, information technology, biotechnology, and other emerging fields, instead, we address the topic of biases on the design of AI algorithms. This means we focus on the designer’s self-centered tendencies and his/her distorted perceptions when designing AI algorithms. Then we will encourage students to design prototypes that will enable subjective design algorithms in artificial intelligence solutions.”

Perhaps those who are concerned about implementing ethics into or around Artificial Intelligence beyond normative manners only, might increase value from engaging into such a post-graduate program. At least I want to believe that leaders and experts in policy-making, academics, the humanities and in tech could benefit and could aid to benefit the common citizens influenced by AI by implementing the findings in such research and in such Master’s degree into the specific realm of their professional, political or communal applications.

Just perhaps, it will then be more conceivable to create an algorithm to discern while still providing means to offer our human cognition with means to discern as well.

Those who are interested in exploring impact investment towards the development of the first Uethics university campus in Mexico can contact Dr. Darlas by requesting further information from me here on LinkedIn. One can also find publicly available materials by doing an internet search on “Dr. Gilda Darlas” or by visiting the URLs:

  • http://eudeglobal.org/2015/la-fundadora/
  • https://www.uethics.org/university

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