Tag Archives: data

text | wondering, The field of AI

<< 7 Musketeers of Data Protection >>


In the EU & UK there are data protection principles set within regulation or law. Some relate back to the UN’s Human Rights:

(right to) Lawfulness;
(right to) Fairness & transparency;
(right to) Purpose limitation;
(right to) Data minimization;
(right to) Accuracy;
(right to) Storage limitation;
(right to) Integrity & confidentiality;
(right to) Accountability

How might Large Language Models (LLMs) measure up?

These innovations were built on scraping the internet for data. The collected data was then processed in a manner to allow the creation of LLMs & their spin-off chatbots. Then products have been layered on top of that which are being capitalized upon. While oversimplified, this paragraph functions as the language model for this text.

This process, hinted at in the previous paragraph, has not been & is not occurring in a transparent fashion. Since the birth of the World Wide Web, and with it the rise of “social” networks, the purpose of users in uploading their data onto the internet was probably not intended with this purpose (i.e., of large data scraping initiatives) in their mind. The data on users is being maximized not minimized.

The resulting output is rehashed in such way that accuracy is seriously questionable. One’s data is potentially stored for unlimited time at unlimited locations. confidentiality seems at least unclear if not destabilized. Who is accountable; this is unclear

I am not yet clear as to how LLMs measure up to the above 7 data protection principles. Are you clear?

If these principles were actually implemented, would they stifle innovation & market? Though, if these seven were not executed, what would be the benefit & what would be the cost (think value, social relations and democracy here, and not only money) to the user-netizen with “democratized” (lack of) access to these “AI”innovations?

Or, shall we declare these 7 musketeers on a path to a death by a thousand transformable cuts? This then occurs in harmony with calls for the need for trustworthy “AI.” Is the latter then rather questionable; to ask it gently?

References

data protection legislation (UK):
Data Protection Act 2018 + the General Data Protection Regulation 2016

https://www.legislation.gov.uk/ukpga/2018/12/contents/enacted

https://www.legislation.gov.uk/eur/2016/679/contents
—
General Data Protection Regulation (GDPR)
https://gdpr-info.eu/

—
Data Protection and Human Rights:
https://publications.parliament.uk/pa/jt200708/jtselect/jtrights/72/72.pdf

https://edps.europa.eu/data-protection/data-protection_en

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

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

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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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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   

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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

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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