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The Field of AI (Part 02-4): A “Pre-History” & a Foundational Context

URLs for A “Pre-History” & a Foundational Context:

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


03 — The Field of AI: A Foundational Context: Control Theory


Control Theory

When thinking at a daily and personal level, one can observe that one’s body, the human body’s physiology, seemingly has a number of controls in place for it to function properly.

Humans, among many other species, could be observed showing different types of control. One can observe control in the biological acts within the body. For instance, by the physiological nature of one’s body’s processes; be they more or less autonomous or automatic processes. Besides, for instance, the beating process of the heart, or the workings of the intestines, one could also consider processes within, e.g. the brain and those degrees of control with and through the human senses.

Humans also exert control by means of, for instance, their perceptions, their interpretations, and by a set of rituals and habitual constraints which in turn might be controlled by a set of social, cultural  or in-group norms, rules, laws and other external or internalized constraints.

Really broadening one’s view onto ‘control’: one can find the need for some form and degree of control not only within humans but also in any form of life; in any organism. In effect, to be an organism is an example of a system of cells working together, in an organized and cooperative manner, instrumental to their collective survival as unified into the organism. Come to think of it, an organism can be considered sufficiently organized and working, if some degrees and some forms of shared, synchronized control is underlying their cooperation.

Interestingly enough, to some perhaps, such control is shared, within the organism, with colonies of supportive bacteria; its microbiome (e.g. the human biome). [1] While this seems very far from the topic of this text at the same time, analogies and links between Control Theory, Machine Learning and the biological world are at the foundation of the academic field of AI.[2]

If one were to somewhat abstract the thinking on the topic of ‘control’, then these controlling systems could be seen as a support towards learning from sets of (exchanged) information or data. These systems might engage in such acts of interchanged learning, with possibly the main aim to sustain forms and degrees of stability, through adaptations, depending on needs and contextual changes. At the very least, the research surrounding complex dynamic systems can use insights in both Control Theory and consequentially, the processing potentials as promised within Machine Learning.

Control could imply the constraining of the influence of certain variables or attributes within, or in context of, a certain process. One attribute (e.g. a variable or constant) could control another attribute and vice versa. These interactions of attributes could then be found to be compounded into a more complex system.

Control seems most commonly allowing for the reduction of risks and could allow for a given form and function (not) to exist. The existence of a certain form and function of control can allow for a system (not) to act within its processes.  

When one zooms in and focuses, one can consider that perhaps similar observations and reflections have brought researchers to constructing what is known as “Control Theory.”

Control Theory is the mathematical field that studies control in systems. This is through the creation of mathematical models, and how these dynamic systems optimize their behavior by controlling processes within a given, influencing environment.[3]

Through mathematics and engineering it allows for a dynamic system to perform in a desired manner (e.g. an AI system, an autonomous vehicle, a robotic system).  Control is exercised over the behavior of a system’s processes of any size, form, function or complexity. Control, as a sub-process, could be inherent to a system itself, controlling itself and learning from itself.

In a broader sense, Control Theory[4], can be found in a number of academic fields. For instance, it is found in the field of Linguistics with, for instance, Noam Chomsky[5] and the control of a grammatical contextual construct over a grammatical function. A deeper study of this aspect, while foundational to the fields of Cognitive Science and AI, is outside of the introductory spirit of this section.

As an extension to a human and their control within their own biological workings, humans and other species have created technologies and processes that allow them to exhort more (perceived) control over certain aspects of (their perceived) reality and their experiences and interactions within it.

Looking closer, as it is found in the area of biology and also psychology, with the study of an organism’s processes and its (perceptions of) positive and negative feedback loops. These control processes allow a life form (control of its perception of) maintaining a balance, where it is not too cold or hot, not too hungry and so on; or to act on a changing situation (e.g. start running because fear is increasing).

As one might notice, “negative” is not something “bad” here. Here the word means that something is being reduced so that a system’s process (e.g. heat of a body) and its balance can be maintained and stabilized (e.g. not too cold and not too hot). Likewise, “Positive” here does not (always) mean something “good”. It means that something is being increased. Systems using these kinds of processes are called homeostatic systems.[6] Such systems, among others, have been studied in the field of Cybernetics;[7] the science of control.[8] This field, in simple terms, studies how a system regulates itself through its control and the communication of information[9] towards such control.

These processes (i.e. negative and positive feedback loops) can be activated if a system predicts (or imagines) something to happen. Note: here is a loose link with probability, thus with data processing and hence with some processes also found in AI solutions.

In a traditional sense, a loop in engineering and its Control Theory could, for instance, be understood as open-loop and closed-loop control. A closed loop control shows a feedback function.  This feedback is provided by means of the data sent from the workings of a sensor, back into the system, controlling the functioning of the system (e.g. some attribute within the system is stopped, started, increased or decreased, etc.).

A feedback loop is one control technique. Artificial Intelligence applications, such as with Machine Learning and its Artificial Neural Networks can be applied to exert degrees of control over a changing and adapting system with these, similar or more complex loops. These AI methods too, use applications that found their roots in Control Theory. These could be traced to the 1950s with the Perceptron system (a kind of Artificial Neural Network), built by Rosenblatt.[10] A number of researchers in Artificial Neural Networks and Machine Learning in general found their creative steppingstones in Control Theory. 

The field of AI has links with Cognitive Science or with some references to brain forms and brain functions (e.g. see the loose links with neurons). Feedback loops, as they are found in biological systems, or loops in general, have consequentially been referenced and applied in fields of engineering as well. Here, associated with the field of AI, Control Theory and these loops, are mainly referring to the associated engineering and mathematics used in the field of AI. In association with the latter, since some researchers are exploring Artificial General Intelligence (AGI), it might also increasingly interest one to maintain some degree of awareness of these and other links between Biology and Artificial Intelligence as a basis for sparking one’s research and creative thinking, in context.

[1]  Huang, S. et al. (February 11, 2020). Human Skin, Oral, and Gut Microbiomes Predict Chronological Age. Retrieved on April 13, 2020 from https://msystems.asm.org/content/msys/5/1/e00630-19.full-text.pdf

[2] See for instance, Dr. Liu, Yang-Yu (刘洋彧). “…his current research efforts focus on the study of human microbiome from the community ecology, dynamic systems and control theory perspectives. His recent work on the universality of human microbial dynamics has been published in Nature…” Retrieved on April 13, 2020 from Harvard University, Harvard Medical School, The Boston Biology and Biotechnology (BBB) Association, The Boston Chapter of the Society of Chinese Bioscientists in America (SCBA; 美洲华人生物科学学会: 波士顿分会) at https://projects.iq.harvard.edu/bbb-scba/people/yang-yu-liu-%E5%88%98%E6%B4%8B%E5%BD%A7-phd and examples of papers at https://scholar.harvard.edu/yyl

[3] Kalman, R. E. (2005). Control Theory (mathematics). Online: Encyclopædia Britannica. Retrieved on March 30, 2020 from https://www.britannica.com/science/control-theory-mathematics

[4] Manzini M. R. (1983). On Control and Control Theory. In Linguistic Inquiry, 14(3), 421-446. Information Retrieved April 1, 2020, from www.jstor.org/stable/4178338

[5] Chomsky, N. (1981, 1993). Lectures on Government and Binding. Holland: Foris Publications. Reprint. 7th Edition. Berlin and New York: Mouton de Gruyter,

[6] Tsakiris, M. et al. (2018). The Interoceptive Mind: From Homeostasis to Awareness. USA: Oxford University Press

[7] Wiener, N. (1961). Cybernetics: or the Control and Communication in the Animal and the Machine: Or Control and Communication in the Animal and the Machine. Cambridge, MA: The MIT Press

[8] The Editors of Encyclopaedia Britannica. (2014). Cybernetics. Retrieved on March 30, 2020 from https://www.britannica.com/science/cybernetics

[9] Kline, R. R. (2015). The Cybernetics Moment: Or Why We Call Our Age the Information Age. New Studies in American Intellectual and Cultural History Series. USA: Johns Hopkins University Press.

[10] Goodfellow, I., et al. (2017). Deep Learning. Cambridge, MA: MIT Press. p. 13

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Image Caption:

“A typical, single-input, single-output feedback loop with descriptions for its various parts.”

Image source:

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