text | wondering, The field of AI

The Field of AI (Part 02-3): 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)


02 — The Field of AI: A Foundational Context:
Philosophy, Psychology & Linguistics

Philosophy:

 In the early days of philosophy (while often associated with the Ancient Greeks, surely found in other comparable forms in many intellectual, knowledge-seeking communities throughout history) and up till present days, people create forms of logic, they study and think about the (existence, developments, meaning, processes, applications, … of) mind, consciousness, cognition, language, reasoning, rationality, learning, knowledge, and so on.   

Logic too, often has been claimed as being an Old Greek invention; specifically by Aristotle (384 B.C to 322 B.C.). It has, however, more or less independent traditions across the globe and across time. Logic lies at the basis of, for instance, Computational Thinking, of coding, of mathematics, of language, and of Artificial Intelligence. In its most basic (and etymologically), logic comes from Ancient Greek “Logos” (λόγος), which simply means “speech”, “reasoning”, “word” or “study”. Logic can, traditionally, be understood as “a method of human thought that involves thinking in a linear, step-by-step manner about how a problem can be solved. Logic is the basis of many principles including the scientific method.”[1] Note, following the result of research and development (R&D) in fields that could be associated with the field of AI and within the field of AI itself, can show that today logic, in its various forms, is not only a linear process. Moreover, at present, the study of logic has been an activity no longer limited to the field of philosophy alone and is studied in various fields including computer science, linguistics or cognitive science as well.

 One author covering a topic of AI, tried to make the link between Philosophy and Artificial Intelligence starkly clear. As a discipline, AI is offered the consideration as possibly being “philosophical engineering.”[2] In this linkage, the field of AI is positioned as one researching more philosophical concepts from any field of science and from Philosophy itself that are then transcoded, from mathematical algorithms to artificial neural networks. This linkage proposes that philosophy covers ideas that are experienced as, for instance, ambiguous or complex or open for deep debate. Historically, philosophy tried to define, or at least explore, many concepts including ‘knowledge,’ ‘meaning,’ and ‘reasoning,’ which are broadly considered to be processes or states of a larger set known as “intelligence”. The latter itself too has been a fertile topic for philosophy. The field of AI as well has been trying to explore or even solve some of these attributes. The moment it solved some expressions of these, it was often perceived as taking away not only the mystery but also the intelligence of the expressed form. The first checker or chess “AI” application is hardly considered “intelligent” these days. The first AI solution beating a champion in such culturally established board games has later been shown to lack sufficient “intelligence” to beat a newer version of an AI application. Maybe that improved version might (or will) be beaten again, perhaps letting the AI applications race on and on? Just perhaps contrary to “philosophical engineering,” would the field of AI be practically engineering the philosophy out of some concepts?

Mini
Project #___:  algorithms in daily life
Find out what “algorithm” in general (in a more non-mathematical or more non-coding sense) means. Can you find it has similarities with the meaning of “logic”? If so, which attributes seem similar?
What do you think ‘algorithm’ could mean and could be in daily life (outside of the realm of Computer Science)? Are there algorithms we use that are not found in a computer?

Mini Project #___: The Non-technological Core of AI
Collect references of what consciousness, intelligence, rationality, reasoning and mind have meant in the history of the communities and cultures around you. 
Share your findings in a collection of references from the entire class. 
Maybe add your findings to the collage (see the Literature project above).
Alternative: the teacher shares a few resources or references of philosophers that covered these topics and that are examples of the pre-history of AI.



Psychology

Psychology has influenced and is influenced by research in AI. To some degree and further developing this is still the case today.[3]

Not only as a field related to cognitive science and the study of the processes involving perception and motor control (i.e. control of muscles and movement) but also the experiments and findings from within the longer history of psychology, have been of influence in the areas of AI.

It is important to note that while there are links between the field of AI and psychology, some attributes in this area of study have been contested, opposed and surpassed by cognitive science and computer science, with its subfield of AI.

An example of a method that can be said to have found its roots in psychology is called “Transfer Learning”. This refers to a process or method learned within one area that is used to solve an issue in an entirely different set of conditions. For a machine the area and conditions are the data sets and how its artificial neural network model is being balanced (i.e. “weighted”). The machine uses a method acquired in working within one data set to work in another data set. In this way the data set does not have to be sufficiently large for the machine to return workable outputs.

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The AI method known as Reinforcement Learning is one that could be said to have some similarities with experiments such as those historically conducted by Pavlov and B.F. Skinner. With Pavlov the process of “Classical Conditioning” was introduced. This milestone in the field of psychology is most famously remembered with Pavlov’s dog that started to produce saliva the moment it heard a sound of a bell (i.e. the action which Pavlov desired to observe). This sound was initially associated with the offering of food; first the bell was introduced, then the food and then the dog would produce saliva. Pavlov showed that the dog indeed did link both the bell and the food. Eventually, the dog would produce saliva at the hearing of the bell without getting any food. What is important here is that the dog has no control over the production of saliva. That means the response was involuntary; it was automatic. This is, in an over-simplified explanation, Classical Conditioning.

That stated, Reinforcement Learning (RL) is a Machine Learning method, where the machine is confronted with degrees of “reward” or the lack thereof. See the section on RL for further details. Studies surrounding reward have been found in historical research conducted by a researcher named Skinner and others. It’s interesting to add that this research has been contested by Chomsky, questioning the scientific validity and transferability to human subjects.[4] Chomsky’s critique has been considered as important in the growth of the fields of cognitive science and AI, back then in the 1950s. In these experiments a process called “Operant Conditioning” was being tested. The researchers were exploring voluntary responses (as opposed to the involuntary ones seen with Pavlov). That is to say, these were responses that were believed to be under the control of the test subject and that would lead to some form of learning, following some form of reward.

Again, these descriptions are too simplistic. They are here to nudge you towards further and deeper exploration, if this angle were to excite you positively towards your learning about areas in the academic field of AI.



Linguistics

With Linguistics come the studies of semiotics. Semiotics could be superficially defined as the study of symbols and various systems for meaning-giving including and beyond the natural languages. One can think of visual languages, such as icons, architecture, or another form is music, etc. Arguably, each sense can have its own meaning-giving system. Some argue that Linguistics is a subfield of semiotics while again others turn that around. Linguistics also comes with semantics, grammatical structures (see: Professor Noam Chomsky and the Chomsky Hierarchy)[5], meaning-giving, knowledge representation and so on.

Linguistics and Computer Science both study the formal properties of language (formal, programming or natural languages). Therefor any field within Computer Science, such as Artificial Intelligence, share many concepts, terminologies and methods from the fields within Linguistics (e.g. grammar, syntax, semantics, and so on). The link between the two is studied via a theory known as the “automata theory”[6], the study of the mathematical properties of such automata. A Turing Machine is a famous example of such an abstract machine model or automaton. It is a machine that can take a given input by executing some rule, as expressed in a given language and that in a step by step manner; called an algorithm, to end up offering an output. Other “languages” that connects these are, for instance, Mathematics and Logic.

Did you know that the word “automaton” is from Ancient Greek and means something like “self-making”, “self-moving”, or “self-willed”? That sounds like some attributes of an idealized Artificial Intelligence application, no? 

Mini
Project #___: What are automatons? 
What do you know you feel could be seen as an “automaton”? 
Can you find any automaton in your society’s history?

[1] Retrieved on January 12, 2020 from https://en.wiktionary.org/wiki/logic

[2] Skanski, S. (2018). Introduction to Deep Learning. From Logical Calculus to Artificial Intelligence. In Mackie, I. et al. Undergraduate Topics in Computer Science Series (UTiCS). Switzerland: Springer. p. v . Retrieved on March 26, 2020 from http://www.springer.com/series/7592  AND https://github.com/skansi/dl_book

[3] Crowder, J. A. et al. (2020). Artificial Psychology: Psychological Modeling and Testing of AI Systems. Springer

[4] Among other texts, Chomsky, N. (1959). Reviews: Verbal behavior by B. F. Skinner. Language. 35 (1): 26–58. A 1967 version retrieved on March 26, 2020 from https://chomsky.info/1967____/

[5] Chomsky, N. (1956). Three models for the description of language. IEEE Transactions on Information Theory, 2(3), 113–124. doi:10.1109/tit.1956.1056813 AND Fitch, W. T., & Friederici, A. D. (2012). Artificial grammar learning meets formal language theory: an overview. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1598), 1933–1955. doi:10.1098/rstb.2012.0103

[6] The automata theory is the study of abstract machines (e.g. “automata”, “automatons”; notice the link with the word “automation”). This study also considers how automata can be used in solving computational problems.