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<< When Critical is not Critical >>

“Traditions and ideas must be revisited and reworked, communicated and debated, entangled and disentangled. (Self)-critique can be carried out neither in narcissistic isolation nor in the silence of the ineffable. In the gap between acknowledging your echoing and refusing to echo, and the gap between one’s own pure voice and its simulacrum, critical educational theory of all persuasions struggles with words. Perhaps it is more critical when its loving words are addressed to others and when it harkens to their response, though in this case too, the teacher-pupil relation is one of articulation. For, to echo Derrida here, ‘a master who forbids himself the phrase would give nothing. He would have no disciples but only slaves’ (1995, p. 147).” —Papastephanou (2004)

Papastephanou, M. (2004). Educational Critique, Critical Thinking and the Critical Philosophical Traditions. Journal of Philosophy of Education, 38(3), 369–378. https://doi.org/10.1111/j.0309-8249.2004.00391.x

The 2024 Tsinghua Higher Education Forum 清华高等教育论坛 . Institute of Education, Tsinghua University 清华大学教育研究院. The Beijing Convention Center 北京会议中心. 30th August 2024,14:25 – 14:50 Prof. Holmes, Wayne: “AI and Education: A critical Studies Approach

Derrida, J. (1995) Violence and Metaphysics, in: Writing and Difference (London, Routledge).

The Field of AI (Part 06): “AI” ; a Definition Machine?

Definitions beyond “AI”: an introduction.

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“You shall know a word by the company it keeps.”

– Krohn, J.[1]

Definitions are artificial meaning-giving constructs. A definition is a specific linguistic form with a specific function. Definitions are patterns of weighted attributes, handpicked by means of (wanted and unwanted) biases. A definition is then as a category of attributes referring to a given concept and which then, in turn, aims at triggering a meaning of that targeted concept.

Definitions are aimed at controlling such meaning-giving of what it could refer to and of what it can contain within its proverbial borders: the specified attributes, narrated into a set (i.e. a category), that makes up its construct as to how some concept is potentially understood.

The preceding sentences could be seen as an attempt to a definition of the concept “definition” with a hint of how some concepts in the field of AI itself are defined (hint: have a look at the definitions of “Artificial Neural Networks” or of “Machine Learning” or of “Supervised and Unsupervised Learning”). Let us continue looking through this lens and expand on it.

Definitions can be constructed in a number of ways. For instance: they can be constructed by identifying or deciding on, and giving a description of, the main attributes of a concept. This could be done, for instance, by analyzing and describing forms and functions of the concept. Definitions could, for instance, be constructed by means of giving examples of usage or application; by stating what some concept is (e.g. synonyms, analogies) and is not (e.g. antonyms); by referring to a historical or linguistic development (e.g. its etymology, grammatical features, historical and cultural or other contexts, etc.); by comparison with other concepts in terms of similarities and differentiators; by describing how the concept is experienced and how not; by describing its needed resources, its possible inputs, its possible outputs, intended aims (as a forecast), actual outcome and larger impact (in retrospect). There are many ways to construct a definition. So too is it with a definition for the concept of “Artificial Intelligence”.

For a moment, as another playful side-note, by using our imagination and by trying to make the link between the process of defining and the usage of AI applications stronger: one could imagine that an AI solution is like a “definition machine.”

One could following imagine that this machine gives definition to a data set –by offering recognized patterns from within the data set– at its output. This AI application could be imagined as organizing data via some techniques.  Moreover, the application can be imagined to be collecting data as if attributes of a resulting pattern. To the human receiver this in turn could then define and offer meaning to a selected data set . Note, it also provides meaning to the data that is not selected into the given pattern at the output. For instance: the date is labelled as “cat” not “dog” while also some data has been ignored (by filtering it out; e.g. the background “noise” of ‘cat’).  Did this imagination exercise allow one to make up a definition of AI? Perhaps. What do you think? Does this definition satisfy your needs? Does it do justice to the entire field of AI from its “birth”, its diversification process along the way, to “now”? Most likely not.

A human designer of a definition likely agrees with the selected attributes (though not necessarily) while, those receiving the designed definition might agree that it offers a pattern but, not necessarily the meaning-giving pattern they would construct. Hence, definitions tend to be contested, fine-tuned, altered, up-dated, dismissed all-together over time and, depending on the perspective, used to review and qualify other yet similar definitions.  It almost seems that some definitions have a life of their own while others are, understandably, safely guarded to be maintained over time.

When learning about something and when looking a bit deeper than a surface, one then quickly is presented with numerous definitions of what was thought to be one and the same thing yet, which show variation and diversity in a field of study. This is OK. We, as individuals within our species, are able to handle, or at least live with ambiguities, uncertainties and change. These, by the way, are also some of the reasons why, for instance and to some extent, the fields of Statistics, Data Science and AI (with presently the sub-field of Machine Learning and Deep Learning) exist.

The “biodiversity” of definitions can be managed in many ways. One can manage different ideas at the same time in one’s head. It is as one can think of black and white and a mix of the two, in various degrees and that done simultaneously; while also introducing a plethora of additional colors. This can still offer harmony in one’s thinking. If that doesn’t work, one can put more importance to one definition over another, depending on some parameters befitting the aim of the learning and the usage of the definition (i.e. one’s practical bias of that moment in spacetime). One can prefer to start simple, with a reduced model as offered in a modest definition while (willingly) ignoring a number of attributes. This one could remind oneself to do so by not equating this simplified model / definition with the larger complexities of that what it only initiates to define.

One can apply a certain quality standard to allow the usage of one definition over another. One could ask a number of questions to decide on a definition. For instance: Can I still find out who made the definition? Was this definition made by an academic expert or not, or is it unknown? Was it made a long time ago or not; and is it still relevant to my aims? Is it defining the entire field or only a small section? What is intended to be achieved with the definition?  Do some people disagree with the definition; why? Does this (part of the) definition aid me in understanding, thinking about or building on the field of AI or does it rather give me a limiting view that does not allow me to continue (a passion for) learning? Does the definition help me initiate creativity, grow eagerness towards research, development and innovation in or with the field of AI? Does this definition allow me to understand one or other AI expert’s work better? If one’s answer is satisfactory at that moment, then use the definition until proven inadequate. When inadequate, reflect, adapt and move on.

With this approach in mind, the text here offers further 10 considerations and “definitions” on the concept of “Artificial Intelligence”. For sure, others and perhaps “better” ones can be identified or constructed.


“AI” Definitions & Considerations

#1 An AI Definition and its Issues.
The problem with many definitions of Artificial Intelligence (AI) is that they are riddled with what is called “suitcase words”. They are “…terms that carry a whole bunch of different meanings that come along even if we intend only one of them. Using such terms increases the risk of misinterpretations…”.[2] This term, “suitcase words”, was created by a world-famous computer scientist, who is considered one of the leading figures in the developments of AI technologies and the field itself: Professor MINSKY, Marvin.

#2 The Absence of a Unified Definition.
On the global stage or among all AI researchers combined, there is no official (unified) definition of what Artificial Intelligence is. It is perhaps better to state that the definition is continuously changing with every invention, discovery or innovation in the realm of Artificial Intelligence. It is also interesting to note that what was once seen as an application of AI is (by some) now no longer seen as such (and sometimes “simply” seen as statistics or as a computer program like any other). On the other end of the spectrum, there are those (mostly non-experts or those with narrowed commercial aims) who will identify almost any computerized process as an AI application.

#3 AI Definitions and its Attributes.
Perhaps a large number of researchers might agree that an AI method or application has been defined as “AI” due to the combination of the following 3 attributes:

it is made by humans or it is the result of a technological process that was originally created by humans,

it has the ability to operate autonomously (without the support of an operator; it has ‘agency’[3]) and

it has the ability to adapt (behaviors) to, and improve within changing contexts (i.e. changes in the environment); and this by means of a kind of technological process that could be understood as a process of “learning”. Such “learning” can occur in a number of ways. One way is to “learn” by trial-and-error or a “rote learning” (e.g. the storing in memory of a solution to a problem). A more complex way of applying “learning” is by means of “Generalization”. This means the system can “come up” with a solution, by generalizing some mathematical rule or set of rules from given examples (i.e. data), to a problem that was previously not yet encountered. The latter would be more supportive towards being adaptable in changing and uncertain environments.

#4 AI Definitions by Example.
Artificial Intelligence could, alternatively, also be defined by listing examples of its applications and methods. As such some might define AI by listing its methods (which are individual methods in the category of AI methods. Also see here below one of the listing of types and methods towards defining the AI framework): AI than, for instance, includes Machine Learning, Deep Learning and so on.

Others might define AI by means of its applications whereby AI is, for instance, a system that can “recognize”, locate or identify specific patterns or distinct objects in (extra-large, digital or digitized) data sets where such data sets could, for instance, be an image or a video of any objects (within a set), a set or string of (linguistic) sounds, be it prerecorded or in real-time, via a camera or other sensor. These objects could be a drawing, some handwriting, a bird sound, a photo of a butterfly, a person uttering a request, a vibration of a tectonic plate, and so on (note: the list is, literally, endless).

#5 AI Defined by referencing Human Thought.
Other definitions define AI as a technology that can “think” as the average humans do (yet, perhaps, with far more processing power and speed)… These would be “…machines with minds, in the full and literal sense… [such] AI clearly aims at genuine intelligence, not a fake imitation.[4] Such a definition creates AI research and developments driven by “observations and hypothesis about human behavior”; as it is done in the empirical sciences.[5]. At the moment of this writing, the practical execution of this definition has not yet been achieved.

#6 AI Defined by Referencing Human Actions.
Further definitions of what AI is, do not necessarily focus on the aspect of ability of thought. Rather some definitions for AI focus on the act that can be performed by an AI technology. Then definitions are something like: an AI application is a technology that can act as the average humans can act or do things with perhaps far more power, strength, speed and without getting tired, bored, annoyed or hurt by features of the act or the context of the act (e.g. work inside a nuclear reactor). Rai Kurzweil, a famous futurist and inventor in technological areas such as AI, defined the field of AI as: “ The art of creating machines that perform functions that require intelligence when performed by people.[6] 

#7 Rational Thinking at the Core of AI Definitions.
Different from the 5th definition is that thought does not necessarily have to be defined through a human lens or anthropocentrically. As humans we tend to anthropomorphize some of our technologies (i.e. give a human-like shape, function, process, etc. to a technology). Though, AI does not need to take on a human-like form, function nor process; unless we want it to. In effect, an AI solution does not need to take on any corporal / physical form at all. An AI solution is not a robot; it could be embedded into a robot.

One could define the study of AI as a study of “mental faculties through the use of computational models.”[7] Another manner of defining the field in this way is stating that it is the study of the “computations that make it possible to perceive, reason and act.”[8] [9]

The idea of rational thought goes all the way back to Aristotle and his aim to formalize reasoning. This could be seen as a beginning of logic. This was adopted early on as one of the possible methods in AI research towards creating AI solutions. It is, however, difficult to implement. This is the case since not everything can be expressed in a formal logic notation and not everything is perfectly certain. Moreover, not all problems are practically solvable by logic principles, even if via such logic principles they might seem solved.[10]

#8 Rational Action at the Core of AI Definitions.
A system is rational if “it does the ‘right thing’, given what it knows.” Here, a ‘rational’ approach is an approach driven by mathematics and engineering. As such “Computational Intelligence is the study of the design of intelligent agents…”[11] To have ‘agency’ means to have the autonomous ability and to be enabled to act / do / communicate with the aim to perform a (collective) task.[12] Scientists, with this focus in the field of AI, research “intelligent behavior in artifacts”.[13]

Such AI solution that can function as a ‘rational agent’ applies a form of logic reasoning and would be an agent that can act according to given guidelines (i.e. input) yet do so autonomously, adapt to environmental changes, work towards a goal (i.e. output) with the best achievable results (i.e. outcome) over a duration of time and this in a given (changing) space influenced by uncertainties. The application of this definition would not always result in a useful AI application. Some complex situations would, for instance, be better to respond to with a reflex rather than with rational deliberation. Think about a hand on a hot stove…[14] 

#9 Artificial Intelligence methods as goal-oriented agents.
Artificial Intelligence methods as goal-oriented agents. “Artificial Intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals. Its foundations include mathematics, logic, philosophy, probability, linguistics, neuroscience and decision theory.”[15]

#10 AI Defined by Specific Research and Development Methods.
We can somewhat understand the possible meaning of the concept “AI” by looking at what some consider the different types or methods of AI, or the different future visions of such types of AI (in alphabetic order)[16]:

Activity Recognition

  • a system that knows what you are doing and acts accordingly. For instance: it senses that you carry many bags, so it automatically opens the door for you (without you needing to verbalize the need).

Affective Computing

  • a system that can identify the emotion someone showcases

Artificial Creativity

  • A system that can output something that is considered creative (e.g. a painting, a music composition, a written work, a joke, etc.)

Artificial Immune System

  • A system that functions in the likes of a biological immune system or that mimics its processes of learning and memorizing.

Artificial Life

  • A system that models a living organism

Artificial Stupidity

  • A system that adapts to the intellectual capacity of the form (life form, human) it interacts with or to the needs in a given context.

Automation

  • The adaptable mechanical acts coordinated by a system without the intervening of a human

Blockhead

  • A “fake” AI that simulates intelligence by referencing (vast) data repositories and regurgitating the information at the appropriate time. This system however does not learn.

Bot

  • A system that functions as a bodiless robot

ChatBot / ChatterBot

  • A system that can communicate with humans via text or speech giving the perception to the human (user) that it is itself also human. Ideally it would pass the Turing test.

Committee Machine

  • A system that combines the output from various neural networks. This could create a large-scale system.

Computer Automated Design

  • A system that can be put to use in areas of creativity, design and architecture that allow and need automation and calculation of complexities

Computer Vision

  • A system that via visual data can identify (specific) objects

Decision Support System

  • A system that adapts to contextual changes and supports human decision making

Deep Learning

  • A system operating on a sub-type of Machine Learning methods (see a future blog post for more info)

Embodied Agent

  • A system that operates in a physical or simulated “body”

Ensemble Learning

  • A system that applies many algorithms for learning at once.

Evolutionary Algorithms

  • A system that mimics biological evolutionary processes: birth, reproduction, mutation, decay, selection, death, etc. (see a future blog post for more info)

Friendly Artificial Intelligence

  • A system that is void of existential risk to humans (or other life forms)

Intelligence Amplification

  • A system that increases human intelligence

Machine Learning

  • A system of algorithms that learns from data sets and which is strikingly different from a traditional program (fixed by its code). (see a future blog post for more info)

Natural Language Processing

  • A system that can identify, understand and create speech patterns in a given language. (see a future blog post for more info)

Neural Network

  • A system that historically mimicked  a brain ‘s structure and function (neurons in a network) though now are driven by statistical and signal processing. (see another of my blog post for more info here)

Neuro Fuzzy

  • A system that applies a neural network to operate in a or fuzzy logic as a non-linear logic, or a non-Boolean logic (values between 0 or 1 and not only 0 or 1). It allows for further  interpretation of vagueness and uncertainty

Recursive Self-Improvement

  • A system that allows for software to write its own code in cycles of self-improvement.

Self-replicating Systems

  • A system that can copy itself (hardware and or software copies). This is researched for (interstellar) space exploration.

Sentiment Analysis

  • A system that can identify emotions and attitudes imbedded into human media (e.g. text)

Strong Artificial Intelligence

  • A system that has a general intelligence as a human does. This is also referred to as AGI or Artificial General Intelligence. This does not yet exist and might, if we continue to pursuit it, take decades to come to fruition. When it does it might start a recursive self-improvement and autonomous reprogramming, creating an exponential expansion in intelligence well beyond the confines of human understanding. (see a future blog post for more info)

Superhuman

  • A system that can do something far better than humans can

Swarm Intelligence

  • A system that can operate across a large number of individual (hardware) units and organizes them to function as a collective

Symbolic Artificial Intelligence

  • An approach used between 1950 and 1980 that limits computations to the manipulation of a defined set of symbols, resembling a language of logic.

Technological Singularity

  • A hypothetical system of super-intelligence and rapid self-improvement out of the control and beyond the understanding of any human. 

Weak Artificial Intelligence

  • A practical system of singular or narrow applications, highly focused on a problem that needs a solution via learning from given and existing data sets. This is also referred to as ANI or Artificial Narrow Intelligence.

Project Concept Examples

Mini
Project #___ : An
Application of a Definition
Do you know any program or technological system that (already) fits this 5th definition? 
How would you try to know whether or not it does?
Mini Project #___: Some Common Definitions of Ai with Examples
Team work      + Q&A: 
What is your team’s definition of AI? 
What seems to be the most accepted definition in       your daily-life community and in a community of AI experts closest to       you?
Reading +      Q&A:: Go through some      popular and less popular definitions with examples
Discussion: which definition of AI feels more acceptable to      your team; why? Which definition seems less acceptable to you and your      team? Why? Has your personal and first definition of Ai changed? How?
Objectives:      The learner can bring together the history, context, types and meaning of      AI into a number of coherent definitions.

References & URLs


[1] Krohn, J., et al.(2019, p.102) the importance of context in meaning-giving; NLP through Machine Learning and Deep Learning techniques

[2] Retrieved from Ville Valtonen at Reaktor and Professor Teemu Roos at the University of Helsinki’s “Elements of AI”, https://www.elementsofai.com/ , on December 12, 2019

[3] agent’ is from Latin ‘agere’ which means ‘to manage’, ‘to drive’, ‘to conduct’, ‘to do’. To have ‘agency’ means to have the autonomous ability and to be enabled to act / do / communicate with the aim to perform a (collective) task.

[4] Haugeland, J. (Ed.). (1985). Artificial Intelligence: The Very Idea. Cambridge, MA: The MIT Press. p. 2 and footnote #1.

[5] Russell, S. and Peter Norvig. (2016). Artificial Intelligence: A Modern Approach. Third Edition. Essex: Pearson Education. p.2

[6] Russell. (2016). pp.2

[7] Winston, P. H. (1992). Artificial Intelligence (Third edition). Addison-Wesley.

[8] These are two definitions respectively from Charniak & McDermott (1985) and Winston (1992) as quoted in Russel, S. and Peter Norvig (2016).

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

[10] Russell (2016). pp.4

[11] Poole, D., Mackworth, A. K., and Goebel, R. (1998). Computational intelligence: A logical approach. Oxford University Press

[12] ‘agent’ is from Latin ‘agere’ which means ‘to manage’, ‘to drive’, ‘to conduct’, ‘to do’

[13] Russell. (2016). pp.2

[14] Russell (2016). pp.4

[15] Maini, V. (Aug 19, 2017). Machine Learning for Humans. Online: Medium.com. Retrieved November 2019 from e-Book https://www.dropbox.com/s/e38nil1dnl7481q/machine_learning.pdf?dl=0 or https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12 https://www.dropbox.com/s/e38nil1dnl7481q/machine_learning.pdf?dl=0

[16] Spacey, J. (2016, March 30). 33 Types of Artificial Intelligence. Retrieved from https://simplicable.com/new/types-of-artificial-intelligence  on February 10, 2020

Header image caption, credits & licensing:

Depicts the node connections of an artificial neural network

LearnDataSci / CC BY-SA (https://creativecommons.org/licenses/by-sa/4.0)

source: https://www.learndatasci.com/

retrieved on May 6, 2020 from here

The Field of AI (Part 05): AI APPROACHES AND METHODS

AI & Neural Networks

A Context with Histories and Definitions

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Figure 01 An example artificial neural network with a hidden layer. Credit: en:User:Cburnett / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/) Retrieved on March 12, 2020 from here

A beautiful and clearly-explained introduction to Neural Networks is offered in a 20 minute video by Grant Sanderson in his “3Blue1Brown” series.[1] One is invited to view this and his other enlightening pieces.

The traditional Artificial Neural Network (ANN)[2] is, at a most basic level, a kind of computational model for parallel computing between interconnected units.  One unit could be given more or less numerical ‘openness’ (read: weight & bias)[3] then another unit, via the connections created between the units. This changing of the weight and the bias of a connection (which means, the allocation of a set of numbers, turning them up or down as if changing a set of dials), is the ‘learning’ of the network by means of a process, through a given algorithm. These changes (in weight and bias) will influence which signal will be propagated forwardly to which units in the network. This could be to all units (in a traditional sense) or to some units (in a more advanced development of a basic neural network, e.g. such as with Convoluted Neural Networks).[4] An algorithm processes signals through this network. At the input or inputs (e.g. the first layer) the data is split across these units. Each unit within the network can hold a signal (e.g. a number) and contains a computational rule, allowing activation (via a set threshold controlled by, for instance, a sigmoid function, or the recently more often applied “Rectified Linear Unit,” or ReLu for short), to send through a signal (e.g. a number) over that connection to a next unit or to a number of following units in a next layer (or output). The combination of all the units, connections and layers might allow the network to label, preferably correctly, the entirety of a focused-on object, at the location of the output or outputs layer. The result is that the object has been identified (again, hopefully, correctly or, at least, according to the needs).

The signal (e.g. a number) could be a representation of, for instance, 1 pixel in an image.[5] Note, an image is digitally composed of many pixels. One can imagine many of these so-called ‘neurons’ are needed to process only 1 object (consisting of many pixels)  in a snapshot of only the visual data (with possibly other objects and backgrounds and other sensory information sources) from an ever changing environment, surrounding an autonomous technology, operated with an Artificial Neural Network. Think of a near-future driverless car driving by in your street. Simultaneously, also imagine how many neurons and even more connections between neurons, a biological brain, as part of a human, might have. Bring to mind a human (brain) operating another car driving by in your street. The complexity of the neural interconnected working, the amount of data to be processed (and to be ignored) might strike one with awe.

The oldest form of such artificial network is a Single-layer Perceptron Network, historically followed by the Multilayer Perceptron Network. One could argue that ‘ANN’ is a collective name for any network that has been artificially made and that has been exhibiting some forms and functions of connection between (conceptual) units.

An ANN was initially aimed (and still is) at mimicking (or modeling, or abstracting) the brain’s neural network (i.e. the information processing architecture in biological learning systems).

Though, the term, Artificial Neural Network, contains the word ‘neural’, we should not get too stuck on the brain-like implications of this word which is derived from the word ‘neuron’. The word ‘neuron’ is not a precise term in the realm of AI and its networks. At times instead of ‘neuron’ the word ‘perceptron’ has been used, especially when referring to as specific type of (early) artificial neural network using thresholds (i.e. a function that allows for the decision to let a signal through or not; for instance, the previously-mentioned sigmoid function).

Figure 02 an electron microscope. Connections between brain cells, into a large neural network, were identified with an older version of this technology.

Image license and attribution: David J Morgan from Cambridge, UK / CC BY-SA (https://creativecommons.org/licenses/by-sa/2.0) Retrieved on April 23, 2020 from here

Nevertheless, maybe some brainy context and association might spark an interest in one or other learner. It might spark a vision for future research and development to contextualize these artificial networks by means of analogies with the slightly more tangible biological world. After all, these biological systems we know as brains, or as nervous systems, are amazing in their signal processing potentials. A hint of this link can also be explored in Neuromorphic Engineering and Computing.

The word “neuron” comes from Ancient Greek and means ‘nerve’. A ‘neuron,’ in anatomy or biology at large, is a nerve cell within the nervous system of a living organism (of the animal kingdom, but not sponges), such as mammals (e.g. humans). By means of small electrical (electro-chemical) pulses (i.e. nerve impulses), these cells communicate with other cells in the nervous system. Such a connection, between these types of cells, is called a synapse. Note, neurons cannot be found among fungi nor among plants (these do exchange signals, even between fungi and plants yet, in different chemical ways)… just maybe they are a steppingstone for one or other learner to imagine an innovative way to process data and compute outputs!

The idea here is that a neuron is “like a logic gate [i.e. ‘a processing element’] that receives input and then, depending on a calculation, decides either to fire or not.[6] Here, the verb “to fire” can be understood as creating an output at the location of the individual neuron. Also note, that a “threshold” is again implied here.

An Artificial Neural Network can then be defined as “…а computing ѕуѕtеm made up of a number of ѕimрlе, highlу intеrсоnnесtеd рrосеѕѕing elements, which рrосеѕѕ infоrmаtiоn by thеir dуnаmiс ѕtаtе response to еxtеrnаl inputs.”[7]

Remember, ‘neuron’, in an ANN, it should be underlined again, is a relatively simple mathematical function. It is, in general, agreed that this function is analogous to a node. Therefore, one can state that an Artificial Neural Network is built up of layers of interconnected nodes.[8] So, one can notice, in or surrounding the field of AI, that words such as unit, node, neuron or perceptron used interchangeably, while these are not identical in their deeper meaning. More recently the word “capsule” has been introduced, presenting an upgraded version of the traditional ‘node,’ the latter equaling one ‘neuron.’ Rather, a capsule is a node in a network equaling a collection of neurons.[9] A little bit of additional information on this can be found here below.

How could an analogy with the brain be historically contextualized? In the early 1950s, with the use of electron microscopy, it was proven that the brain exists of cells, which preceding were labelled as “neurons”.[10] It unequivocally showed the interconnectedness (via the neuron’s extensions, called axons and dendrites) between these neurons, into a network of a large number of these cells. A single of these type of locations of connection between neurons has been labeled as a “synapse”.

Since then it has been established that, for instance, the human cerebral cortex contains about 160 trillion synapses (that’s a ‘160’ and another 12 zeros: 160000000000000) between about a 100 billion neurons (100000000000). Synapses are the locations between neurons where the communication between the cells is said to occur.[11] In comparison some flies have about 100000 neurons and some worms a few hundreds.[12] The brain is a “complex, nonlinear, and parallel computer (information-processing system)”.[13] The complexity of the network comes with the degree of interconnectedness (remember, in a brain that’s synapses).

Whereas it is hard for (most) humans to multiply numbers at astronomically fast speeds, it is easy for a present-day computer. While it is doable for (most) humans to identify what a car is and what it might be doing next, this is (far) less evident for a computer to (yet) handle. This is where, as one of many examples, the study and developments of neural networks (and now also Deep Learning) within the field of AI has come in handy, with increasingly impressive results. The work is far from finished and much can still be done.

The field of study of Artificial Neural Networks is widely believed to have started a bit earlier than the proof of connectivity of the brain and its neurons. It is said to have begun with the 1943 publication by Dr. McCulloh and Dr. Pitts, and their Threshold Logic Unit (TLU). It was then followed by Rosenblatt’s iterations of their model (i.e. the classical perceptron organized in a single layered network) which in turn was iterated upon by Minsky and Papert. Generalized, these were academic proposals for what one could understand as an artificial ‘neuron’, or rather, a mathematical function that aimed to mimic a biological neuron, and the network made therewith, as somewhat analogously found within the brain.[14]

Note, the word ‘threshold’ is of use to consider a bit further. It implies some of the working of both the brain’s neurons and of ANNs’ units. A threshold in these contexts, implies the activation of an output if the signal crosses the mathematically-defined “line” (aka threshold). Mathematically, this activation function can be plotted by, for instance, what is known as a sigmoid function (presently less used). The sigmoid function was particularly used in the units (read in this case: ‘nodes’ or ‘neurons’ or ‘perceptrons’) of the first Deep Learning Artificial Neural Networks. Presently, the sigmoid function is at times being substituted with improved methods such as what is known as “ReLu” which is short for ‘Rectified Linear Unit’. The latter is said to allow for better results and is said to be easier to manage in very deep networks.[15]

Turning back to the historical narrative, it was but 15 years later than the time of the proposal of the 1943 Threshold Logic Unit, in 1958, with Rosenblatt’s invention and hardware design of the Mark I Perceptron —a machine aimed at pattern recognition in images (i.e. image recognition) — that a more or less practical application of such network had been built.[16] As suggested, this is considered being a single-layered neural network.

This was followed by a conceptual design from Minsky and Papert, considering the multilayered perceptron (MLP), using a supervised learning technique. The name gives it away, this is the introduction of the multi-layered neural network. While hinting at nonlinear functionality,[17] yet this design was still without the ability to perform some basic non-linear logical functions. Nevertheless, the MLP was forming the basis for the neural network designs as they are developed presently. Presently, Deep Learning research and development has advanced beyond these models.

Simon Haykin puts it with a slight variation in defining a neural network when he writes that it is a “massively parallel distributed processor, made up of simple processing units, that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: 1. Knowledge is acquired by the network from its environment through a learning process. 2. Inter-neuron connection strengths, known as synaptic weights, are used to store the acquired knowledge.”[18]

Let us shortly touch on the process of learning in the context of an ANN and that with a simplified analogy. One way to begin understanding the learning process, or training, of these neural networks, in a most basic sense, can be done by looking at how a network would (ignorantly) guess the conversion constant between kilometers and miles without using algebra. One author, Tariq Rashid, offers the following beautifully simple example in far more detail. The author details an example where one can imagine the network honing in on the conversion constant between, for instance, kilometers and miles.

Summarized here: The neural network could be referencing examples. Let us, as a simple example, assume it ‘knows’ that 0 km equals 0 miles. It also ‘knows’, from another given example, that 100 km is 62.137 miles. It could ‘guess’ a number for the constant, given that it is known that 100 (km) x constant = some miles. The network randomly could, very fast, offer a pseudo-constant guessed as 0.5. Obviously, that would create an error compared to the given example. In a second guess it could offer 0.7. This would create a different kind of error. The first is too small and the second is too large. The network consecutively undershot and then overshot the needed value for the constant.

By repeating a similar process, whereby a next set of numbers (= adjusted parameters internal to the network) is between 0.5 and 0.7 with one closer to the 0.5 and the others closer to 0.7, the network gets closer in estimating the accurate value for its needed output (e.g. 0.55 and 0.65; then next 0.57 and 0.63, and so on). The adjusting of the parameters would be decided by how right or wrong the output of the network model is compared to the known example that is also known to be true (e.g. a given data set for training). Mr. Rashid’s publication continues the gentle introduction into supervised training and eventually building an artificial neural network.

In training the neural network to become better at giving the desired output, the network’s weights and biases (i.e. its parameters) are tweaked. If the output has a too large an error, the tweaking processes is repeated until the error in the output is acceptable and the network has turned out to be a workable model to make a prediction or give another type of output.

In the above example one moves forward and backward until the smallest reasonable error is obtained. This is, again somewhat over-simplified how a backpropagation algorithm functions in the training process of a network towards making it a workable model. Note, “propagate” means to grow, extend, spread, reproduce (which, inherently, are forward movements over time).

These types of network, ANNs or other, are becoming both increasingly powerful and diversified. They also are becoming increasingly accurate in identifying and recognizing patterns in certain data sets of visual (i.e. photos, videos), audio (i.e. spoken word, musical instruments, etc.) or other nature. These are becoming more and more able to identify patterns, as well as humans are able to and beyond what humans are able to handle.[19]

Dr. HINTON, Geoffrey[20]  is widely considered as one of the leading academics in Artificial Neural Networks (ANNs) and specifically seen as a leading pioneer in Deep Learning.[21] Deep Learning, a type of Machine Learning, is highly dependent on various types of Artificial Neural Network.[22] Dr. Hinton’s student, Alex Krizhevsky, noticeably helped to boost the field of computer vision by winning the 2012 ImageNet Competition and this by being the first to use a neural network.

To round the specific ‘ANN’ introduction up, let us imagine, perhaps in the processes of AI research and specifically in its area similar to those of ANNs, solutions can be thought up or are already being thought of that are less (or more) brain-like or for which the researchers might feel less (or more) of a need to make an analogy with a biological brain. Considering processes of innovation, one might want to keep an open-mind to these seemingly different meanderings of thought and creation.

Going beyond the thinking of ANNs, one might want to fine-tune an understanding and also consider diversity in forms and functions of these or other such networks. There are, for instance, types going around with names such as ‘Deep Neural Networks’ (DNNs) which, are usually extremely large and are usually applied to process very large sets of data.[23] One can also find terminologies such as the ‘Feedforward Neural Networks’ (FFNNs), which is said to be slightly more complex than the traditional and old-school perceptron networks;[24] ‘Convolutional Neural Networks’ (CNNs), which are common in image recognition; ‘Recurrent Neural Networks’ (RNNs) and its sub-type of ‘Long Short-term Memory’ networks (LSTM), which apply feedback connection and which are used in Natural Language Processing. These latter networks are claimed to still apply sigmoid functions, contrary to the increased popularity of other functions.[25] All of these and more are studied and developed in the fields of Machine Learning and Deep Learning. All these networks would take us rather deep into the technicalities of the field. You are invited to dig deeper and explore some of the offered resources.

It might be worthwhile to share that CNN solutions are particularly well-established in computer vision. The neurons specialized in the visual cortex of the brain and how these do or do not react to the stimuli coming into their brain region from the eyes, were used as an inspiration in the development of the CNN. This design helped to reduce some of the problems that were experienced with the traditional artificial neural networks. CNNs do have some shortcomings, as many of these cutting-edge inventions stil need to be further researched and fine-tuned.[26]

Capsule Networks (CapsNets)

In the process of improvement, innovation and fine-tuning, there are new networks continuously being invented. For instance, in answering some of the weaknesses of ‘Convolutional Neural Networks’ (CNNs), the “Capsule Networks (CapsNets)” are a relative recent invented answer, from a few years ago, by Hinton and his team.[27] It is also believed that these CapsNets mimic better how a human brain processes vision then what the CNNs have been enabled to offer up till now.

To put it too simple, it’s an improvement onto the previous versions of  nodes in  a network (a.k.a. ‘neurons’) and a neural network. It tries to “perform inverse graphics”, where inverse graphics is a process of extracting parameters from a visual that can identify location of an object within that visual. A capsule is a function that aids in the prediction of the “presence and …parameters of a particular object at a given location.[28] The network hints at outperforming the traditional CNN in a number of ways such as the increased ability to identify additional yet functional parameters associated with an object. One can think of orientation of an object but also of its thickness, size, rotation and skew, spatial relationship, to name but a few.[29] Although a CNN can be of use to identify an object, it cannot offer an identification of that object’s location. Say a mother with a baby can be identified. The CNN cannot support the identification whether they are on the left of one’s visual field versus the same humans but on the right side of the image.[30] One might imagine the eventual use of this type of architectures in, for instance, autonomous vehicles.

Generative Adversarial Networks (GANs)

This type of machine learning method, a Generative Adversarial Network (GAN), was invented in 2014 by Dr. Goodfellow and Dr. Bengio, among others.[31]

Figure 03 This young lady does not (exactly) physically exist. The image was created by a GAN; a StyleGAN based on the analysis of photos of existing individuals. Source: public domain (since it is created by an AI method, and a method is not a person, it is not owned). Retrieved March 10, 2020 from here

It’s an unsupervised learning technique that allows to go beyond historical data (note, it is debatable that, most if not all data is inherently historical from the moment following its creation). In a most basic sense, it is a type of interaction, by means of algorithms (i.e. Generative Algorithms), between two Artificial Neural Networks.

The GANs allow to create new data (or what some refer to as “lookalike data”)[32] by applying features, by means of certain identified features, from the historical referenced data. For instance, a data set, existing of what we humans perceive as images, and then of a specific style, can allow this GANs’ process to generate a new (set of) image(s) in the style of the studied set. Images are but one media. It can handle digital music, digitized artworks, voices, faces, video… you name it. It can also cross-pollinate between media types, resulting in a hybrid between a set of digitized artworks and a landscape, resulting in a landscape “photo” in a style of the artwork data set. The re-combinations and reshufflings are quasi unlimited. Some more examples are of GANs types are those that can

  • …allow for black and white imagery to be turned into colorful ones in various visual methods and styles.[33]
  • …turn descriptive text of, say different birds into photo-realistic bird images.[34]
  • …create new images of food based on their recipes and reference images.[35]
  • …turn a digitized oil painting into a photo-realistic version of itself; turning a winter landscape into a summer landscape, and so on.[36]

If executed properly, for instance, the resulting image could make an observer (i.e. a discriminator) decide that the new image (or data set) is as (authentic as) the referenced image(s) or data set(s) (note: arguably, in the digital or analog world, an image or any other media of content is a data set).

It is also a technique whereby two neural networks contest with each other. They do so in a game-like setting as it is known in the mathematical study of models of strategic decision-making, entitled “Game Theory.” Game Theory is not to be confused with the academic field of Ludology, the latter which is the social, anthropological and cultural study of play and game design. While often one network’s gain is the other network’s loss (i.e. a zero-sum game), this is not always necessarily the case with GANs.

It is said that GANs can also function and offer workable output with relatively small data sets (which ic an advantage compared to some other techniques).[37]

It has huge applications in the arts, advertising, film, animation, fashion design, video gaming, etc. These professional fields are each individually known as multi-billion dollar industries. Besides entertainment it is also of use in the sciences such as physics, astronomy and so on.

Applications

One can learn how to understand and build ANNs online via a number of resources. Here below are a few hand-picked projects that might offer a beginner’s taste to the technology.

Project #___: Making Machine Learning Neural Networks (for K12 students by
Oxford University)
Project source: 
https://ecraft2learn.github.io/ai/AI-Teacher-Guide/chapter-6.html
Project #___: Rashid, T. (2016). Make Your Own Neural Network
A project-driven book examining the very basics of neural networks and aiding a learning step by step into creating a network. Published as eBook or paper via CreateSpace Independent Publishing Platform.
This might be easily digested by Middle Schools students or learners who cannot spend too much effort yet do want to learn about neural networks in an AI context.
information retrieved on April 2, 2020 from http://makeyourownneuralnetwork.blogspot.com/
Project #___:  A
small example: Training a model to estimate square roots (click on the image to
enter the SNAP! environment)
Project source: 
https://ecraft2learn.github.io/ai/AI-Teacher-Guide/chapter-6.html
Project #___:  Training
a model to label data (click on the image to enter the SNAP! environment)
Project source: 
https://ecraft2learn.github.io/ai/AI-Teacher-Guide/chapter-6.html
Project #___:  Training
a model to predict how you would rate abstract "art"
Project source: 
https://ecraft2learn.github.io/ai/AI-Teacher-Guide/chapter-6.html
Project #___: A Neural Network to recognize hand-written digits
This
project comes with an online book and code by Michael Nielsen. 
Source code: 
https://github.com/mnielsen/neural-networks-and-deep-learning  
Updated source code: https://github.com/MichalDanielDobrzanski/DeepLearningPython35 
Database:
http://yann.lecun.com/exdb/mnist/ (a training set of 60,000 examples, and a test set of 10,000 examples)
Study material: 
http://neuralnetworksanddeeplearning.com/chap1.html   
Project #___: MuZero: Build a Neural Network using Python[1] 
project source: https://medium.com/applied-data-science/how-to-build-your-own-muzero-in-python-f77d5718061a
[1] Schrittwieser, J. et al. (2020). Mastering Atari, Go, Chess and Shogi by
Planning with a Learned Model. Online: arXiv.org, Cornell University;
Retrieved on April 1, 2020 from  https://arxiv.org/abs/1911.08265 

References & URLs

[1]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 the entire series: https://search.bilibili.com/all?keyword=3Blue1Brown&from_source=nav_suggest_new AND https://www.youtube.com/watch?v=aircAruvnKk Information Retrieved from https://www.3blue1brown.com/about

[2] Nielsen, M. (2019). Neural Networks and Deep Learning. Online: Determination Press. Retrieved on April 24, 2020 from http://neuralnetworksanddeeplearning.com/  AND https://github.com/mnielsen/neural-networks-and-deep-learning AND http://michaelnielsen.org/

[3] Marsland, S. (2015). Machine Learning. An Algorithmic Perspective. Boca Raton, FL, USA: CRC Press. p.14

[4] Charniak, E. (2018). Introduction to Deep Learning. Cambridge, MA: The MIT Press p.51

[5] Sanderson, G.  (? Post-2016). 

[6] Du Sautoy, M. (2019). The Creative Code. How AI is Learning to Write, Paint and Think. London: HarperCollins Publishers. pp.117

[7]Dr. Hecht-Nielsen, Robert in Caudill, M. (December, 1987).  “Neural Network Primer: Part I”. in AI Expert Vol. 2, No. 12, pp 46–52. USA:      Miller Freeman, Inc. Information Retrieved on April 20, 2020 from https://dl.acm.org/toc/aiex/1987/2/12 and  https://dl.acm.org/doi/10.5555/38292.38295 ; citation retrieved from https://www.oreilly.com/library/view/hands-on-automated-machine/9781788629898/0444a745-5a23-4514-bae3-390ace2dcc61.xhtml

[8] Rashid, T.  (2016). Make Your Own Neural Network.  CreateSpace Independent Publishing Platform

[9] Sabour, S. et al. (2017). Dynamic Routing Between Capsules. Online: arXiv.org, Cornell University; Retrieved on April 22, 2020 from https://arxiv.org/pdf/1710.09829.pdf

[10] Sabbatini, R. (Feb 2003). Neurons and Synapses. The History of Its Discovery. IV. The Discovery of the Synapse. Online: cerebromente.org. Retrieved on April 23, 2020 from http://cerebromente.org.br/n17/history/neurons4_i.htm

[11] Tang Y. et al (2001). Total regional and global number of synapses in the human brain neocortex. In Synapse 2001;41:258–273.

[12] Zheng, Z., et al. (2018). A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster. In Cell, 174(3), 730–743.e22

[13] Haykin, S. (2008). Neural Networks and Learning Machines. New York: Pearson Prentice Hall. p.1

[14] McCulloch, W.. & Pitts, W. (1943; reprint: 1990). A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, Vol. 5, pp.115-133. Retrieved online on February 20, 2020 from  https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf  

[15] Sanderson, G.  (? Post-2016). 

[16] Rosenblatt, F. (January, 1957). The Perceptron. A Perceiving and Recognizing Automaton. Report No. 85-460-1. Buffalo (NY): Cornell Aeronautical Laboratory, Inc. Retrieved on January 17, 2020 from https://blogs.umass.edu/brain-wars/files/2016/03/rosenblatt-1957.pdf 

[17] Samek, W. et al (2019). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Artificial Intelligence. Switzerland: Springer. p.9

[18] Haykin, S. (2008). p.2

[19] Gerrish, S. (2018). How Smart Machines Think. Cambridge, MA: The MIT Press. pp. 18

[20] Gerrish, S. (2018). pp. 73

[21] Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (9 October 1986). “Learning representations by back-propagating errors”. Nature. 323 (6088): 533–536

[22] Montavon, G. et al. (2012). Neural Networks: Tricks of the Trade. New York: Springer. Retrieved on March 27, 2020 from https://link.springer.com/book/10.1007/978-3-642-35289-8 AND https://machinelearningmastery.com/neural-networks-tricks-of-the-trade-review/   

[23] de Marchi, L. et al. (2019). Hands-on Neural Networks. Learn How to Build and Train Your First Neural Network Model Using Python. Birmingham & Mumbai: Packt Publishing. p. 9.

[24] Charniak, E. (2018). Introduction to Deep Learning. Cambridge, MA: The MIT Press. p. 10

[25] de Marchi, L. et al. (2019). p. 118-119.

[26] Géron, A. (February, 2018). Introducing capsule networks. How CapsNets can overcome some shortcomings of CNNs, including requiring less training data, preserving image details, and handling ambiguity. Online: O’Reilly Media. Retrieved on April 22, 2020 from https://www.oreilly.com/content/introducing-capsule-networks/

[27] Sabour, S. et al. (2017)

[28] Géron, A. (2017). Capsule Networks (CapsNets) – Tutorial (video). Retrieved on April 22, 2020 from https://www.bilibili.com/video/av17961595/ AND  https://www.youtube.com/watch?v=pPN8d0E3900

[29] Géron, A. (February, 2018).

[30] Tan, K. (November, 2017).  Capsule Networks Explained. Online. Retrieved on April 22, 2020 from https://kndrck.co/posts/capsule_networks_explained/ AND https://gist.github.com/kendricktan/9a776ec6322abaaf03cc9befd35508d4

[31] Goodfellow, I. et al. (June 2014). Generative Adversarial Nets. Online: Neural Information Processing Systems Foundation, Inc  Retrieved on March 11, 2020 from https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf AND Online: arXiv.org, Cornell University;  https://arxiv.org/abs/1406.2661

[32] Skanski, S. (2020). Guide to Deep Learning. Basic Logical, Historical and Philosophical Perspectives. Switzerland: Springer Nature. p. 127

[33] Isola, P. et al. (2016, 2018). Image-to-Image Translation with Conditional Adversarial Networks. Online: arXiv.org, Cornell University; Retrieved on April 16, 2020 from https://arxiv.org/abs/1611.07004

[34] Zhang, H. et al. (2017). StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. Online: arXiv.org, Cornell University; Retrieved on April 16, 2020 from https://arxiv.org/pdf/1612.03242.pdf

[35] Bar El, O. et al. (2019). GILT: Generating Images from Long Text. Online: arXiv.org, Cornell University; Retrieved on April 16, 2020 from https://arxiv.org/abs/1901.02404

[36] Zhu, J. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. Online: arXiv.org, Cornell University; Retrieved on April 16, 2020 from https://arxiv.org/pdf/1703.10593.pdf

[37] Skanski, S. (2020). p.127

The Field of AI (Part 04): AI APPROACHES AND METHODS

AI & Games

A History of AI Research with Games

Games (computer games and board games alike) have been used in AI research and development since the early 1950s. Scientist and engineers focus on games to measure certain stages of success in AI developments. Game settings form a closed testing environment, as if it were a lab, within a specific set of rules and steps. Games have a clear objective or a clear set of goals. Games also allow to research and understand possible applications of probability (e.g. calculate the chances of winning if certain parameters are met or followed). Since very specific and focused problems need to be solved in specific game architectures, games are ideal to test Narrow AI applications.

Narrow AI solutions, are what have been achieved by scientist, so far, as opposed to a ‘General AI’ solution. A General AI solution (or ‘Strong AI’) would be a super-intelligent construct able to solve many, if not any, humanly-thinkable problem and / or beyond. The latter is still science fiction (until it is not). The former, Narrow AI solutions, exists in many applications and can be tested in a game setting. Results in such AI designs, within game play, can following be transcoded into other areas (e.g. solutions for language translation, speech recognition, weather forecast, sales predictions, autonomously operating mechanical arms, managing efficiency in a country’s electric grid[1] or other systems).

Some Narrow AI solutions use a method of Machine Learning that is called “Reinforcement Learning.” In simple terms, it is a way of learning by rewards or scoring. For that reason too, games are an obvious environment that can be used infinitely, to test and improve an AI application. Games lead to rewards or scores; one can even win them.

Moreover, a (computer) game can be played by multiple copies or versions of an AI solution, speeding up the process to reach the best solution or strategy (to win). The latter can, for instance, be achieved by means of “evolutionary algorithms.”  These are algorithms that are improving themselves through, for instance, mutations or  a process of selection as if through a biological natural selection of the fittest (i.e. an autonomous selecting, by means of a process, of a version or offspring of an algorithm that is better at solving something, while ignoring another that is not). Though, if the AI a plays a computer game that has a bug, it might exploit the bug to win, instead of learning the game[2].

Chess has been one of the first games, besides checkers, to have been approached by the AI research community.[3] As mentioned previously, in the mid-1950s Dr. SAMUEL, Arthur wrote a checkers program. A few years earlier (circa 1951) trials were made to write applications for both chess (by Dr. PRINZ, Dietrich) and checkers (by Dr. STRACHEY, Christopher). While these earliest attempts are presently perhaps dismissed as not really being a type of AI application (since, at times, some coding tricks were used), in those days they were a modest, yet first, benchmark of what was to come in the following decades.

For instance, on May 11 1997, the computer named “Deep Blue” beat Mr. KASPAROV,[4] the chess world champion of that time. A number of such achievements have followed covering a number of games. Compared to today’s developments Deep Blue is no longer that impressive. A few years ago, in 2014, by using a form of Machine Learning, namely Deep Learning, AlphaGo defeated the world champion Mr. Lee Sedol at Wéiqí (also known as the game of Go). That AI solution was later surpassed, by AlphaGo Zero (aka AlphaZero). This system used yet another form of Machine Learning, namely Reinforcement Learning (a method mentioned here previously). This AI architecture played against itself and then against AlphaGo. AlphaZero won all of the Wéiqí games from AlphaGo.

In 2017, LěngPūDàshī, the poker-playing AI, defeated some of the world’s top players in the Texas Hold ‘Em poker game. Now scientists are trying to defeat complex real-time online strategy video games players with AI solutions. While such games might not often be taken seriously by some people, they are, technically and through the lens of AI developments, far more complex then, for instance, a chess game. Some successes have already been booked: On April 17, 2019 an AI solution defeated Dota 2 champions. Earlier that same year, human players were defeated at a game of StarCraft II.  Note, the same algorithm that was trained to play Dota 2 can also be taught to move a mechanical hand. Improvements, in benchmarking AI solutions with games, do not stop.[5]

As blood glucose concentration rises, the pancreas secretes insulin to decrease the concentration of blood sugar online viagra australia and reduces cholesterol level. Vitamin E – Like vitamin C, this fat-soluble vitamin is a powerful antioxidant that works to fight diseases, as well cialis prices discount here as signs of early aging. So, men should be more careful about their sexual health and they picked pure natural herbs to take care of the fraud companies that can kill the enthusiasm of millions of such couples & deprive them of their sex. purchase female viagra If a product is promoted as something that can cure impotence sildenafil online india permanently and you can experience a normal sexual drive.

Hands-on Learning with AI Research through Games

Project #___ : Build your own Game
AI with TIC TAC TOE (Arduino version):
Project source: 


Project #___ : TIC TAC TOE Iteration #2 (SCRATCH implementations):
Project Source:

Project #___ : TIC TAC TOE
Iteration #3 (Berkeley’s SNAP! + Oxford AI implementation for K12):
Project Source:

[1] 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/

[2] Vincent, J. (February 28, 2018). A Video game-playing AI beat Q*bert in a way no one’s ever seen before. Online: The Verge. Retrieved February 14, 2020 from https://www.theverge.com/tldr/2018/2/28/17062338/ai-agent-atari-q-bert-cracked-bug-cheat

[3] Copeland, J. (May, 2000). What is Artificial Intelligence? Sections: Chess. Online: AlanTuring.net Retrieved February 14, 2020 from http://www.alanturing.net/turing_archive/pages/Reference%20Articles/what_is_AI/What%20is%20AI12.html

[4] Kasparov, G. (March 25, 1996). The Day I Sensed a New Kind of Intelligence. Online: Time Retrieved February 14, 2020 from http://content.time.com/time/subscriber/article/0,33009,984305-1,00.html

[5] An example of the process of continued developments is very well unfolded here: https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go ; URL last checked on March 10, 2020

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

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

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


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

Mathematics & Statistics

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

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

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

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

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

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

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

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

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

[4] For instance (in alphabetical order):

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

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

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

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

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

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

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

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

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

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

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

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

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

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

See:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Field of AI (Part 02-5): 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)


04 — The Field of AI: A Foundational Context: Cognitive Science

Cognitive Science

Cognitive Science combines various fields of academic research into one.[1] This is therefore called an interdisciplinary field, or even more coherently integrated into one: a transdisciplinary field with possibly the involvement of non-academic participants.[2] It touches on the fields of anthropology, psychology, neurology or neuro sciences, biology, health sciences, philosophy, linguistics, computer sciences, and so on.

The work by Roger Shepherd or by Terry Winograd[3] or David Marr, among many others, is considered to have been crucial in the development of this academic field.[4] It is also claimed that Noam Chomsky, as well as the founders of the field of AI, had a tremendous influence on the development of Cognitive Science.[5] The links between the field of Cognitive Science and the field of AI are noticeable in a number of research projects (e.g. see a future post on AGI) and publications.[6]

It is the field that scientifically studies the biological “mental operations” (human and other) as well as the processes and its attributes assigned to or associated with “thinking” and the acquisition of or processes of “language”, “consciousness”, “perception”, “memory”, “learning”, “understanding”, “knowledge”, “creativity”, “emotions”, “mind”, “intelligence”, “motor control,” “vision,” models of intentional processes, the application of Bayesian methods to mental processes or other intellectual functions.[7] Any of these and related terms, through scientific lenses –while seemingly obvious in meaning in a daily use– are very complex, if not debated or contested[8]. The field does research and developments of the “mental architecture” which includes a model both of “information processing and of how the mind is organized.”[9]

Hence, the need for fields such as Cognitive Science. Since these areas are implying different systems, the need for various fields (or disciplines) being a source for Cognitive Science is not only inevitable, it is necessary. The contexts of each individual system (or field, or discipline) is potentially the core research area of a field covering another system. As suggested above, this implies an overlap and integration of other systems (or fields or disciplines, etc.) into one. Following, this requires an increased scientific awareness and practice of inter-dependence between fields of research.

Cognitive Science has developed advances in computational modeling, the creation of cognitive models and the study of computational cognition.[10]

The field of AI, through its history, found inspiration in Cognitive Science for its study of artificial systems. One example is the loose analogy with neurons (i.e. some of the cells making up a brain) and with neural networks (i.e. the connection of such cells) for its mathematical models.

To some extent an AI researcher could take the models distilled, following research in Cognitive Science, for their own research in artificial systems. The bridge between the two are arguably the models and specifically the mathematical models.

Figure 1 Cognitive Science is a multi-disciplinary academic field at the nexus of a number of other fields, including these shown here above. Image in the Public Domain Retrieved on March 18, 2020 from here

Simultaneously, researchers in Cognitive Science can also use solutions found in the field of AI to conduct their research.

Research in Artificial General Intelligence (AGI) partially aims to recreate functions and the implied processes with their This is achieved by firstly inhibiting c-GMP molecules which causes release of nitric oxide in the penile tissues can lead to an outflow of blood from the heart to the body) and Veins (that carry blood back to the heart). generic viagra from usa cute-n-tiny.com There are many results that say that pharmacy online viagra http://cute-n-tiny.com/cute-animals/my-cute-new-kitten/attachment/lilububbles/ knowing the reason for erection along with the usage of kamagra tablets. This type of ED in men with 30s last for a few tadalafil 5mg days only and would not need any sort of medical assistance. It viagra mastercard españa really is through this manner that human being is capable of reproduce. output, which Cognitive Science studies in biological neural networks (i.e. brains).

Some have argued that the field of AI is a sub-field of the field of Cognitive Science, many do not subscribe to this notion. [11] The argument has been made since in the field of AI one can find the research of processes that are innate to the processes found in a brain: sound pattern recognition, speech recognition, object recognition, gesture recognition, and so on which are in turn studied in other fields, such as Cognitive Science. It is more commonly agreed that AI is a sub-field of Computer Science. Still, as stated in the opening lines of this chapter, many do agree with the strong interdisciplinary or transdisciplinary links between the two.[12]


[1] Bermudez J.L.(2014). Cognitive Science. An Introduction to the Science of the Mind. Cambridge: Cambridge University Press. p. 2 Retrieved on March 23, 2020 from https://www.cambridge.org/us/academic/textbooks/cognitivescience

[2] https://semanticcomputing.wixsite.com/website-4

[3] He conducted some of his work at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology (MIT) research program. See Winograd, T. (1972). Understanding Natural Language. In Cognitive Psychology; Volume 3, Issue 1, January 1972, pp. 1 – 191. Boston: MIT; Online” Elsevier. Retrieved on March 25, 2020 from https://www.sciencedirect.com/science/article/abs/pii/0010028572900023   

[4] Bermudez J.L.(2014). pp. 3, 16, and on.

[5] Thagard, Paul, (Spring 2019 Edition). Cognitive Science. In Edward N. Zalta (ed.). The Stanford Encyclopedia of Philosophy. Online: Stanford University. Retrieved on March 23, 2020 from https://plato.stanford.edu/archives/spr2019/entries/cognitive-science/

[6] Gurumoorthy, S. et al. (2018). Cognitive Science and Artificial Intelligence: Advances and Applications. Springer

[7] Green, C. D. (2000). Dispelling the “Mystery” of Computational Cognitive Science. History of Psychology, 3(1), 62–66.

[8] Crowther-Heyck, H. (1999). George A. Miller, language, and the computer metaphor and mind. History of Psychology, 2(1), 37–64

[9] Bermudez J.L.(2014). p. xxix

[10] Houdé, O., et al (Ed.). (2004). Dictionary of cognitive science; neuroscience, psychology, artificial intelligence, linguistics, and philosophy. New York and Hove: Psychology Press;  Taylor & Francis Group.

[11] Zimbardo, P., et al. (2008). Psychologie. München: Pearson Education.

[12]An example thereof is the Bachelor of Science program in “Cognitive Science and Artificial Intelligence” at the Tilburg University, The Netherlands. Retrieved on March 23, 2020 from  https://www.tilburguniversity.edu/education/bachelors-programs/cognitive-science-and-artificial-intelligence

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:

Retrieved on March 30, 2020 from here License & attribution: Orzetto / CC BY-SA (https://creativecommons.org/licenses/by-sa/4.0)

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.

The Field of AI (Part 02-2): 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)


My very rough and severely flawed mapping of some of the fields and applications associated with the Field of AI. (use, for instance, CRTL+ to zoom in)
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01 — The Field of AI: A Foundational Context: Literature, Mythology, Visual Arts

Figure 1 Talos or Talus, the artificial lifeform described in the Greek mythology. Here depicted with the mythological character of Medea or Medeia by Sybil Tawse image: public domain

 The early Greek Myths (about 2500 years ago) showcase stories of artificially intelligent bronze automatons or statues that were brought to life which then in turn exhibited degrees of “intelligence”. If you want to dig deeper search for Pygmalion’s Galateia, or look up the imaginary stories of Talos (Talus)[1].

China’s literary classics; for instance, Volume 5 “The Questions of Tang” of the Lièzǐ, perhaps unwittingly also explored the imagination of Artificial Intelligence. See, the example mentioned in the post on the Field f AI and a Pre-History.

The many thousands years old Jewish myth of the “golem” (גולם‎), fantasized about a creature made of clay that magically came to life. It could be interpreted as an imagination of the raw material for a controllable automaton and an artificial form of some degree of intelligence. While its cultural symbolism is far richer than given justice here, it could be imagined as symbolizing a collective human capability to envision giving some form and function of intelligence to materials that we, in general, do not tend to equate with comparable capability (i.e. raw materials for engineered design).

Golem (Prague Golem reproduction) photo: public domain

It is suggested in some sources[2] that artificial intelligence (in the literary packaging of imagined automatons or other) was also explored in European literary works such as in the 1816 German Der Sandmann (The Sandman) by Ernst Theodor Amadeus Hoffman,[3]  with the story’s character Olympia. The artificial is also explored by the fictional character Dr. Wagner, who creates Homunculus (a little man-like automaton), in Faust by Goethe,[4] and in Mary Shelley’s Frankenstein.[5] Much earlier yet, far less literary and rather philosophically, the artificial was suggested in the 1747 publication entitled L’Homme Machine (Man—Machine) by the French Julien Offray de la Mettrie, who posited the hypothesis that a human being as any other animal, are automatons or machines.

The next post will cover some hints of Philosophy in association with the Field of AI

Mini
Project #___ : Exploring the Pre-History of AI in your own and your larger
context.
Collect any other old stories from within China, Asia or elsewhere (from a location or culture that is not necessarily your own) that reference similar imaginations of “artificial intelligence” as constructed in the creative minds of our ancestors. 
Share your findings in a collection of references from the entire class. 
Maybe make a large collage that can be hung up on the wall, showing “artificial intelligence” from the past, through-out the ages.
Alternative: the teacher shares a few resources or references from the Arts (painting, sculpture, literature, mythology, etc.) that covered these topics and that are examples of the pre-history of AI.

[1] Parada, C. (Dec 10, 1993). Genealogical Guide to Greek Mythology. Studies in Mediterranean Archaeology, Vol 107. Coronet Books

[2] McCorduck, P. (2004). Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. Natick: A K Peters, Ltd. p. xxv

[3]Hoffmann,  E.T.A. (1816). Der Sandmann. In Hoffmann (1817). Die Nachtstücke.  Retrieved on April 8, 2020 from https://germanstories.vcu.edu/hoffmann/sand_pics.html  translated here https://germanstories.vcu.edu/hoffmann/sand_e_pics.html additional information: http://self.gutenberg.org/articles/The_Sandman_(short_story) 

[4] Nielsen, W. C. (2016). Goethe, Faust, and Motherless Creations. Goethe Yearbook, 23(1), 59–75. North American Goethe Society.  Information retrieved on April 8, 2020 from https://muse.jhu.edu/article/619344/pdf

[5] An artistic interpretation of the link between the artificial life of the Frankenstein character and AI is explored here: http://frankenstein.ai/  Retrieved on April 8, 2020