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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

text | wondering, The field of AI

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]

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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

text | wondering, The field of AI

The Field of AI (Part 03): A Recent History


A Consideration on Stories of Recent Histories

This story is not a fixed point. This one story here below is neither one that controls all possible AI stories. We are able to note that a history, such as this one, is a handpicking from a source that is richer than the story that is consequentially put in front of you, here, on a linear and engineered chronology. The entirety of the field of AI is more contextualized with parallel storylines, faded-in trials, and faded-out errors, with many small compounded successes and numerous complexities. Histories tend to be messy. This story does not pretend to do justice to that richness.

Just like a numerical dataset, history is a (swirling) pool of data. Just as an information processing unit, hopefully enabled to identify a relevant pattern that still could be prone to an unwanted bias, ambiguities, and probabilities with given uncertainties, so too is this narrative of a history on the dynamic field of AI studies, its researches and its developments. In realizing this, one can only wish that the reader of this story shares the wish to grow towards an increased self-reliant literacy, nurtured with “data” (read the word “data” here as “historical resources” among more numerical sources) and analytical ability.

A Suggested Mini Project Concept for Students:

Mini Project #___ : 
Datasets & Pattern Recognition Opportunities are Everywhere
The above consideration could be part of any storytelling, and its implication is not an insurmountable problem. It’s an opportunity, especially in the area of data sciences associated with AI research and development. See, this story here as an invitation to go out into the field and study more, to get a more nuanced sense of this history’s depths and its adventures within it. Try to see its datasets and their correlations, fluidities, contradictions and complexities. The ability to do so are essential skills in the field of AI as well as important skills as a human in a complex and changing world.
What “algorithm” might the author of this story here have used when recognizing a pattern from a given dataset in creating this story? (there is no right or wrong answer)
It’s almost obvious that a learner can only aspire toward something they know as something that existed, exists or could be imagined to exist. That is simultaneously through for learning from the data selection processes from another, for instance, the authoring of and the applying of the following history of the field of AI.
Can you create your own version of an AI history? What kind of filter, weight, bias or algorithm have you decided to use in creating your version of an AI history?
Figure 01 Cells from a pigeon brain. Drawing made in 1899, of Purkinje cells (A) and granule cells (B) from pigeon cerebellum by Santiago Ramón y Cajal; Instituto Cajal, Madrid, Spain. Image: Public Domain Retrieved on March 27, 2020 from here


A Recent History of the Field of AI

Just like a present-day AI solution does, a human learner too needs datasets to see their own pattern of their path within the larger field. Who knows, digging into the layers of AI history might spark a drive to innovate on an idea some researchers had touched on in the past yet, have not further developed. This has been known to happen in a number of academic fields of which the field of AI is no exception.[1] Here it is opted to present a recent history of the field of AI[2] with a few milestones from the 20th century and the 21st century:

By the end of the 1930s and during the decade of 1940-1950, scientists and engineers joined with mathematicians, neurologists, psychologists, economists and political scientists to theoretically discuss the development of an artificial brain or of the comparison between the brain, intelligence and what computers could be (note, these did not yet exist in these earliest years of the 1940s).

In 1943, McCulloch & Pitts offered a theoretical proposal for a Boolean logic[3] circuit model of the brain.[4] These could be seen as the theoretical beginnings of what we know today as the Artificial Neural Networks.

In 1950 Turing wrote his seminal paper entitled “Computing Machinery and Intelligence.”[5] Slightly later, in the 1950s, early AI programs included Samuel’s checkers game program, Newell & Simon’s Logic Theorist, or Gelernter’s Geometry Engine. It has been suggested that perhaps the first AI program was the Checkers game software. Games, such as Chess, Wéiqí (aka GO) and others (e.g. LěngPūDàshī, the poker-playing AI[6]) have been, and continue to be, important in the field of AI research and development.

While relatively little about it is said to have sustained the test of time, in 1951, a predecessor of the first Artificial Neural Network was created by Marvin Minsky and Dean Edmonds.[7] It was named “SNARC” which is short for “Stochastic Neural Analog Reinforcement Computer”.[8] The hardware system solved mazes. It simulated a rat finding its way through a maze. This machine was not yet a programmable computer as we know it today.

The academic field of “Artificial Intelligence Research” was created between 1955 and 1956.[9] That year, in 1956, the term “AI” was suggested by John McCarthy. He is claimed to have done so during a Dartmouth College conference that same year in Hanover, New Hampshire, the USA. McCarthy defined AI as “… the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable…”[10]

At that same time, the “Logic Theorist” was introduced by other scientists as the first Artificial Intelligence application.[11] It was able to proof a number of mathematical theorems.

In January 1957 Frank Rosenblatt proposed the concept of a single-layered neural network. He invented the photoperceptron (“perceptron” for short), an electronic automaton and model analogous to the brain, in a simplest sense thinkable, that would have the ability to “learn” visual patterns and to process such “…human-like functions as perception, recognition, concept formation, and the ability to generalize from experience… [This system would get its inputs] directly from the physical world rather than requiring the intervention of a human agent to digest and code the necessary information.” [12] In short, the machine was aimed to recognize and classify a given geometric shape, following the input from a camera.

It is natural and healthy in the sciences to inquire with intellectual integrity and wonder, to insist for verification, corroboration and falsifiability[13] of theories and designs. So too did the photoperceptron design not escape scrutiny and the common peer review.[14] At one point, the perceptron was perceived to be of debated applicability and of contested interest as a basis for further research and developments within AI.[15] Several decades later, following a couple of “AI Winters” and academic meanderings, substantial increase in computational power and processing techniques, this will turn out to be a fruitful basis for a specific area of AI research and development: Machine Learning, its area of Deep Learning and its multilayered Artificial Neural Networks.[16]

1959: The term “Machine Learning” was invented by the IBM electrical engineer and Stanford Professor, Arthur Lee Samuel. He wrote the first successful self-learning program. It played a Checkers game.[17] This was an early demonstration of an AI-type application which will become a hot topic in the 21st century and into present-day academic work.

The period from the nineteen fifties (1950s) into the earliest years of the nineteen seventies (early 1970s): during this two-decades-long period there was a lot of excitement around the promises suggested within the research and developments in the Artificial Intelligence field.

In 1965 Robinson invented an algorithm[18] for logical reasoning that laid the groundwork for a form of computer programming,[19] allowing for the automated proving of mathematical theorems.

Around 1970 Minsky and Papert considered the multilayered perceptron (MLP)[20] which could be seen as a theoretical predecessor to the multilayered neural networks as they are researched and developed today, in the AI sub-field of Machine Learning and its Deep Learning techniques.

Reflecting back onto the years around 1973,[21] voices tend to speak of the first “AI Winter”[22] while others don’t seem to mention this period at all.[23] Either way, it means that during this time, it is perceived that two forces supposedly collided: one was that of some academics and other people with a wish to do research in specific directions in the field of AI. They continued needing money. However, other academics with some understandable doubts[24] and those controlling the funds,[25]  no longer believed much in the (inflated) promises made within the given AI research of that period in history. Since money as a resource became limited, so too did research and development slow down. More focus and result-oriented work was required to obtain funds. At least, so it seemed for a period of time, until after the mid-seventies or until the early Eighties (1980s).[26] Depending on the historical source this time period has been demarcated rather differently (and perhaps views on what counts as significant might differ).[27]

Fading in from the early 1970s and lasting until the early 1990s, the AI research and developmental focus was on what is referred to as Knowledge-based approaches.  Those designing these type of solutions sought to “hard-code knowledge about the world in formal languages…” However, “…none of these projects has led to a major success. One of the most famous such projects is Cyc…”[28] Humans had to physically code the solutions which created a number of concerns and problems. The experts could not sufficiently and accurately code all the nuances of the reality of the world around the topic which the application was supposed to “intelligently” manage.

With the earliest introductions in 1965 by Edward Feigenbaum, one could continue finding further, yet still early, developments of these “Knowledge-based Systems”[29] (KBS). The development of these systems continued into the 1970s, some of which then came to further (commercial) fruition during the 1980s in the form of what was by then called “Expert Systems”(ES). The two systems, KBS and ES, are not exactly the same but they are historically connected.  These later systems were claimed to represent how a human expert would think through a highly specific problem. In this case the processing method was conducted by means of IF-THEN rules. During the 1980s the mainstream AI research and development focused on these “Logic-based, Programmed Expert Systems”. Prolog, a programming language, initially aimed at Natural Language Processing,[30] has been one of the favorites in designing Expert Systems.[31]  All expert systems are knowledge-based systems, the reverse is not true. By the mid-1980s Professor John McCarthy would criticize these systems as not living up to their promises.[32]

In the late Eighties (late 1980s), Carver Mead[33] introduced the idea to mimic the structure and functions of neuro-biological architecture (e.g. of brains or of the eye’s retina and visual perception) in the research and development of AI solutions (both in hardware and software). This approach (especially in chip design) has been increasingly and presently known as “Neuromorphic Engineering”. This is considered a sub-field of Electrical Engineering.

Jumping forward to present-day research and development, “neuromorphic computing” implies the promise of a processing of data in more of an analog manner rather than the digital manner traditionally known in our daily computers. It could, for instance, imply the implementation of artificial neural networks onto a computer chip.  This, for instance, could mean that the intensity of the signal is not bluntly on or off (read: 1 or 0) but rather that there could be a varying intensity.   One could read more in relation to this and some forms of artificial neural networks by, for instance, looking at gates, thresholds, and the practical application of the mathematical sigmoid function; to name but a few.

Simultaneously, these later years, following 1988 until about 1995, some claim, can be referred to as the second surge of an “AI Winter”.[34]  Some seem to put the period a few years earlier.[35] The accuracy of years put aside, resources became temporarily limited again. Research and its output was perceived to be at a low.  Concurrently, one might realize that this does not imply that all research and developments in both computing hardware and software halted during these so-called proverbial winters. The work continued, albeit for some with some additional constraints or under a different name or field (not as “AI”). One might agree that in science, research and development across academic fields seems to ebb, flow and meander yet, persist with grit. 

From 1990 onward slowly, but surely, the concept of probability and “uncertainty” took more of the center-stage (i.e. Bayesian networks). Statistical approaches became increasingly important in work towards AI methods. “Evolution-based methods, such as genetic algorithms and genetic programming” helped to move AI research forward.[36]  It was increasingly hypothesized that a learning agent could adapt to (or read: learn from) the changing attributes in its environment. Change implies the varying higher probabilities of a number of events occurring and the varying lower probability of some other attributes, as events, occurring.

AI solutions started to extract patterns from data sets rather than be guided by a line of code only. This probabilistic approach in combination with further algorithmic developments was gradually heralding a radically different approach from the previous “Knowledge-based Systems’. This approach to AI solutions has warranted in what some refer to as the AI Spring[37] some perceive the past few years up till present day.[38]

In the twenty-first century to present-day, Artificial Neural Networks have been explored in academic research and this with increasing success. More and more, it became clear that huge data sets of high quality  were needed to make a specific area of research in AI, known as Machine Learning, more powerful.

During the first decade of this century Professor Li Feifei oversaw the creation of such a huge and high quality image dataset, which would be one of the milestones in boosting confidence back into the field of AI and the quality of algorithm design.[39]

This story now arrived at the more recent years of AI Research and Development.

Following the first decade of this twenty-first century, an increasing usage of GPUs (graphical processing units) as hardware to power Machine Learning applications could be seen. Presently, even more advanced processing hardware is suggested and applied.

Special types of Machine Learning solution are being developed and improved upon. Specifically Deep Learning appeared on the proverbial stage. The developments in the Artificial Neural Networks and the layering of these networks become another important boost in the perception of potentials surrounding applications coming out of AI Research and Development (R&D).[40]

Deep Learning is increasingly becoming its own unique area of creative and innovative endeavor within the larger Field of AI.

Globally, major investment (in the tens of billions of dollars) have been made into AI R&D. There is a continued and even increasing hunger for academics, experts and professionals in various fields related to or within the field of AI.

The historical context of the field of AI, of which the above is a handpicked narrative, has brought us to where we are today. How this research is applied and will be developed for increased application will need to be studied, tried and reflected on, with continued care, considerate debate, creative spirit and innovative drive.

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Footnotes & URLs to Additional Resources

[1] Olazaran, M. (1996). A Sociological Study of the Official History of the Perceptrons Controversy. Social Studies of Science, 26(3), 611–659. London: Sage Publications

[2]  Here loosely based on: Professor Dan Klein and Professor Pieter Abbee. (January 21st, 2014)  CS188 “Intro to AI” Lecture. UC Berkeley.

[3] George Boole (1815 – 1864) came up with a kind of algebraic logic that we now know as Boolean logic in his works entitled The Mathematical Analysis of Logic (1847) and An Investigation of the Laws of Thought (1854). He also explored general methods in probability. A Boolean circuit is a mathematical model, with calculus of truth values (1 = true; 0 = false) and set membership, which can be applied to a (digital) logical electronic circuitry.

[4] 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   

[5] Turing, A.M. (1950). Computing Machinery and Intelligence. Mind 49: 433-460. Retrieved November 13, 2019 from http://cogprints.org/499/1/turing.html and https://www.csee.umbc.edu/courses/471/papers/turing.pdf

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

[7] Martinez, E. (2019). History of AI. Retrieved on April 14, 2020 from https://historyof.ai/snarc/

[8] Minsky, M. (2011). Building my randomly wired neural network machine. Online: Web of Stories   Retrieved on April 14, 2020 from https://www.webofstories.com/play/marvin.minsky/136;jsessionid=E0C48D4B3D9635BA883747C9A925B064

[9] Russell, S. and Peter Norvig. (2016) and McCorduck, Pamela. (2004). Machines Who Think. Natick, MA.: A K Peters, Ltd.

[10] McCarthy, J. (2007). What is AI? Retrieved on December 5th, 2019 from http://www-formal.stanford.edu/jmc/whatisai/node1.html This webpage also offers a nice, foundational and simple conversation about intelligence, IQ and related matters. 

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

[12] Rosenblatt, F. (January, 1957). The Perceptron. A Perceiving and Recognizing Automaton. Report No. 85-460-1. Buffalo (NY): Cornell Aeronautical Laboratory, Inc. p. 1 & 30 Retrieved on January 17, 2020 from https://blogs.umass.edu/brain-wars/files/2016/03/rosenblatt-1957.pdf  

[13] Popper, K. (1959, 2011). The Logic of Scientific Discovery. Taylor and Francis

[14] Minsky, M. and Papert, S.A. (1971). Artificial Intelligence Progress Report. Boston, MA:MIT Artificial Intelligence Laboratory. Memo No. 252.  pp. 32 -34 Retrieved on April 9, 2020 from https://web.media.mit.edu/~minsky/papers/PR1971.html or  http://bitsavers.trailing-edge.com/pdf/mit/ai/aim/AIM-252.pdf

[15] Minsky, M. and Papert, S.A. (1969, 1987). Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: The MIT Press

[16] Olazaran, M. (1996). A Sociological Study of the Official History of the Perceptrons Controversy. Social Studies of Science, 26(3), 611–659. London: Sage Publications

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

[18] It is known as the “unification algorithm”. Robinson, John Alan (January 1965). A Machine-Oriented Logic Based on the Resolution Principle. J. ACM. 12 (1): 23–41 Retrieved on March 24, 2020 from https://dl.acm.org/doi/10.1145/321250.321253 and https://web.stanford.edu/class/linguist289/robinson65.pdf

[19] The form is what now could be referred to as a logic-based declarative programming paradigm = the code is telling a system what you want it does and that by means of formal logic facts and rules for some problem and not exactly by stating how step by step it needs to do it. There are at least 2 main paradigms with each their own sub-categories. This logic-based one is a subcategory of the declarative programming set of coding patterns and standards. The other main paradigm (with its subsets) is imperative programming which includes object-oriented and procedural programming. The latter includes the C language. See Online: Curlie Retrieved on March 24, 2020 from https://curlie.org/Computers/Programming/Languages/Procedural  Examples of (class-based) object-oriented imperative programming languages are C++, Python and R. See: https://curlie.org/en/Computers/Programming/Languages/Object-Oriented/Class-based/

[20] Minsky, M. and Papert, S.A. (1969, 1987) p. 231 “Other Multilayer Machines”.

[21] Lighthill, Sir J. (1972). Lighthill Report: Artificial Intelligence: A General Survey. Retrieved on April 9, 2020 from http://www.chilton-computing.org.uk/inf/literature/reports/lighthill_report/p001.htm and https://pdfs.semanticscholar.org/b586/d050caa00a827fd2b318742dc80a304a3675.pdf and http://www.aiai.ed.ac.uk/events/lighthill1973/

[22] Bostrom, N. (2014). Superintelligence. Paths, Dangers, Strategies. Oxford: Oxford University Press. p. 22

[23] McCorduck, P. (2004). pp. xxviii – xxix

[24] Minsky, M. and Papert, S.A. (1969, 1987)

[25] Historic Examples: Pierce, J. R. et al (1966). Language and Machines: Computers in Translation and Linguistics. Washington D. C.: The Automatic Language Processing Advisory Committee (ALPAC). Retrieved on April 9, 2020 from The National Academies of Sciences, Engineering and Medicine at   https://www.nap.edu/read/9547/chapter/1 alternatively: http://www.mt-archive.info/ALPAC-1966.pdf

[26] Hutchins, W. J. (1995). Machine Translation: A Brief History. In Koerner, E. E.K. .et al (eds). (1995). Concise history of the language sciences: from the Sumerians to the cognitivists. Pages 431-445. Oxford: Pergamon, Elsevier Science Ltd. p. 436

[27] Russell, S. et al. (2016, p.24) doesn’t seem to mention this first “AI Winter” and only mentions the later one, by the end of the 1980s nor does McCorduck, Pamela. (2004) pp. xxviii – xxix. Ghatak, A. (2019, p. vii) however, identifies more than one, as do Maini, V., et al. (Aug 19, 2017) and Mueller, J. P. et al. (2019, p. 133), Chollet, F. (2018). P12 Perhaps these authors, who are mainly focusing on Deep Learning, see the absence of research following the Rosenblatt’s perceptron as a “winter”.

[28] Goodfellow, I., et al. (2016, 2017). Deep Learning. Cambridge, MA: The MIT Press. p. 2

[29] More in-depth information can be found in the journal of the same name: https://www.journals.elsevier.com/knowledge-based-systems

[30] Hutchins, W. J. (1995). p. 436

[31] Some Prolog resources related to expert systems: https://www.metalevel.at/prolog/expertsystems AND https://en.wikibooks.org/wiki/Prolog

[32] McCarthy, J. (1996). Some Expert Systems need Common Sense. Online: Stanford University, Computer Science Department. Retrieved on April 7, 2020 from   http://www-formal.stanford.edu/jmc/someneed/someneed.html

[33] Mead, C. Information Retrieved on April 8, 2020 from http://carvermead.caltech.edu/ also see Mead, C. (1998). Analog VLSI and Neural Systems. Reading, MA: Addison-Wesley from https://dl.acm.org/doi/book/10.5555/64998

[34] Russell (2016) p. 24

[35] McCorduck (2004) p. 418

[36] Bostrom, N. (2014). Superintelligence. Paths, Dangers, Strategies. Oxford: Oxford University Press. pp.24

[37] Manyika, J. et al (2019). The Coming of AI Spring. Online: McKinsey Global Institute. Retrieved on April 9, 2020 from https://www.mckinsey.com/mgi/overview/in-the-news/the-coming-of-ai-spring

[38] Olhede, S., & Wolfe, P. (2018). The AI spring of 2018. Significance, 15(3), 6–7. Retrieved on April 9, 2020 from https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1740-9713.2018.01140.x

[39] Deng, J. et al. (2009). ImageNet: A Large-Scale Hierarchical Image Database. Online: Stanford Vision Lab, Stanford University & Princeton University Department of Computer Science. Retrieved April 7, 2020 from http://www.image-net.org/papers/imagenet_cvpr09.pdf

[40] Trask, A. W. (2019). Grokking Deep Learning. USA: Manning Publications Co. p. 170

text | wondering, The field of AI

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 

text | wondering, The field of AI

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

text | wondering, The field of AI

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)

text | wondering, The field of AI

The Field of AI (Part 02-3): A “Pre-History” & a Foundational Context

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

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


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

Philosophy:

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

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

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

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

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



Psychology

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

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

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

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

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

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

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



Linguistics

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

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

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

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

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

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

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

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

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

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

text | wondering, The field of AI

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)
The above image as a higher resolution in *.pdf formatDownload
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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

text | wondering, The field of AI

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

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last update: Friday, April 24, 2020

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:
  • This post 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)
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The Field of AI: A “Pre-History”.

A “pre-history” and a foundational context of Artificial Intelligence can arguably by traced back to a number of events in the past as well as to a number of academic fields of study. In this post only a few have been handpicked.

This post will offer a very short “pre-history” while following posts will dig into individual academic fields that are believed to offer the historical and present-day context for the field of AI.

It is not too far-fetched to link the roots of AI, as the present-day field of study, with the human imagination of artificial creatures referred to as “automatons” (or what could be understood as predecessors to more complex robots).

While it will become clear here that the imaginary idea of automatons in China is remarkably older, it has been often claimed that the historic development towards the field of AI, as it is intellectually nurtured today, commenced more than 2000 years ago in Greece, with Aristotle and his formulation of the human thought activity known as “Logic”.

Presently, with logic, math and data one could make a machine appear to have some degree of “intelligence”. Note, it is rational to realize that the perception of an appearance does not mean the machine is intelligent. What’s more, it could be refreshing to consider that not all intelligent activity is (intended to be seen as) logical.

It’s fun, yet important, to add that to some extent, initial studies into logic could asynchronously be found in China’s history with the work by Mòzǐ (墨子), who conducted his philosophical reflections a bit more than 2400 years ago. 

Coming back to the Ancient Greeks: besides their study of this mode of thinking, they also experimented with the creation of basic automatons.

Automatons (i.e. self-operating yet artificial mechanical creatures) were likewise envisioned in China and some basic forms were created in its long history of science and technology.[1] An early mentioning can be found in, Volume 5 “The Questions of Tang” (汤问; 卷第五 湯問篇) of the Lièzǐ (列子)[2], an important historical Daoist text.

In this work there is mentioning of this kind of (imagined) technologies or “scientific illusions”.[3] The king in this story became upset by the appearance of intelligence and needed to be reassured that the automaton was only that, a machine …

Figure 1 King Mù of Zhōu, who reigned a little more than 2950 years ago ( 周穆王; Zhōu Mù Wáng ) , introduced by Yen Shi, is meeting an automaton (i.e. the figure depicted with straighter lines, on the top-left), as mentioned in the fictional book Lièzǐ. Image retrieved on March 5, 2020 from here
Figure 2 Liè Yǔkòu (列圄寇/列禦寇), aka the Daoist philosopher Lièzĭ (列子) who imagined an (artificial) humanoid automaton. This visual was painted with “ink and light colors on gold-flecked paper,” by Zhāng Lù (张路); during the Míng Dynasty (Míng cháo, 明朝; 1368–1644). Retrieved on January 12, 2020 from here ; image license: public domain.

Jumping forward to the year 1206, the Arabian inventor, Al-Jazari, supposedly designed the first programmable humanoid robot in the form of a boat, powered by water flow, and carrying four mechanical musicians. He wrote about it in his work entitled “The Book of Knowledge of Ingenious Mechanical Devices.”

It is believed that Leonardo Da Vinci was strongly influenced by his work.[4] Al-Jazari additionally designed clocks with water or candles. Some of these clocks could be considered programmable in a most basic sense.

figure 3 Al-jazari’s mechanical musicians machine (1206). Photo Retrieved on March 4, 2020 from here; image: public domain

One could argue that the further advances of the clock (around the 15th and 16th century) with its gear mechanisms, that were used in the creation of automatons as well, were detrimental to the earliest foundations, moving us in the direction of where we are exploring AI and (robotic) automation or autonomous vehicles today.

Between the 16th and the 18th centuries, automatons became more and more common.  René Descartes, in 1637, considered thinking machines in his book entitled “Discourse on the Method of Reasoning“. In 1642, Pascal created the first mechanical digital calculating machine.

Figure 4 Rene Descartes; oil on canvas; painted by Frans Hals the Elder (1582 – 1666; A painter from Flanders, now northern Belgium, working in Haarlem, the Netherlands. This work: circa 1649-1700; photographed by André Hatala . File retrieved on January 14, 2020 from here. Image license: public Domain

Between 1801 and 1805 the first programmable machine was invented by Joseph-Marie Jacquard. He was strongly influenced by Jacques de Vaucanson with his work on automated looms and automata. Joseph-Marie’s loom was not even close to a computer as we know it today. It was a programmable loom with punched paper cards that automated the action of the textile making by the loom. What is important here was the system with cards (the punched card mechanism) that influenced the technique used to develop the first programmable computers.

Figure 5 Close-up view of the punch cards used by Jacquard loom on display at the Museum of Science and Industry in Manchester, England. This public domain photo was retrieved n March 12, 2020 from here; image: public domain

In the first half of the 1800s, the Belgian mathematician, Pierre François Verhulst discovered the logistic function (e.g. the sigmoid function),[1] which will turn out to be quintessential in the early-day developments of Artificial Neural Networks and specifically those called “perceptrons” with a threshold function, that is hence used to activate the output of a signal, and which operate in a more analog rather than digital manner, mimicking the biological brain’s neurons. It should be noted that present-day developments in this area do not only prefer the sigmoid function and might even prefer other activation functions instead.

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

In 1936 Alan Turing proposed his Turing Machine. The Universal Turing Machine is accepted as the origin of the idea of a stored-program computer. This would later, in 1946, be used by John von Neumann for his “Electronic Computing Instrument“.[6] Around that same time the first general purpose computers started to be invented and designed. With these last events we could somewhat artificially and arbitrarily claim the departure from “pre-history” into the start of the (recent) history of AI.

figure 6 Alan Turing at the age of 16. Image Credit: PhotoColor [CC BY-SA (https://creativecommons.org/licenses/by-sa/4.0)] ; Image source Retrieved April 10, 2020 from here


As for fields of study that have laid some “pre-historical” foundations for AI research and development, which continue to be enriched by AI or that enrich the field of AI, there are arguably a number of them. A few will be explored in following posts. The first posts will touch on a few hints of Literature, Mythology and the Arts.

[1] Needham, J. (1991). Science and Civilisation in China: Volume 2, History of Scientific Thought. Cambridge, UK: Cambridge University.

[2] Liè Yǔkòu (列圄寇 / 列禦寇). (5th Century BCE). 列子 (Lièzǐ). Retrieved on March 5, 2020 from https://www.gutenberg.org/cache/epub/7341/pg7341-images.html  and 卷第五 湯問篇 from https://chinesenotes.com/liezi/liezi005.html   and an English translation (not the latest) from  https://archive.org/details/taoistteachings00liehuoft/page/n6/mode/2up  

[3] Zhāng, Z. (张 朝 阳).  ( November 2005). “Allegories in ‘The Book of Master Liè’ and the Ancient Robots”. Online: Journal of Heilongjiang College of Education. Vol.24 #6. Retrieved March 5, 2020 from https://wenku.baidu.com/view/b178f219f18583d049645952.html

[4] McKenna, A. (September 26, 2013). Al-Jazarī Arab inventor. In The Editors of Encyclopaedia Britannica. Online: Encyclopaedia Britannica Retrieved on March 25, 2020 from https://www.britannica.com/biography/al-Jazari AND:

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

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

[6] Davis, M. (2018). The Universal Computer: the road from Leibniz to Turing. Boca Raton, FL: CRC Press, Taylor & Francis Group

text | wondering, The field of AI

The Field of AI (part 01): Context, Learning & Evolution

One could state that Artificial Intelligence (AI) methods enable the finding of and interaction with patterns in the information available from contexts to an event, object or fact. These can be shaped into data points and sets. Many of these sets are tremendously large data sets. So large are these pools of data, so interconnected and so changing that it is not possible for any human to see the patterns that are actually there or that are meaningful, or that can actually be projected to anticipate the actuality of an imagined upcoming event.

While not promising that technologies coming out from the field of AI are the only answer, nor the answer to everything, one could know their existence and perhaps apply some of the methods used in creating them. One could, furthermore, use aspects from within the field of AI to learn about a number of topics, even about the processes of learning itself, about how to find unbiased or biased patterns in the information presented to us. Studying some basics about this field could offer yet another angle of meaning-giving in the world around and within us.  What is a pattern, if not an artificial promise to offer some form of meaning?

It’s not too far-fetched to state that the study of Artificial Intelligence is partly the study of cognitive systems[1] as well as the context within which these (could) operate. While considering AI[2], one might want to shortly consider “context.”

Here “context” is the set of conditions and circumstances preceding, surrounding or following a cognitive system and that related to its processed, experienced, imagined or anticipated events. One might want to weigh how crucial conditions and circumstances are or could be to both machine and human.[3]  The field of AI is one of the fields of study that could perhaps offer one such opportunity.

A context is a source for a cognitive system to collect its (hopefully relevant) information, or at least, its data from. Cognitive Computing (CC) systems are said to be those systems that try to simulate the human thought processes, to solve problems, via computerized models.[4] It is understandable that some classify this as a subset of Computer Science while some will obviously classify CC as a (sometimes business-oriented) subset of the field of AI.[5] Others might link this closer to the academic work done in Cognitive Science. Whether biological or artificial, to a number of researchers the brain-like potentials are their core concern.[6]

As can be seen in a few of the definitions and as argued by some experts, the broad field of AI technologies do not necessarily have to mimic *human* thought processes or human intelligence alone. As such, AI methods might solve a problem in a different way from how a human might do it.

However similar or different, the meaning-giving information, gotten from a context, is important to both an AI solution as well as to a biological brain. One might wonder that it is their main reason for being: finding and offering meaning.

The contextual information an AI system collects could be (defined by or categorized as) time, locations, user profiles, rules, regulations, tasks, aims, sensory input, various other big to extremely huge data sets and the relationships between each of these data sets in terms of influencing or conflicting with one another. All of these sources for data are simultaneously creating increasing complexities, due to real-time changes (i.e. due to ambiguity, uncertainty, and shifts). AI technologies offer insights through their outputs of the *best* solution, rather than the one and only certain solution for a situation, in a context at a moment in spacetime.

The wish to understand and control “intelligence” has attracted humans for a long time. It is then reasonable to think that it will attract our species’ creative and innovative minds for a long time to come. It is in our nature to wonder, in general, and to wonder about intelligence and wisdom in specific; whatever their possible interlocked or independent definitions might be(come) and whichever their technological answers might be.

In considering this, one might want to be reminded that the scientific name of our species itself is a bit of a give-away of this (idealized) intention or aspiration: “Homo Sapiens.” This is the scientific name of our animal species. Somewhat loosely translated, it could be understood to mean: “Person of Wisdom”. 

In the midst of some experts who think that presently our intelligence is larger than our wisdom, others feel that, if handled with care, consideration and contextualization, AI research and developments just might positively answer such claim or promise and might at least augment our human desires towards becoming wiser.[7] Just perhaps, some claim,[8] it might take us above and beyond[9] being Homo Sapiens.[10]

For now, we are humans exploring learning with and by machines in support of our daily yet global needs.

For you and I, the steps to such aim need to be practical. The resources to take the steps need to be graspable here and now.

At the foundation, to evaluate the validity or use of such claims, we need to understand a bit what we are dealing with. Besides the need for the nurturing of a number of dimensions in our human development, we might want to nurture our Technological Literacy (or “Technology Literacy”).[11]

A number of educators[12] seem to agree that,[13] while considering human experiences and their environments, this area of literacy is not too bad a place to start off with.[14] In doing so, we could specifically unveil a few points of insight associated with Artificial Intelligence; that human-made technological exploration of ambiguous intelligence.

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[1] Sun F., Liu, H., Hu, D.  (eds). (2019). Cognitive Systems and Signal Processing: 4th International Conference, ICCSIP 2018, Beijing, China, November 29 – December 1, 2018, Revised Selected Papers, Part 1 & Part 2. Singapore: Springer

[2] DeAngelis, S. F. (April 2014). Will 2014 be the Year you Fall in Love with Cognitive Computing? Online: WIRED. Retrieved November 22, 2019 from https://www.wired.com/insights/2014/04/will-2014-year-fall-love-cognitive-computing/

[3] Desouza, K. (October 13, 2016). How can cognitive computing improve public services? Online Brookings Institute’s Techtank Retrieved November 22, 2019 from https://www.brookings.edu/blog/techtank/2016/10/13/how-can-cognitive-computing-improve-public-services/

[4] Gokani, J. (2017). Cognitive Computing: Augmenting Human Intelligence. Online: Stanford University; Stanford Management Science and Engineering; MS&E 238 Blog. Retrieved November 22, 2019 from https://www.datarobot.com/wiki/cognitive-computing/

[5] https://www.datarobot.com/wiki/cognitive-computing/

[6] One example is: Poo, Mu-ming. (November 2, 2016). China Brain Project: Basic Neuroscience, Brain Diseases, and Brain-Inspired Computing. Neuron 92, NeuroView, pp. 591-596.  Online: Elsevier Inc. Retrieved on February 25, 2020 from https://www.cell.com/neuron/pdf/S0896-6273(16)30800-5.pdf  . Another example is: The work engaged at China’s Research Center for Brain-Inspired Intelligence (RCBII), by the teams led by Dr XU, Bo and Dr. ZENG, Yi. Founded in April 2015, at the CAS’ Institute of Automation, the center contains 4 research teams: 1. The Cognitive Brain Modeling Group (aka Brain-Inspired Cognitive Computation); 2. The Brain-Inspired Information Processing Group; 3. The Neuro-robotics Group (aka Brain-Inspired Robotics and Interaction) and 4. Micro-Scale Brain Structure Reconstruction. Find some references here: bii.ia.ac.cn

[7] Harari, Y. N. (2015). Sapiens. A Brief History of Humankind. New York: HarperCollings Publisher

[8] Gillings, M. R., et al. (2016). Information in the Biosphere: Biological and Digital Worlds. Online: University California, Davis (UCD). Retrieved on March 25, 2020 from https://escholarship.org/uc/item/38f4b791

[9] (01 June 2008). Tech Luminaries Address Singularity. Online: Institute of Electrical and Electronics Engineers (IEEE Spectrum). Retrieved on March 25, 2020 from  https://spectrum.ieee.org/static/singularity

[10] Maynard Smith, J. et al. (1995). The Major Transitions in Evolution. Oxford, England: Oxford University Press  AND Calcott, B., et al. (2011). The Major Transitions in Evolution Revisited. The Vienna Series in Theoretical Biology. Boston, MA: The MIT Press.

[11]  National Academy of Engineering and National Research Council. (2002). Technically Speaking: Why All Americans Need to Know More About Technology. Washington, DC: The National Academies Press   Online: NAP Retrieved on March 25, 2020 from https://www.nap.edu/read/10250/chapter/3

[12] Search, for instance, the search string “Technological Literacy” through this online platform: The Education Resources Information Center (ERIC), USA https://eric.ed.gov/?q=Technological+Literacy

[13] Dugger, W. E. Jr. et al (2003). Advancing Excellence in Technology Literacy. In Phi Delta Kappan, v85 n4 p316-20 Dec 2003 Retrieved on March 25, 2020 from https://eric.ed.gov/?q=Technology+LIteracy&ff1=subTechnological+Literacy&ff2=autDugger%2c+William+E.%2c+Jr.&pg=2

[14] Cydis, S. (2015). Authentic Instruction and Technology Literacy. In Journal of Learning Design 2015 Vol. 8 No.1 pp. 68 – 78. Online: Institute of Education Science (IES) & The Education Resources Information Center (ERIC), USA. Retrieved on March 25, 2020 from https://files.eric.ed.gov/fulltext/EJ1060125.pdf


IMAGE CREDITS:

An example artificial neural network with a hidden layer.

en:User:Cburnett / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/) Retrieved on March 12, 2020 from https://upload.wikimedia.org/wikipedia/commons/e/e4/Artificial_neural_network.svg