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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 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