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The Field of AI (part 02): A “Pre-History” & a Foundational Context.
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)
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 …
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.
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.
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.
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.
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
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.
Few people know that heart comes http://appalachianmagazine.com/2016/12/25/why-stink-bugs-are-taking-over-the-eastern-united-states/ online prescription for viagra under pressure, when stress control of the mind. The uses of alcohol, smoking, tobacco, and http://appalachianmagazine.com/2019/03/10/mountain-tradition-eating-ramps-in-springtime/ super generic viagra illegal drugs have great influence of carrying this sexual complication. You on line viagra can also get discount on bulk order for this product. While viagra lowest prices some states don’t need that this online drug is not good as their branded counterparts are.[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
