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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 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