Artificial Intelligence turned 50 in 2006 (Cordeschi, 2007), making its chronological age 67 in the current year, 2023.  The 1956 summer Dartmouth Conference was to examine “the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it,” as one reads in the document (McCarthy et al., 1955).

The experiments are mentioned in the Dartmouth preparatory document of August 1955, but another important event took place around that time:  The symposium on “The Design of Machines to Simulate the Behavior of the Human Brain,” sponsored by the PGEC at the IRE National Convention, held in 1955 (Cordeschi, 2007).  One of the main issues dealt with at the symposium was the possibility of using computers for different aims, and what might be “the neurophysiologists’ contribution” to the building of machines reproducing brain functions (Cordeschi, 2007).  Computers are used for testing brain functions, through the use of scanned images called, primarily called the Magnetic Resonance Imaging (MRI System) and other individually developed tools run by computers to evaluate brain responses to stimuli.  Very few standard instruments and brain testing software are publicly available for presentation or discussion, although there are a vast number of studies on artificial intelligence and machine learning.  Heavy investment has been placed in research and development in the learning areas and artificial intelligence simulation, comparing human learning to machine learning, where it is theorized humans and machines are different in their processing and functionality in these two areas, similar to their calculation abilities.  A human’s ability to ‘sum things up’ or ‘summarize’ what was read, but also ‘comp-rehend’ and then apply the knowledge.  All parts of terms used in computer science.

Humans use their brains to use computers and it would seem that computers should be able to replicate human intelligence and brain processing only if computer science is able to fully understand the brain, intelligence, learning, and the many and varied psychological and biological functions.  Artificial Intelligence is simply the knowledge area, which is what is sought after to understand human design, in order to create a computerized simulator.  It is unknown if learning or redesigning the learning processes is the beginning of creating a programmable human-like machine that can learn, but of more value is a machine that can interact and mimic the human on multiple levels, aligned with what is known in psychology and biology.  The perception of ‘intelligence’ is what seems to be the area of heavy investment, programming, and interest, and this is where humans and computers or human life science, computer science, and psychology merge or are ‘simular’ – where special emphasis should be placed on the word ‘simular’ and our goal of simulation, with a slight variation to a calculation function of ‘sum,’ which is now a machine function that works faster than any human.  Processing or functioning speed seems to be what is highly sought after, as is the automatic functioning and processing of data and information.

There is an obvious merging of disciplines, necessary to achieve the goals of artificial intelligence, yet there is no official name for a total AI system.  It is known as machine learning, artificially intelligent systems, or robots.  Some understand it as intuitive software applications that resemble human like reasoning and problem solving, built, and presented as applications, which can operate using human-like presentation.  Cybernetics used concepts developed by computer science to model brain functions elucidated in neuroscience, but is limited to feedback controls. Computer science and linguistics were already linked through computational linguistics.  It is believed that all fifteen possible links could be instantiated with respectable research, and the eleven links we saw as existing in 1978 have been greatly strengthened, defined in 1978 and that involved the tools of the two disciplines it linked together (Miller, 2003).  The science of AI could be described as “synthetic psychology,” “experimental philosophy,” or “computational epistemology”– epistemology is the study of knowledge. AI can be seen as a way to study the nature of knowledge and intelligence, but with a more powerful experimental tool than was previously available (Poole & Mackworth, 2018).

It seems apparent that researchers and scientists are replicating themselves through the use of scientific explanation of ‘neurology’ and computer science in many disciplines that involve psychology, philosophy, and some psuedo-scientific programming methods, like neural linguistic processing, by the use of more than just terminology, but also internet web page design, and sequential, object oriented programming.  The problem in explaining the phenomena, is that it does not fit the scientific study model or methods used to describe it, such as if there are only two ways or things:  humans and computers, or qualitative and quantitative methods to prove scientific theories.

Understanding just the difference, use, and references of ‘hypo’ and ‘hyper’ gets us closer in computer programming and healthcare, and it is not a simple conclusion that ‘they are used differently’ and there is no purpose other than that are synonymously based with syntax variations and are merely semantics with no greater or historical purpose and reason or simply stated, the slight variation and difference is not by accident.  It is believed that Search Engine Management (SEM), a part of STEM, and ‘research’ are closely aligned with human brain mechanisms, aligned with our ways of deciding based upon proof, and programming models are also closely aligned with psychological studies of visual advertising, or optical imaging systems of pain and pleasure principles on multiple levels using technology interchangeably in short and long term mental conditioning.  

It is unknown how much time and money has been invested in Artificial Intelligence and Machine Learning, but in year 2021, the U.S. National Science Foundation (NSF), in collaboration with other federal agencies and higher education institutions and other stakeholders, announced a $140 million investment to establish seven new National Artificial Intelligence Research Institutes (AI Institutes). The announcement was part of a broader effort across the federal government to advance a cohesive approach to AI-related opportunities and risks (NSF, 2023).

Just a tiny dissection and historical view of computers, once referenced as ‘terminals’ and our product or outputs of learning, being term papers, and our most advanced learning credential, being a ‘terminal degree,’ it is clear there is convergence, but the concern is that artificial automated intelligence leads to termination, which it has been proven that automation has led to employment, job, or manpower reduction due to the replacement of humans by automated and computerized tasks, beyond mathematical calculations, but now automated workflows and processes, which has been proven to save both time and money.

In neurology, there are similar termination references, such as the neuron firing system, which is used to describe the electric and chemical processing in human brains that show ‘action potential’ leading to a response, on a cellular and micro level.  Advanced reports show research being done to create neural networks, used to describe the internal human brain functioning and applied to artificial intelligence and machine learning.  Group communication theories are also advancing and closely replicating computers to humans through the use of ‘networking principles.’

An intelligent system could be made of ‘text’ book information, but it would not be an artificially intelligent machine because information is one thing, but knowledge or intelligence is another, with necessary proof and applicability or use protocols where a computer application can be designed to manage such information, on multiple levels in various formats. 


Cordeschi, R. (2007).  Ai Turns Fifty:  Revisiting Its Origins.  Applied Artificial Intelligence, 21(4-5), 259-279.

McCarthy, J., M. L.Minsky, N.Rochester, and C. E.Shannon. 1955. A proposal for the Dartmouth summer research project on artificial intelligence.

Artificial Intelligence: Foundations of Computational Agents, Poole & Mackworth, November 2018,

National Science Foundation, Artificial Intelligence, NSF Announces 7 New Artificial Intelligent Research Institutes, May 2023.