The study of human (natural) neural networks is a motivation for the development of artificial intelligence. This scientific conception is transdisciplinary. In this essay, the role of (S)AI training infrastructure based on artificial neural networks in air warfare is – partially - discussed. Airspace, in the modern times of warfare, hides both threats and opportunities. In addition, people's perception of space and time has changed, as they have got further from their natural environment.
This essay would rather briefly analyze and compare the contemporary processes of human decision making and artificial neural networks with natural networks in the context of modelling these processes than describe the air force infrastructure, its equipment and the role of (S)AI in the creation of tactical alternatives.
American studies have shown a steady decline in pilot preparedness within the frame of US Air Force, which has already affected national defense and offensive capabilities. Furthermore, the "robust" training volume was reconfigured, with three pillars:
S(AI) (pattern-based artificial intelligence), ML (logarithmic machine learning) and augmented reality (AR). Nevertheless, the use of "Advanced Technologies" was expected to corelate better with the ever-changing knowledge needs of pilots.
CONTENT
ABSTRACT
1. Artificial Intelligence S(AI)
1.1. "Unless it does"
1.2. Natural and artificial networks
1.3. Natural intelligence vs. activation function
1.4. The importance of simulation to support learning
2. TRANSDISCIPLINARITY
2.1. Reconstruction of knowledge and tactics
2.2. Closed order tactical acts
2.3. Operational constructivism
Bibliography
ABSTRACT
The study of human (natural) neural networks is a motivation for the development of artificial intelligence. This scientific conception is transdisciplinary. In this essay, the role of (S)AI training infrastructure based on artificial neural networks in air warfare is – partially - discussed.
Airspace, in the modern times of warfare, hides both threats and opportunities. In addition, people's perception of space and time has changed, as they have got further from their natural environment.
This essay would rather briefly analyze and compare the contemporary processes of human decision making and artificial neural networks with natural networks in the context of modelling these processes than describe the air force infrastructure, its equipment and the role of (S)AI in the creation of tactical alternatives.
KEYWORDS
Artificial intelligence and the human nervous system, logarithmic machine learning (ML), augmented reality (AR).
1. Artificial Intelligence S(AI)
American studies have shown a steady decline in pilot preparedness within the frame of US Air Force, which has already affected national defense and offensive capabilities. [1]
Furthermore, the "robust" training volume was reconfigured, with three pillars:
S(AI) (pattern-based artificial intelligence)[2], ML (logarithmic machine learning) and augmented reality (AR). Nevertheless, the use of "Advanced Technologies " [3] was expected to corelate better with the ever-changing knowledge needs of pilots.
(S)AI performs comparisons by using a suitable infrastructure that works without human intervention. In its current status of development, we have to emphasize that its recognition capability is closely linked to its capacity and suitability of processing digitized raw data.
Moreover, the algorithms created by natural intelligence (NI) could also enable (S)AI to create new algorithms. The intelligence of 'AI' is derived from this.
We know the theory of rational limits of human cognition. Also, the "triggers" from the environment could be prioritized, however, we could only guess what is more important and what is less important. In addition, we could notice that this simplification reduces the chances of making (and repeating) error-free decisions. It is, however, not yet known when we could be sure that the diversity of the natural and social environment could be fully known and modelled in the future.
Could we ever identify the external influences that determine the correctness of our decisions?
This would happen if in a theoretical situation the “worlds” dominated by (S)AI technologies would bounce out of bias. (we could imagine this in a theoretical model, however, it does contradict the laws of nature) In contrast, it does not mean that the complexity of the natural environment could never be modelled.
1.1. "Unless it does"
Unless we do achieve the over-expansion of this technology, human-designed (S)AI algorithms (merely) in their supporting role, will not be exceeded for a long time. They are useful, no doubt, they still have their ability to accelerate natural decisions and improve their accuracy. However, they will not develop their system-building power. In addition, algorithms will create new algorithms, however, in this form they do not induce the differentiation that occurs in nature, nor do they play a determining role (sui jure) in inducing social adaptation.
1.2. Natural and artificial networks
Nevertheless, simulating human intelligence could be derived from the function of nervous system. The human brain consists of approximately eighty-six billion neurons which communicate through electrical and electrochemical signals. These connections are called synapses. Moreover, the relevant triggers for the transmission of signals - in physical-natural environment - are "translated" for the dendrites, these short-branched extensions. In addition, when these electrical signals reach a certain threshold, the nerve cell generates an action potential. Consequently, these travel along a single (in the body) longer stretch of the axon, which eventually passes the information on to following cell.
On the contrary, in the case of (S)AI5[4], algorithms and their subsystems decide if the information induces an action potential or not. Hence, the human nervous system is able to prioritize the (natural) stimuli that force the nervous system to respond. Furthermore, the human rational cognitive potential is sufficient for subjective selection between stimuli from the direct and indirect environment. Based on their pre-knowledge, humans are capable of constructing a multitude of models, algorithms and subsystems for (S)AI. The bulge of information taken from the indirect environment exceeds any independent potential for interpretation.
1.3. Natural intelligence vs. activation function
A "node" is nothing more than what copies (make it pattern based on a pre-designed simplification) information, the information is stored by the "node" and transmitted. Following this, it is transmitted adaptively and weightily to new "selection recipes" created by artificial algorithms or subsystems, which could generate the (artificial) action potential. Therefore, the inputs to an artificial neuron do have a weight, which determines how much the input affects the output of the neuron. Moreover, we calculate the output of neuron as the sum of all the inputs multiplied by their all weights. We add a constant value (called "bias") to this summed input, which controls the threshold for activation of the neuron. The bias helps fine-tune the decision making limits. Finally, the activation function converts the summed input into the final output of the artificial neuron. Moreover, this is passed on to the neurons in the next layer. The "bias" is initially set randomly in order to train the artificial neural network (simulating a natural environment to help the adaptation accuracy, therefore reducing the network bias).
Consequently, the data are propagated from the input layer [5] (forward propagation) to the output layer. This is where the earlier mentioned “weighting” takes place. A so-called "loss function" is used to measure the deviation between fine-tuning ("prior knowledge") anticipation and the real values. Nevertheless, the ultimate goal is to minimize this value. We could only achieve this by repeating these processes (interactive optimization). Furthermore, the network's ratio system is also modified (process of back-propagation)[6]. This learning process, the epochs, reduces the possibility of error step by step as by running over and over again through the "iterations" of the data sets. Over-modelled systems, on the contrary, could become insensitive to new situations – we mean adaptation.
As a consequence, the contribution of “non-natural intelligence” to the “simulation of reality” could not be underestimated - even as known today, despite its apparent deficiencies, it could supersede the role of traditional tools in education, excluding real-life practice, certainly.
By repetition of problem situations, by the unchanged demonstration of expected outcomes.
1.4. The importance of simulation to support learning
Today, we could not think of pilot training without simulators that provide us with the real experience of flying. Their advantages:
1) they allow the practice of basic tactical (BFM - Basic Fighter Maneuvering) operations in a safe environment, also modelling emergency situations during different phases of flight, such as Air Combat Maneuvering (ACM).
In addition, understanding of the concept of expendable physical energy (potential and kinetic energy), the geometric vision in aerodynamic space as well as the relationship between time and tactics - which could be a constant challenge in the future - are very specific skills.
Nevertheless, simulators also provide us with the exercise of other knowledge:
2) which is cost-effective to operate;
3) which enables the measurability of maneuvers and procedures (systemic
approach to behaviors, ability to make sense of information, adaptation,
stress loading as well as the "quantitative reflection" of physical and
visual loads).
Furthermore, "AI" is now playing a dominant role at three levels of tactical decision-making.
These are modelling, simulation and wargaming (WG – War Games).
We have to note that there is a consensus on the reality modelling potential of wargaming[7]:
these are interdependent tactical infrastructures attempting to determine the most optimal outcome.
" Over time, the lessons from M&S and wargaming are assimilated using "AI" to mine data from M&S experiments, so as to refine theory and data for subsequent cycles " [8]
This short essay intends is to specify a problem, a question that could only be answered partially today. Nevertheless, the (S)AI-supported systems (sometimes competing, sometimes supporting each other), modelling, simulation, war games (WG) could not yet leave the optimal starting point.
There are two reasons for this. First, the distribution of information (protected information blocks the development of accurate tactics - it is essentially covered data) and secondly, no form of (S)AI could deal with the diversionary power of human subjectivity and affectivity as emotional motivation.
2. TRANSDISCIPLINARITY
Undoubtedly, one of the basic needs of people in modern society is that their lives should be predictable. Their knowledge should provide a realistic chance of success in their life and also to be able to meet the expectations of his personal or professional environment.
When modelling natural neuron networks we rely on a transdisciplinary methodology in the design of artificial neuron networks. In addition, the composition of knowledge required in modern (turbulent) society is changing rapidly. Artificial neural networks - as previously described - would reduce the discrepancy between prediction and real values by copying the operation of natural neuroanatomy.
2.1. Reconstruction of knowledge and tactics
Why is the air force chosen for the analysis above?
In the first text of this volume we introduced the concept of "closed order tactical acts"[9].
By emphasizing the clarity of "tactical acts", out aim was to improve their measurability.
We could reply with a simple "yes" or "no" to the questions of effectiveness.
2.2. Closed order tactical acts
"During "closed-order tactical acts" the order and nature of (relevant) events do not change much in the environment surrounding the acts - there are no surprises. Once certain signs of the environment have been recognized,
the sequence of movements previously rehearsed can be easily performed and the knowledge planned to be used can be mobilized, thereby reducing the potential for error."
Nevertheless, the need for predictability is a priority for modern man. Almost all of the concepts mentioned in this paper could be explained in the context of general sociality, however, without this context they might seem non-real. Thus, our goal is not to describe the Air Force training systems.
On the contrary, the aim is to attempt to reconstruct human knowledge, to show how it is created, how it is organized. Moreover, the goal was also the general analysis of knowledge patterns and to argue for their transdisciplinary character.
Furthermore, the success and failure of the tactic affects many social subsystems, triggering out a series of interactions. Success and failure provide different scenarios in each social subsystem in their essential elements. Hence, the impact of tactics goes beyond their military significance.
Choosing the right tactics is the result of collective collaboration, in military decision making, data collection and interpretation, and in many cases (S)AI points the way to "information hurricane "[10].
Moreover, the success of tactics could be measured via several variables, some of them military, others socio-political. The finalization of tactical decisions – which are in accordance with the accepted strategy - could provoke debate among decision-makers. Thus, the main reason for this is to question the validity of the raw data, with disagreements which might escalate even over the selection and sufficiency of the data. This course of thoughts, however, might be unclear for the reader.
Information and its accurate interpretation is a strategic expectation which follows the interests and normative expectations of a sub-system of society. The operationalization of actions should be in accordance with them.
2.3. Operational constructivism
Principally, this volume has attempted to place these practices in a causal context according to Niklas Luhmann's holistic approach. We could not imagine the development of the right tactics without valid information, however, social and political circumstances as well as the potential of its own infrastructure should be taken into account, not to mention the mass and distribution of its human resources and its knowledge.
Furthermore, the precisely so-called "closed order tactical acts"
demonstrated the intention that operational optimization
(cost-effectiveness) is the starting point: no doubts that this paper has
drawn the attention to the value of general knowledge patterns. Also, this
essay has attempted to show the (non-tiny) differences between the natural
(NI) and - what we call - the neural (S)AI approach.
If it has only achieved the goal to erect doubts: for the creators of
artificial neural networks, the natural, human nervous system could not be
more than a motive, since its complexity could not yet be copied.
For all this, it is beyond any doubts that artificial intelligence is a breakthrough in trainings and anticipating complex sequences of events that involve many social subsystems. However, its reliability remains still in doubt:
"Anticipating possibilities has great potential; reliable prediction does not. "[11]
Bibliography
Attila, Varga Krisztian, Dombrádi: The role of AI in decision making for military operations, 2024: Pages: 21
- Catalog Number: V1495581, ISBN (eBook) 9783389053782, ISBN (Book) 9783389053799, Grin Verlag.
David E. Rumelhart, Richard Durbin, Richard Golden, Yves Chauvin: Backpropagation: The Basic Theory 1995, Imprint Psychology Press, eBook ISBN 9780203763247.
CREID JOHNSON:Can you elaborate on the key foundational items that are critical for rebuilding future fighter learning and training? n.: THE CORE PILLARS OF REBUILDING FUTURE PILOT TRAINING Later see conference in 2025. https:// www.defenceiq.com/events-military-flight-training-london
Krisztian Dombradi :The Communication Theory of Combined Arms Warfare Universität of Szeged (HUNGARIAN ACADEMY OF SCIENCES (MTA-SZTE)) 2024, Pages: 13. Catalog Number: V1488108 ISBN (eBook): 9783389045190 Grin Verlag
Vikashraj Luhaniwal:Forward propagation in neural networks — Simplified math and code version, by, Towards Data Science, May 7, 2019.
Artificial intelligence for wargaming and modeling by Paul K Davis and Paul Bracken Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 1–16 2022 DOI: 10.1177/15485129211073126 journals.sagepub.com/home/dm
[1] CREID JOHNSON: Can you elaborate on the key foundational items that are critical for rebuilding future fighter learning and training? n.: THE CORE PILLARS OF REBUILDING FUTURE PILOT TRAINING Later see conference in 2025. https:// www.defenceiq.com/events-military-flight-training-london
[2] Attila, Varga Krisztian, Dombrádi : The role of AI in decision making for military operations,2024: Pages: 21 - Catalog Number: V1495581, ISBN (eBook) 9783389053782, ISBN (Book) 9783389053799, Grin Verlag
[3] Artificial intelligence (AI) Machine learning (ML), Augmented and virtual reality (AR/VR), Robotics and automation, Quantum computing, Nanotechnology, Advanced materials such as graphene and carbon nanotubes 5G wireless networks, Energy storage technologies such as lithium-ion batteries.
[4] Attila, Varga Krisztian, Dombrádi : The role of AI in decision making for military operations, 2024: Pages: 21
- Catalog Number: V1495581, ISBN (eBook) 9783389053782, ISBN (Book) 9783389053799, Grin Verlag
[5] Forward propagation in neural networks — Simplified math and code version, by: Vikashraj Luhaniwal, Towards Data Science, May 7, 2019.
[6] Backpropagation: The Basic Theory, David E. Rumelhart, Richard Durbin, Richard Golden, Yves Chauvin 1995, Imprint Psychology Press, eBook ISBN 9780203763247.
[7] „’We have also benefited greatly from wargaming, in part through long associations with Herman Kahn (P.B.), RAND, and Andrew Marshall, but the quality of wargames ranges from being a waste of time or even counterproductive to being a rich source of insights. Although such insights cannot be trusted without follow-up study, that is true also of insights from modeling.” in: Artificial intelligence for wargaming and modeling by Paul K Davis and Paul Bracken
[8] Artificial intelligence for wargaming and modeling by Paul K Davis and Paul Bracken
Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 1–16
[9] The Communication Theory of Combined Arms Warfare
Universität of Szeged (HUNGARIAN ACADEMY OF SCIENCES (MTA-SZTE)) Krisztian Dombradi 2024, Pages: 13. Catalog Number: V1488108 ISBN (eBook): 9783389045190 Grin Verlag
[10] ATTILA, VARGA – KRISZTIAN, DOMBRADI: THE ROLE OF "AI" IN DECISION MAKING, Grin Verlag, 2024. https:// www.grin.com/document/1495581
[11] artificial intelligence for wargaming and modeling by Paul K Davis and Paul Bracken Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 1–16 2022 DOI: 10.1177/15485129211073126 journals.sagepub.com/home/dm
Frequently asked questions
What is the main topic of the document?
The document explores the relationship between artificial intelligence (AI) and human (natural) intelligence, specifically in the context of air warfare and tactical decision-making. It examines the role of AI training infrastructure based on artificial neural networks.
What are the key themes discussed in the document?
The key themes include artificial intelligence (AI), transdisciplinarity, natural vs. artificial neural networks, the importance of simulation in learning, and the reconstruction of knowledge and tactics.
What is the document's abstract about?
The abstract discusses the study of human neural networks as a motivation for developing artificial intelligence. It also touches upon the role of AI training infrastructure in air warfare, highlighting the perception of space and time changes in modern warfare.
What are some of the keywords associated with this document?
The keywords are Artificial intelligence and the human nervous system, logarithmic machine learning (ML), augmented reality (AR).
What is the significance of S(AI) in the context of this document?
S(AI) refers to pattern-based artificial intelligence and plays a crucial role in comparing data without human intervention. Its recognition capability is closely linked to its capacity to process digitized raw data.
How does the document compare natural and artificial networks?
The document contrasts the human nervous system, which consists of neurons communicating through electrical and electrochemical signals, with artificial networks where algorithms determine action potentials. It emphasizes the human nervous system's ability to prioritize natural stimuli.
What is the role of simulation in pilot training?
Simulators are essential in pilot training, providing a safe environment to practice tactical operations and model emergency situations. They also offer cost-effective operation, measurability of maneuvers, and the exercise of critical knowledge related to energy management and geometric vision.
What is the meaning of "closed order tactical acts" within the document?
"Closed order tactical acts" refer to situations where the order and nature of events surrounding the acts remain relatively unchanged, allowing for rehearsed sequences of movements and planned knowledge mobilization to reduce potential for error.
What is the transdisciplinary approach mentioned in the document?
The transdisciplinary approach refers to relying on a transdisciplinary methodology in the design of artificial neuron networks when modelling natural neuron networks. It emphasizes the interdisciplinary nature of reconstructing knowledge and tactics in modern society.
What is the document trying to prove?
The paper aims to show the (non-tiny) differences between the natural (NI) and - what we call - the neural (S)AI approach, and to prove that the natural, human nervous system could not be more than a motive for the creators of artificial neural networks.
- Quote paper
- Krisztián Dombrádi (Author), 2024, Transdisciplionarity Practical Comparison of Natural and Artificial Neural Networks. Thoughts on the Air Force Renewing Training System, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/1498397
