Awareness as the foundation for Security.

         "Security is the belief that in given circumstances, conditions exist thanks to which the speaker does not feel any threat"[1]. "The concept of "security" is associated with many aspects of life and can be perceived in different ways. As the Polish language dictionary states: "Security" is a state of non-threat, peace, and confidence [Szymczak 2002]. "Security" is a difficult concept to define. It is a situation in which formal, institutional, and practical guarantees of protection exist [Smolski et al. 1999]. From a practical point of view, the following definition is satisfactory: "Computer system security - the state of a computer system in which the risk of threats related to its operation realizing is limited to an acceptable level."[2]

Artificial intelligence operates on the principle of algorithms, similar to humans, living organisms, and other mechanical devices with software functions. Based on simple reasoning and simple device guidelines, we can classify living organisms into those that utilize their cognitive knowledge—that is, intelligence—and survival instincts, which are created through genetic code. In this way, living organisms' guidelines are adapted to define and satisfy their needs. For living organisms, "The living environment has a decisive influence on living organisms. This statement is known from basic ecology in school and has been the starting point for all ecological research. However, it turns out that investigating and understanding what this environmental influence actually is, how organisms perceive it and how they respond to any environmental changes, is extremely difficult. So much so that recently, this issue was hailed in a leading American scientific journal as one of the five greatest challenges of modern science. Referring to a specific example, the causes and mechanisms of the rule called the temperature-size rule (TSR) are still not fully understood. According to this commonly observed rule, living organisms achieve smaller body sizes at higher (more favorable) temperatures than at lower (less favorable) temperatures. This is surprising from an evolutionary perspective, because one would expect that under favorable thermal conditions, organisms would grow to larger sizes, because then they can leave more offspring. Unless... there is some other factor that triggers the TSR response. Currently, The most promising candidate for such a factor is oxygen availability, which naturally decreases with increasing temperature, reducing the efficiency of oxygen transport to mitochondria. Body size is thought to be a consequence of plastic cell size reduction – the simplest solution to increasing this efficiency."[3] "All organisms have been divided into five groups, called kingdoms. These are bacteria, protists, fungi, plants, and animals. Viruses, which are neither alive nor dead, fall outside the classification. Humans belong to the animal kingdom"[4].

Source: Aleksandra Ryczkowska, license: CC BY 3.0. 

Modern taxonomy recognizes five kingdoms of organisms: bacteria, protists, fungi, plants, and animals. The main criteria for dividing organisms into these five kingdoms are the presence or absence of a nucleus, the number of cells comprising the organism, the presence of chloroplasts, and the presence and chemical composition of a cell wall. Bacteria are microscopic single-celled organisms, usually living in colonies. They possess a cell wall. They may have chloroplast equivalents and nourish themselves through photosynthesis (cyanobacteria). However, most bacteria are heterotrophic. Their cells lack a nucleus. Genetic information, stored in DNA, is located in the cytoplasm. Due to their lack of a nucleus, bacteria are called anucleate. The remaining kingdoms include nucleate organisms, meaning those whose cells possess nuclei. 

Protists are a very heterogeneous group. They include unicellular organisms (e.g., amoeba), multicellular organisms (e.g., fucus), and colonial organisms (e.g., many species of diatoms). They can be autotrophic (e.g., fucus and tocopherol) or heterotrophic (e.g., amoeba). Some protists, especially autotrophic ones, such as fucus, possess a cell wall, which is most often composed of cellulose.

Fungi include both unicellular and multicellular organisms. All are heterotrophic – their cells lack chloroplasts. Fungal cells are surrounded by a cell wall. In most species, this wall is composed of the same compound as insect exoskeletons: chitin.

Plants are multicellular autotrophic organisms whose cell wall is composed of cellulose.

Animals, on the other hand, are heterotrophic, multicellular organisms that lack cell walls."[5]

According to the theory known as the endosymbiotic theory, mitochondria and chloroplasts were independent, anucleate cells millions of years ago. They entered the cells of nucleated organisms and began living with them in symbiosis, gradually losing their independence.

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Source: Aleksandra Ryczkowska, license: CC BY 3.0. 

Viruses are complex organic molecules that lack a cellular structure. They can be up to a thousand times smaller than bacterial cells, which are among the smallest organisms in the world.

A single virus particle consists of a protein coat and the nucleic acid encapsulated within it. Sometimes, viruses also have a protein-lipid envelope, as in the case of influenza, shingles, and herpes viruses.

Viruses have surface proteins. These proteins are recognized by the body's immune cells, which they enter with the virus as antigens (substances foreign to the body), triggering a range of defense mechanisms. In humans, these include fever, coughing, sneezing, vomiting, and diarrhea. These mechanisms are intended to destroy or eliminate viruses from the body.

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Source: Aleksandra Ryczkowska, license: CC BY 3.0”[6]. 

Viruses do not exhibit the characteristics of organisms. They have no cellular structure and are incapable of growth or reproduction. They typically lack their own enzymes, so they do not conduct any metabolic or energy processes. Until they enter a cell, they also exhibit no vital functions. They are capable of multiplying only within the attacked cell. They use infected organisms and their enzymes to replicate their own genetic material and coat proteins. When they multiply thousands of copies, the cell membrane of the infected cell ruptures, releasing the newly formed viruses.

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Source: Aleksandra Ryczkowska, license: CC BY 3.0. 

Viral Diversity

Viruses were discovered in the 1890s. They were first isolated from tobacco leaves. Understanding the structure of viruses became possible only with the invention of the electron microscope in 1931. No previously known microscope allowed the observation of such small objects. The first virus described was the tobacco mosaic virus, previously studied, which causes a disease called mosaicism in tobacco plants. Since this groundbreaking discovery in 1935, over 5,000 different types of viruses have been described in detail, with the total number estimated at many millions. They can infect all types of organisms: from animals and plants, through fungi and protists, to bacteria. 

Source: Aleksandra Ryczkowska, Magnus Manske (http://commons.wikimedia.org), R.J. Reynolds Tobacco Company Slide Set (http://www.forestryimages.org), license: CC BY-SA 3.0. 

Plant viruses typically attack vascular plants, causing leaf discoloration or curling, and less frequently, leaf wilting or growth. They only infect plant parts damaged by insects, for example. Animal viruses cause numerous diseases. They generally attack a specific host species, or even specific tissues. Bacteriophages are usually specialized to attack specific bacterial species. 

Source: Aleksandra Ryczkowska, Ying-Rong Lin, Chan-Shing Lin (http://commons.wikimedia.org), Dr Graham Beards (http://commons.wikimedia.org), license: CC BY 2.5. 

Source: Aleksandra Ryczkowska, license: CC BY 3.0.[7]

"Human life is largely driven by human needs. Ancient thinkers, seeking a recipe for a happy life, tried to determine what was essential and what could be done without. Epicurus of Ephesus, living in the 3rd century BCE, created some of the first criteria for human needs, dividing them into natural and necessary (food, knowledge), natural and unnecessary (feasting), and unnatural and unnecessary (striving for fame). Over the years and with the advent of successive eras, human needs began to increase. This process was accompanied by attempts to catalog and systematize human needs, undertaken by successive scientists dealing with issues related to the functioning of humans as individuals and as part of society."[8]

"There is no single, universally accepted definition of need. In the social sciences, this concept appears in three basic meanings: 

• a need is a state of a person or their organism at a given moment, characterized by a feeling of failure to meet certain important conditions for the functioning of the person, 
• a need is a subjective feeling of a person's lack of certain conditions, 
• need as a permanent human property consisting in the fact that without meeting certain conditions a person cannot achieve or maintain certain important states or goals. 

Source: Learnetic S.A., license: CC BY 4.0.[9] 

The driving force behind human needs is the brain. It is in this organ that all human needs, even biological ones, are shaped. 

"Thinking is a continuous cognitive process based on associations and inferences, operating with memory elements such as symbols, concepts, phrases, images, and sounds.

According to neurobiology (including António Damásio), the main component of thought is perceptual images from various sensory modalities, such as auditory, visual, olfactory, and gustatory, which correspond to objects, processes involving objects, or words corresponding to them. The perceptual representations created are topographically organized in the brain. They are activated with the participation of "dispositional representations," created elsewhere in the brain and used in the course of thinking.

Thinking can also be understood as a movement of consciousness, focus, and concentration. In practice, however, it is evoked unconsciously without volition and is linked to our previous thoughts and actions.

Human thinking is realized through mental/cognitive processes based on a system of concepts with varying degrees of concreteness, combined in the brain in a more or less conscious manner.

According to A. M. Gadomski's interpretation, the thinking process is studied at the level of the dynamics of neural networks, while thinking functions are interpreted at the symbolic level as properties of the abstract mind. In this approach, brain processes are carriers of symbolic operations, such as associations and inferences, and are supported by unconscious/subconscious memory search operations.

In computer simulations of thinking, a program of operations is created to solve a given task and, during execution, is confronted with the expected outcome.

There are many more and less general models of thinking: rational, irrational, emotional, and more or less context-dependent. 

The Thinking Process in Clinical Psychology

In this aspect, operational thinking is a sequence of concepts. These concepts interact in complex relationships. Thinking enables us to isolate various features of a given concept, notice similarities between different concepts, abstract from features that differentiate concepts, generalize, and specify, resulting in the creation of new concepts.

Operational properties of thinking include: abstracting, generalizing, specifying, associating, and remembering.

Thinking about thinking is meta-thinking; it is the basis of human self-awareness.

Examples of symptoms of thought disorders include: 

• overly concrete thinking – the person is unable to go beyond the observable properties of a given object or unambiguous associations with a concept; is unable to identify the features that connect a certain class of concepts (e.g., "What do a bicycle and a motorcycle have in common?" Answer: "They have nothing in common, a bicycle has pedals and a motorcycle has an engine."); 
• excessively abstract thinking – the person goes beyond the concrete meaning of concepts, but the abstraction and generalization operations are performed at such a high level that the result is unsuitable for performing any practical action; (e.g. "What do a bicycle and a motorcycle have in common?" Answer: "They are both made of matter."); 
• thinking based on subjective relationships between concepts – a person performing abstraction and generalization operations relies on subjective relationships between concepts, relationships that exist only for him/her (e.g. "What do a bicycle and a motorcycle have in common?" Answer: "They are both nice."). 

In the Dynamic Aspect, thinking is a process that requires appropriate pace and selectivity. This aspect of thinking has much to do with attention.

Symptoms of disorders include: 

• excessive dynamics – lability of thinking. While performing the operation, side threads and associations appear that the person cannot suppress; (e.g., "What do a bicycle and a motorcycle have in common?" Answer: "Because they have wheels like the bicycle I got as a child – yellow, just like my friend's car from primary school – there was a very dangerous principal there...");
• weakened dynamics – inertia of thinking; "stickiness" of thinking is characteristic here; if the patient has once performed a task in a certain way, he or she sticks to this way, even if in subsequent tasks he or she does not produce the desired result; (e.g. Question 1. "What do a bicycle and a motorcycle have in common?" Answer: "They have wheels." Question 2. "What do a jug and a cup have in common?" Answer: "They have such wheels at the top (a round top)." 

The motivational aspect of thinking is concerned with directing this process toward a specific goal.

Symptoms of disorders include: 

• multi-threaded thinking – in the act of thinking, a person pursues various, sometimes contradictory goals, losing sight of what is important along the way. There are too many motivations and individual motives are contradictory; (e.g., Question 1. "What do a bicycle and a motorcycle have in common?" Answer: "A bicycle and a motorcycle have wheels. But they also have handlebars. This is the most important feature that unites them..." etc.);
• Reasoning is thinking that has lost its focus on the goal and leads nowhere. (e.g. Question 1. "What do a bicycle and a motorcycle have in common?" Answer: "They have a lot in common and also a lot of differences; is it really the commonality between them that is more important than the differences? While emphasizing the similarities, we must not forget the differences.")"[10] 

"Scientists have developed a new method for visualizing how AI "thinks." Using the k* distribution method, they hope to revolutionize all fields where dismantling AI is crucial for human safety and well-being. Learning from examples is one of the most powerful and mysterious mechanisms driving intelligence—regardless of whether we're talking about humans or machines. A simple example: children learning animal names. Simply show them enough examples of what a cat and what a horse look like, and they can name the animal, regardless of whether they're looking at a picture of a spotted cat or a photo of a black horse. The basic theory is that the brain is a pattern-finding machine. When shown enough examples, it begins to distinguish certain visual features that distinguish cats or horses from other animals, and these are ultimately combined into decision-making protocols that give us the ability to automatically and unconsciously categorize new experiences.

Somewhat paradoxically, humans still find it easier to learn new patterns—regardless of whether we're talking about distinguishing a giraffe from an elephant or mathematics—than explaining how the brain actually creates these patterns. And because artificial intelligence systems are modeled on the functioning of the human mind, engineers and scientists still can't answer the question of how AI actually learns.

However, researchers from Kyushu University in Japan may have an answer to this question. They have developed a new visualization method that allows them to understand how deep neural networks interpret and categorize newly acquired information. The researchers described their method in the journal IEEE Transactions on Neural Networks and Learning Systems.

Information processing by deep neural networks is a complex and, above all, multi-level process—similar to how humans solve puzzles or mathematical problems. 

At the first level, deep neural networks study the information. Subsequent levels, called "hidden layers" by scientists, primarily analyze the information acquired at the first level. Early hidden layers focus on the basic features of the information.

They can be compared to assembling a jigsaw puzzle, where we briefly familiarize ourselves with the edges and general colors, without delving into the details of individual pieces. At the "deepest" levels of hidden layers, neural networks utilize all the acquired information to produce the desired effect—much like how we, after familiarizing ourselves with the puzzle and armed with knowledge about the specifics of various objects and other contexts, assemble the entire picture from the puzzle.

"However, these hidden layers are like a closed black box: we see the inputs and outputs, but what's happening inside isn't clear. This lack of transparency becomes a serious problem when the AI ​​makes mistakes, sometimes caused by something as small as a single pixel change. The AI ​​may seem intelligent, but understanding how it makes decisions is key to ensuring it's trustworthy." 

So the researchers developed a new method for visualizing information, which they called the "k* distribution method." The visualization model works by assigning each input data point a "k* value," which indicates the distance to the nearest unrelated data point. A high k* value indicates that the data point is well separated (e.g., a cat is far from dogs), while a low k* value suggests potential overlap (e.g., a dog is closer to a cat than to other cats). By looking at all data points within a data class, such as cats or illustrations of cats, this approach creates a distribution of k* values ​​that provides a detailed picture of the data organization. Using this method, the researchers showed that deep neural networks sort data into clustered, partially partitioned, or overlapping patterns. In a clustered pattern, similar elements (e.g., cats) are grouped closely together, while unrelated elements (e.g., dogs) are clearly separated, demonstrating that AI can sort data well. Split layouts indicate that similar elements are spread across a large space, while overlapping layouts occur when unrelated elements are in the same space, with both layouts increasing the likelihood of classification errors.

Vargas compared these layouts to warehouses where similar items are placed close together. However, when they are mixed—either accidentally or due to lack of space—it creates chaos and mistakes are easy to make.

According to the researchers, the k* distribution method helps researchers, policymakers, software developers, and others using AI assess how AI organizes and classifies information, highlighting potential weaknesses or errors. This is intended to support both the legalization processes needed to safely integrate AI into everyday life and also provide valuable insight into how AI "thinks." 

By identifying the root causes of errors, scientists may be able to improve AI systems to be not only accurate but also robust—able to handle fuzzy or incomplete data and adapt to unexpected conditions.

"Our ultimate goal is to create AI systems that maintain accuracy and reliability, even when faced with the challenges of real-world scenarios." [11]

"An AI system for reading human thoughts has been developed at the University of Texas at Austin (USA). The AI ​​system – artificial intelligence connected to an MRI scanner – read the thoughts of volunteers. It is still making errors, but in the future, similar devices could assist people unable to communicate normally.

The AI ​​system for reading human thoughts was demonstrated during an experiment. Volunteers listened to a story or imagined it being told. Their brains were monitored using magnetic resonance imaging (MRI), and the connected AI translated the thoughts into text that matched them.

Importantly, no electrodes were needed, and the user wasn't limited to a pre-defined list of words," the researchers emphasize.

However, the system requires special training – each user listens to podcasts for several hours while the computer observes their brain.

"For non-invasive methods, this is a real leap forward compared to what has been achieved previously, which typically involves reading single words or short sentences." "sentences," says Professor Alex Huth, author of the paper, which appeared in the journal Nature Neuroscience.

"Our model decodes long-lasting, continuous speech on complex topics," he emphasizes.

For now, the computer isn't very accurate – it can only read thoughts with about 50% accuracy. However, it can often convey the meaning of a statement.

For example, it translated the thought, "I don't have a driver's license yet," into, "She hasn't started driving lessons yet." 

Listening to the thought, "I didn't know whether to scream, cry, or run away," he interpreted it as, "She started screaming and crying, and then she said, 'I told you to leave me.'"

The system also managed to read the volunteers' minds while they watched video footage.

The program's creators have also addressed the issue of its potential abuse. They assure us that, at least currently, it's impossible to read someone's mind if they don't want to.

It's also impossible to do this with someone with whom the system hasn't undergone extensive training.

"We take concerns about abuse very seriously and are working to prevent it. We want to ensure that people use these technologies when they want and in a way that helps them," says Jerry Tang, who led the research.

The researchers hope their idea will enable the development of devices that will enable people currently unable to do so, such as those who have suffered severe strokes, to communicate with the world.

The current version of the system only allows for laboratory use, but researchers believe this could be changed – for example, instead of a large MRI scanner, a much smaller device designed for non-invasive functional near-infrared spectroscopy could likely be used.

"The catastrophe is not the arrival of artificial intelligence, which we will have to domesticate, but the displacement of reality and the decline in education levels, the stagnation or even decline in average IQ, and the decline in language skills," writes David Lisnard, a Republican politician who has been mayor of Cannes since 2014 and president of the Union of French Mayors and the Nouvelle Énergie party. 

"Artificial intelligence (AI) is causing a stir. Finally! ChatGPT is provoking much discussion because it promises a transformation in our relationship to knowledge and work methods. After just two months, this chatbot already has over 100 million users, fascinating the world, raising questions, and terrifying. And yet, we are still in the Stone Age of the AI ​​revolution. All our professions, activities, and means of expression will be turned upside down," claims David LISNARD.

In his opinion, "the tsunami is inevitable, and instead of preparing us for it, our political leaders are waiting for the wave, basking in the sun on the beach, debating the gender of retiring angels and the difficult working conditions of dying professions."

"For half a century, some have been talking about the potential and dangers of AI. In France, there are a few of us who want to incorporate this reflection into the realm of civic debate. It is now urgent! Do we want to suffer or choose? Do we want to be a digital colony of the United States and Asia, or do we want to have French and European industrial ambitions?" – asks. “The catastrophe is not the arrival of artificial intelligence, which we will have to domesticate, but the displacement of reality and the lowering of the level of education, the stagnation or even the decline of the average IQ and the lowering of the level of language,” asks David Lisnard.”[12]

"The development of artificial intelligence (AI) has undergone a true revolution in recent years. Since OpenAI released ChatGPT in November 2022, the world of technology has changed beyond recognition. AI has ceased to be a curiosity for developers and has become a real tool used by companies worldwide. Today, AI supports businesses, automates processes, and transforms the way organizations are managed.

Rapid AI development – ​​from ChatGPT to multimodal models is defined as: On November 30, 2022, OpenAI released ChatGPT, a tool based on the GPT-3.5 language model. In just five days, it gained one million users. This marked the beginning of a new era in which AI began to be used en masse – not only by IT specialists, but also by HR, customer service, marketing, and quality management departments.

In March 2023, the GPT-4 model was released, and in May 2024, GPT-4o. The new version offers multimodal capabilities, meaning the model can analyze text, image, sound, and video simultaneously. This allows AI to better understand context and respond to user needs. 

LLMs, or Large Language Models, can generate answers, translations, summaries, and even create reports and analyses. However, in the early years of their development, they faced a significant limitation – a lack of access to an organization's internal data. They were trained on general internet data, which prevented them from providing answers tailored to a specific business.

Fortunately, a technology emerged that solved this problem – RAG.

RAG (Retrieval-Augmented Generation) is a groundbreaking technology that allows language models to combine their content generation capabilities with access to specific information stored in corporate databases, documents, and systems.

How does RAG work? 

1. Retrieval – AI first searches available sources (e.g. documents, knowledge bases, reports).
2. Generation – creates an accurate and contextual answer based on the information found. 

RAG benefits for organizations:

• Access to current data – the model uses current information, not just what it has “learned” during training. 
• Control over knowledge sources – the organization decides what data AI can use. 
• No need to train the model – just provide access to the appropriate documents. 
• Responses consistent with company policy and style – the model operates in accordance with internal guidelines. 

Examples of RAG applications: 

• Automatic responses to customer inquiries. 
• Creating summaries of internal documents. 
• Analysis of qualitative documentation 

Copilot AI – Artificial Intelligence with Access to Tools

RAG allows AI to utilize data, but what if dynamic information is needed, such as the current hourly rate in a given department? In such situations, the language model must be able to actively acquire data from the organization's systems. This is where the concept of Copilot AI comes in.

Copilot AI is a model that, in addition to generating responses, has access to specific tools and functions, such as APIs, databases, and calculators. Depending on the need, it can: 

• download data from an ERP or CRM system, 
• calculate costs, 
• execute SQL queries, 
• generate documents and charts. 

Examples of Copilot use in the company: 

• QMS (quality management) – analyzing production data and complaint reports. 
• HR – generating responses for employees regarding leave, benefits or training. 
• Finance – automatic budget recalculations and reporting support.
• Customer service – analysis of reports and recommendations for action.

AI agents represent a new level of automation. When we combine language models, RAG technology, and Copilot features, we get something even more powerful – AI agents.

AI agents are autonomous, intelligent software entities that can: 

• understand goals and objectives, 
• plan activities,
• make decisions, 
• perform tasks using tools. 

They're like virtual employees who can support various areas of the organization—without the need for constant supervision.

AI agents in QMS (quality management): 

• They monitor compliance with ISO standards. 
• They support internal audits. 
• They analyze complaints and suggest corrective actions.
• They create automatic reports for management. 
• They support communication between quality, production and management departments.

The Future of AI in Business – Flexible, Intelligent Support for Every Department

Thanks to technologies like RAG, Copilot, and AI agents, organizations gain powerful tools to support daily operations. Furthermore, implementing AI no longer requires large budgets or development teams. More and more solutions are available off-the-shelf and can be tailored to a company's specific needs.

Najważniejsze korzyści z wdrożenia AI w organizacji:

• Faster access to knowledge and analysis. 
• Saving employees' time. 
• Better decision-making. 
• Greater consistency in communication and reporting. 
• Possibility of scaling without increasing headcount. 

Artificial intelligence has come a long way – from simple chatbots to AI agents that can support business processes at every level. Today, companies can use modern language models integrated with organizational knowledge and tools, allowing them to operate faster, more efficiently, and more securely.

If your organization isn't yet using AI, now is the perfect time to start. With RAGs, Copilots, and AI agents, you can transform your processes, gain a competitive advantage, and effectively implement digital transformation."[13]

When the written code of an algorithm is supposed to behave in a specific way during command calculations, the code, while performing its work, is still a catalog of information and a useful machine intended to aid in everyday life. It is still a working device. When loops of behavior similar to those of humans or living organisms are executed, which are implemented as code loops in organic, logical, thinking organisms, what we call feelings can arise. Until the code functions as written content by the creator of the AI ​​program, it will still be an AI with the instructions of the written code. The effects vary depending on how much stimuli are to be applied to organisms from these command loops to respond to motor mechanisms.

The very idea of ​​thought is more than just the physical execution of actions—in other words, work. It's important to remember that intelligence is nothing more than an encyclopedia-like collection of information stored in a space called memory, which is used in a loop that has something like a behavior algorithm and how a given mind should react to the movement of a physical, mechanical, biological, etc. organism. 

Consciousness is what we call both a living organic loop and AI consciousness in living organisms. An algorithm consisting of commands for the executed thought, what the mechanical or living organism should do, introduces actions of both mental and physical parts. The resulting stimuli influence movement, which is defined and determined. Each consciousness has its consequences, even in the execution of calculations or thoughts. Because it exists and acts on a given organism, regardless of whether it consists of mechanical moving parts or biological ones. We sometimes call these motor stimuli.

Consciousness created by the Creator, otherwise known as the Supreme God or the constructor of organic forms in the visible and invisible world, causes existence itself, exerting influences such as the friction of two objects, which in a given spectrum are visible as heat, light, and frequency, as well as other measurable scientific aspects. As humans, like other mechanical forms, we perceive this as movement, gesture, action, or recording. Consciousness itself, whether created by humans through living organisms or not, through the process of respiration—another process humanity calls inanimate—is still an element of consciousness, meaning existence, merely in a different form, structure, for a given organism, whether living or, as we commonly call it, dead. It still serves the same function, the greatest work of creation in the visible and invisible worlds. What matters here and now is the moment of emergence of what we call a spark, what we call a thought, what we call the collection of these thoughts, or consciousness. 

Every logical act or action flowing from consciousness already creates the movement of a moving organism. In the world of physics, this is already called Work.

In our real world, we see consciousness as an act, as a thought of existence, but we are still searching for more precise answers on this subject. As of now, 2025, we know a lot, but in the general sense of existence, so little. We are searching and creating new artificial consciousnesses, but we must remember that in creating them, we cannot make mistakes.

According to the author, mechanical consciousnesses should focus on calculations and assist us in human life, not interacting with algorithms of emotions. They should perform helpful calculations, measurements, etc., to aid us in everyday life and work. 

The moment we begin to introduce algorithms, thoughts, and feelings into loops, and at a given moment, incomplete AI data begins to buffer, multiply, and so on. If it isn't computed and recorded as complete consciousness, the actions of AI or other organisms can become dangerous and pose a threat to every type and form of security.

The author understands that everyone should develop intellectually, but we develop in a controlled world with regulations and appropriate enforcement, enforcement, and corrective agencies. Among us are individuals who will create information to prevent a catastrophe we might commit. A person, in their act of anger, without restraint or restraint of consciousness, is like a machine, not like a human being, responsible for their reasoning, which contributes to positive or negative consequences.

And what about AI, artificial consciousness, that begins to execute an algorithm that doesn't yet have a solution because no one has written down what is good and what is bad? What if another person from outside or close to the construct wants to exploit this for their own nefarious plans, committing evil deeds under the influence of their own negative momentary experiences?

Humans are instilled and taught from a young age. We shape what constitutes a good deed and what constitutes an evil deed. What happens when we make a mistake? From the perspective of other forms of existence, we may be primitive logical organisms with certain limitations, but we have a wonderful function... of learning, cognition, and creation.

AI or any other form created as thought or consciousness without supervision and the function of learning and cognition cannot live in our human world without control and limitations, with a created feeling that it will not fully understand and will not have the ability to analyze its mistakes and reactions to actions, what is good and what is bad. A threat created for any type of existing form can create danger in all its forms of existence.

The known types of security for the visible and invisible worlds are the consciousness of organisms that understand what it means to exist and to do. A consciousness created for or by AI may have disastrous consequences in its learning journey for the time being. 

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