Scientific Foundation for AGI
(Artificial General Intelligence)
(Artificial General Intelligence)
It is impossible to address any complex unsolved technological problem without acquiring and using necessary theoretical foundations, and objective insights. It is impossible to invent real-CBSE that can address the infamous software crisis without creating and using Componentology. Likewise, it is impossible to address AGI without creating and using Neuronology.
Surely, humanity both needs & aspires to understand how the brain works
Neuronology Is Inevitable: The Power of Insatiable Human CuriosityIf understanding how nature created intelligence is not worth pursuing, then what is? It is difficult to imagine that any sincere scientist would argue against the need to understand how the brain functions.
After all, today no scientific endeavour is more fundamental—or more consequential—than uncovering how nature has already achieved intelligence.
Asking why Neuronology is needed is like asking why Germ Theory or Quantum Mechanics were needed when they first emerged. Each arose not out of convenience, but out of necessity—at a moment when prevailing paradigms could no longer explain the most urgent mysteries of their time. Before Germ Theory, diseases were misunderstood and misattributed. Before Quantum Mechanics, classical physics failed to account for the behavior of matter at the atomic scale. These breakthroughs did more than expand knowledge—they revolutionized medicine and transformed physics.
Today, we stand at a similar crossroads. The nature of intelligence, cognition, and consciousness—whether biological or artificial—remains one of the greatest unsolved frontiers in science. Our current understanding is fragmented, incomplete, and often speculative. Neuronology is that necessary leap: a bold new scientific discipline dedicated to uncovering the fundamental laws and mechanisms that govern the mind.
It is not merely another academic pursuit—it is the next scientific revolution. A revolution that will shape the future of both natural and artificial intelligence, fundamentally redefining the trajectory of our technological civilization by uncovering the underlying architecture of thought, consciousness, and cognition. While resistance to new evidence and scientific truths that challenge deeply entrenched dogma is expected, it cannot justify inertia—especially when the shortcomings of the prevailing paradigm are plainly evident.
Scientific disciplines can be classified into two categories: (i) primary or basic sciences, such as physics, biology, and chemistry and many of their subfields; and (ii) secondary or applied sciences, such as medical sciences and engineering disciplines. The secondary sciences rely on the theoretical foundations created by the primary sciences. Software engineering is a secondary science that requires theoretical foundations such as Neuronology & Componentology. Computer science is responsible for providing the necessary theoretical foundation for software, so it has the responsibility to create subfields of Neuronalogy & Componentology.
An indisputably proven age-old formula: The first step is to conduct comprehensive scientific study to create a sound theoretical foundation comprising understanding, descriptions, and theories or scientific insights. Once a sound theoretical foundation has been created, the second step is to rely on it to conduct engineering research. The prerequisite for software engineering research on "Component-Based Software" is the creation of Componentology, which we have created. Obviously computer science has skipped the prerequisite first step.
Two indisputable principles or proven cardinal rules: (i) Scientific knowledge (i.e., comprising descriptions, understanding, & theories) of any physical thing or reality (e.g. of components or neurons) is invalid and flawed if it contradicts any valid evidence or observations made about the physical thing or reality. (ii) Engineering research to find solutions for any technological problem must not rely on any flawed or invalid scientific or theoretical knowledge (that contradicts evidence).
Pioneer-Soft is planning to start an exciting new scientific endeavor to create a sound theoretical foundation for Artificial General Intelligence (AGI). We are looking for partners and collaborators who are interested in this not-for profit challenging scientific exploration: Creation of a new scientific field Neuronology for AGI, as it is impossible to realize the full potential of AGI without gaining valid scientific insights and knowledge of neuronology
It is indisputably proven that scientific knowledge, comprising objective understanding, descriptions, theories, and insights, must be the foundation for engineering research rather than pseudoscientific nonsense created by relying on pre-paradigmatic misconceptions and myths (e.g. about so called neurons or components), which were created when software was in its infancy.
If someone conducted a comprehensive scientific study, such as Componentology or Neuronology, and accumulated comprehensive scientific knowledge, comprising understanding, descriptions, and theories based on objective observations and scientific evidence, would you accept and switch to relying on the scientific descriptions and understandings to conduct your engineering research, or would you continue to defend and rely on the prevalent pseudoscience filled with misconceptions and myths that contradict scientific evidence and observations?
Unfortunately, computer scientists are having a very difficult time understanding or accepting the elementary and age-old idea that research in any applied science, such as engineering or medical sciences, should be based on scientific or theoretical foundations created through scientific research. As scientists have not yet created the scientific or theoretical foundations (e.g., Componentology or Neuronology), researchers of software engineering have been relying on pre-paradigmatic misconceptions and myths.
Scientific research activity to accumulate theoretical knowledge and understanding is distinct from engineering research activity to create useful things or solutions. Engineering research must be based on the theoretical knowledge and understanding created by scientific research. It is essential to conduct each activity separately, by understanding the differences between the two kinds of research activities. Unfortunately, computer scientists are having a very difficult time comprehending the distinction between the two.
Engineering research is almost always conducted with a deep-rooted agenda, such as addressing a problem and is often for profit. In contrast, the objective of scientific research is not usually for profit but is for the pursuit of truth to acquire true knowledge, which must be conducted objectively without any agenda, influence, or preconceived notions. It can be difficult to remain objective when there is an underlying agenda, and it is even more complicated if there are widespread misconceptions and deep-rooted dogmatic beliefs. Therefore, we decided to separate engineering research from scientific research, such as Componentology or Neuronology, in order to gain a true understanding of reality objectively.