By David Stephen —
GPT-5 is now a participant in the progress of this world. This reality is a perturbation to the status quo of human society. GPT-5, like other AI chatbots, is making it evident that intelligence is what matters the most, even though society subsumes other concerns outside of it. What it means that any existence is significant is intelligence. What it takes to be the fittest — among others — is largely by intelligence, even when other factors are at play. ChatGPT has carved a new era in the history of existence, and this era, intelligence, would demand more of existence from everyone than the era of capitalism — or whatever else preceded it.
But what is intelligence? There are several vague definitions across disciplines, with mentions of creativity and so on. However, what does it mean that there is an objective, and then a barrier, but intelligence is used to attain the objective, keeping out risk? A standard, here, to define intelligence is the use of memory for expected, desired and advantageous outcomes. Simply, intelligence uses what is in memory, for goals that are desired, or expected of an individual in a group, or goals that are advantageous to the individual, or the group at a present time or subsequently.
This conceptual definition of intelligence is universal across organisms albeit the mechanism of intelligence varies among organisms. Humans dominated the world, so humans [by evidence] have the highest intelligence among species. Human intelligence is particularly aided by language in ways that information can be retained, passed and [to] manage instructions, without having to begin from zero, all the time, for most people. Human intelligence is [by evidence] based in the human brain. What it means to be intelligent, contrasted from other mind processes can be explored through conceptual brain science.
The purpose will be to look at the components of the brain responsible for intelligence and how they mechanize desired, expected and advantageous outcomes — using memory. This can also be important to seek how to measure intelligence, on average, boost it for learning and problem-solving as well as explore areas of strength that human intelligence may cover, as AI ploughs on.
Theoretical Neuroscience of Components and Relays of Human Intelligence
In empirical neuroscience, all brain functions [for human life and experiences] are established to be mechanistically correlated with neurons [in clusters] — and their electrical and chemical signals. Simply, functions of the brain, for coordination of internal and external sensory inputs, are operated by neurons, with their signals. Therefore, it can be postulated that electrical and chemical signals are directly responsible for construction and transportation of functions. Neurons are excluded because they are cells. They are not as mobile as signals, transporting over large distances and they are not as flexible, with the ability to hold respective architectures or representations for several types of distinct functions.
Therefore, because neurons are in clusters, it is assumed that electrical and chemical signals are in sets or loops, in those clusters, with each set holding distinct functional types. Functions can be said to have four major divisions: memory, emotions, feelings and regulations of internal senses. These divisions have several subdivisions. Functions are said to be obtained by the interactions of electrical and chemical signals in sets. Whenever they interact, electrical signals and chemical signals have states at the time of the interactions, those states become the attributes that grade the extent to which they interact.
Simply, interactions produce functions, but the extents of those interactions are determined by the states of the signals at the time of the interactions. Some of the attributes include prioritization of a set, which means the set with the most intensity of electrical signals or the set with the most volume of chemical signals or the set with the most volume — of a major activating chemical signal — glutamate. Prioritization explains the label attention. Another attribute is the split of electrical signals, in a set with some going before, to interact earlier than others, for quicker interpretation, before others follow. If the interpretation matches the input, then the incoming one goes in the same direction and does nothing, if not, it goes in another direction, correcting the error. Splits explain the concept of predictive coding, processing and prediction error. This concept of splits is based on the established neuroscience of saltatory conduction, where electrical signals go faster in myelinated axons. It is theorized that it enables some to go ahead of others as well.
Other attributes include sequences, which could be old and new, thick and thin sets for memory, space of constant diameter for free will, intent or control, side-to-side volume variation for subjectivity and so forth. There is a close relationship between interactions and attributes of signals. Although interactions occur to obtain the configuration for the function, attributes are also ways that interactions get used. Simply, while interactions produce functions, attributes ensure that they are used in the right ways for purposes.
For example, a spelling of something is a configuration, but it is possible that the spelling is not in one location because of thick sets, so an attribute like sequences or splits is a transport that picks, to use what it consists, to get the right thing. Intelligence, as the use of memory, is based more with attributes than with interactions, conceptually, even though they are interdependent.
How Does Human Intelligence Work?
With the postulation that electrical and chemical signals are configurators of human intelligence — where their interaction produces memory that is used via relaying attributes — it is possible to design what exactly human intelligence is and how it works. There are two types of human intelligence based on this postulate, operational intelligence and improvement intelligence. Thick sets make it tougher to remember faces since most faces need to have thin set variants.
Operational intelligence is a result of basic or general status of interactions and attributes, where there is nothing outside regular. This means that processes, once trained or learned are possible to get done, since signals have the configurations, and attributes can access, to use them. Simply, most processes are operational intelligence, such that while there are slight differences from other non-intelligence processes, operational intelligence is basic. Splits, sequences, thick sets, thin sets, intensity, volume, side-to-side variation, and much more. Operational intelligence include most aspects of nurture, passing exams, learning new skills, building familiarity and so forth
Thick sets are the reasons that human memory isn’t as accurate as digital. It is what also makes AI far knowledgeable, than any human. Thick sets collect whatever is common between two or more thin sets, among any function. It is more prominent for memory, emotions and feelings. For memory, chair is a thick set, as well as table and others. So, what it is, with all the properties are collected into a set and anything unique about one is left in a thin set. Now, whenever there would be an interpretation, the thick set is used, not the thin set. From birth, thin sets collect into thick sets, becoming a reason that older memories are rare. Thick sets ensure optimization for attributes towards faster operational intelligence and probable improvement intelligence, not necessarily for rote.
Operational intelligence can be mild and can also be complex. Several professional roles use operational intelligence, but some of the relays that ensure expertise on those may border on improvement intelligence. Operational intelligence can be used to seek improvement intelligence with the use of the attribute, new sequences, where instead of using familiar or old paths, new paths are pushed to see if outcomes would be different.
Improvement intelligence also has mild and extensive. It ranges problem-solving, creativity, innovation, advancement and so forth. Improvement intelligence is almost a natural attribute of new sequences, where instead of relays going through old or defined sequences, they use new sequences, so what should be successive is not, but something else, resulting in another insight.
Also, splits splatter in different directions of distributions, where some non-concerned sets are included in interactions, sometimes exploring the relevance to ensure that usages are spread, instead of limited or narrow follows of operational intelligence. There is also the addition of thick sets, where they collect much more similarities even outside of their scope. For example, a thick set of chair could collect thin sets from those of a vehicle or those of a color. This would ensure that in trying to use the set, more is possible, for outcomes than just as chairs, leading to the possibility for improvement. GPT-5 can be said to be running possibilities on both types of intelligence.
Human Intelligence Research Lab
For now, across science, anywhere in the world, there is no explanation for human intelligence, compared to, or beyond the details above. The potential of this, in an era of artificial intelligence, could be the decisive factor in how to prepare for the sweep of AI across nations and industries.
Formularizing this into an effort to improve human intelligence can be possible by incorporating a new research lab for human intelligence. The objective will be to provide the explanation with displays, describe what happens during learning, what induces or inhibits understanding as well as what to do to boost problem-solving, creativity, innovation and much else. Venture Capital for this non-AI lab will be profitable within a quarter, with a timeline to start by November 2025, and ship the first products by December 2025.
Although for now, AI tutors are getting adopted, they do not appear with potential to make a lot of difference without falling into the quag of past facilities [they all get held back by the limits of human intelligence].
This human intelligence research lab will be for profit. The lab will be for humanity but tower frontier AI models as well as find more ways that humans can better prepare.
The first Human Intelligence Research Lab in the world will be monumental. The foundation for its existence is already established by conceptual brain science. The opportunity is limitless and for the first time in the history of humanity and in the era of intelligence, the mechanism of human intelligence, in the brain, is at least within accurate view.
There is a new [October, 2025] report by Oxford University Press, Teaching the AI-Native Generation Empowering Schools in the Age of AI, stating that, “2,000 students aged 13–18 across the UK were surveyed in August 2025. 8 in 10 young people use AI tools in their schoolwork. Almost as many use AI tools to help with homework. Fewer than half of UK pupils can identify AI misinformation. One third (32%) stated they can’t tell if AI content is true, whilst a fifth (21%) were unsure. Almost half (48%) of pupils want support from their teachers to help them understand what content generated by AI is trustworthy and reliable. 51% of pupils want more clarity from their school on when they should be using AI tools in their schoolwork. Among the top concerns for students (60%) was AI tools encouraging copying rather than original work and over half (51%) worry that AI resources may be biased or reinforce untrue stereotypes. 48% of young people are concerned pupils in their year are secretly using AI to do their schoolwork, with students in London worrying the most about this, and almost as many (47%) pupils are concerned their teachers are unable to spot when this happen. Over 90% of students believe AI has helped to develop a skill in relation to their schoolwork.”
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