by David Stéphen
The biggest part of the problem facing humanity with artificial intelligence is that there is no architecture of what human intelligence is in the brain.
The dominance of speculation in the debate about human intelligence over AI shows how much is not understood about what human intelligence is, in the brain.
It is true that advances in the world are an evidence of the excellence of human intelligence. But, how much of that excellence is from the monopoly of that level of intelligence, among organisms?
Now that there is a rival for human intelligence in artificial intelligence, even if the capabilities of AI are less than n% of human intelligence, but improving, it is important to seek to understand what human intelligence is in the brain, conceptually.
There are several labels that are used to describe human intelligence, like creativity, innovation, reasoning and so forth.
They tell what AI does not have and some are saying what AI won’t have. But creativity, innovation or reasoning given what capacity?
This question means that rather than just assume that because humans make things, then creativity is default. The quest is to know what creativity is in the brain or how it works at least conceptually. This is so that it can be ascertained or nearly, how far AI is, or could get against it, in the future. The same for innovation, reasoning and much else.
The first objective is to have a general definition human intelligence.
Human intelligence is defined as the use of memory for desired, expected or advantageous outcomes.
So, the way memory is used is what intelligence is. Memory can be said to be at destinations. Relays across those, for utility means intelligence.
Now, where must that memory be to be used, within the mind or external? Also, what relays are significant, for exceptional intelligence outcomes?
It is known that the mind interprets external stimuli, but some interpretations in memory for specific intelligence [say physics or economics], should mean that some of those are available, to use what is coming in.
Still, there are at least two major types of intelligence [or say memory use]. First, is improvement or advancement intelligence and the second is operational or procession intelligence.
Operational means routine, like learning something and using it, at the level of what everyone else can do. For example, driving, cooking, reading and much else. Just operational. Although there are mild, mid and extreme levels of operational intelligence.
The next is improvement or advancement intelligence. This means the process of making something novel. There are also mild, mid and extreme ranges of this. While some extreme operational intelligence may intersect with some mild advancement intelligence, they are mostly distinct.
For example, building a new automobile, a house or something where the process is already understood is operational intelligence. Though the complexity of the process might make it extreme from just say, cutting paper or changing the shape of a can.
So, if intelligence is operational or improvement, then what part can AI do, for now? What is its trajectory for the future?
Also, because of economics, what parts of human intelligence are valued, among humans? Human competing with humans means there are choice intelligence.
What aspects of choice intelligence can AI scrape and deliver across endeavors? While several operational intelligence require physical activities, several aspect of it require the directions of knowing what to do [or memory contents use].
This is where AI thrives. So, in trying to have a model of what human intelligence is in the brain, exploring basic definitions and types would be fruitful. Then to find ways to grow improvement intelligence, to solve problems.
Now, in the brain, neuroscience has established that neurons are involved in all functions of the brain for human life and experiences. This includes intelligence. But neurons do so with their electrical and chemical signals.
This means that either a model of three elements of intelligence is built or of two. While it has been difficult if not impossible to theorize brain functions for neurons, in part because they are cells, the next options are electrical and chemical signals.
So, conceptually, the human mind is the collection of all the electrical and chemical signals, with their interactions and attributes, in sets, in clusters of neurons, across the central and peripheral nervous systems. Simply, the human mind is the sets of signals.
Human intelligence in the brain can be defined as the relays of electrical signals, with attributes like sequences, splits and intensity, to sets [for interactions] where memories are obtained, for advantageous experiential outcomes.
Simply, the interactions of electrical and chemical signals result in functions. There are states that signals are at the time of interactions that determine the extent of the interactions.
So, destinations specific to memory, can be said to get relays that provide intelligence. Relays are possible by electrical signals, with their attributes like sequences and splits.
To develop a model of how human intelligence works, it is feasible to use electrochemical psychiatry, based on signals with the postulate in Conceptual Biomarkers and Theoretical Biological Factors for Psychiatric and Intelligence Nosology.
It is likely to get a principal part of this prepared before January, 2027.
AI model welfare
The campaign to care for AI rights, morals and feelings does not consider that the most important effort for now is to care about human intelligence in the brain, to at least prepare for what to do, for problem-solving, creativity and innovation, to place alternatives as AI cuts into some of the productive tasks of humans.
What happens to general work aspects or operational intelligence of humans as AI improves? In what ways would pivot be possible? The central goal is to seek out what human intelligence is, in the brain before the end of 2026, as AI labs, Anthropic and OpenAI are looking to IPO within months.
There is a new [October, 2026] report on Reuters, Fed’s Cook sees AI inflationary push as a top 2027 risk, stating that, “Federal Reserve Governor Lisa Cook said on Thursday that she sees AI, and its inflationary push, as a top risk for 2027, and noted that increasingly frequent supply shocks have had surprisingly persistent effects and become more salient for policy.”
“The AI build out is potentially creating inflationary pressures that may not resolve very quickly,” Cook told New York Fed President John Williams at an event at the regional Fed bank. “So I think this is one of the main things that concerns me right now for 2027.”
“Cook joined a unanimous vote last month at the Fed to raise the policy rateby a quarter of a point to support a “timelier” return of inflation to the Fed’s 2% goal. Inflation by the Fed’s targeted measure was 3.4% in August and has been above the goal for more than 5-1/2 years.”