The Making, the Rise, and the Future of the Speakingman-Sixth Edition is a bold exploration of how geomagnetic events, atmospheric radiation, and environmental shifts may have influenced the emergence of language, culture, and modern cognition. You describe several “cognitive revolutions” in human history. What defines these transitions?

The cognition improves by two means: one refers to an increased number of neurons, the other refers to an increase in the density of neurons through multiple folding.

In both cases, the neuronal network changes reflect a change in the brain’s energy consumption, because the neurons consume energy, and the transmission of information from one neuron to another even consumes more energy.

In the meantime, the climate has a major role, because at warm temperatures, the brain must consume energy for its own cooling. Thus, at ambient cooler temperatures, the brain can afford to grow its number of neurons and tracts for inter-neuronal communication, because it does not need to cool its networks; this saves energy that can increase the network size, and consequently the brain size. This is the first part of the process, where global cooling on Earth favored the build- up of larger brains.

The second part refers to the modality in which the brain processes the perceived information from the environment. It is known that the brain mostly processes that information in a nonlinear manner (some 66% of it), while the rest is processed linearly. However, linear processing produces an elimination of variables, which do not allow prediction. The prediction is essential for survival and is common for all biota. Thus, humans have in their primitive stage a dominant linear mental processing. As the circuits and neurons multiply, some of them become not entrained into an immediate response to the environmental causes; thus, they process the contextual aspects, allowing the build-up of symbolic representations, and later, abstract representations. As it seems, the increase in the size of the population and the consequent need for socialization played an important role in symbolic and abstract representations.

The third part refers to the cognitive revolutions themselves. They represent a change in dominance between linear and nonlinear mental processing, which changes have produced different modalities to see and interpret the perceived reality. For example, I defined the first cognitive revolution as an occurrence that developed around 70,000 years ago, and where the manifestation of abstract/nonlinear thinking caused artistic achievements, but also made the sapiens consider more contextual aspects, helping them have a holistic view of the environment and spread around the planet. The second cognitive revolution started to develop around 45,000 years ago, when the need for better communication gradually transformed their ‘static’ language into a ‘dynamic’ language around 30,000 years ago. The language was and is linear and is a manifestation of ‘symbolic thinking.’ As it is recorded in cave painting, around 37,000 years ago, this transition occurred from abstract/nonlinear thinking to symbolic/linear thinking.

The linear thinking evolved toward more complex forms until around 15-10,000 years ago, when domestication of animals and plants gradually developed. At this point, linearity reached its peak and led to another change in thinking, where gradually the nonlinear processing expressed its dominance. This was the transition from Matriarchate to the Megalithic Culture that evolved in various stages until 4,000 years ago, when the current ‘material/linear’ era took over. This Megalith Culture I defined as a nonreligious culture, where spirituality, as an aspect dividing the role of humans in the universe and on this planet, was primitively exposed by human thinking. In fact, it was in our current understanding as a ‘primitive information era.

As one can observe, I hypothesized four distinct manifestations of dominance between linear and nonlinear.

What challenges come with presenting a theory that spans climate science, neuroscience, and anthropology?

Presenting a theory that spans climate science, neuroscience, and anthropology is the role of a multidisciplinary approach that produces a unified understanding of a multiplicity of aspects.

Regular fractional understanding prevents one from seeing the ’emergent’ image of our evolution. As the science of complexity explains, the emergent is not the sum of the interacting parts, and its result is very distinct and uncorrelated to any of those parts. As this definition says, my result is an ’emergent’ and cannot be compared to any of the aspects I have analyzed in this process.

First, the ’emergent’ answer to the previous question is that Brain Energetic Consumption has driven our evolution.

Do you believe AI could trigger another “cognitive revolution”? If so, what should we be paying attention to today?

As I explained in the book, in my opinion, the era of Atmospheric Experiments with the Atomic Bombs (AEAB) (1950-1962) produced a doubling of the atmospheric concentration of C14 isotopes, on par with the geomagnetic events from our prehistory, and consequently generated significant pulses of neurogenesis. Such pulses occurred during the period when the concentration of C14 remained elevated up to 20% from its peak in 1962-1964. Hence, the pulses of neurogenesis were in effect on the human brain from approximately 1955 to 1985, or for 30 years. The result affected the neuronal networks of all those born in this time interval. Unfortunately, during the mentioned period, the scientific community ignored the possible neuronal effect of such doubling of the C14 concentration. As unrelated experiments indicate, neuronal effects to various forcing factors vary between bio-positive and bio-negative effects and depend on each individual. Consequently, I postulated that as a result of the AEAB, we should have today a group of geniuses and groups of people with mental illnesses, while these two groups would be larger than in any previous epochs of the 20th century. In my opinion, I related this hypothesis to a chain of ‘technological revolutions’ manifested between 1980 and 2010, when those born 1955-1985 reached mental maturation, being able to produce significant discoveries. In this context, AI falls as a continuation of the previous technological revolutions.

However, here is another ’emergent’ aspect not mentioned before. It refers to the process of ‘quantification’ or the transformation of ‘qualities’ generated by our emotional thinking process into ‘linear qualities.’

Every switching of mental processing toward linearization resulted in ‘quantification.’

Now, digitalization (as another type of ‘quantification’ processing) is fundamentally distinct from AI, because AI attempts to transform the last ‘qualities’ preserved deep in the mind into ‘quantities.’ The potential geniuses, as the actors involved in Al, are still active at present.

There will occur a ‘cognitive revolution’, but this one is fundamentally distinct from any previous one, because here the ultimate target is the transformation of information into energy and energy into matter, and the reverse of this process will be attempted, too.

However, the primary elements of the ‘Al’ era, which are predominantly nonlinear and parallel-computed, are forming up around us, too. Thus, it is a complex transition from one era to another.

The ‘Al’ era requires a distinct type of society because it will imply social and collective intelligence, assembling a socially non-religious spirituality. Here, simultaneously, the new society is building up and will replace capitalism as we know it. This new society, currently evolving around us as smart cities, smart technologies, 4.0 Industry, etc., is tagged as the Information Trade Society. The ‘Al’ is embedded in it.

While ‘digital strategy’ is nonlinear, its underlying technical processes are linear, like the algorithms, and help produce linear complexities. Thus, ‘digital’ in itself is a transition from analog/linear systems to nonlinear systems, like Al. It blends linear and nonlinear to help the transition occur more smoothly.

By contrast, ‘Al’ relies on neural networks with nonlinear activation. At present, ‘Al’ can analyze highly nonlinear, chaotic data and reduce them to simple, linear equations, like matrix multiplication. ‘Al’ is highly efficient at solving linear problems. It can replace complex operations with simpler, linear-complexity additions.

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