Human and non-human forms of intelligence exist in all facets of our lives. It is nowadays standard practice to differentiate something as intelligent or unintelligent in its making. In this context, intelligence nourishes from mental and physical stimuli, the interaction between our genes and environment, and the internal stasis of strictly cognitive and affective aspects. Therefore, in the wild, we confront indirect evidence rather than direct laboratory records of an observed capacity of learning and adaptation, which is continually repeated until it converges into an intelligent clue.
Earlier evidence of human Intelligence can be traced back to prehistoric art. The depiction of humans and animals in hunting scenes follows natural laws with a well-developed aesthetic sense and a level of intellect beyond survival needs. According to a postulate of the Kantian conception of history, nature acts for man until he is able to act as free intelligence. When man awakens from the torpor of the senses, recognizes himself as a man and looks around, he finds himself in the state which has come to be formed under the coercion of needs, according to purely natural laws. Man, who is acquiring consciousness of himself as a rational and moral being, cannot be content with that state which has arisen through a natural determination; he longs to build the self-according to the pure laws of reason and freedom. But if physical man is real, moral man is problematic. The self, which provident nature has formed as suitable and sufficient for physical man, cannot be abolished before the ideal of moral man is fully realized—capable of reforming and recreating according to the laws of reason. The depiction of human intelligence through morality itself follows its interpersonal and intrapersonal forms evoked through Gardner’s theory of emotional intelligence.
In this making, we step into the field of cognitive ethology for extending morality and modes of reason beyond non-humans, such as animal species and artificial man-made machines. We begin with the minimal brain structures of insects or invertebrates. For instance, bees have no knowledge of the semantic aspects of stimulation in their ability to discriminate a number of objects, such as dots on a screen. But bees are able to recognize the physical characteristics, including size and the contours of a total surface area; most notably, they are capable of transitioning from discrete to continuous processing. Learning to discriminate from a perceptual point of view does not require a big brain, but it reveals surprising connections between animal and human cognition. With a tiny cubic-millimeter-sized brain containing just about one million neurons, we should question how a bee employs all these neurons. Our view is that the neurons left over from the basic computation of the thought process are simply large memory stores. This outlook comes from their ability to discriminate objects (bridges, faces, and more). Keeping unused information in memory is also part of the relational life of humans. This led us to question innate knowledge, along with how much we learn and how much we know at the moment we are born. When ducklings hatch from their eggs, they already have developed sensorial and motor points. This is the simplest form of non-human animals’ free intelligence based entirely on the need to fulfill present and future needs. However, we can all question animal intelligence through observation, just as Brooks (1991) noted, “intelligence is in the eyes of the observer”. We concur with Brooks while making the transition into a more diversified form of non-human intelligence. On the perspective of simulating natural intelligence, Wiener introduced the concept of Cybernetics, which featured the study of communication and control in the animal and the machine. In broad terms, cybernetics, denoted as biological cybernetics, was adopted for formulating aspects of communication and control in biological organisms. This construct of an idealistic nature seems to be commanded by an imminent teleology for imitating biological systems, as Craik defined “…is not ask what kind of thing a number is, but to think what kind of mechanism could represent so many physically possible or impossible, and yet self-consistent, processes as number does”. A number is a symbolic representation that could be manipulated without intuition outside the mind. The origin of representationalism dates back to the Good Old Fashioned Artificial Intelligence (GOFAI). This cartesian form of free intelligence revealed other sources of cognitive accomplishment. Newell proposed a physical symbol system as the primary architecture of human cognition “capable of universal computation”. This approach hypothesized that intelligence would be realized by a universal computer. Essentially, the brain is removed and replaced with a computer. However, as Hutchins quoted, “…and the emotions all fell away when the brain was replaced by a computer”. A machine like a computer that reasons in a mechanical way cannot be compared to the brain, because computers do not have or develop emotions. According to Damasio’s Theory (somatic marker hypothesis), there are different centres in the brain that allow us to perceive emotions that are linked to bodily sensations and play a very important role in the development of thoughts. Within this context, Darwinwrote The Expression of the Emotions, where he defined six emotions arising from human reactions.
Over thirty years ago, Simon and Kaplan claimed that “The computer was made in the image of the human” but humans have different and multiple guises. This is a new path for artificial human-made technologies melding free intelligence in either the illusion of a pure Promethean openness or into the reduction to a finite delimitation.
