“Intelligence is what you use when you don't know what to do.”
― Jean Piaget
The human brain is remarkable in its complexity design. A myriad of constantly evolving, reciprocally sophisticated computational systems, engineered by natural selection to use information to adaptively regulate physiology, behavior and cognition. Our brain defines our humanity. Systematically, through multitudinous generations, both the human brain structure (hardware) and its neural algorithms (software) have been fine-tuned by evolution to enable us adapt better to environment.
For an extended period of time, the structural elements of the human brain, such as size and shape, proved to resemble more closely to those of the rest members in the Hominidae family. Albeit, starting with specimen of Australopithecus afarensis, the brain has began to evolve and transfigure. It increased in size and developed new areas. The main dissimilarity was the development of the neocortex, the frontal and prefrontal cortex - today these areas are associated with higher levels of cognition, such as judgment, reasoning, and decision-making.
Following the Australopithecus, Homo habilis saw a further increase in brain size and a structural expansion in the area of the brain associated with expressive language. Gradually, the brain development reached and stabilized in the range of its modern measurements, those of early Homo sapiens. The regions of the brain that completed their growth at that stage were those associated with planning, communication, and advanced cognitive functions, while the prefrontal areas, that are bigger in humans than in other apes, affected planning, language, attention, social information processing, temporal information processing, namely improvisational intelligence.
Information processing has been a guiding aspect of human evolution, fundamentally structured around acquiring sensory input from the environment and then interpreting and organizing it by the brain. A brain may be referenced in function to a computer in that both are types of computing machines generating complex patterns of output, after dissemination of correlating complex patterns of input, and after querying to stored information. Such organizational structure can be affected by our 'in-flow data filter' that regulates how much attention we pay our surroundings, without overloading our systems. For instance, when you engage in a conversation in a public place, your brain filters out background noise focusing your sensory input acquisition on the required interactive action. So-to-speak attention algorithms can be determined by you voluntarily in what is known as top-down processing or it may be automatic, in what is known as bottom-up processing. Recurrently, most of the external data that our brain captures and uses is not completely conscious. In many instances, we make decisions influenced by information with no conscious awareness. And there is an evolutionary basis for this attentional choice among many others.
The best associating indicators of intelligence could be connected with the simpler but less predictable problems that animals encounter, novel situations where evolution has not provided a standard blueprint and the animal has to improvise by using its intellectual wherewithal. While humans often use the term intelligence to define both a broad spectrum of abilities and the efficiency with which they're deployed, it also implies flexibility and creativity, an "ability to slip the bonds of instinct and generate novel solutions to problems" (Gould and Gould, 1994).
Human behavior is the most astonishingly flexible behavior among any animal species. Heuristic intelligence, or improvisational intelligence, is the exemplary core for a phenomenon of human behavior in the evolutionary cognitive process. Heuristics are rules-of-thumb and simplified cognitive shortcuts we use to arrive at decisions and conclusions, helping us save energy and processing power. Cosmides and Tobby (2002) divide intelligences into two distinct categories: dedicated intelligences and improvisational intelligences, wherein dedicated intelligence refers to "the ability of a computational system to solve predefined, target set of problems" and improvisational intelligence refers to “the ability of a computational system to improvise solutions to novel problems”. They argue that the latter form of reasoning is employed whenever al allocated processing module doesn't exist to solve a particular problem. Our computational brain hierarchy is composed of a structure of innate neural networks, which have distinct established evolutionary developed functions, or massive modularity. The mind is not composed of "general-purposes" mechanisms, such as a general-purpose reasoning mechanism or a general-purpose learning mechanism, but instead consists of hundreds or thousands of highly specialized modules that provide us with innate knowledge and innate abilities in various sporadic domains. Most of these modules evolved during human development time in Pleistocene hunter-gatherer societies, applying universality over all human populations. They constitute an invariant human nature.
Within such modularity, improvisational intelligence essentially conceives a more domain-general kind of intelligence as being 'bundled-together' of several dedicated intelligences to solve evolutionary novel problems such as driving cars, using smartphones or launching rockets to space. Improvisational intelligence enables humans to solve such novel problems by processing information that is transiently and contingently valid. It is designed to represent the unique features of particular combinations of evolutionary recurrent categories and requires mechanisms that translate data from dedicated intelligences into common standards. Modular adaptations are invariably by specific external stimuli and improvisational intelligence, by contrast, permits the use of knowledge derived from domain specific inference systems in the absence of triggering stimuli. Hence, humans, unlike existing today machines embedded with artificial intelligence, are able to reason about the consequences of what is unknown, what can be anticipated to become known in the future or what is not physically present.
But is there a way to bootstrap improvisational intelligence and incorporate improvisation mechanisms? Non-evalutionary improvisation must be only memory-based as an emergent process guided by the expanding collection of background knowledge. Learning in the current context of machine learning is like querying an expert for an answer - an independent and purposeful activity in itself, the end product being newly created knowledge. In artificial agents case, by bundling novel memory (the ability to retrieve relevant background knowledge) and novel analogical reasoning (the ability to transfer knowledge from a similar situation in the past to the current situatio) of artificial non-evolutionary intelligent systems are fundamental to novel problem reformulation, which in turn is the basis for improvisational intelligence. The further humans extend their existence, subsequently unlinking evolution-based dedicated intelligences, the higher are chances for intelligent agents to establish human-like improvisational intelligence.