The function of the brain has fascinated humans for centuries. Philosophers have debated the questions of knowledge and reality as it is perceived by the mind. Scientists have also strived to replicate the intricacies of human intelligence with research into the burgeoning fields of theoretical biology, computer science, and Artificial Intelligence (AI).

Cognitive function and AI share a common denominator in the minds of researchers. Each one relies on computation. There is a certain set of stimuli or environmental factors for which some intelligent agent responds. It’s the pathway from A to B that has fueled research into learning how the human brain processes these data.

What Is Artificial Intelligence?

To understand the relationship between cognition and AI, let’s begin by defining it. Artificial intelligence describes the process by which an intelligent agent perceives the environment and performs a desirable action or goal. Agents are any organism, device, or entity that can do something. It includes non-living units like thermostats or smoke detectors.

It can also be a cat, a horse, or even a human. It can involve a group such as an ant colony or beehive. It may even be larger domains such as society or a community. Research focuses on studying these relationships between the environment and the intelligent agents.

Defining Intelligence

Research into this field isn’t new. Early pioneers such as Charles Babbage laid the foundations for future development. He conceived the idea of the analytical machine that would later become the computer. The resemblance to cognitive function was rudimentary with simple arithmetic problems and pre-defined actions.

It wasn’t long before science turned toward exploring the mechanisms of intelligence. Innovators like Alan Turing paved the way for bridging the gap between machines and intelligence with his work in mathematical biology. It became evident quickly that human cognitive function relied on something more than boolean logic.

Computational Intelligence

Computational Intelligence (CI) arose in part because of a desire to replicate the fundamental processes of human reasoning. The concept itself fostered the development of other technologies including the five main principles on which it operates. They include:

  • Neural Networks
  • Fuzzy Systems
  • Evolutionary Computation
  • Learning Theory
  • Probabilistic Methods

Each one brings a different aspect of cognitive function to the AI table. They toss aside the notion of black and white thinking to something more realistic and adaptable. In these ways, CI is a better representation of the human thought process. The key lies in its flexibility. The rigid dogma of AI of years past has evolved into more sophisticated technologies.

Neural Networks

For many years, scientists believed that brain development stopped once a person reached adulthood. The concept of neuroplasticity tells a different story. Rather than a static organ, the brain is responsive to changes and stimuli in both its internal and external environment. If damage occurs, surviving parts of the brain will reorganize and assume the lost functionality.

Likewise, the brain responds to learning. If you acquire a new skill, it responds by creating new neural pathways. CI replicates this process to increase its efficiency, not unlike neuroplasticity.

Fuzzy Systems

Rarely does a problem have a simple solution. That’s where fuzzy logic comes into the picture. It sheds the binary system of true-false answers to one with varying degrees of truth in between. It’s a closer approximation to cognitive function in humans.

Evolutionary Computation

Evolutionary computation (EC) recognizes the other forces at work that direct intelligence by adding adaptation to the mix. The human brain responds to changes in its environment through evolution. EC seeks to replicate these processes with similar ways of modifying the behavior of AI systems to respond more efficiently.

Learning Theory

Learning theory describes how knowledge is gained and absorbed by the human mind. As with EC, outside factors influence how these processes occur. They include social, environmental, and emotional aspects. The key is that both internal and external forces are at work. AI embraces this technology by accounting for these effects and adapting accordingly.

Probabilistic Methods

Virtually any model has some degree of uncertainty in it. The same applies to cognitive function. There aren’t always guaranteed responses. Probabilistic methods seek to identify these influences and find ways to account for them using prior knowledge as the driver of more appropriate responses. The same factors affect decisions that humans make.

A bad experience with making one choice will cause you to pause before following the same path. The problem is that it might be the right course of action in a similar situation. That’s where AI has the upper hand, however, without having the emotional aspects that can trump other influences.

The Advantages of Artificial Intelligence

AI can account for some of the biases and logical fallacies that plague critical thinking in humans. People may fall victim to illogical thought processes from hardwired responses to environmental stimuli. Confirmation bias, for example, can cloud proper assessment of a situation by failing to see what doesn’t conform to preconceived ideas.

AI can bypass such pitfalls by monitoring all changes irrespective of emotions and prejudices. It can also avoid the traps of a false dichotomy by failing to see alternative solutions. It can respond to the environment without distractions from other stimuli. For example, a motion sensor will still detect movement in another room if an occupant is focused on another task.

Some of the greatest advantages of AI involve its ability to establish appropriate responses no matter what the circumstances. A CO detector will still sound an alarm even if you don’t notice the presence of this poison. A smart thermostat will still adjust the temperature even if you forget to change it yourself.

AI has much ground to cover before it can truly replicate the essential processes of cognition. The state of the art recognizes the flexibility and adaptability of the human brain to the changes in its environment. Therein lies the crux of the relationship between the two. AI embraces the superior reasoning ability of the mind while seeking to minimize the impacts of its fallibilities.

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