Artificial Intelligence vs Human Intelligence

Artificial Intelligence is the art of programming computers in order to produce intelligent behavior, whereas brain theory is the study of the brains function, to understand how the brain functions, the stimulations that go within and how outputs are produced via mathematical modeling and computer stimulation. It can be argued that both contain similar characteristics for functionality, both works together, this can be seen through artificial intelligence used to understand the roles of brain mechanisms. Furthermore both are involved in high cognitive task, such as reasoning, problem solving and decision making. Although some philosophers have accepted that machines can do everything that humans can do, some disagree with this view arguing that such high sophisticated behavior such as love, emotions discovery and moral decisions can only be carried out by humans.

Artificial Intelligence vs Human Intelligence

AI for many years has been pursuing the study of intelligent behaviour, but using artificial methodology. Intelligence can be defined as the ‘ability to learn and understand, to solve problems and make decisions’, both AI and the human brain share this characteristic. In order to study human intelligence some use artificial intelligence to understand human processes. One of the most significant papers on machine intelligence has been explored by Alan Turning; however his theories still remain universal. Turning (1950) predicted that by 2000, a computer could be programmed to have a conversation with a human interrogator for five minutes and that it would be able to deceive the interrogator that it is human, this suggest a link between AI and brain mechanisms. Both the human brain and machines are able to solve complex mathematical calculations; a machine may be designed to solve these calculations faster than the human brain. Although evidence suggests that our brain stores the equivalent of about of over 1018 bits and can process information at the equivalent of about 1015 bits per second. Therefore both AI and brain mechanisms can work together to produce efficient results, as it is evident that both accept input and generate the correct output. It is required that an intelligent machine should help human make decisions, to search for information, to control complex objects, and finally to understand the meaning of words.

One of the possible definitions of AI refers to cognitive processes and especially to reasoning. Before making any decisions, people also reason, it is therefore natural to explore the links between both. Since the early 1950’s, there has been a vast development of AI where it has become a valuable tool to support humans making decisions, similarly specific brain mechanisms are involved in decision making in the brain, one can argue that both working together will lead to more valid and faster decisions. To support this, research shows that more sophisticated and user-friendly forms of computer-assisted decision aiding technologies are being developed, examples include decision support systems and external information retrieval system, this then can work alongside brain mechanisms.

AI has largely been used for problem solving; such machines have been created than can go beyond the human brain ability to solve problems. These include solving mathematical problems of high complexity; these are programmed to do exactly what we want them to do. AI is now involved in solving real life issues, which are usually dealt by human brain mechanisms, some may suggest that it may reach to such an extent that it outperforms the best traders and investors. Evidence suggest that they are already involved in to forecast the economy and analyze credit risk, these just being to examples. This is a rapid growing field which needs to be given attention. More money is being spent on this as the developments in this field have been immense. Neural networks, is one specific type of AI that mimics many characteristics of the human brain. Studies suggests that neural networks are able to draw conclusions of data that is incomplete and may learn from previous mistakes, thus imitating performance outputs by brain mechanisms.

AI systems are increasingly being developed and increasing rapidly, this is because of the variety of applications it includes, such as symbolic reasoning, flexibility and explanation capabilities, thus both AI and brain mechanisms can work together and produce efficient results that would make every day life seem less effortless and produce faster results. The goal of building AI agents was that it provides efficiency and most importantly works parallel to brain mechanisms, such as contains features such as cognitive abilities which will play a role in decision making and help in difficult and complex human situations. Furthermore cognitive abilities such as perception, reasoning, learning and planning turn technical systems into systems that ‘know what they are doing’; therefore they may function in parallel to the ways brain mechanisms do. Through the years there has been vast developments and more work is being put into these systems in order to it contains certain amount of represented knowledge , learn from previous experiences in order to prevent mistakes previously made and so that it makes further improvement and changes. Furthermore developers have aimed to develop AI even further, such as justify the actions and decisions made, be aware of the capabilities it contains and be able to reflect on its behaviour, these are the same roles that brain mechanisms play, thus they aim to create a parallel model.

Although such developments have been made through many years, it may be argued that AI may still not be able to compete with the advanced functionality of brain mechanisms. Despite the speed and memory capacity, AI struggles to contend. The human brain contains around 20 billion neurons, each neuron being connected through synapses of approximately 10,000 other neurons, which AI cannot possibly mimic. However there has been continuous progress and advances of AI, an example might be face recognition software, which detects photos; the brain does this automatically, and relies on memory similar to AI where it uses its stored memory for collection. In AI this has been used for security purposes, which have demonstrated to be very successful. Previous advances include voice recognition, which both AI and brain mechanisms share. For AI this has not only be used in terms of security reasons but also in order to function the device and making it more easier and convenient for use, this has been done through using different applications, where it involves programming, the same way the brain mechanisms programme in order to carry out specified roles. In addition although AI applications use many techniques, the fundamental building block is called the neural network, likewise brain mechanisms functions the same way.

Things that were difficult to reason earlier on have become easier to understand through advances in technology. As the human brain being such a complex mechanism it has been difficult to observe human brain activity. New advances have been made in recent years, FMRI (Functional Magnetic Resonance Imaging) being one of these discoveries which has helped immensely to capture brain activity, this has been significant help for AI as it enables to have an insight on brain activity, which may help advance and help imitate brain mechanisms. Furthermore, this then shifts the balance between building artificial intelligent systems and studying natural intelligence. It should be expected that in the future, there should be numerous studies on relating artificial intelligence to natural intelligence. Current evidence suggests that both can work together to demonstrate different cognitive states in humans, here artificial intelligence has been used for learning algorithms to distinguish between various cognitive states observed through FMRI. Looking at this area further, natural and artificial intelligence are both said to be closely related in most objects and in everyday life generally. Both get impacted if are hit physically. Human behavior is said to be artificial at times, both function through language and communication. Communication is essential for both AI and brain mechanisms in order to function. Through communication they are able to send out essential messages, which helps to maintain these systems and allows them to function effectively and plays a key role in all aspects.

AI shares many characteristics with brain mechanisms; one may argue that an essential feature which only human mechanisms can produce is emotions. Emotion is defined as a person’s feelings and behavior which has a direct affect on the performance, emotions may act as an obstacle to produce intelligent outcome, thus it can be argued that because AI lacks to produce emotions this may not have an impact, therefore not having an influence on the outcomes it produces. However it is essential, that in order to emulate human behavior and to work alongside humans that AI should have emotions, it is required that not only should it think and reason but also be able to show emotions.

Overall evidence suggests that both artificial intelligence and brain mechanisms are closely related, both work together in order to produce efficient outcomes. AI and brain mechanisms share many key characteristics such as reasoning, problem solving and decision making and intelligence. Recent evidence suggest that AI has allowed us to understand complex brain processes, this then enables to understand human actions and decisions in more depth. However many researchers still argue that the high level cognition can only be produced by brain mechanisms, such as emotions and feelings that AI fails to produce.

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