Introduction
Artificial Intelligence is a broad-based division of computer science specializing in the development of intelligent machines with the ability to perform tasks requiring human intellect. It is a science with many disciplines, approaches, and programmable functions and a capability to learn, reason, solve problems, and make decisions. This paper will primarily focus on the discussion of Artificial General Intelligence (AGI) technology.
Main body
Artificial intelligence tools have also had an immense influence globally, especially in enabling businesses to perform better with regard to the production and provision of quality products and services. Super Intelligence, Artificial Narrow, and Artificial General are the main types of Artificial Intelligence present (O’Carroll, 2017). Currently, Narrow intelligence is the only type of artificial intelligence that has been effectively implemented by most organizations and businesses around the world.
Artificial intelligence is a model of a computer that replicates human acumen designed with a real intelligence system. It also has the capabilities to imitate human actions, learn and grasp any issue using its aptitude. AGI can think, comprehend, and respond in an indistinguishable way from that of a human being in any provided situation. According to O’Carroll (2017), AGI research aims to form and demonstrate the technologies which will exhibit a broad spectrum of general intelligence inherent in human beings. Thus, the aim of creating AGI matches that of the machine intelligence movements in the early 1950s. After many attempts, researchers finally managed to develop the Narrow AI system, which had the potential to show or exceed human ability in a particular task (Adamson, 2018). Therefore, the application of the skill to other forms of work was the only problem at the time.
The pioneers of the AI sector in the 1950s and 60s were mainly concerned with the establishment of hardware or software which could replicate the general human-like intellect. Ever since, the industry has concentrated on the discovery of this intelligence’s ability to execute functional operations (Adamson, 2018). Many interesting developments have resulted from this technology though they have been unsuccessful in respect to the field’s initial core objectives (O’Carroll, 2017). A number of approaches within the AGI community have been examined by researchers. Including the construction of cognitive architectures driven by science and neuroscience. According to O’Carroll (2017), the first researchers in the Artificial Intelligence generation were persuaded by its feasibility and that it would come into existence in a few decades. In the early 1970s, however, it became clear that the project’s complexity had been significantly underestimated by experts (O’Carroll, 2017). In the end, financial institutions funding the project became doubtful and started putting scientists under immense pressure to generate effective practical AI.
At the beginning of the 1980s, Japan’s fifth-generation computer project again developed an interest in AGI, consequently establishing a ten-year timeframe that included AGI targets. In reaction to this and the performance of specialist systems, the industry, in conjunction with the government, invested capital back into the field (O’Carroll, 2017). Nevertheless, towards the end of the 1980s, trust in AI dropped; hence the goals of the project were never achieved. The researchers who had expected the imminent achievement of AGI were again mistaken for the second time in 20 years (O’Carroll, 2017). In the 1990s and at the beginning of the 21st century, contemporary AI gained better business performance and academic integrity by solving specific problems in which verifiable results could be obtained (Adamson, 2018). In addition, the development of trade-related applications such as computational machine learning and artificial neural networks were other challenges AI aimed to solve (O’Carroll, 2017). The emergence of such problems helped artificial intelligence to achieve tremendous success within the 21st-century dimension, including everyday use in the technology sector. Today, AI is heavily funded for the purpose of industrial and academic development.
Considering the benefits of AI, a challenge arises when it comes to Strong AI because it is still considered an emerging technology. However, researchers will have to establish how to make intelligent machines programmed with a range of cognitive skills in order to succeed. The reason why Strong Al should be further explored is its ability to utilize a system theory of mind (Bernard Marr & Co, 2020). It refers to the power of machines to understand desires, thoughts, beliefs, and ideas, including greater use of intelligence ( Bernard Marr & Co, 2020). Therefore, the idea is about training computers to understand individuals better and not only be limited to computing and emulation.
The current AI enhances the programming of people’s daily lives, notably through time-saving, for instance, by using smartphone applications as maps during traveling. Aligning this with AGI would entail the phone recommending alternate approaches if it happens that an accident has occurred on a particular route that a person wishes to use (Marr, 2020). O’Carroll (2017) states that virtual digital assistants such as Alexa and Cortana are commonly used to respond to any raised questions and implement an individual’s tasks or solutions based on commands or inquiries. They are also capable of interpreting human speech into different languages and responding through synthetic voices ( Bernard Marr & Co, 2020). Therefore, in responding to questions, tracking domestic automation systems and media devices by voice, the two assistants can be essential.
Detection of fraud in public sector agencies and companies will be another advantage of AGI. The use of historical data storage computers through the combination of machine learning algorithms with unattended learning in digital organizations helps in the achievement of a higher degree of intellect and clarification on the absolute risk of consumer conduct (O’Carroll, 2017). AI, specifically AGI, will, hence, be able to identify fraud incidents in real-time and consequently intercept them in less than a second through the applications such as Omnicare.
AGI has also penetrated the healthcare systems as many scientists and tech companies are now focused on making huge investments, which could help discover ways in which artificial intelligence can strengthen the Medicare system. Machines using this intelligence will be able to determine customized procedures for drug prescription and improve the tools for disease diagnosis, thereby reducing errors during critical processes such as surgery (O’Carroll, 2017). Intelligent robots will now also be capable of performing surgeries and even providing customer care services.
Conclusion
In conclusion, it is not surprising how extraordinarily challenging it is to achieve Strong AI, considering the human brain to be the model for producing intellectual ability. Researchers have been trying unsuccessfully to replicate the critical functions of visibility and mobility without accurate information about the human brain’s complexities. Despite all the challenges, scientists are still more determined to develop a replica of the human mind. As such, there is always the hope of the project developing into a reality in the near future.
References
Adamson, G. (2018). Achieving trust in Artificial General Intelligence: Secrets, precautions, and public scrutiny. 2018 IEEE International Symposium on Technology and Society (ISTAS), 66–70. Web.
Bernard Marr & Co. (2020). What Is the Importance of Artificial Intelligence (AI). Web.
O’Carroll, B. (2017). What are the 3 types of AI? A guide to narrow, general, and super artificial intelligence. Codebots. Web.