Past, Present, and Future of Artificial Intelligence

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Past, Present, and Future of Artificial Intelligence

The concept behind the working of artificial intelligence (AI) has been in existence since the 1950s. During this period, scientists, philosophers and mathematicians explored the probability that machines could emulate and perform tasks just as human beings, including making decisions and solving problems. As part of his exploits, Alan Turing, a British Polymath wrote an article titled Computing Machinery and Intelligence (Castelfranchi, 2013). This book, published in 1950, is believed to have been an eye opener that facilitated the advancement of artificial intelligence. Guided by this backdrop, this research paper analyses the past, present, and future of artificial intelligence.

Past

The idea behind AI originated in the 1940s. It was officially embraced in the 1950s in the academic field. It attracted extensive funding from the government in the 1960s. The funding was discontinued in the 1970s forcing the application of AI to transform from academics to business (Aguis, 2019). A detailed account of past evolution of AI shows that after the idea was conceived in 1950s, the motivation to develop it further faded as the computers at that time, could execute commands but failed to store data. In addition, the prospects of exploring and developing AI were hindered by lack of funding. Computers begun flourishing in early 1970s as they were faster, could store more data, and were relatively affordable. Advancements in computing power motivated Research and Development Corporation (RAND) to finance researches that led to introduction of Herbert Simon’s and Allen Newell’s General Problem Solver. In addition, Joseph Weizenbaum also came up with a near similar prototype (Haenlein & Kaplan, 2019). These programs increased the prospect that machines could be used in future to interpret languages spoken by other machines and to solve problems. However, a lot was needed to embed machines with the capability to think autonomously, process natural languages, and self-recognize.

In the 1980’s research in AI was boosted significantly by an increased funding by private investors, and businesses. Investments were made in improving algorithmic tools. David Rumelhart and John Hopfield begun popularizing deep learning techniques (Howard, 2019). Combining algorithms and deep learning enabled machines to learn through analysis and replication of previous experiences. Additional research by Edward Feigenbaum led to the introduction of expert systems that mimicked the decision making processes of very intelligent human beings. This included solving complex problems such as predicting stock prices and playing chess. In the same period spanning 1980s and 1990s, businesses begun introducing robotics dependent on artificial intelligence (Howard, 2019). These events created fear among employees thus attracting criticism and backlash. In spite of these developments and challenges, landmark goals towards AI were achieved in the 2000’s after the introduction of the internet.

Present

Today, AI continues to be applied in robotics and other diverse applications such as surgeries. There is a third generation of artificial intelligence known as deep learning. It has further sped the process of innovations in AI as organizations such as Amazon and Google are using the deep learning algorithms to invent predictive models (Aguis, 2019). Today, some of the common applications of AI include photography, self-driving cars, personal assistant technologies, smart cars, and home assistance technologies such as intelligent vacuuming. AI is equally changing the business environment thus facilitating the rise of disruptive business models and processes.

Currently, companies are constantly investing in developing AI. Aguis (2019) reports that in the last five years, investments in AI have increased by 12.9% annually across the whole globe. In the coming four years, China is anticipated to become the largest producer of AI technologies replacing the USA as the second largest producer and coming second after Europe. Currently, Europe has the largest international collaboration in developing AI systems. Research on AI is focused on computer vision, probabilistic reasoning, knowledge representation, search and optimization, precision decision making, and neural networks (Haenlein & Kaplan, 2019). These developments have changed the work environment since they enhance effectiveness of decision making, increase productivity, and facilitate monitoring of processes.

Future of AI

It is expected that AI will contribute towards the realization of a smarter future. As the world transitions into the Fourth Industrial Revolution, it is anticipated that AI will continue to push limits in functioning like human beings. With the advancements in Internet of Things, Internet of Systems, Deep Learning, and Cyber-physical technologies, it is inevitable that AI will be used to create rule-based systems to manage domestic applications, algorithms will be used for context awareness and retention especially in roboadvisors and chatbots. Additionally, AI will be used as reasoning machines and domain specific experts. Haenlein and Kaplan (2019) second these sentiments noting that AI will grow significantly in the coming years and further change business models. Generally, it will improve the quality of life by easing work. There are also fears that it will replace human beings especially in sectors with repetitive work that can be coded and programmed to be done by machines.

Conclusion

In conclusion, this research paper has analyzed the history, current state, and the future of artificial intelligence. It notes that AI was first introduced in the 1940s as an ideology that has grown to its current state. Businesses are making huge investments in these technologies with the aim of using its features to ease decision making and enhance productivity among other benefits. In the future, AI will complement smart technologies to change the future of working.