Artificial Intelligence in the Digital Market

Several key events were happening in 2011 that further incorporated artificial intelligence into everyday life. The first event was when IBM Watson, a supercomputer designed with natural language processing software, won the television show Jeopardy!, defeating three Harvard business students (IBM's Watson, 2011). This demonstrated that computers could match and even exceed human intelligence and memory. Also, in 2011, Apple first began incorporating Siri into its iOS for the iPhone 4S (Mutchler, 2017). Microsoft released a similar voice assistance product called Cortana, followed by Amazon's Alexa in 2014. These voice assistants are capable of a wide range of useful applications, including sending and receiving text messages, making phone calls, answering simple questions (such as "What is the weather?"), setting appointment reminders, controlling music and video playback, and even ordering Uber and Lyft rides (Lekach, 2011). By 2011, natural language processing software had introduced several meaningful solutions to real-world problems. Due to the success of voice assistants like Alexa and Cortana, there is growing corporate interest in future applications of A.I. tech pieces that incorporate natural language processing (Goasduff, 2019). This revived interest in voice assistants can open up more options for future research on conversational A.I. (Ram et al., 2018). Google announced a brand-new class of A.I. technology dubbed AlphaGo in 2016. In an old (but well-liked) Chinese board game called Go, it defeated the current world champion five times in a row (Granter, Beck, & Papke, 2017). In terms of deep learning and machine learning, this constituted a huge advancement. AlphaGo was not programmed to play go, despite IBM's "Deep 20 Blue" being trained to play chess. Instead, AlphaGo developed its software to play the board game Go and learn the rules (Gibney, 2016).

The International Journal of Information Management published an article in 2019 about the history and future of artificial intelligence. In the article, authors Edwards and Dwivedi (2019) write that A.I. seems to have made significant improvements in the past several years, such that A.I. is becoming a topic of increased cultural and scientific relevance. Throughout the history of artificial intelligence, there have been naysayers and alarmists. Due to the lack of immediate progress, there have been several seasons of doubt about the entire field of artificial intelligence. Researchers call these periods "A.I. winters." Of the notion that A.I. is mostly in the rearview mirror, Raymond Kurtzweil wrote, "there's this stupid myth out there that A.I. has failed, but A.I. is around you every second of the day" (Kurzweil 2005, p. 263). According to currently available research, A.I. as a field will likely continue to progress as it has across the previous six decades, at a moderate pace.

Artificial Intelligence Today

Presently, it isn't easy to find a major sector of the industry that has not in some way been incorporating artificial intelligence in their dealings. A.I. has recently been used in some surprising and helpful ways. For example, researchers are now considering using A.I. for United States military applications. Some topics the military is interested in include crowd-based modeling (McKenzie et al., 2008), surveillance and imagery technology (Keller, 2019), and assessing military readiness through the aggregation of many data points (Strickland, Mariani, & Jenkins). 21 These technology upgrades will improve insights for the U.S. military significantly, which is likely to improve the functionality of future operations.

As stated, A.I. is also useful in fraud detection (Krausz, Schneider, & Colthart, 2018). Its relevance in corporate finance could be beginning to take shape as well, with one source predicting that banks that use A.I. may yield 34 percent more revenue and 14 percent more employment by 2022 (Realizing the full value, 2018). With more and more companies investing in A.I. products for the growing financial market, new research estimates productivity in the financial sector in Germany will skyrocket in the coming decades due to increased investment in A.I. technology (Bredt, 2019).

Notably, the industry that has likely invested most heavily in research in artificial intelligence technology is the medical industry, with computers capable of improving patient care through machine learning-powered insights (Durant, 2019; Mincholé & Rodriguez, 2019). Currently, A.I. functions medically as a useful tool for medical doctors and surgeons; it is nowhere near functioning on the level of a medical doctor, but it does promise faster computing time for decision-making processes and information grouping processes for human beings who work in the medical industry (Levine, 2019). Research is growing in the medical field around ethics in A.I. use (Matsuzaki, 2018; Whitaker, 2019).

Besides having achievements in the field of medicine and mathematics, studies all across the world confirm that artificial intelligence now possesses the ability to accurately perceive and judge human emotion, to the point that A.I. software is now capable of performing sentiment analysis on movie reviews (María et al., 2016). A.I. can also perform textual emotion mining using NLR (natural language processing) software (Chawla & Mehrotra, 2018). Well-known companies whose artificial technology is now integral in their businesses include Amazon, Tesla, 22 Cogito, Netflix, and Pandora (Adams, 2017). A.I. looks to be a valuable asset to business and economics; it can help accountants better identify and evaluate complex financial problems (Dirican, 2015; Ransbotham et al., 2018).

Access to enormous amounts of data, cheaper and faster computers, and advanced machine techniques were successfully applied to several economic problems in the current century. The present world deals with deep learning, big data, and Artificial Intelligence. Today, A.I. is a technology that is transforming every walk of life. A.I. is a broad range of tools that helps people integrate information, analyze data and use the resulting insights to improve decision-making (Patel, 2020). A.I. technology allows machines to learn from experience, adjust to new inputs and perform human-related tasks.

In today's world, the most common A.I. technologies have been applied in chess-playing computers and self-driving vehicles, which rely heavily on deep learning, natural language processing, and automation in the manufacturing sector. Using A.I. technologies, computers can be trained to accomplish complex tasks by processing enormous amounts of data and recognizing the data. Each industry has a high demand for artificial intelligence capabilities, including systems used in industrial automation, learning, risk notification, and research. A.I. technology has been applied in various industries, including health care, retail, manu