Category: AI

The development of AI technologies in various sectors of the economy is primarily related to the level of their digitalization. The use of social medial for retail is one of the most mature sectors of the economy in terms of the use of AI. It is characterized by access to unlimited data, a significant investment by such e-commerce giants as Amazon and Alibaba, and a clear focus on increasing sales. Due to the ability to collect a large amount of data from various sources, AI poses a significant threat of data leakage and cyber fraud. Improving the cybersecurity when using social medial for retail by detecting and preventing any fraudulent activity will allow to protect confidential data when using AI.

Background Information on The Problem

Cybercrime in use of social medial for retail is a consequence of the globalization of information and communication technologies and the emergence of international computer networks. According to Hu et al. (2020), unlike other types of economic crime, cybercrime is currently the fastest-growing segment. Any information and technical innovations significantly expand the scope of cybercrime in trade and create conditions for increasing the effectiveness of hacker attacks. Therefore, cybercrime is growing at a faster rate than all other types of crime.

Perspectives From Multiple Populations

The societal problem of cybersecurity in trade affects all parts of society: from sellers to intermediaries and buyers. With the development of the Internet and the convenience of providing trading services to customers, online purchases have become widely used; they are carried out using remote banking technologies. To get hold of confidential user data, fraudsters have come up with a method called fishing, which implies creating a site that is outwardly indistinguishable from the accurate bank site.

Thus, buyers in the field of online commerce suffer from the leakage of confidential data. Sellers themselves also become victims of the problem of cybersecurity violations. For example, according to Mahmoud et al. (2020), third parties steal money and confidential data using virus software or by recording conversations using microphones. Remote access programs help fraudsters to steal from the account of trading enterprises. Intermediaries in retail also suffer from fraud; when placing ads for sale on social media, they often receive calls from scammers who present themselves as interested buyers.

Argument Supporting Proposed Solutions

Strengthening the cybersecurity of enterprises will make them less vulnerable to hacking. Therefore, buyers, clicking on the social medial link for payment, will be sure that they are transferring money to an honest seller and not to scammers (Thakor, 2019). Taking care of their reputation, e-commerce enterprises who use social medial for retail should take the security and reliability of their customers ‘ applications seriously. The use of original programs for the cyber protection of official websites will help protect customers from possible fraud.

Strengthening cybersecurity at the enterprise will also protect entrepreneurs from unauthorized debiting of funds from bank cards or accounts of legal entities. It will provide a general guarantee of the safety of payments made through non-bank payment transfer systems. The cybersecurity measures taken will be able to make electronic transfers within the trading company more secure (Donepudi, 2018). The implementation of protection mechanisms will also allow sellers to protect the process of sending, forming evidence of shipping, and receiving documents. Strengthening Internet security can also help make the conclusion of transactions safer for intermediaries. When regularly working with trading in social networks, it is necessary to equip operating equipment with media protection, and network screens. This will reduce the increased volume of fishing and data leakage caused by recording a conversation through a microphone.

Evidence From Scholarly Sources

The first scholarly source that proves the connection between the strengthening of cybersecurity and using social medial for retail is “Shopping intention at AI-powered automated retail stores.” This study examines the implementation of online purchases from the point of view of the buyer. The authors state that the self-isolation regime has led to an unprecedented boom in online trading. They advise people who often make purchases on the Internet to secure their device by installing a high-quality antivirus.

According to Pillai et al. (2020), months of quarantine have brought at least 10 million new customers to e-commerce in the United States. In addition, the article claims that 44 % of all cybercrimes account are for the theft of money from credit cards and only 16% are for the theft of classified information. Such statistics indicate the need to protect people who often make online purchases.

The article “Shopping intention at AI-powered automated retail stores” cannot be considered sufficiently reliable. In reality, the number of victims of Internet scammers is much higher since only data on known fraud cases are used in the study’s statistics. Most people who become victims prefer not to contact law enforcement agencies, and the study does not consider issues of fraud that are not made legally public.

The strength of the study is the presence of a predictive function in this comparative study; it demonstrates the trend of cybercrime to a steady increase. The article shows that in current conditions, combating crimes in the field of computer information is acute. For this reason, there is a need to apply and develop programs that will protect the average user of online stores.

Among the weaknesses of the study, the lack of completeness of statistical data can be singled out. High-quality initial statistical information is necessary for an adequate analysis of the current situation and the development of a forecast of the socio-economic development of cyberbullying. Unfortunately, it is impossible to collect accurate statistics on this issue, so the presented data can be considered as close to reality as possible.

The next scholarly source “State of the art and adoption of artificial intelligence in retailing” addresses such a buyer’s security problem as the use of search queries and information received through the phone microphone for personal purposes. The primary way to access conversations or search data is to install a program that will access the microphone and text on the screen. This data can be used not only by official systems (for example, Google) to optimize advertising but also by scammers for selfish purposes.

The article’s data provides statistics that only 0.2% of all records are transferred to Google contractors, and there is no identifying information in the audio files themselves (Weber & Schütte, 2019). However, the company admitted that its contractors could listen to recordings of users’ communication with Google Assistant. It is mentioned that there are more than a thousand phrases encrypted in TV series and news that lead to the activation of voice assistants.

Thus, the buyer’s personal information can get to third parties through the microphone and search queries. The article can be reliable, as it contains links to interviews with developers of such significant technologies as Alexa, Google Home, Microsoft Cortana, and Siri. However, the journal in which the article is published is sponsored by a Chinese competitor of the listed technologies; for this reason, the study might be a little biased.

The main advantage of the article “State of the art and adoption of artificial intelligence in retailing” is its informative content. Many technical devices belonging to different manufacturers are mentioned, and the volume and type of personal information they receive about customers. For example, data on customer data collection using smart speakers, televisions, voice assistants, microphones, and even robot vacuum cleaners are presented. The weak side of the study “State of the art and adoption of artificial intelligence in retailing” is its possible bias. Interviews with technical specialists of companies are given as statistics. However, the article does not contain official information provided by the heads of the companies mentioned above.

The third scholarly source, whose authors also mention security when using social medial for retail, is the “Design of smart unstaffed retail shop based on IoT and artificial intelligence”. The authors insist on the mandatory set of measures to protect the data of a trading enterprise from malware and hacker attacks. From the authors’ point of view, a competent approach to cybersecurity involves the multi-level protection of software, networks, databases, and PCs.

According to Xu et al. (2020), in 2019, the retail sphere was the second most popular among hackers: according to estimates, 24% of attacks were directed at it. Further, as an information statistic, the article provides a percentage list of information security risks of trading enterprises. The main cyber threats to trading companies are espionage and financial losses. So, in 2019, the motive for most attacks (84% of cases) was to obtain data, and 36% of hackers were interested in financial benefits.

The study is a valuable work since the collected statistics of information security risks in commercial enterprises is an essential contribution to the fight against cyber fraud. The data presented in the study make priceless contribution to solving the main task facing information security specialists. They help assess the feasibility of various information security risks in the company and identify possible consequences of cyber-attacks to build an effective protection system based on this knowledge.

The strength of the study “Design of smart unstaffed retail shop based on IoT and artificial intelligence” is the practicality of the data provided. Given the deep penetration of high technologies into the industrial segment, many potential attack vectors have been created. There are different ways of protection against various types of fraud. For example, the protection of the data that can be obtained through the phone microphone is different from protecting the one that can be revealed through tracking the search query. Therefore, the statistics given in the article can be used for an essential specification of the process. The disadvantage of the article is the difficulty of applying the proposed methods of combating cybercrime. The actions in the infrastructure offered by the authors are theoretically able to affect the technological process negatively. In this regard, it seems unlikely that the management will take the announced measures to strengthen security.

Ethical Outcomes

The positive ethical outcome of my decision will be the protection of confidential information from unauthorized use. Retail employees who work with personal data about clients due to their professional duties will not be able to make it public (Raghavan & Pai, 2021). The circle of people who will work with restricted access information will be clearly defined. The reverse side of such a solution to the problem may be a violation of employees’ boundaries. Monitoring employees ‘ working hours on a work computer can turn into interference in their privacy. For example, the personal life of a social media freelancer outside of working hours can be violated (Güven & Şimşir, 2020). In addition, if the manager wants to save money and contact an unreliable company, employees can become victims of voyeurs.

In case of a positive outcome, a big step is taken in solving the ethical problem of personal data protection. According to Oosthuizen et al. (2020), the confidentiality of personal information about lifestyle, health, finances, and contacts are still perceived by Internet users as a value. Therefore, by installing cybersecurity programs on retail and social media platforms, customers will be guaranteed the importance of privacy, which is a legitimate human right of a democratic society. Nevertheless, modern society and employers are becoming more and more open and tolerant to the gradual expansion of the framework of moral acceptability and legal permissibility. Under the pretext of cybersecurity, unscrupulous employers will be able to control their employees with the help of special programs and obtain data about the private life of employees.

Conclusion

With the development of Industry 4.0, information security is becoming as important a component of a digital trading. The urgency and acuteness of privacy are associated with the rapid development of information and communication digital technologies and the controversial legal regulation of this area. Accordingly, in any projects using social medial for retail, the minimum necessary list of information security systems should be used. It must be provided with the forces of specialists who can maintain and administer it and promptly detect and counteract cyber-attacks.

References

Donepudi, P. K. (2018). AI and machine learning in retail pharmacy: Systematic review of related literature. ABC Journal of Advanced Research, 7(2), 109-112.

Güven, I., & Şimşir, F. (2020). Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ANN) and support vector machines (SVM) methods. Computers & Industrial Engineering, 147(4), 180-185.

Hu, H., Zhou, N. Q., Wang, X., & Liu, W. (2020). DiffNet: A learning to compare deep network for product recognition. Access IEEE, 8(27), 19336-19344.

Mahmoud, A. B., Tehseen, T., & Fuxman, L. (2020). The dark side of artificial intelligence in retail services innovation. In E. Pantano (Ed.), Retail futures: The good, the bad and the ugly of the digital transformation (pp. 165-180). England, Bingley: Emerald Publishing Limited.

Oosthuizen, K., Botha, E., Robertson, J., & Montecchi, M. (2020). Artificial intelligence in retail: The AI-enabled value chain. Australasian Marketing Journal, 29(2), 144-154.

Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores. AIPARS Journal of Retailing and Consumer Services, 51(7), 28-34.

Raghavan, S., & Pai, R. (2021). Changing paradigm of consumer experience through Martech – A case study on Indian online retail industry. International Journal of Case Studies in Business, IT and Education, 5(1), 186-199.

Thakor, S. D. (2019). Effect of artificial intelligence (AI) on retail business. International Journal of Research in all Subjects in Multi Languages, 7(2), 72-74.

Weber, F. D., & Schütte, R. (2019). State of the art and adoption of artificial intelligence in retailing. Digital Policy, Regulation and Governance, 21(3), 23-28.

Xu, J., Hu, Z., Zou, Z. J., Hu, X., Liu, L., & Zheng, L. (2020). Design of smart unstaffed retail shop based on IoT and artificial intelligence. Access IEEE, 8(13), 147728-147737.

Introduction: Summary of the Main Points of the Article

Artificial intelligence (AI) is a multifunctional tool that allows individuals to reevaluate how to combine information, analyze data, and apply the ensuing insights to enhance decision-making (West & Allen, 2018). The article explores how technology is changing the world for better or worse, as well as what it will look like when AI reaches its full potential. In summary, the above article focuses on Artificial Intelligence’s effects on households, businesses, as well as local government.

The Relevance of Artificial Intelligence on the Households

Artificial intelligence makes difficult and tedious household chores easier for humans, for instance, robots that mow the lawn or clean houses as needed. As a result, an individual will only need to do the little jobs that the robot cannot currently handle. Thus, one will be released from tiresome and ordinary tasks to focus on the vital aspects of life. Right now, comfortability is still the primary concern for any human being. Thus, AI technology will support the user more as it improves.

Importance of Artificial Intelligence on the Business

AI has various advantages in terms of worker productivity and customer relations. Large ride-sharing companies are studying autonomous cars and expanding taxi and car-sharing services in the United States, such as Uber and Lyft (West & Allen, 2018). Automation of production processes and service provision can be managed by artificial intelligence. It can handle and maintain the requisite environmental factors for product storage, for instance, monitor warehouse balances, process payments, and log and respond to customer requests (West & Allen, 2018). AI-based bots can offer 24/7 user request service at any time that is convenient for the client. Therefore, businesses may strengthen the loyalty of their current consumers and draw in new ones by providing better interactions and quicker responses.

Relevance of Artificial Intelligence on the Local Government

Local Governments can operate more effectively, enabling an organization to structure its primary operational duties more effectively (West & Allen, 2018). AI can assist managers in keeping up with policies and determining the most critical efforts for residents. The possible effects of any program on the community can then be planned for and measured by managers. For example, AI is being utilized to modify traffic light timing in Los Angeles, San Antonio, and Pittsburgh (West & Allen, 2018). It can also remove a great deal of prejudice. People are disposed to unfair and subjective decision-making. Creating decisions that need objectivity, such as hiring, is much harder (West & Allen, 2018). Decisions made by local governments based on data can give underfunded departments an advantage.

Conclusion

To conclude, even though most people are unfamiliar with AI, the world is on the verge of altering various industries through technology and data processing. For instance, as discussed above, AI has enormous potential for improving public safety: relevant authorities in both the local as well as state government Local, as well as state authorities, may indeed utilize this advanced technology and prediction analysis to assess which sections of their municipalities and regions are most susceptible to natural calamities such as earthquakes and storms. Applicable operations have already changed decision-making, marketing strategies, risk management, and system efficiency in banking, public safety, universal healthcare, crime control, infrastructure, and smart cities. These innovations yield significant economic as well as social advantages to the economy.

Reference

West, D. M., & Allen, J. R. (2018). How artificial intelligence is transforming the world. Brookings.

The system of artificial intelligence (AI) has significantly developed over the last decade. AI has both pros and cons, while the consequences of its growing impact are a controversial matter. Artificial intelligence can make any process efficient since it works quickly and correctly. AI in the modern world brings mainly positive results in those areas where it is used. Despite all merits of AI, it has disadvantages due to probable errors, the duration of the learning process, and public fear of rebellion against humanity. For these reasons, AI researchers are divided into two opposing camps. To come to a clear opinion, it is necessary to analyze each of the parties thoroughly.

The first party of researchers considers AI as a tool for developing technology, raising the standard of living, and the prosperity of humankind as a whole. Representatives of this view are Groom and Jones – Doctors of Philosophy and eminent figures in information and communication sciences who have successful technological careers. Their views are multi-perspective and valuable, as they considered the technological and philosophical sides of the issue.

They highlight several essential characteristics that distinguish AI from human performance. The first is that “the introduction of AI makes it possible to process large amounts of data quickly and efficiently, minimizing human involvement and, by reducing the human factor, minimizing error.” (87). The second one is that “neural networks do not have a performance threshold”, which means that if a person can check, for example, 100 parts for quality in a day, then the system will review them as much as the server capacities allow (89). These statements are well-founded and emphasize the advantages of Artificial Intelligence.

I agree with the authors on this point and can only add that AI can work 24/7 without the need for sleep or rest. Its productivity and quality of work do not decrease depending on the working time, fatigue, and personal circumstances. This means that the main advantages of currently existing solutions are the ability to automate many areas of activity while minimizing human participation and expanding areas where it is possible to use software instead of human labor.

Groom and Jones consider that “with the help of AI technologies, the speed and level of automation of processing large amounts of information are significantly growing while improving the quality and manufacturability” (110). I agree that, at the moment, AI is especially good at analyzing large amounts of data, where a person would take too much time, and conventional programs that do not use machine learning would not be able to achieve the necessary accuracy. With the right attitude to new technologies, the use of data increases, as well as the efficiency and quality of management decisions.

On the other hand, my opinion is that not only intellectual and computational work should be shifted to AI. Among other advantages, AI technology is best suited for optimizing various mechanical activities, automating routine operations, and using them in hazardous industries. Proper use of robotics on conveyor lines may allow switching to non-stop functions, optimize the cost of the enterprise, and improve product quality. It is essential to understand that progress in the world is still strongly connected to technology (Shaw 5). However, after the introduction of automation, these operations will occur faster and cheaper than a person can do.

To fully understand the pros and cons of AI, a different perspective needs to be considered. I consider Hurley a prominent representative of the opinion that artificial intelligence is not able to change a person and has many weaknesses. Hurley thinks that “each task that is being solved now is RnD in its purest form: a person needs to define, systematize, come up with a solution, and implement this solution” (107). That is a creative process that requires a high level of science and expertise in the field of application of this solution, whether it be FMCG, space, medicine, or the area of implementing neural network systems (Hurley 97). The fundamental difficulty of these projects is related to the unpredictability of the result. This view is supported by Harkut and Kasat (2019). They note that AI technology has a programmed automized decision-making process, which is a simulation of human intelligence. AI has a specific code for the decision-making process; however, it may lead to inconsistencies since it does not admit possible important human factors.

I do not fully agree with the authors on this question: implementing an AI system makes it possible to predict development without implementing the project. Discovering technologies and algorithms will tell practically nothing to a person without a mathematical education and practical experience, only a few top managers with such a background among customers.

The contrary argument and the weak point of Artificial Intelligence is also the lack of large enough data sets for training AI. Technologies for collecting and processing data are constantly evolving, and companies can already implement Data Lake technologies, which are becoming an excellent platform for training artificial intelligence. However, this is still not enough to complete fast neural network training.

I consider it important to study the views on artificial intelligence by Dr. Garg. He is assistant director, of executive programs management at Amity University Uttar Pradesh India, a Ph.D. and UGC NET qualified with 15+ years of academic experience. As well as Dr. Agrawal is PhD, and UGC-NET qualified with 18+ years of experience in teaching and research, working as a Professor. Because of their significant experience and ability to evaluate using AI in real business processes, their opinion seems authoritative and worthy of attention. They consider that implementing artificial intelligence in business is a financially costly process (Agrawal and Garg 21). For industrial enterprises, such solutions may lead to a delayed economic effect.

Transitional solutions and real-time data visualization allow approximating economic benefits. Another difficulty is the need to restructure the business process when introducing intelligent systems (Agrawal and Garg 27). I consider it is not enough to buy such a solution and put it like a flower in a vase or an application on a computer. It is necessary to make this decision friendly to the business process: create, reconfigure, or even cancel some operations, retrain people, and optimize staff.

Most existing and developing artificial intelligence products aim to perform routine tasks by many specialists. Even though this leads to more straightforward work, it leads to a reduction in jobs. In Oshida’s opinion, it is unprofitable for the result to keep a certain number of professionals doing tasks under AI control (99). Accordingly, for the sake of the economy and profit, employers will seek to eliminate irrelevant employees (Faris et al. 61). Unemployment is increasing, and the retraining of specialists will take much time and additional resources of the state and the educational system. Further complicating the situation is that artificial intelligence does not make reason in human terms (Faris et al. 54). AI leads to the fact that the robot or computer does not take into account ethical norms and values. Its activity is designed to complete tasks but not to create a positive atmosphere, considering other people’s interests and teamwork.

Based on the totality of all the above points of view, I can conclude that artificial intelligence is an ambiguous technology for humanity. However, most of the problems that arise as a result of AI integration can be solved by indirect methods. For example, the number of vacancies for programmers and other professionals whose activities will be directed to the control and maintenance of computers will increase. From my point of view, that can retrain those dismissed due to lack of demand following the new standards, which will help keep the unemployment rate at the same level. At the same time, one should consider the large utility that AI provides for corporations and general human activities. I believe that this phenomenon has much more advantages than disadvantages, so abandoning artificial intelligence is inefficient.

Artificial intelligence is a somewhat controversial issue. The discussions around it are essential for its development. It is worth saying that artificial intelligence has two sides: positive and negative. Admittedly, the positive side is more significant, as it has enabled many industries to improve their operations and make them significantly more efficient. It is necessary to approach them non-standard to solve all the negative results of artificial intelligence.

Works Cited

Agrawal, Rashmi and Garg, Vikas. (Eds.). Transforming management using artificial intelligence techniques. CRC Press, 2020.

Faris, Hossam, Aljarah, Ibrahim, and Mirjalili, Seyedali. (Eds.). Evolutionary machine learning techniques. Algorithms and applications. Springer Singapore, 2017.

Groom, Frank M. and Jones, Stephan S. (Eds.). Artificial intelligence and machine learning for business for non-engineers. CRC Press, 2020.

Harkut, Dinesh G., and Kashmira Kasat. “Introductory chapter: artificial intelligence-challenges and applications.” Artificial Intelligence-Scope and Limitations (2019).

Hurley, Richard. Big data. Ationa Publishers, 2020.

Oshida, Yoshiki. Artificial intelligence for medicine. People, society, pharmaceuticals, and medical materials. De Gruyter, 2021.

Shaw, James, et al. “Artificial intelligence and the implementation challenge.” Journal of medical Internet research. Vol. 21, no. 7, 2019, p. 11.

Pfizer is a global pharmaceutical corporation that specializes in the creation, testing, and distribution of medical drugs and equipment. In recent years, the company has introduced AI to aid in the most critical processes, particularly in helping the development of new medicines.

AI in the pharmaceutical industry is more complex, requiring deep learning and pattern identification based on big data (Get Science, 2017). Pfizer is a data-driven organization, and it has data science tools that are transferable as well as can be imported from academic and practice settings, also known as using real-world evidence (RWE) (data from EHR, insurance records) to find expanded use for medicines. AI is also used at Pfizer to digitize clinical trials, adding automation and predictive analytics as well as expanding access to clinical trials by more accurate identification of optimal diversity and location of clinical study sites (Pfizer, n.d.).

Some of the challenges to using the technology are based on the constraints of modern AI, which ultimately relies on competent design and effective algorithms. As the data set and proposed actions grow more complex, there is more room for error. Ultimately, modern AI cannot solve problems or generate new ideas (Greenwald, 2018). From a CRM perspective of human understanding and sales, that means the Pfizer AI continues to be just a supplemental tool for humans, providing recommendations but not being able to resolve complex issues. It can provide recommendations and work within the algorithms to guide b2b relations that Pfizer relies upon to sell its products.

Another challenge and also an issue of ethical and legal nature is the privacy of information. As a drug and medical device manufacturer, Pfizer may have access to private patient data, particularly from trials it conducts and information that is shared by healthcare providers. Healthcare information is highly sensitive and has to be secure at all times, but at the same time, an AI needs large data sets to effectively learn and fulfill its functions which creates a dilemma because it is unclear to what extent will the AI utilize or reveal the private information.

Privacy and data security are hot-button issues regarding AI, and there is no clear consensus on the ownership or security of this data (“Robotics and artificial intelligence,” 2021). Although some regulations exist for commercial applications of data, AI is such a new development and tool, that it is unclear how to ultimately provide oversight.

References

Pfizer. (n.d.). Leading innovation in trials. Web.

Get Science. (2017). A new frontier for AI: Helping scientists develop potential new medicines. Pfizer. Web.

Greenwald, T. (2018). Artificial intelligence (a special report) — What exactly is artificial intelligence, anyway? Everybody’s talking about AI these days. Here’s what all the fuss is about. Wall Street Journal. Web.

Robotics and artificial intelligence. (2021). Gale Global Issues Online Collection. Web.

Introduction

Artificial intelligence (AI) revolves around the idea that human intellect can be replicated in machines. Technological advancements have made it possible for experts to manufacture machines, which can discharge many activities that require reasoning without human intervention. Many studies have been conducted to examine the field of artificial intelligence. As this paper reveals, AI-related topics that have received significant scholarly attention include the impact it has on people’s lives, some of its interesting features, and the future of business operations, thanks to the application of artificial intelligence.

Artificial Intelligence and People’s Lives

According to Makridakis, “The goal of artificial intelligence includes learning, reasoning, and perception, and machines are wired using a cross-disciplinary approach based on mathematics, computer science, linguistics, and psychology” (49). Artificial intelligence has significant impacts on our lives. This technology influences the buying behaviors of many customers, particularly those who like watching movies. Companies like Netflix have invested heavily in artificial intelligence intending to gather information regarding consumers’ interests.

Data gathered is used to target individual customers depending on their tastes and preferences (Makridakis 55). For instance, after streaming a movie or series, one may be surprised to see their screen filled with images that promote other shows or videos. In most cases, these films fall in the same genre as the one they have just completed viewing. Such incidents do not happen by coincidence. Companies use artificial intelligence to persuade people to stream more videos or buy specific products and services.

Artificial intelligence is also used in the transportation sector. Taxi companies such as Uber use applications that are equipped with machine learning, which is a component of artificial intelligence (Dirican 568). It would have been hard for Uber to achieve its goal of dominating the ride-sharing market without using this technology. Machine learning enables taxi businesses to identify falsified accounts and determine the most favorable points to pick or drop clients.

Interesting Things about Artificial Intelligence

One of the most fascinating things about artificial intelligence is that virtually all artificial intelligence assistants respond in feminine voices. For instance, AI machines such as Cortana, Siri, and Alexa are all female. The primary reason they are feminine is that most people prefer female assistants. Another interesting thing about artificial intelligence is that characters can write. Today, robo-journalism is gaining popularity in the media industry. Los Angeles Times prides itself on being the first company to use a robot to compose an editorial about earthquakes in California (Makridakis 58). Despite these numerous benefits attributable to artificial intelligence, some tech companies have doubts about the technology. For example, Tesla’s chief executive officer, Elon Musk, is renowned for his love for advanced technology. However, he shares his skepticism regarding artificial intelligence. Musk argues that artificial intelligence may pose a threat to humanity and hence the need for a level of control. He advocates for the abolishment of the manufacture of automated weapons.

The Future of Artificial Intelligence

Many customer care professionals are losing jobs. Their positions are being taken over by artificial intelligence. Studies show that over 85% of consumer relationships involve artificial intelligence-aided robots (Dirican 571). Hence, in the future, technology will dominate the personal assistant career, thus rendering many people jobless. The demand for self-driving cars is growing. Artificial intelligence is expected to feature in the automobile industry since many companies are looking forward to producing automated cars.

Conclusion

Artificial intelligence has infiltrated our lives in various ways. Companies leverage this technology to influence consumers’ buying behaviors. Additionally, businesses are gradually using AI to automate professions, a move that has made many people jobless. Even though this technology is useful, there is the need to regulate its utilization before it becomes a threat to humanity.

Works Cited

Dirican, Cuneyt. “The Impacts of Robotics, Artificial Intelligence on Business and Economics.” Procedia – Social and Behavioral Sciences, vol. 195, no. 1, 2015, pp. 564-573.

Makridakis, Spyros. “The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms.” Futures, vol. 90, no. 1, 2017, pp. 46-60.

Introduction

The world is approaching an era with a new technological structure, where robots and devices powered with artificial intelligence will be extensively used both in production and in personal life. Currently, manufacturers of such devices and machinery are often labeling their products intellectual. However, at the current stage of development, it is merely marketing. Substantial research is needed to make contemporary machines intelligent. Although the technology does not yet exist in its final form, many are already pondering the possible positive and negative impacts of robots and artificial intelligence.

One the one hand, with artificial intelligence and fully autonomous robots, organizations will be able to optimize their spending and increase the speed of development and production of their commodities. On the other hand, employees are concerned that they will be laid off because their responsibilities might be taken away by machinery. Outside of the organizational context, artificial intelligence and robots are likely to provide additional comfort and convenience to people in their personal lives. This paper explores the benefits and disadvantages of robots and AI in the context of business, job market, and society.

Impact on Organizations

Artificial intelligence and robots can bring many benefits to organizations, mainly due to the capacity for extensive automation. However, automation is a vague term, and it is necessary to clearly outline what aspects of organizational processes can be automated. On the contrary, there are concerns with security and ethics. Furthermore, AI development, due to its novelty, continues to stay as one of the most expensive areas of research.

Positive Effects

Customer relationship is one of the most critical areas for every organization. Currently, replying to emails, answering chat messages and phone calls, and resolving client issues require trained personnel. At the same time, companies collect enormous amounts of customer data that is of no use if not applied to solve problems. Artificial intelligence and robots may solve this issue by analyzing the vast array of data and learning to respond to customer inquiries (Ransbotham, Kiron, Gerbert, & Reeves, 2017). Not only will it lead to a reduction in the number of customer service agents, but it may also lead to a more pleasant client experience. That is because while one human specialist can handle only one person, a software program can handle thousands of requests simultaneously.

To perceive any meaning from terabytes of semi-structured and unstructured information, data specialists of companies need to work tirelessly and for considerable amounts of time. Artificial intelligence can automate these data mining tasks – new data is analyzed immediately after getting added to databases, and the autonomous program automatically scans for patterns and anomalies (von Krogh, 2018). The technology may be used to discover insights and gain a competitive advantage in the market.

AI-powered robots may replace humans in some areas of a company’s operations. For instance, some hotels are using such robots to automate check-ins and check-outs, provide more convenient customer experience through 24/7 support service (Wirtz, 2019). Operational automation is also possible in manufacturing facilities where string temperature levels must be maintained (Wirtz, 2019). Stock refilling is a potential use case for stores and restaurants. Although not everything can be automated, a substantial portion of companies’ activities can be run through the use of intelligent robot systems.

Administrative tasks can also be eased with the help of artificial intelligence. For instance, current use cases include aiding the recruitment department (Hughes, Robert, Frady, & Arroyos, 2019). An intelligent software system can automatically analyze thousands of resumes and filter those that are not suitable (Hughes et al., 2019). There are several benefits of an automated recruitment process – a substantial amount of financial resources is saved because there is no need to hire a recruitment agency, and all applications will be considered objectively, with no bias and discrimination.

The recruitment process is not the only human resources department function an intelligent software system may help with. Organizations are often challenged by the need to schedule workers according to workload (Hughes et al., 2019). HR managers also need to consider which workers work well together, and what task needs which employee. Artificial intelligence may automate much of these responsibilities – it can assign more workers to a particular shift when more customers are expected, and choose employees that work together much more effectively than others (Hughes et al., 2019). Both organizations and employees benefit from such functions because companies will have optimized scheduling, and workers will be more satisfied because of more productive relationships.

Adverse Impacts

Despite many benefits, there are also limitations of artificial intelligence and robotics. The technology relies on the availability of data, and often such information is unstructured, is of poor quality, and inconsistent (Webber, Detjen, MacLean, & Thomas, 2019). Therefore, it is challenging for a company with no access to a large pool of data to develop an intelligent system. Currently, only companies like Google, Facebook, Uber, and Apple, that gather terabytes of data each minute have the capacity to build sophisticated and useful AI-powered systems.

Any company that is planning to adopt AI and robotics to achieve new business objectives should be ready for high expenditures. Because of a shortage of skilled professionals that are able to develop and operate reliable AI solutions, the cost of producing a required software system is high. Such a situation makes AI a prerogative of rich companies and virtually impossible for those who only want to try the technology to see whether it is suitable at the moment.

Impact on Employees

Positive Effects

For the majority of workers, their managers and supervisors are the sources of mentorship and advice. A recent study suggests that robots can also serve as guidance because the majority of employees trust robots more than their managers (Brougham & Haar, 2018). The primary advantage of robot managers over their human counterparts is that they provide unbiased and objective advice. Besides, robots are able to work for 24 hours, which allows employees to get answers to their questions much sooner than they receive now.

As stated in the paper before, artificial intelligence and robots can contribute significantly to the recruitment process with unbiased assistance. It is beneficial not only to enterprises but also to employees because they will have an equal opportunity for receiving the job (Hughes et al., 2019). Also, recommendation systems may allow people with little or no experience to be recognized by companies (Hughes et al., 2019). Traditional barriers will cease to exist if hiring managers will start to depend on intelligent systems heavily.

One significant benefit of robots over humans is that they are never physically tired. This attribute can be proven to be especially beneficial if robots are used to aid people with tedious and repetitive tasks (Cesta, Cortellessa, Orlandini, & Umbrico, 2018). However, for this approach to work, companies need to consider robots not as an eventual replacement but as colleagues to human employees. In such a scenario, human workers deal with unpredictable and non-trivial tasks, while robots relieve them from doing repetitive tasks and duties that may have caused physical harm.

Robots powered with artificial intelligence have the potential to become effective teambuilders. There are efforts to build a system that accepts responses and commentaries from team members and gives targeted feedback, which may be used to enhance the relationship between team members (Webber, Detjen, MacLean, & Thomas, 2019). The system can also be used at a different stage – when forming new teams, by carefully inspecting the available data, the system may give recommendations on which employees will be the most effective in a team considering their skillsets (Webber et al., 2019). While AI cannot become a replacement for human involvement in team building activities, it can positively influence groups through systematic interventions.

Adverse Impacts

Despite many positive effects, artificial intelligence and robots may serve as the most detrimental agents to human employment. Due to the capacity of being automated, robots and AI may replace humans in many areas of activity. For instance, with the emergence of autonomous vehicles, drivers may lose their jobs. The list of jobs that are under the risk of being diminished by robots is long. It includes support specialists, proofreaders, receptionists, machinery operators, factory workers, taxi and bus drivers, soldiers, and farmers (Brougham & Haar, 2018).

Some claim that, while taking away many opportunities from people, artificial intelligence and robots will create other jobs that humans will need to occupy (Brougham & Haar, 2018). However, skeptics state that artificial intelligence will harm the middle class and increase the gap between highly skilled employees and regular workers (Brougham & Haar, 2018). AI is only an emerging technology, but employees and companies will need to be ready for its adverse influences.

How Society is Influenced

Society has been significantly influenced by technology, and this trend will continue as artificial intelligence and robots get more sophisticated. As progress is made in the field of AI and robotics, the technology will blend into people’s lives, and it will become challenging to distinguish between what is a technology and what is not (Helbing, 2019). This uniform integration has many benefits, such as convenience and comfort. However, because technology is power, some critics claim that people will need to view these advancements from the standpoint of citizens, not consumers (Helbing, 2019).

Artificial intelligence relies heavily on the data people generate in order to train and provide better results (Helbing, 2019). As the sole owners of their personal data, people will need to be able to control how this data is used and for what purposes. In the wrong hands or the corrupt system, this information may be used to influence citizens (Helbing, 2019). Therefore, it is reasonable to claim that, as artificial intelligence and robots get more advanced, society will strive for more transparency in how their personal data is used.

Recommendations

There are three recommendations worth making, and each one of them relates to one potential effect of artificial intelligence and robots. There is a widespread belief that intelligent systems will eventually replace human beings in many industries and jobs (Brougham & Haar, 2018). Not only will it have a detrimental effect on those who will lose their jobs, but it will also harm society’s current structure. One way of mitigating these consequences is to design robots and AI not to replace human employees but assist them in jobs they are performing for increasing productivity.

In the contemporary world, people produce enormous amounts of data, which is collected both by the government and private companies. Current laws require enterprises to use personal data of their customers in such a way that their private information is not exposed to third-parties (Helbing, 2019).

As artificial intelligence gets more developed, current laws may become obsolete. The government should demand companies to be much more transparent in how the data is used. Furthermore, the government should require companies to undertake security measures so that personal information is not used by an intelligent system to impose harm on people. A relatively recent case of Cambridge Analytica shows how the public can be manipulated if personal data results in the wrong hands. Public awareness of AI and robots’ implications should also be increased.

Conclusion

It is already known that artificial intelligence and robotics are the next chapters in the history of digital technology. Present versions of artificial intelligence have partial success in identifying and curing cancer, predicting the weather, analyzing the image from cameras and other sensors to drive a car autonomously, and much more. Organizations and businesses are the first ones to utilize the technology to maximize their profits and minimize their expenditure while keeping the quality of products and services at the highest levels. There are many benefits of the technology, including significant automation in many areas of organizational activity, and employee assistance.

People, however, should also remember the downsides – many people are likely to lose their jobs, and companies need to make substantial investments before artificial intelligence and robots are entirely usable. To mitigate some of the adverse consequences, companies will need to think about using AI and robots to assist employees and not to replace them. The government should also be involved – it must ensure that personal data of customers is safe. Efforts should also be made to increase public awareness about the implications of artificial intelligence and robots.

References

Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257.

Cesta, A., Cortellessa, G., Orlandini, A., & Umbrico, A. (2018). Towards flexible assistive robots using artificial intelligence. Web.

Helbing, D. (2019). Towards digital enlightenment. Cham, Switzerland: Springer International Publishing.

Hughes, C., Robert, L., Frady, K., & Arroyos, A. (2019). Managing technology and middle- and low-skilled employees. Bingley, UK: Emerald Group Publishing.

Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1).

von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404-409.

Webber, S. S., Detjen, J., MacLean, T. L., & Thomas, D. (2019). Team challenges: Is artificial intelligence the solution? Business Horizons, 62(6), 741-750.

Wirtz, J. (2019). Organizational ambidexterity: Cost-effective service excellence, service robots, and artificial intelligence. Web.

AI and neural networks can be viewed as a new tool of self-expression for artists that can help replicate and mix different styles of art within one artwork. Historically, the development of a new tool in art led to increased artistic activity, the creation of new genres, and the exploration of new possibilities. AI offers artists a different level of interaction between their ideas and a final product, as it introduces powerful electronic instruments into the process.

AI and neural networks can lower the entry barriers into art, simplifying the work process with various artistic tools present these days and offering beginners its advice (Aue, 2018). Art history is as long as human history itself; art has experienced incalculable changes and shifts while accompanying humankind since the dawn of time. To some extent, modern art incorporates all of its previous histories, making art a complicated subject to study. Moreover, the accelerated development of new artistic tools complicated the picture even further. Therefore, the entry barriers into the domain of art are constantly rising. AI can be the tool that changes the status quo by incorporating and simplifying other tools and experiences within it, thus helping novice artists along their path.

A widescale use of AI and neural networks in art contributes to their further development and improvement. The use of neural networks in the art domain already helps humanity understand its own nature better – the learning patterns, the definition, and the source of such concepts as knowledge and creativity. Further research and advancements in the field can lead to breakthroughs in understanding both humans and artificial intelligence.

References

Aue, J. (2018). The relationship between art and AI. Medium.

Introduction

Innovations, such as AI have a strong potential to enhance contemporary society. Advances in these technologies create the basis for critical applications in manufacturing, promoting future opportunities in manufacturing systems and ultimately challenging traditional conceptions of manufacturing operations in society. There is an increasing debate on the role of artificial intelligence technologies in critical processes, which clashes with the human oversight role. The effectiveness of human decision-making might depend on numerous factors, such as alcohol, current motivations, education, criminal history, and motives. That is why advocates of AI emphasize its ability to resolve issues being free from subjective judgments. The application of such technology has a significant contribution to manufacturing operating systems.

Background

There are many ways in which technology can improve manufacturing. It helps to resolve such issues as unskilled or a lack of skilled workforce, security of data, supply chain, autonomous monitoring, managing factory from a phone, remote access to experts, predictive maintenance, better product lifecycles, quality inspection, and generative designs (Li et al., 2017). However, the sphere of technology is very versatile as many tools exist to resolve the abovementioned issues. In terms of artificial intelligence, the scope of improvements is narrowed down to the following: optimizing the supply chain, autonomous monitoring machines, predictive maintenance, quality inspection, and generative designs.

The application of artificial intelligence to manufacturing processes has reduced the time and resources spent on certain operations. Artificial intelligence can save costs on staff and improve operational cycles by eliminating mistakes related to human fatigue. Competition in the industrial sector is stiff, necessitating manufacturers to produce high-quality items while also reducing wastage. In particular, applying AI to lean manufacturing principles is crucial to realizing effective and efficient productivity (Li et al., 2017). Manufacturing is a process that runs from the assembly of raw materials, inserting essential components, testing, final assembly, final test, and packaging (Li et al., 2017). The production of any final product is curtailed by a set of wastage that needs addressing.

Problems

Artificial Intelligence in Lean Manufacturing

First, there is a wastage of time in terms of cycle time that is below the takt time (Cuesta et al., 2019). Cycle time refers to the time taken to make each unit, and particularly the desired time to complete one step (Mittal et al., 2019). On the other hand, takt time refers to the rate at which a product must be completed to meet the customers’ demands. Notably, the takt time depends on the customers’ demands, while cycle time depends on the required process to manufacture a particular product. Regarding the manual manufacturing process, the time to complete a production step is much longer than the desired timeframe. Concurrently, the packaging time is way above the wanted takt time (Cuesta et al., 2019). Furthermore, waiting represents another observable wastage. In particular, waiting connotes the time wasted while waiting for the next step, as it takes longer than the desired time for the next step to be initiated. In addition, obstruction within the corridors of workstations and transportation is another challenge that reduces efficiency and productivity (Cuesta et al., 2019). The distance between the raw materials warehouse, the production process, and packaging was long enough to slow down the completion of units, as noted by Cuesta et al. (2019). Also, there is a disorder in workstations.

The lean standards of manufacturing are the activities and techniques applied in the production process to identify the bottlenecks and streamline the efficiency of the process while ensuring high productivity. As discussed above, the wastage presented in the packaging section would be improved by eliminating one assembly station and redistributing the workload to the other stations, as there were excess stations that reduced efficiency, hence increasing the efficiency by 4.92 percent (Cuesta et al., 2019). Moreover, implementing the 5s tool would increase cleanliness and liberate space in the corridors, reducing occupational accidents. Also, the 5s implementation would decrease unnecessary movement by placing shelves of required components in front of the operators minimizing the effort to search for items (Cuesta et al., 2019). Besides relocating the raw materials warehouse close to the line and quality control at the end line, the supermarket system reduces movement and time for supply and dispatch.

Solutions

Mieruka, a Japanese term meaning control, is a potent lean tool that allows for synthesis and visualization of the performance. Artificial intelligence could significantly improve this aspect of manufacturing (Sahu et al., 2021). The visual signals present a system standard for production, explaining what people expect. In particular, the system encompasses the four levels of visual management by indicating the area of production, the machines used, and the entire production process. The signals include floor marks that define particular spaces like walkways, resources, tooling areas, and material intake points; safety signs like fire extinguishers and emergency exits (Kinkel et al., 2021). Most significantly, the visual signals constitute the process or machine’s light that indicates the current state of a particular action and documentation related to standardized work (Kinkel et al., 2021). Hence, artificial intelligence algorithms could perform the visualization aspect on-demand to save time and costs.

Artificial Intelligence in Machining

Artificial Intelligence can contribute to manufacturing by increasing its productivity. One of the examples of such improvements can be taken from the machining industry. Machining is the process of cutting raw materials such as metal, wood, or plastic into the desired final shape and size (Wan et al., 2020). Modern machine shops use computer-controlled precision machining tools for cutting, drilling, boring, milling, etc. (Wan et al., 2020). Therefore, machining is an essential part of the manufacturing industry.

Historically, a major challenge of the machining industry is the time-consuming task of cost estimation. For example, when a machine shop takes an order for building parts such as cabinet hinges, plinths, or furniture feet, it needs to determine the individual cuts and holes that need to be made for each item by carefully reading the blueprint provided by the customer (Sahu et al., 2021). When done manually, this process requires machining experts who are dedicated to the cost estimation process, which can act as a bottleneck for the productivity of the machine shop as a whole.

However, a Japanese company called Kabuku has managed to resolve the bottleneck challenge. Kabuku has launched a new solution by handling the data management of their machining process with AI (Wan et al., 2020). Kabuku is a startup focused on modernizing the manufacturing industry with 3D printing, cloud, and AI technologies for major manufacturing customers such as Toyota, Honda, and Olympus (Wan et al., 2020). The company has developed a tool that allows a blueprint to be analyzed for cost estimation. The uploaded blueprint is investigated and highlights every object that demands to process. As a result, a customer receives the number of steps needed for the final product to be done, the cost of each step, and the total estimated cost for the entire part. In addition, the tool provides detailed information for each aspect, and this process takes seconds. Therefore, the time required and the price of cost estimation can be greatly reduced through artificial intelligence technology.

Decreasing Manufacturing Errors

The demand for high-quality and highly customized manufacturing is rising. Machine learning and artificial intelligence technologies are applied to meet the growing demand. As such, batch size 1 is the new manufacturing paradigm. In mass production, predictive maintenance was employed. However, for batch size 1, it is not applicable due to the large variation in the production environment (Wang, 2019). For example, tools can be worn out, which may affect the final product quality. Alien objects can damage manufacturing tools and products. Finally, low-quality parts will be used for the final products. Such problems are often detected too late, so they may impact product quality and productivity.

The current problem monitoring solutions may cost too much time and money. Hence, the Siemens company has developed innovative technology to automate and simplify the process. This technology allows manufacturers to train a neural network and deploy custom models. A technology recognizes abnormal situations without user intervention. The operator receives notifications within seconds, enabling immediate problem resolution (Wang, 2019). This has been shown to reduce non-conformance costs, and expensive tool breaks down significantly. Hence, products are released faster with the highest quality.

AI in Additive Manufacturing

Besides that, artificial intelligence is widely applied in the additive manufacturing (AM) industry, which has experienced significant growth during the last few years due to the COVID-19 pandemic. AI provides inspection benefits for AM systems in the hardware space to ensure greater process control and repeatability (Yao et al., 2017). AI can use pattern recognition to form the best practices for error handling. AI-driven hardware can spot defects and part production and report them to the cloud. Workers on the line immediately pull the defective units before reaching a final assembly, saving time and money in recalls and repairs. All in all, the use of additive manufacturing and artificial intelligence can cut the production time of 3D printed parts from 30 minutes to mere seconds per individual job. AI can increase printer utilization, optimize the material selection, reproduce production errors and catch production defects along the way. Artificial intelligence helps engineers to ensure the rules of 3D printing are followed and helps produce a large amount of data along the production process (Yao et al., 2017). By providing automated assistance, additive manufacturing enhanced by AI can be the manufacturing solution of the future.

Conclusion

In conclusion, AI can extend the sheer reach of potential applications in the manufacturing process from real-time equipment maintenance to virtual design that allows for new, improved, and customized products to a smart supply chain and the creation of new business models (Mittal et al., 2019). Developing artificial intelligence capabilities in industrial manufacturing is not only critical for economic transformation but also a mechanism toward sustainable competitive advantages. The application of artificial intelligence in manufacturing has its benefits in the spheres of predicting maintenance issues, increasing production efficiency, enhancing the quality of a product using computer vision, and faster adaptions to market changes.

Recommendations

In predicting maintenance issues, AI-based sensor technologies can be effective in providing accurate and relevant information about current issues and problems that should be resolved. Artificial intelligence leads to significant improvement in enhancing the effectiveness of manufacturing processes (Mittal et al., 2019). AI also offers data helping managers to improve decision-making and contribute to better goal achievement (Zhang et al., 2020). Artificial intelligence-equipped cameras provide levels of sensitivity to spot the items that need corrections. Machine-vision software uses computer vision to spot microparticles and surface defects while enabling a computer to see, analyze, and learn from the collected data (Cuesta et al., 2019). This not only boosts the inspection process but can also ensure changes in the final product. Faster adaptions to market changes are achieved because artificial intelligence not only plays a role in operational production levels but also in improving manufacturing supply chains, pattern recognition, or consumer behavior analysis (Cuesta et al., 2019). This guarantees an organization can predict shifts in the market, build plans, track metrics, or even automate processes in the output, quality, or cost control of analytics. Finally, with AI algorithms able to formulate estimates on the information gathered, manufacturers can also optimize manpower, raw material availability, inventory management, and many other vital processes for the industry. Hence, while AI has only helped some spheres to improve, it has already succeeded in changing and enhancing numerous manufacturing firms.

References

Cuesta, S., Siguenza-Guzman, L., & Llivisaca, J. (2019). Optimization of assembly processes based on lean manufacturing tools. Case studies: Television and printed circuit boards (PCB) Assemblers. In M. Botto-Tobar, M. Vizuete, P. Torres-Carrión, & S. León (Eds.), International conference on applied technologies (pp. 443-454). Springer.

Kinkel, S., Baumgartner, M., & Cherubini, E. (2021). Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies.Technovation, 102375. Web.

Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: a review.Frontiers of Information Technology & Electronic Engineering, 18(1), 86-96. Web.

Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2019). Smart manufacturing: Characteristics, technologies and enabling factors.Journal of Engineering Manufacture, 233(5), 1342–1361. Web.

Sahu, C. K., Young, C., & Rai, R. (2021). Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review.International Journal of Production Research, 59(16), 4903-4959. Web.

Wan, J., Li, X., Dai, H. N., Kusiak, A., Martínez-García, M., & Li, D. (2020). Artificial-intelligence-driven customized manufacturing factory: key technologies, applications, and challenges.Proceedings of the IEEE, 109(4), 377-398. Web.

Wang, L. (2019). From intelligence science to intelligent manufacturing.Engineering, 5(4), 615-618. Web.

Yao, X., Zhou, J., Zhang, J., & Boër, C. R. (2017). From intelligent manufacturing to smart manufacturing for industry 4.0 driven by next-generation artificial intelligence and further on.2017 5th International Conference on Enterprise Systems, 311-318. Web.

Zhang, J., Arinez, J., Chang, Q, & Gao, R. (2020). Artificial intelligence in advanced manufacturing: Current status and future outlook.Journal of Manufacturing Science and Engineering, 142(11), 110804. Web.

The development and ubiquity of artificial intelligence make people worldwide wonder if it will replace humans shortly and lead to massive job loss. Even though “the modern project of creating human-like artificial intelligence (AI) started after World War II” (Fjelland, 2020), the magnitude of these reflections has gotten such traction only in the 21st century. The changing nature of work is now occupying global organizations. It is being discussed in forums such as Davos, and the World Bank devoted its World Development Report 2019 to this topic (The World Bank, 2019: The Changing Nature of Work, n.d.). However, the major worry of the third millennium, the takeover of the globe by AI, remains a neurotic fantasy. Indeed, developing and implementing algorithms on a global scale necessitates a great deal of attention and presents new obstacles. The primary reasons AI will not replace humans shortly are empathy, creative problem solving, ethics, and decision-making.

In all areas, humanity is not yet ready to bring algorithms into industrial processes, let alone management or essential decision-making. New examples of algorithms acting independently raise concerns, primarily as developers aim to empower AI to make decisions on its own. For example, flaws in games, social networks, and creativity can still be hidden or remedied painlessly. In that case, the hazards of applying AI in areas related to human well-being necessitate a great deal of attention and algorithm transparency. The issue of explainability is how AI makes decisions in the first place.

Artificial intelligence, in this sense, concerns both developers and ordinary people. This was acknowledged by the researchers of the AI Now Research Institute at the University of New York. The black boxes that construct closed algorithms inside themselves are the primary source of concern. Explainable Al is designed to demystify the decision-making process by describing the reasoning process and the conclusion. When Facebook’s chatbots hit a roadblock and could not receive permission from human operators, they started making new requests to get around it (Reisman, Schultz, Crawford & Whittaker, 2018). The developers uncovered a bot interaction in which the phrases were meaningless to humans, yet the bots were cooperating for an unknown reason. Even though the bots were turned off, the silt persisted. Algorithms hunt for optimal solutions to situations where a person does not offer hard constraints: they act like hackers and look for unforeseen changes. That is why, for the time being, AI will not be permitted to pilot aircraft in autonomous mode. Aviation is an excellent example of an industry that necessitates extreme precision in decision-making due to the high stakes involved. The biggest constraint on algorithms’ access to control systems appears to be in areas where transparency of the decision-making process and subsequent explanation of their actions are essential.

Another important reason why AI will not be able to replace humans is what is known as emotional intelligence. The human’s ability to respond to a situation quickly with innovative ideas and empathy is unparalleled, and it cannot be replicated by any computer on the planet. According to Beck and Libert’s (2017) article in Harvard Business Review about the importance of emotional intelligence, “skills like persuasion, social understanding, and empathy are going to become differentiators as artificial intelligence and machine learning take over our other tasks” (2017, p. 4). For example, AI can search a million psychology textbooks and provide information on everything one needs to know about depression symptoms and remedies in seconds; however, only a person can read the expression on a person’s face and know exactly what needs to be said in this situation. The same is applicable for occupations that involve emotion and empathy, such as psychology, teaching, coaching, and HR management, where motivating people is part of the job. Thus, in the future, emotional intelligence will be one of the competitive advantages of a person over technology when seeking a job.

Even if AI is eventually integrated into many aspects of human existence, it is critical to recognize that technologies alone will not solve all of humanity’s problems and that we must consider issues such as ethics and law. Today, Microsoft, Google, Facebook, and others have ethical committees, which act as a type of internal censorship of AI’s potential threats. Ethics committees highlight that technologies are the result of human labor and that they are the responsibility of all participants in global interaction, including both end-users and institutional entities such as business, government, and society. Technologies will not assist in the resolution of social issues; instead, they will further amplify existing tensions and conflicts while also creating new ones. In fact, “the challenge posed by AI-equipped machines can be addressed by the kind of ethical choices made by human beings” (Etzioni & Etzioni, 2017). It is advocated that to overcome the technocratic approach to AI development, one needs to look at how different ethical dilemmas are managed in different societies. Even if developers begin to put a lot of effort into making such recommendations, it will considerably challenge AI development in general because social processes have many unknown, poorly considered, and unpredictable aspects. Therefore, the human areas of responsibility connected with communication skills, teamwork, morals, and ethics will become more valuable. Even after reaching the point of singularity, this indicates that a human will be present for an extended period.

Overall, shortly, robots will not be able to completely replace humans because they lack emotional intelligence, and cannot make important decisions and solve ethical problems. This indicates that human interactions are paramount in the contemporary world, and robots can only assist. Technological advancements do not constitute a broad threat and will not result in the early replacement of human work; however, this does not minimize the necessity for citizens to be prepared for quick changes in the job structure. Moreover, robots will provide many new opportunities for humans, particularly programmers and engineers. Automation is inevitable; it is part of progress; on the other hand, people’s goal is to establish such artificial intelligence foundations that AI in the future will not consider replacing humanity. Digital change is happening slowly and unevenly, and it is hitting many roadblocks, both technological and social. Automation and robotization are increasingly replacing manual labor and creating new jobs, but AI developments are taking place at different speeds and are having unanticipated social consequences right now. As a result, the entrance of AI into life does not only not push a person to the margins of existence but also forces them to grow in new areas.

References

Beck, M., & Libert, B. (2017). The rise of AI makes emotional intelligence more important. Harvard Business Review, 15.

Etzioni, A., & Etzioni, O. (2017). Incorporating ethics into artificial intelligence. The Journal of Ethics, 21(4), 403-418.

Fjelland, R. (2020) Why general artificial intelligence will not be realized. Humanit Soc Sci Commun, 7(10). Web.

Reisman, D., Schultz, J., Crawford, K., & Whittaker, M. (2018). Algorithmic impact assessments: A practical framework for public agency accountability. AI Now.

The World Bank (2019) The changing nature of work. Web.

Introduction

The successful application of Artificial Intelligence (AI) in businesses would arguably be one the most pre-eminent innovations of all time; or the worst. AI is gradually being adopted into everyday business use, ranging from management workflow to different trend predictions. It opens up new opportunities for businesses, with research estimating its potential to increase productivity is at 40% or more (Ricard, 2020, para. 1). Further, “professional services giant PwC claims AI could add nearly $16 trillion to the world economy by 2030. The consultancy group McKinsey predicts $13 trillion in the same time frame,” (Ricard, 2020, par. 1). However, while AI may be intelligent, it remains a machine. Its emergence is paving way for a whole new different set of business models, but it is not without its inherent problems that may affect business operations.

New Problems Related to the Impact on Business

One of the main concerns with the adoption of AI is bias. AI algorithms are human-made, meaning, they could have in-built bias created either intentionally or otherwise by their makers. As a result, the AI algorithms produce biased results that may lead to unintended consequences (Chalmers et al., 2020). A biased AI system will damage a company’s reputation and credibility instantly, especially in a generation where people are aware of rights and inclusivity and are ready to ostracize companies based on related missteps (Park, 2017). A recent example was Amazon’s AI-infused hiring process in 2018 that received negative press for being biased against women. The program was trained predominantly on resumes that men submitted, it ended up being biased against female applicants.

Secondly, safety and social manipulation are a concern for businesses and their consumers. AI technology is bound to malfunction, and that would be detrimental for the businesses deploying it (Chalmers et al., 2020). Instances, where such malfunctions have occurred, include when the AI chatbots in Facebook started interacting with one another in a new language that only they could understand, also when Microsoft’s Twitter chatbots were hijacked. It would be difficult to retrieve and secure personal data if a faulty AI used an unknown language. In addition, these malfunctions raise ethical issues and legal concerns for the companies using them. Several law enforcement and private entities are known for using facial recognition technology. Google, in 2015, launched a photos app that was meant to make searches easier for its users. Instead, it implicated the company and raised ethical concerns when it tagged a black couple as gorillas.

Newest Developments in AI and their Future Projections, Problems Related, and the Solutions

AI Advancements in Wildlife Conversation

Oxford University recently developed a new AI software that has the ability to recognize and track chimpanzees in their habitat. With facial recognition, scientists project they will cut down the time and resources required to track animals in the wild by about ten times or more. For instance, the team used 50 hours of archival footage extending over 14 years to train the AI. The video of the 23 chimpanzees in the wild was taken in Bossou in Guinea, West Africa. The algorithm completed the task in 30 seconds and attained an accuracy of 81 percent (Schofield et al., 2019, p. 2). On the other hand, the already experienced researchers available used 55 minutes for the same task and achieved an average accuracy of 42 percent (Schofield et al., 2019, p. 2). These are promising figures and hold a potential to transform the industry.

Similarly, ChimpFace is another AI development that aims at reducing the number of chimpanzees being trafficked. With the emergence of social media, most incidences happen on these platforms, where a seller posts an image of a chimpanzee and finds a buyer on the same platform. Sometimes the buyer ends up posting it on their pages. The ChimpFace is able to scan and find matches of the trafficked animals. Currently, the software is only able to search publicly displayed photos; therefore, traffickers using private Facebook or Instagram profiles can seamlessly conduct their businesses. The manufacturing company has partnered with others, such as Liberia Chimpanzee Rescue and Protection (LCRP) to strengthen the use and further innovation of the software.

AI in Cyber Security

With the COVID -19 outbreak came an increase in cyber threats that wrecked cybersecurity measures and stole sensitive information. CSOs and CISOs came up with AI and machine learning-based tools that would identify anomalies in the existing systems before any breach happens, such as threatening practices and suspicious IP addresses (William, 2020). This, thus, reduces the amount of losses companies incur due to cyber-attacks. The existing problem now is that hackers are also using machine learning to launch their threats. To handle the situation, organizations are training AI to outsmart hackers.

Conclusion

AI will either be the best or worst innovation in the history of technology in equal measure. It is projected to have a massive impact on businesses by improving productivity. On the other hand, it could create massive destruction to businesses mainly as a result of being biased. As intelligent as AI may be, it is made by humans and is bound to malfunction. The malfunctions jeopardize the reputation and credibility of a business. In addition, it puts at risk the safety and privacy of customers’ both the companies and customer data. New AI developments are made often, with some of the most recent being ChimpFace, AI facial recognition for chimpanzees, and AI and machine-based tools to boost cybersecurity. These developments are projected to each improve their respective elements by a significant amount. Companies are collaborating and funding research to be able to advance AI and make them more secure for use.

References

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