Category: AI

Introduction

The author’s work is devoted to the role of artificial intelligence (AI) in human life:

• He writes about the development of AI, especially noting how computer technology has caused a renaissance of influence on processing data through AI. The author’s narrative is consistent and multidimensional; he gradually concludes.
• He analyzes the theoretical aspects of artificial intelligence, mentioning its characteristics.
• The writer says that AI technology is incapable of forming memories and discusses four existing varieties of AI.
• Despite the high quality of the letter, it is necessary to pay attention to some of the essay’s shortcomings.

A Reflective Reading Response

Reading the article made me think again about the importance of artificial intelligence in human life. Due to the large flow of information from countless sources, it is easy to focus on other high-tech inventions or concepts. Indeed, this would be an urgent problem if technology had initially been a subject of human interest. However, I enjoyed how the author approached the research topic: he outlined the application of the analyzed technology and expressed his position regarding the problems arising during artificial intelligence. The creation of such essays awakens interest in technology, which is essential because, as noted by the author in the final part of the essay, the scope of AI is broad.

Artificial intelligence aims to create technical systems capable of solving non-computational problems and performing actions that require the processing of meaningful information and are considered the prerogative of the human brain. I confess that for me, many facts about AI were not known before reading the article.

Assessment of the Paper

I agree with the author’s position expressed by him in the essay. In particular, I found it interesting that the author noted that autonomous weapons, known as AI, are dangerous and cause fear among members of society. Even though in 2021, many people are much more worried about, for example, environmental problems, they should not ignore the existence of military conflicts in different parts of the world. Moreover, interstate conflicts are developing incredibly quickly; sometimes, one incident is enough to escalate aggression. Therefore, considering that we live in an era of technological progress, it is essential to keep military AI technologies under maximum control.

I did not find any severe logical errors in the text, but it seemed that the writer should have indicated a link to the source in some fragments of the text. If found that they are not original, Borrowing ideas without referencing them is plagiarism and is not acceptable for academic writing. However, I detected semantic correspondence between the articles used and the paraphrased author’s extracts from these texts. Indeed, Bakken claims that companies increasingly opt for AI in business decision-making (Bakken).

As noted by the author, Shabbir and Tarique provide information on the strategy for successfully implementing AI in companies and organizations worldwide (5). To the disadvantages of the work, I would attribute the use of terms without a preliminary explanation and the limited application of the research results. The author himself notes that it will take a lot of time and many resources for society to benefit from the initiative.

The author presents the analyzed problems of using AI in terms of their benefits and harms, which reduces the likelihood of narrative bias; however, storytelling has certain drawbacks. In particular, the author mentions how broad is the scope of AI in business and what a vast role AI will play in achieving progress. However, the writer does not indicate that business uses weak artificial intelligence, which can only solve narrowly technical problems using extensive data methods and machine learning algorithms. Vital artificial intelligence, in turn, suggests that computers can acquire the ability to think and be aware of themselves as separate individuals. Someone might argue that only a weak AI is enough to solve business problems, but it seems that it should have been determined in what state AI is now.

Presumably, the development of technologies will bring humanity to the moment when AI receives the right to make decisions, including strategic ones. This process is facilitated by the fact that more and more collecting and analyzing information is transferred to artificial intelligence. Therefore, it seems reasonable and logical for the writer to conclude that investing in AI in security agencies ensures that members of society are protected from unwanted security threats. AI allows one to assess the situation and make a decision faster and quickly. As the author rightly points out, the information collected and processed by AI must be translated into a human-readable format so that a person can correctly evaluate and comprehend the information.

Suggestions for Changes

Concerning the use of terms in the text, the following should be noted. First, the author introduces the concept of “Turing’s question” but does not first explain its meaning, which may confuse readers. If I were to write this work, I would describe its importance to the readers beforehand since it is evident that not everyone can understand it every time the term is used. I would point out that “Turing’s question” is an empirical test suggested by Alan Turing. However, someone can argue with me by saying that the author explains the test’s purpose in the following sentence. Still, this test’s essence and standard interpretation are left without attention. And this is unacceptable since it interferes with the reader’s perception of the text.

For a more precise understanding, the author should have mentioned other approaches to AI perception since there is no single answer to what artificial intelligence does. However, nearly every author who writes a book on AI considers this phenomenon in the context of science achievement at the time of the book’s creation. Among such approaches, one should mention the symbolic method, which appeared first in the era of digital machines, or the logical one.

In the paper, the author identifies only three sections, including “exigence,” “positions,” and “evaluation.” Dividing the text into specific semantic parts improves the perception of the text and allows readers to understand in advance what will be discussed in the paragraph. However, in my opinion, it was worth dividing the “positions” paragraph into additional parts since this piece of text turned out to be quite voluminous compared to others.

Thus, the first paragraph should be called “the computer’s perception of the world and the scope of AI,” The second paragraph is “the possibility of using AI initiatives.” The heading of the third paragraph would be related to the use of AI in armed conflict, and the title of the fourth paragraph would give readers an understanding that the section would be devoted to the use of AI in business. However, this is just my assumption, and I do not exclude that the author’s preference in dividing parts of the text will seem sufficient to someone.

Works Cited

Bakken, Rebecca. “Business Applications for Artificial Intelligence: An Update for 2020.” Professional Development Hazard Division of Continuing Education, 2019. Web.

Shabbir, Jahanzaib, and Tarique Anwer. “Artificial intelligence and its role in near future.” arXiv preprint arXiv:1804.01396 (2018). Web.

Artificial intelligence (AI) technology penetrates different aspects of people’s business and everyday life. It can also be applied to such aspects of healthcare as caring for patients and managing administrative tasks. This innovative technology can enhance the continuous improvement of services and working conditions of healthcare providers and realize a new opportunity to predict and identify diseases and abnormalities. AI is a significant contribution to the development of healthcare because it provides the chance to surpass the abilities of humans and reduce the number of errors caused by human failures. The drivers for the innovation are the increase in the aging population, the National Health Service’s strategies to enhance the well-being of citizens and healthcare services’ quality, and the expansion of modern technologies in other domains (Al-Amoudi & Latsis, 2019).

The Institute of Cancer Research requires introducing this innovation to cope with the challenges of diagnosing and treating patients. The institute is located in London and specializes in all kinds of cancers and tumors. My role in the service would be to prepare the project management plan to implement the innovation. The agile approach helps introduce AI innovations because it guarantees the project’s adaptability to changes, following five stages of change, and the ability of communication, collaboration, and trust to influence the product.

Project management plays an essential role in the process of planning the implementation of innovation. It is the combination of techniques, methods, knowledge, and expertise used by the manager to organize the realization of a specific project (Demirkesen & Ozorhon, 2017). The most significant characteristics of project management are the particular timescale and budget limits, which frame the time dedicated to the project and the financing of its implementation.

The project is a type of endeavor with specific purposes and benefits for the organization (Tereso et al., 2018). One of the essential components of project management is identifying the project’s purpose, which helps to understand what peculiar outcomes are anticipated. Then, it includes leading and motivating the team organized to implement the plan (Lock, 2017). Next, a manager has to manage conflicts, risks, and issues developed in the process of realization. Finally, a manager has to monitor the progress of the team and compare it with the plan.

The most common approaches to project management include traditional and agile methods of implementation, which differ in their structure. The traditional approach presupposes the application of linear strategy when all the processes occur in sequence. According to this method, all the projects evolve according to the predetermined lifecycle, including plan, design, testing, production, and support (Musawir et al., 2017). The agile strategy, on the other hand, is known for its flexibility. According to this approach, such elements as teamwork, collaboration with the customer, and project adaptability are essential (Stoddard, Gillis, and Cohn, 2019).

These characteristics signify that achieving the necessary result is the central focus of the managers choosing this method. They comprehend that the requirements and situations may change during the process of implementation. Consequently, they may bring changes to guarantee that the predetermined outcome is achieved.

The agile approaches are more popular among managers because these methods make the projects more flexible. In particular, a flexible strategy allows managers to make changes when other interconnected components of the project change their course or do not correspond to the predictions (Mergel, Ganapati, and Whitford, 2020). This ability to make changes without interfering with other aspects of work provides the chance to continue the project.

The next advantage concerns the complex plans that comprise multiple stages of implementation and complex interactions between team members. When a client introduces new requirements, the plan that follows the traditional plan should be changed completely, and all the processes should start from the beginning because the project is linear (Kaim, Härting, and Reichstein, 2019). On the other hand, agile methods allow managers to adapt to changes and proceed. Additionally, these methods enable the clients to validate the plan’s steps (Loiro et al., 2019). It allows them to observe whether these elements of the project correspond to the requirements and expectations.

The application of agile strategies in introducing AI in healthcare settings provides the opportunity to test the changes to bring the most appropriate solutions. Since the traditional method is inflexible, it does not suit the case of introducing innovations because it fails to manage the changes. Consequently, the flexible model is suitable because it allows the manager to make necessary changes during the project’s implementation (Holden, Boustani, and Azar, 2021).

In particular, the introduction of machine learning might require healthcare professionals’ supervision and inspection of the functionality of the technique. The complex form of machine learning, deep learning, helps to identify cancer using radiology images. The supervisors have to check the application to determine whether the diagnosis is relevant (Verma et al., 2021). In addition, they have to understand whether the recommended treatments are sufficient and the drug development is timely. If some errors are detected, the flexible strategies allow the managers to make necessary changes that prove beneficial for the clinic.

The introduction of agile techniques enhances communication and cooperation between the team members. The project requires the organization of a specific team, which should include healthcare workers, managers, data scientists, and engineers (Glazkova, Fortin, and Podladchikova, 2019). The application of agile methods allows the creation of several teams to work on different AI innovations. For instance, the introduction of diagnosis and treatment applications might help apply machine learning to identify the types of cancer and the methods of treating a disease (Davenport & Kalakota, 2019).

The development of these teams provides the opportunity to make the implementation process divided into several iterative cycles. Completing these cycles requires cooperation and interaction between team members and different groups (Wiencierz, 2021). These groups can be self-organized; they can diagram workflows and create tests for the software and implementation process. The weekly meetings provide the chance to review progress and analyze the completed stages discussing the achievements and necessary corrections. Consequently, the cooperation and communication between the team members promoted by agile methods help shorten the time needed to exchange essential data, improve understanding, and enhance collaboration.

The implementation of AI in the institute project consists of several stages. The business case and project charter would be developed during the first stage, the project initiation phase (Altunel, 2017). The project charter would include implementing AI in the cancer institute, project constraints, including risks and customer satisfaction, timeline, and budget. During the next stage, project planning, the team should work together to identify the client’s characteristics, what the customers expect from the AI’s use, and prioritize the plan’s elements based on their value. The development phase comprises working on the product’s first iteration (Bergmann & Karwowski, 2018).

In particular, the deep learning techniques and diagnosis and treatment applications might evolve into a usable product, revised during successive iterations. During this period, the teams would cooperate, test the product, and follow the guidelines to prepare the solution, which is ready to be released into production (Gren, Goldman, and Jacobsson, 2019). In the fourth phase, production, the patients might use the applications, and the teams should monitor their functioning. The final stage, project closing, comprises the completion of the plan and analysis of its results.

Communication, collaboration, and trust are the most significant human elements in the agile project of implementing AI in healthcare settings. According to Malik, Sarwar, and Orr (2021), face-to-face conversations and the exchange of information are crucial for project performance. Healthcare professionals, engineers, and managers have to interact during the plan’s implementation to guarantee that they share their ideas and observations and follow the same purpose.

Noguera, Guerrero-Roldán, and Masó (2018) explain that collaboration and the agile approach are inseparable in this type of project. In particular, collaboration provides the opportunity to enhance productivity and ensure that healthcare providers and engineers work together to find the most appropriate solution in applying AI in healthcare settings. Trust influences how the team members communicate, share thoughts, and comprehend each other (Imam & Zaheer, 2021). The development of trust might help the healthcare workers be ready to share their findings, observations, and examinations during all the stages of project implementation. Consequently, combining these human factors might bring the project to success and guarantee the appropriate introduction of AI in the institute.

Thus, agile strategies would make the project successful because they make the plan adaptable, divided into five specific stages, and effective due to communication, collaboration, and trust. The Institute of Cancer Research requires the introduction of AI innovations to improve the process of diagnosing and treating patients. The agile approach is the most suitable method of implementing new applications because it allows making the necessary changes and enhances cooperation between team members.

The plan’s implementation would be divided into five stages, project initiation, planning, development, production, and project closing. Such human elements as communication, collaboration, and trust might be considered to guarantee that the project achieves its goal. Implementing these aspects might promote healthcare services’ quality because it would ensure that patients experience better quality care and that hospitals benefit from innovative technologies.

Reference List

Al-Amoudi, I., and Latsis, J., 2019. Anormative black boxes: Artificial intelligence and health policy. In Post-Human Institutions and Organizations (pp. 119-142). Routledge.

Altunel, H., 2017. Agile project management in product life cycle. International Journal of Information Technology Project Management, 8(2), pp.50-63.

Bergmann, T. and Karwowski, W., 2018. Agile project management and project success: A Literature Review. Advances in Intelligent Systems and Computing, pp. 405-414.

Demirkesen, S. and Ozorhon, B., 2017. Impact of integration management on construction project management performance. International Journal of Project Management, 35(8), pp.1639-1654.

Davenport, T. and Kalakota, R., 2019. The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), pp. 94-98.

Glazkova, N., Fortin, C. and Podladchikova, T., 2019. Application of lean-agile approach for medical wearable device development. 2019 14th Annual Conference System of Systems Engineering (SoSE).

Gren, L., Goldman, A. and Jacobsson, C., 2019. Agile ways of working: A team maturity perspective. Journal of Software: Evolution and Process, 32(6).

Holden, R., Boustani, M. and Azar, J., 2021. Agile Innovation to transform healthcare: innovating in complex adaptive systems is an everyday process, not a light bulb event. BMJ Innovations, 7(2), pp. 499-505.

Imam, H. and Zaheer, M., 2021. Shared leadership and project success: The roles of knowledge sharing, cohesion and trust in the team. International Journal of Project Management.

Kaim, R., Härting, R. and Reichstein, C., 2019. Benefits of Agile Project Management in an Environment of Increasing Complexity—A Transaction Cost Analysis. Intelligent Decision Technologies 2019, pp.195-204.

Lock, D., 2017. The essentials of project management. Routledge.

Loiro, C., Castro, H., Ávila, P., Cruz-Cunha, M., Putnik, G. and Ferreira, L., 2019. Agile project management: A communicational workflow proposal. Procedia Computer Science, 164, pp.485-490.

Malik, M., Sarwar, S. and Orr, S., 2021. Agile practices and performance: Examining the role of psychological empowerment. International Journal of Project Management, 39(1), pp.10-20.

Mergel, I., Ganapati, S. and Whitford, A., 2020. Agile: A New Way of Governing. Public Administration Review, 81(1), pp.161-165.

Musawir, A., Serra, C., Zwikael, O. and Ali, I., 2017. Project governance, benefit management, and project success: Towards a framework for supporting organizational strategy implementation. International Journal of Project Management, 35(8), pp.1658-1672.

Noguera, I., Guerrero-Roldán, A. and Masó, R., 2018. Collaborative agile learning in online environments: Strategies for improving team regulation and project management. Computers & Education, 116, pp.110-129.

Stoddard, M., Gillis, B. and Cohn, P., 2019. Agile project management in libraries: Creating collaborative, resilient, responsive organizations. Journal of Library Administration, 59(5), pp.492-511.

Tereso, A., Ribeiro, P., Fernandes, G., Loureiro, I. and Ferreira, M., 2018. Project management practices in private organizations. Project Management Journal, 50(1), pp.6-22.

Verma, S., Popli, R. and Kumar, H., 2021. The Agile Deployment Using Machine Learning in Healthcare Service. Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences, pp.879-890.

Wiencierz, C., Röttger, U. and Fuhrmann, C., 2021. Agile Cooperation between Communication Agencies and Companies. International Journal of Strategic Communication, 15(2), pp.144-158.

Introduction

The artificial intelligence (AI) development process has a direct exposure to supply chain regulation. With regard to globalization and multiplication of the possible delivery possibilities, human resources or thoroughly developed programs cannot compete with the analyzing power of artificial intelligence. Even though AI technologies began their history during World War II with the establishment of the Turing test, they began to be directly implied in supply chain processes only in the last decade of the 21st century (Baryannis et al., 2018). The analysis is aimed to measure the current impact of artificial intelligence presence in supply chain processes and ponder the perspectives of AI development in terms of the leading power of supply chain regulation.

Current Situation

The supply chain is one of the most important factors of the world economy operation since it links valuable parts of almost all business processes in the world and delivers them to the selling markets, where the final products are realized. As a result, without the cutting-edge level of supply chain functioning, the economy would experience significant stagnation due to the inability to assemble or furnish the final product to the customer.

One of the most notable characteristics of AI efficiency is a statistically proven increase in a company’s profitability due to the organic transition from human resources to new analyzing instruments. To improve communication between counterparties, new communication methods are being employed. For instance, installing virtual chatbots that customize and reflect the preferences of their customers produce a 10% greater return on equity and 10 percent more revenue than other companies from the survey pool (Modgil et al., 2021). Another important trend for AI integration emphasizes that due to the digitalization of supply chains as a result of the industry 4.0 strategy, they are evolving into supply chain ecosystems, which are made up of interconnected businesses that coordinate operations and face similar adaptive difficulties.

A precise value proposition and a specified, dynamic collection of agents with various responsibilities describe this governance paradigm with regard to different roles, such as producer, supplier, orchestrator, and complementor. As a result, AI developing companies and intermediates offering any form of the new position in such supply chain ecosystem has an increasingly high demand for industry 4.0 solutions that would effectively integrate into businesses’ operating ecosystems (Hofmann et al., 2019). From a data harvesting perspective, artificial intelligence helps to observe not only the individual characteristics, which is a common process for programmed applications and behavioral analytics but also the pricing proprieties of different companies, such as packaging and delivering sector corporations.

For instance, Modgil et al. revealed that last-mile delivery is the costliest logistics step, accounting for roughly half of the total package delivery cost (2021). Last but not least, today’s businesses are becoming more globalized but also less vertically integrated, which develops the complexity of distribution networks and exposes them to far more risks (Calatauyd et al., 2019). As a result, AI systems possess and apply their technical advantage in scanning a large pool of possibilities to address goods from ‘point A to point B’ with the lowest duration time and regulatory or natural risks.

The Future Implementation of Artificial Intelligence into Supply Chain Functionality

When it comes to the possibilities of future implementation of AI technologies into supply chain operating activities, it is critical to focus on those aspects that have significant exposure to the industry’s development. In fact, there are five of the most effective methods of organic AI application into supply chain functionality. First and foremost, a major part of the technological potential might be realized through the implication of inventory planning utilities. More specifically, customization has perspectives to be fully performed through AI technologies, which significantly helps determine future buying patterns, whether large purchases are required to retain stock in advance or whether the current inventory capacity percentage is maintained properly (Modgil et al., 2021). Secondly, internet commerce made a significant impact in experiencing supply chain disruptions during the global pandemic in 2020 and 2021. Despite the partial stagnation of global production, customers began ordering packages on internet marketplaces, which has disclosed another important niche for supply chain functioning (Modgil et al., 2021). More specifically, AI might be utilized to discover local suppliers, as many things do not require the use of worldwide vendors.

In addition, artificial intelligence assists in creating an effective and robust supply chain from local suppliers through vendor management solutions, such as credit management or vendor evaluation. As a result, this operating function could be attached to software analysis ecosystems to enforce the synergy effect of efficiency increase. Thirdly, artificial intelligence might be utilized to increase routine operation execution, such as package tracking. In fact, during the high-intense periods of deliveries, many companies face significant issues with achieving to execute the final package distribution before the previewed time. In many cases, customers experience deliveries’ delays and begin tracking their orders to understand their current situation. In this case, standard programmable software cannot demonstrate constant operating success due to unstable information updates.

However, artificial intelligence technologies might execute this routine operation with a relatively higher efficiency percentage. The cutting-edge technology would estimate shipment delivery time variations and preview the amount of risk connected with a cargo based on trend research. Fourthly, artificial intelligence technologies would certainly advance the quality of risk management in supply chain functioning. The adoption of AI developments is based on its future capacity to evaluate data, find the exact source of risk, and promote transparency among supply chain partners. These functions might be very useful in detecting the risk associated with various intersections of the distribution network and providing effective and timely remedies (Modgil et al., 2021). Last but not least, AI’s capacity to execute numerous experiments and calculate the possible outcomes would influence the process of market analysis.

For example, sophisticated analytics using AI may be used to anticipate future outcomes and market tendencies. At the same time, data may be examined utilizing the unrivaled computational capacity to forecast future demand or better understand client purchasing habits. In today’s world, it is crucial to not only anticipate the changes but also define the specific applications of AI technology in the supply chain functioning to benefit from its development.

Conclusion

To summarize, artificial intelligence is one of the most powerful tools for supply chain development, and its opportunities are already partially realized. From the current perspective, artificial intelligence helps to understand the functioning of standard operations and collaboratively define certain quantitative measures based on customers’ expected behavior. However, the whole potential is currently unrealized, which makes artificial intelligence a perspective domain for developing risk management in supply chain, routine operations execution, vendor performance analysis, packages tracking, and estimating market behavior.

References

Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2018). Supply chain risk management and artificial intelligence: state of the art and future research directions.International Journal of Production Research, 57(7), 2179–2202. Web.

Calatayud, A., Mangan, J., & Christopher, M. (2019). The self-thinking supply chain.Supply Chain Management: An International Journal, 24(1), 22–38. Web.

Hofmann, E., Sternberg, H., Chen, H., Pflaum, A., & Prockl, G. (2019). Supply chain management and Industry 4.0: conducting research in the digital age.International Journal of Physical Distribution & Logistics Management, 49(10), 945–955. Web.

Modgil, S., Singh, R. K., & Hannibal, C. (2021). Artificial intelligence for supply chain resilience: learning from Covid-19. The International Journal of Logistics Management, of. Web.

Introduction

Rooted in computer science, Artificial Intelligence (AI) is defined by the development of digital systems that can perform tasks, which are dependent on human intelligence (Rexford, 2018). Interest in the adoption of AI in the education sector started in the 1980s when researchers were exploring the possibilities of adopting robotic technologies in learning (Mikropoulos, 2018). Their mission was to help learners to study conveniently and efficiently. Today, some of the events and impact of AI on the education sector are concentrated in the fields of online learning, task automation, and personalization learning (Chen, Chen and Lin, 2020). The COVID-19 pandemic is a recent news event that has drawn attention to AI and its role in facilitating online learning among other virtual educational programs. This paper seeks to find out the possible impact of artificial intelligence on the education sector from the perspectives of teachers and learners.

AI’s Impact on Education

Technology has transformed the education sector in unique ways and AI is no exception. As highlighted above, AI is a relatively new area of technological development, which has attracted global interest in academic and teaching circles. Increased awareness of the benefits of AI in the education sector and the integration of high-performance computing systems in administrative work have accelerated the pace of transformation in the field (Fengchun et al., 2021). This change has affected different facets of learning to the extent that government agencies and companies are looking to replicate the same success in their respective fields (IBM, 2020). However, while the advantages of AI are widely reported in the corporate scene, few people understand its impact on the interactions between students and teachers. This research gap can be filled by understanding the impact of AI on the education sector, as a holistic ecosystem of learning.

As these gaps in education are minimized, AI is contributing to the growth of the education sector. Particularly, it has increased the number of online learning platforms using big data intelligence systems (Chen, Chen and Lin, 2020). This outcome has been achieved by exploiting opportunities in big data analysis to enhance educational outcomes (IBM, 2020). Overall, the positive contributions that AI has had to the education sector mean that it has expanded opportunities for growth and development in the education sector (Rexford, 2018). Therefore, teachers are likely to benefit from increased opportunities for learning and growth that would emerge from the adoption of AI in the education system.

The Impact of AI on Teachers

The impact of AI on teachers can be estimated by examining its effects on the learning environment. Some of the positive outcomes that teachers have associated with AI adoption include increased work efficiency, expanded opportunities for career growth, and an improved rate of innovation adoption (Chen, Chen and Lin, 2020). These benefits are achievable because AI makes it possible to automate learning activities. This process gives teachers the freedom to complete supplementary tasks that support their core activities. At the same time, the freedom they enjoy may be used to enhance creativity and innovation in their teaching practice. Despite the positive outcomes of AI adoption in learning, it undermines the relevance of teachers as educators (Fengchun et al., 2021). This concern is shared among educators because the increased reliance on robotics and automation through AI adoption has created conditions for learning to occur without human input. Therefore, there is a risk that teacher participation may be replaced by machine input.

Performance Evaluation emerges as a critical area where teachers can benefit from AI adoption. This outcome is feasible because AI empowers teachers to monitor the behaviors of their learners and the differences in their scores over a specific time (Mikropoulos, 2018). This comparative analysis is achievable using advanced data management techniques in AI-backed performance appraisal systems (Fengchun et al., 2021). Researchers have used these systems to enhance adaptive group formation programs where groups of students are formed based on a balance of the strengths and weaknesses of the members (Live Tiles, 2021). The information collected using AI-backed data analysis techniques can be recalibrated to capture different types of data. For example, teachers have used AI to understand students’ learning patterns and the correlation between these configurations with the individual understanding of learning concepts (Rexford, 2018). Furthermore, advanced biometric techniques in AI have made it possible for teachers to assess their student’s learning attentiveness.

Overall, the contributions of AI to the teaching practice empower teachers to redesign their learning programs to fill the gaps identified in the performance assessments. Employing the capabilities of AI in their teaching programs has also made it possible to personalize their curriculums to empower students to learn more effectively (Live Tiles, 2021). Nonetheless, the benefits of AI to teachers could be undermined by the possibility of job losses due to the replacement of human labor with machines and robots (Gulson et al., 2018). These fears are yet to materialize but indications suggest that AI adoption may elevate the importance of machines above those of human beings in learning.

The Impact of AI on Students

The benefits of AI to teachers can be replicated in student learning because learners are recipients of the teaching strategies adopted by teachers. In this regard, AI has created unique benefits for different groups of learners based on the supportive role it plays in the education sector (Fengchun et al., 2021). For example, it has created conditions necessary for the use of virtual reality in learning. This development has created an opportunity for students to learn at their pace (Live Tiles, 2021). Allowing students to learn at their pace has enhanced their learning experiences because of varied learning speeds. The creation of virtual reality using AI learning has played a significant role in promoting equality in learning by adapting to different learning needs (Live Tiles, 2021). For example, it has helped students to better track their performances at home and identify areas of improvement in the process. In this regard, the adoption of AI in learning has allowed for the customization of learning styles to improve students’ attention and involvement in learning.

AI also benefits students by personalizing education activities to suit different learning styles and competencies. In this analysis, AI holds the promise to develop personalized learning at scale by customizing tools and features of learning in contemporary education systems (du Boulay, 2016). Personalized learning offers several benefits to students, including a reduction in learning time, increased levels of engagement with teachers, improved knowledge retention, and increased motivation to study (Fengchun et al., 2021). The presence of these benefits means that AI enriches students’ learning experiences. Furthermore, AI shares the promise of expanding educational opportunities for people who would have otherwise been unable to access learning opportunities. For example, disabled people are unable to access the same quality of education as ordinary students do. Today, technology has made it possible for these underserved learners to access education services.

Based on the findings highlighted above, AI has made it possible to customize education services to suit the needs of unique groups of learners. By extension, AI has made it possible for teachers to select the most appropriate teaching methods to use for these student groups (du Boulay, 2016). Teachers have reported positive outcomes of using AI to meet the needs of these underserved learners (Fengchun et al., 2021). For example, through online learning, some of them have learned to be more patient and tolerant when interacting with disabled students (Fengchun et al., 2021). AI has also made it possible to integrate the educational and curriculum development plans of disabled and mainstream students, thereby standardizing the education outcomes across the divide. Broadly, these statements indicate that the expansion of opportunities via AI adoption has increased access to education services for underserved groups of learners.

Overall, AI holds the promise to solve most educational challenges that affect the world today. UNESCO (2021) affirms this statement by saying that AI can address most problems in learning through innovation. Therefore, there is hope that the adoption of new technology would accelerate the process of streamlining the education sector. This outcome could be achieved by improving the design of AI learning programs to make them more effective in meeting student and teachers’ needs. This contribution to learning will help to maximize the positive impact and minimize the negative effects of AI on both parties.

Conclusion

The findings of this study demonstrate that the application of AI in education has a largely positive impact on students and teachers. The positive effects are summarized as follows: improved access to education for underserved populations improved teaching practices/instructional learning, and enhanced enthusiasm for students to stay in school. Despite the existence of these positive views, negative outcomes have also been highlighted in this paper. They include the potential for job losses, an increase in education inequalities, and the high cost of installing AI systems. These concerns are relevant to the adoption of AI in the education sector but the benefits of integration outweigh them. Therefore, there should be more support given to educational institutions that intend to adopt AI. Overall, this study demonstrates that AI is beneficial to the education sector. It will improve the quality of teaching, help students to understand knowledge quickly, and spread knowledge via the expansion of educational opportunities.

Reference List

Chen, L., Chen, P. and Lin, Z. (2020) ‘Artificial intelligence in education: a review’, Institute of Electrical and Electronics Engineers Access, 8(1), pp. 75264-75278.

du Boulay, B. (2016) Artificial intelligence as an effective classroom assistant. Institute of Electrical and Electronics Engineers Intelligent Systems, 31(6), pp.76–81.

Fengchun, M. et al. (2021) AI and education: a guide for policymakers. Paris: UNESCO Publishing.

Gulson, K. et al. (2018) Education, work and Australian society in an AI world. Web.

IBM. (2020) Artificial intelligence. Web.

Live Tiles. (2021) 15 pros and 6 cons of artificial intelligence in the classroom. Web.

Mikropoulos, T. A. (2018) Research on e-Learning and ICT in education: technological, pedagogical and instructional perspectives. New York, NY: Springer.

Rexford, J. (2018) The role of education in AI (and vice versa). Web.

Seo, K. et al. (2021) The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(54), pp. 1-12.

UNESCO. (2021) Artificial intelligence in education. Web.

In Liu and Asimov’s short stories, the authors define human beings and how artificial intelligence differs from the human mind. The author of The Algorithms for Love, Liu Ken, writes that humans are too young and too immature to understand the global laws of the universe. However, it seems to many that human self-sufficiency and autonomy in desires and actions is prohibitive. Asimov’s Liar is unique in that it is one of the author’s first attempts to reflect from different angles on the modern world’s clash of high technology and morality. Liu and Asimov approach the problem of artificial intelligence differently, but they agree that there is a strong similarity between the thinking of robots and humans.

Liu Ken wonders where the boundary is between artificial and natural intelligence. He is interested whether the robot’s self-learning program can be considered a person (Ken 5). After all, in essence, the decisions it makes are based on experience and knowledge – humans reason the same way. The author goes on to ask his readers whether humans can be considered robots with artificial intelligence, in case one considers that there is no essential difference between the latter and people (Ken 5). Thus, Liu Ken brings readers to his final question of who programmed humans in this case.

Ken’s story is about a young woman named Elena, a programmer and designer for a company that creates unusual toys – robot androids. Improving from model to model, she finally manages to create a robot that can easily cope with the Turing test, able to communicate like a real person. However, she is the only one who understands that this apparent intelligence is only the result of talented and competent programming. Analyzing the results of her work, Elena comes to a frightening question. She begins to worry that there is a possibility that people are not intelligent either and only work out the algorithms embedded in their internal program from day to day. In this story, Ken Liu once again managed to combine serious science fiction with subtle psychology and the dramatic fate of the main character in a small story. This literary work makes its readers embrace the idea of the identity of the artificial and human intelligence.

Among the works of Isaac Asimov, one of the most significant and unique can be considered a collection of short stories I, Robot. This work most fully expresses the social and philosophical views of the writer in the early stage of his work (Baysal 172). The sixth story in the collection, Liar, makes it clear that human psychology, philosophy, and social laws interest Asimov more than the technical side of the literary work. Some of Asimov’s thoughts in this story are conveyed through the statements of the robot RB-34. Herbie says that science is of little interest for him in one of his monologues (Asimov 65). This hero is much more concerned with fiction and the intertwining and interplay of human motivations and feelings. The monologue under analysis reflects Asimov’s penchant for social and philosophical issues and his belief in art and commitment to the ideas of humanism.

The authors are united by the themes of the stories and the issues they raise, although the writers’ attention span is slightly different. Asimov pushes readers to the idea that the ideal robot is a human being. At the same time, Liu cautions about the consequences of recognizing the equality between how humans think and how robots do. Nevertheless, both authors try to define what is human and what are the fundamental advantages and disadvantages of being people.

Works Cited

Asimov, Iisac. I, Robot. Gnome Press, 1950.

Baysal, Kubra. “Technophobia and Robot Agency in Asimov’s I, Robot.” Special issue of IBAD Journal of Social Sciences, 2020, pp. 171-179. Web.

Liu, Ken. The Algorithms for Love. Strange Horizons, 2004.

Summary of the Article

The article “Spell, Graphcore Partner to Build Next-Gen AI Infrastructure” by Chris J. Preimesberger tells about announcing a partnership between two companies that work with artificial intelligence (AI). The author highlights the purpose of this collaboration: to create “the next generation of AI infrastructure” (Preimesberger). It implies that these companies will integrate each other’s technologies to “make advanced AI development faster, easier, and less expensive” (Preimesberger). The article emphasizes that new demands arise among different companies with AI improvement and machine learning (ML) deployment. To conclude and straighten the importance of such a solution, Preimesberger refers to analysis from Gartner’s research that predicts that the Spell and Graphcore partnership will result in value capture from AI.

Discussion of the Business Implications

The development of AI and ML gradually affects business and probably will have a substantial influence in the future. The possibilities of AI lead to reducing costs, automation, reduction in the number of required workers, quality development, and avoiding human errors. Large enterprises such as Google, Amazon, and Microsoft use AI. Still, in this particular case, Spell and Graphcore aim to allow smaller companies to take advantage of AI and ML products. They plan to develop this field and offer a free trial for their advanced integrated technologies. Nowadays, technologies are improving rapidly; informational technologies are already used in every business field. AI and ML appear as the next step in advanced technologies that will infiltrate every field of activity with the purpose of facilitation and improvement.

Personal Opinion

I consider that such solutions are moving progress in the right direction. Advancing AI and ML technologies allow new opportunities that can be used in different branches of life and business. It matters because the deployment of AI may lead to overall improvement and replace the human workforce in performing physically challenging or complicated tasks, which will result in lower costs and process automation.

Work Cited

Preinesberger, Chris J. “Spell, Graphcore Partner to Build Next-Gen AI Infrastructure”. Venture Beat. 2022. Web.

The purpose of this essay is to critically analyze an article by James Vlahos, “How Scientists are Bringing AI Assistants to Life.” The author claims artificial intelligence assistants are being brought to life by scientists. The author’s targeted audience includes both developers and users of AIs. The author offers advice to users on how to best interact with virtual assistants. In the same fashion, the author highlights how researchers and developers explore new ways to enhance the efficiency and personable interactions of AIs.

Creating an AI with tastes and preferences in art and can share its background story is almost like breathing life into an object. In the development of virtual assistants, the next big challenge is enabling them to communicate and familiarize themselves better with humans, possess more accuracy in predicting human needs, and enhance their performance of complex tasks (Pieraccini 6). Microsoft went big on personality in the development of AI assistants because of how consumers personify technology. AIs are built with character traits that are likable by humans. This characteristic makes users engage more with virtual assistants, enabling the AI to learn more about human interactions.

The author posts a strong argument to support his claim that scientists breathe life into an object. Developing an intelligence with a personality the same as a human is an astounding achievement. Creating lifelike personas that make one seamlessly communicate with an AI as a human and the advancement of technology from response generation to speech synthesis is evidence enough to support the author’s opinion. The ability to create lifelike personas for AIs highlights just how close to reality the AIs are. Asked whether she is alive, Alexa, an AI assistant, replied, “I’m not really alive, but I can be lively sometimes.” AI’s do not try to outsmart humans when answering questions, despite their personality traits being just imaginary and almost informal.

Work Cited

Pieraccini, Roberto. AI Assistants. MIT Press Essential Knowledge Series, 2021.

Using innovative technologies, ADNOC is planning to apply its electronic developments in all spheres of activity and production as early as next year. An equally noteworthy example of the diversity and multifunctionality of the use of artificial intelligence is the predictive ability of computer programs. The company is developing a computer capable of processing an impressive amount of large data and suggesting strategies for the company’s actions depending on the forecast. The developers of software for ADNOC strive to build a machine that would work according to a certain theory but would be able to offer the possibility of effective data reading. That is why, within the framework of the company, a powerful program was developed not only for material processing but also for its visualization. By using more accessible visual media to convey complex information, the development makes the future and present of the company more clearly visible and intelligible.

The use of computer technologies in the development of a large company is noticeable at all levels of its existence. ADNOC uses computer simulations to improve the operation of the network chain and visualize data for a clearer picture of the company’s position. All these developments are implemented in each of the branches of the company’s operation, increasing the speed of performance and the effectiveness of actions that are more beneficial. Their project to computerize and update all sorts of areas of the company’s activities should be fully implemented in 2022 (ADNOC, 2020). This underlines the firm’s commitment to anticipating and adapting to the future. The company strives to develop a forecasting model that would include the largest possible amount of statistical data.

The results of big data processing show that ADNOC has significantly accelerated the speed and the number of financial transactions through its software. Based on the logic of increasing the turnover of money, one can conclude that this software improves the financial performance of the company. In addition, it should be noted that as part of the accelerated exchange of money, an innovative blockchain technology is used, which provides a permanent digital transfer of currency. The program’s ability to make smart business decisions is already reflected in its $ 1 billion worth of stocks (Arabian Business, 2020). The varied opportunities for financial enrichment brought by this program are potentially enormous.

Reference

ADNOC completes the first phase of the artificial Intelligence predictive maintenance project. (2020). ADNOC.

Adnoc’s AI, big data-powered Panorama center generates $1bn in 3 years. (2020). Arabian Business.

Artificial intelligence (AI) continues to grow more and more popular in the spheres that involve processing large amounts of information within limited timelines. Marketing is among those; the key to success in it lies in constant awareness of the recent tendencies in the market as well as in consumer behavior, which calls for never-ending data analysis. The use of AI, meanwhile, helps automate the process, consequently improving the productivity of businesses, possibly by up to 40% (Ramyalg, 2022, para. 3). The improvement means not only saving time and money, but also better quality of customer service due to the possibility to invest more resources in it. Another essential benefit from using AI is the reduction of error rates by minimizing the human factor (“AI could reduce,” 2018). The algorithm does not skip anything and cannot be tired or distracted, which, along with the incomparably higher speed, makes it substantially more effective than humans are.

The latter nuance actually is the reason why many assume that AI will be able to replace marketing jobs in the future. In addition, it is worth noting that the human factor involves bias and prejudice, which can distort judgement and, consequently, influence the final decision (“AI could reduce,” 2018). Considering all of the above, it seems to be relevant to guess that artificial intelligence could replace people in marketing jobs because it is substantially more reliable. In the opinion of those who support this idea, such a shift could result in substantially better understanding of consumer behavior, notably, a broader perspective on the recent tendencies and the drivers of change.

In fact, however, the use of AI cannot eliminate the involvement of people in marketing activities. The main reason is that it is able exclusively to simplify and quicken the accomplishment of certain tasks, but not organize and control the performance of a business on its own. Notably, only humans can be creative thinkers and generate the ideas that set the directions for further activity, for instance, design marketing campaigns. AI algorithms, meanwhile, are “built for repetitive tasks” and can only assist in analytical routine, but not in decision making (“Will digital marketers be out,” 2021, para. 1). The latter requires not only creativity, but also critical thinking, of which machines remain incapable.

Another consequence of the fact the previous paragraph mentions is the need for humans to program AI. Simply stated, someone has to write and adjust the algorithms that it will follow. This actually compromises the above ability of machines to minimize bias because algorithms may involve it in case their authors are prejudiced towards certain topics or categories of people; therefore, their usefulness is limited.

Relationships with customers that should be based on trust for maximal effectiveness also are a noteworthy area. Building them requires emotional response and interpersonal connections, which skills are unavailable for AI as well. It can analyze questions and generate answers, relying on the accessible data, but emotion-driven communication is beyond its functionality (“Will digital marketers be out,” 2021, para. 1). Meanwhile, personal interactions are critical in marketing, similar to any sphere that involves work with customers, due to which humans are irreplaceable in it. Furthermore, using AI may even create new jobs due to the need to program it as well as monitor its performance (ibid). The answer to the question whether it will replace marketing jobs in the future, therefore, doubtlessly is negative.

References

AI could reduce human error rate.(2018). Hello Future. Web.

Ramyalg, J. (2022). Artificial intelligence in marketing: Boost the growth in 2022. Mobidev. Web.

Will digital marketers be out of jobs because of artificial intelligence?(2021). Outreach Bee. Web.

The technical revolution has brought about a fundamental change in the world. Driven by the development of technology, the process of mastering the natural environment, the complexity of human social life, filled with artificial technical inventions, have reached their apogee in modern times. At a certain point in the process of inventing and introducing various technological devices intended for the means of mastering and subjugating the surrounding space, for means of communication and calculating his actions, a person produced a very unusual, previously unknown phenomenon – artificial intelligence.

The use of artificial intelligence has not bypassed the field of medicine. Nowadays, AI helps research and create new drugs, analyze a patient’s medical history, data from various devices, recognize the patient’s speech during admission, automate a doctor’s work, and recognize diverse diseases on medical images. AI has the potential not only to improve the accuracy of diagnostics and improve the quality of treatment, but also to significantly reduce its cost by optimizing tedious and time-consuming processes in medicine. Thus, the study of artificial intelligence seems to me interesting since this area of knowledge is of particular relevance today. It is this area that largely determines the course of development of technology and society.

It is worth noting that my background was a bachelor of biomedical engineering and my senior project took second place. Moreover, I have worked as a biomedical engineer for about six years. As is known, traditional bioengineering methods are slow and labor-intensive since the primary approach to choosing suitable materials here is trial and error. In this regard, artificial intelligence technologies adapted to the needs of synthetic biology come to the rescue. Artificial intelligence technologies can efficiently perform routine parts of synthetic biology research, freeing up scientists’ time for more creative scientific research processes. Thus, the program will help me reduce my work time and achieve revolutionary results.

In current conditions, the digitalization of medicine is not a populist slogan but an actual and urgent need. AI developments to create drugs, including those used to treat covid-infected, vaccines, cancer-fighting technologies, and other pharmaceutical and medical products, received an enormous volume of private investment in 2020. According to Zhang et al. (2021), it is more than $ 13.8 billion. Therefore, it seems that through the study of this, I will be able to contribute to the fight against the so-called plague of the 21st century.

Reference

Zhang, D., Mishra, S., Brynjolfsson, E., Etchemendy, J., Ganguli, D., Grosz, B., Lyons, T., Manyika, J., Niebles, J. C., Sellitto, M., Shoham, Y., Clark, J. & Perrault, R. (2021). Artificial intelligence: Index report 2021 [PDF document].