Business Insights of Artificial Intelligence and the Future of Humans

Authors

  • Upendar Rao Thaduri Web Developer, Amalgamated Bank, New York, USA

DOI:

https://doi.org/10.18034/ajtp.v5i3.669

Keywords:

Artificial Intelligence, AI Machine Learning, Natural Language Processing, Uses of Field, Quantum Computing

Abstract

Recent technological advancements and the increasing pace of adopting artificial intelligence (AI) technologies constitute a need to identify and analyze the issues regarding their implementation in the education sector. This is because the education sector is associated with highly dynamic business environments controlled and maintained by information systems. In addition, the education sector is a sector that is associated with information systems. On the other hand, it was discovered through an analysis of the current research that a moderate amount of investigation has been conducted in this field. We have highlighted the benefits and obstacles of adopting artificial intelligence in the education sector to fill this hole. Before this, a brief discussion was presented on the fundamental ideas of AI and its development over time. In addition, we have evaluated the usefulness of contemporary AI technologies for students and teachers, which are currently on the software market. These technologies are currently available. We have built a strategy implementation model, outlined by a standard five-step method and the corresponding configuration guide. This is the very last thing that we have done. To check and ensure the accuracy of their design, we independently devised three implementation plans for three distinct institutions of higher education. The results acquired will contribute to a more profound knowledge of the particulars of AI systems, services, and tools, which will, in turn, pave the way for implementing these things more efficiently.

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References

Alanazi, H. O., Abdullah, A. H., Qureshi, K. N. (2017). A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care. Journal of Medical Systems, 41(4), 1-10. https://doi.org/10.1007/s10916-017-0715-6 DOI: https://doi.org/10.1007/s10916-017-0715-6

Al-Gargoor, R. G., Saleem, N. N. (2013). Software Reliability Prediction Using Artificial Techniques. International Journal of Computer Science Issues (IJCSI), 10(4), 274-281.

Balara, D., Timko, J., Žilková, J., Lešo, M. (2017). Neural Networks Application for Mechanical Parameters Identification of Asynchronous Motor. Neural Network World: International Journal on Neural and Mass-Parallel Computing and Information Systems, 27(3), 259-270. http://dx.doi.org/10.14311/nnw.2017.27.013 DOI: https://doi.org/10.14311/NNW.2017.27.013

Ballamudi, V. K. R. (2016). Utilization of Machine Learning in a Responsible Manner in the Healthcare Sector. Malaysian Journal of Medical and Biological Research, 3(2), 117-122. https://mjmbr.my/index.php/mjmbr/article/view/677

Ballamudi, V. K. R., & Desamsetti, H. (2017). Security and Privacy in Cloud Computing: Challenges and Opportunities. American Journal of Trade and Policy, 4(3), 129–136. https://doi.org/10.18034/ajtp.v4i3.667 DOI: https://doi.org/10.18034/ajtp.v4i3.667

David, R. K., Barbara, S., Peter, S., Bhatt, R. A., Adele I. F. (2016). Natural Language Processing–Enabled and Conventional Data Capture Methods for Input to Electronic Health Records: A Comparative Usability Study. JMIR Medical Informatics, 4(4). https://doi.org/10.2196/medinform.5544 DOI: https://doi.org/10.2196/medinform.5544

Dekkati, S., & Thaduri, U. R. (2017). Innovative Method for the Prediction of Software Defects Based on Class Imbalance Datasets. Technology & Management Review, 2, 1–5. https://upright.pub/index.php/tmr/article/view/78

Dekkati, S., Thaduri, U. R., & Lal, K. (2016). Business Value of Digitization: Curse or Blessing?. Global Disclosure of Economics and Business, 5(2), 133-138. https://doi.org/10.18034/gdeb.v5i2.702 DOI: https://doi.org/10.18034/gdeb.v5i2.702

Desamsetti, H. (2016a). A Fused Homomorphic Encryption Technique to Increase Secure Data Storage in Cloud Based Systems. The International Journal of Science & Technoledge, 4(10), 151-155.

Desamsetti, H. (2016b). Issues with the Cloud Computing Technology. International Research Journal of Engineering and Technology (IRJET), 3(5), 321-323.

Desamsetti, H., & Mandapuram, M. (2017). A Review of Meta-Model Designed for the Model-Based Testing Technique. Engineering International, 5(2), 107–110. https://doi.org/10.18034/ei.v5i2.661 DOI: https://doi.org/10.18034/ei.v5i2.661

Dunja, M., Marko, G. (2013). Automatic Text Analysis by Artificial Intelligence. Informatica, 37(1), 27-33.

Gutlapalli, S. S. (2016a). An Examination of Nanotechnology’s Role as an Integral Part of Electronics. ABC Research Alert, 4(3), 21–27. https://doi.org/10.18034/ra.v4i3.651 DOI: https://doi.org/10.18034/ra.v4i3.651

Gutlapalli, S. S. (2016b). Commercial Applications of Blockchain and Distributed Ledger Technology. Engineering International, 4(2), 89–94. https://doi.org/10.18034/ei.v4i2.653 DOI: https://doi.org/10.18034/ei.v4i2.653

Gutlapalli, S. S. (2017a). Analysis of Multimodal Data Using Deep Learning and Machine Learning. Asian Journal of Humanity, Art and Literature, 4(2), 171–176. https://doi.org/10.18034/ajhal.v4i2.658 DOI: https://doi.org/10.18034/ajhal.v4i2.658

Gutlapalli, S. S. (2017b). The Role of Deep Learning in the Fourth Industrial Revolution: A Digital Transformation Approach. Asian Accounting and Auditing Advancement, 8(1), 52–56. Retrieved from https://4ajournal.com/article/view/77

Gutlapalli, S. S. (2017c). An Early Cautionary Scan of the Security Risks of the Internet of Things. Asian Journal of Applied Science and Engineering, 6, 163–168. Retrieved from https://ajase.net/article/view/14

Iztok, F. Jr. (2017). Computational Intelligence Algorithms for the Development of an Artificial Sport Trainer. Informatica, suppl. Special Issue: Superintelligenc, 41(4), 517-518.

John, Z., Ming, F., Bin, G., Vijay, M., Bin, Z. (2018). Business Values/Implications of AI and Machine Learning. Data and Information Management, 2(3), 121-129. https://doi.org/10.2478/dim-2018-0016 DOI: https://doi.org/10.2478/dim-2018-0016

Lal, K. (2015). How Does Cloud Infrastructure Work?. Asia Pacific Journal of Energy and Environment, 2(2), 61-64. https://doi.org/10.18034/apjee.v2i2.697 DOI: https://doi.org/10.18034/apjee.v2i2.697

Lal, K. (2016). Impact of Multi-Cloud Infrastructure on Business Organizations to Use Cloud Platforms to Fulfill Their Cloud Needs. American Journal of Trade and Policy, 3(3), 121–126. https://doi.org/10.18034/ajtp.v3i3.663 DOI: https://doi.org/10.18034/ajtp.v3i3.663

Lal, K., & Ballamudi, V. K. R. (2017). Unlock Data’s Full Potential with Segment: A Cloud Data Integration Approach. Technology & Management Review, 2(1), 6–12. https://upright.pub/index.php/tmr/article/view/80

Mandapuram, M. (2016). Applications of Blockchain and Distributed Ledger Technology (DLT) in Commercial Settings. Asian Accounting and Auditing Advancement, 7(1), 50–57. https://4ajournal.com/article/view/76

Mandapuram, M. (2017a). Application of Artificial Intelligence in Contemporary Business: An Analysis for Content Management System Optimization. Asian Business Review, 7(3), 117–122. https://doi.org/10.18034/abr.v7i3.650 DOI: https://doi.org/10.18034/abr.v7i3.650

Mandapuram, M. (2017b). Security Risk Analysis of the Internet of Things: An Early Cautionary Scan. ABC Research Alert, 5(3), 49–55. https://doi.org/10.18034/ra.v5i3.650 DOI: https://doi.org/10.18034/ra.v5i3.650

Mikhail, B., Alexey, T., Sergey, M., Alisa, Z., David, D. (2017). Artificial Intelligence in Life Extension: from Deep Learning to Superintelligence. Informatica, suppl. Special Issue: Superintelligence, 41(4), 401-417.

Raouf, B., Mohammad A. S., Noura, L., Sara, A., Shahriar, N. (2018). A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities. Journal of Internet Services and Applications, 9(1), 1-99. https://doi.org/10.1186/s13174-018-0087-2 DOI: https://doi.org/10.1186/s13174-018-0087-2

Sarma, G. P., Hay, N. J. (2017). Robust Computer Algebra, Theorem Proving, and Oracle AI. Informatica, suppl. Special Issue: Superintelligence, 41(4), 451-461. https://www.proquest.com/docview/2002969430/420194A50A204AC8PQ/3 DOI: https://doi.org/10.2139/ssrn.3038545

Thaduri, U. R. (2017). Business Security Threat Overview Using IT and Business Intelligence. Global Disclosure of Economics and Business, 6(2), 123-132. https://doi.org/10.18034/gdeb.v6i2.703 DOI: https://doi.org/10.18034/gdeb.v6i2.703

Thaduri, U. R., Ballamudi, V. K. R., Dekkati, S., & Mandapuram, M. (2016). Making the Cloud Adoption Decisions: Gaining Advantages from Taking an Integrated Approach. International Journal of Reciprocal Symmetry and Theoretical Physics, 3, 11–16. https://upright.pub/index.php/ijrstp/article/view/77

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Published

2018-12-31

How to Cite

Thaduri, U. R. (2018). Business Insights of Artificial Intelligence and the Future of Humans. American Journal of Trade and Policy, 5(3), 143–150. https://doi.org/10.18034/ajtp.v5i3.669

Issue

Section

Policy and Practice Reviews