The strategic transformation of accounting into a learned profession

2020 ◽  
pp. 095042222095431
Author(s):  
Melissa Aldredge ◽  
Courtenay Rogers ◽  
James Smith
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Machine Learning ◽  
Data Analytics ◽  
Broad Sense ◽  
Skills Gap ◽  
Accounting Profession ◽  
Current Technology ◽  
Accounting Curriculum ◽  
Strategic Transformation

The skills gap in the accounting profession is not a new issue. More than 30 years of research and studies all point to an ever-increasing disparity between what accountants do and what the mainstream accounting curriculum teaches. Technology and businesses are changing and evolving rapidly, as are the expectations for accountants. Advances in the areas of automation and machine learning, artificial intelligence, data analytics and blockchain are examples of current technology disruptions in the accounting industry. The competencies and skills needed today for the accounting profession in the broad sense are not being taught by most universities. The purpose of this paper is to explore what these competencies and skills are, and why accounting curricula needs a strategic transformation into higher education for a learned profession.

Blockchain Technology, Business Data Analytics, and Artificial Intelligence: Use in the Accounting Profession and Ideas for Inclusion into the Accounting Curriculum

2019 ◽  
Vol 17 (1) ◽  
pp. 107-117 ◽  
Author(s):  
Amer Qasim ◽  
Faten F. Kharbat
Keyword(s):  
Artificial Intelligence ◽  
Data Analytics ◽  
New Technologies ◽  
Future Research ◽  
Accounting Profession ◽  
Curriculum Redesign ◽  
Blockchain Technology ◽  
Accounting Curriculum ◽  
Business Data ◽  
Jel Classifications

ABSTRACT Recently, accounting academics have reported increased attention in the accounting profession toward the employment of various technologies. These studies only highlight the exploitation of these technologies in the profession and areas for future research, while missing the need for modernizing the accounting curriculum to meet the industry's technological needs. This paper follows an integrated model for curriculum redesign to reflect blockchain technology, business data analytics, and artificial intelligence employment in the accounting profession. By building on the main four educational curricula designs components, we propose ways to incorporate these technologies into the accounting curriculum. Current industry implementation of new technologies should be considered by academia when designing accounting curriculum to prepare graduates for the market and to ensure their employability. This paper calls for radical changes in the accounting curriculum to reach a balance between existing accounting knowledge and information technology skills relevant to the profession. JEL Classifications: M4; I23; O33.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

scholarly journals Data Analytics and Public Safety

2021 ◽  
Vol 3 (3) ◽  
pp. 59-62
Author(s):  
Mark Masongsong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
West Coast ◽  
Data Analytics ◽  
Public Safety ◽  
Learning Technologies ◽  
Key Points

On November 27, 2020, UrbanLogiq CEO Mark Masongsong spoke on the topic of Data Analytics and Public Safety at the 2020 CASIS West Coast Security Conference. The key points of discussion focused on the challenges of artificial intelligence and machine learning technologies and their utility towards public safety. 

scholarly journals Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions

2021 ◽  
Vol 13 (18) ◽  
pp. 10424
Author(s):  
Valentin Kuleto ◽  
Milena Ilić ◽  
Mihail Dumangiu ◽  
Marko Ranković ◽  
Oliva M. D. Martins ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Machine Learning ◽  
Information Technologies ◽  
Higher Education Institutions ◽  
Common Knowledge ◽  
Educational Process ◽  
Research Survey ◽  
Students Attitudes ◽  
Commercial Sources

The way people travel, organise their time, and acquire information has changed due to information technologies. Artificial intelligence (AI) and machine learning (ML) are mechanisms that evolved from data management and developing processes. Incorporating these mechanisms into business is a trend many different industries, including education, have identified as game-changers. As a result, education platforms and applications are more closely aligned with learners’ needs and knowledge, making the educational process more efficient. Therefore, AI and ML have great potential in e-learning and higher education institutions (HEI). Thus, the article aims to determine its potential and use areas in higher education based on secondary research and document analysis (literature review), content analysis, and primary research (survey). As referent points for this research, multiple academic, scientific, and commercial sources were used to obtain a broader picture of the research subject. Furthermore, the survey was implemented among students in the Republic of Serbia, with 103 respondents to generate data and information on how much knowledge of AI and ML is held by the student population, mainly to understand both opportunities and challenges involved in AI and ML in HEI. The study addresses critical issues, like common knowledge and stance of research bases regarding AI and ML in HEI; best practices regarding usage of AI and ML in HEI; students’ knowledge of AI and ML; and students’ attitudes regarding AI and ML opportunities and challenges in HEI. In statistical considerations, aiming to evaluate if the indicators were considered reflexive and, in this case, belong to the same theoretical dimension, the Correlation Matrix was presented, followed by the Composite Reliability. Finally, the results were evaluated by regression analysis. The results indicated that AI and ML are essential technologies that enhance learning, primarily through students’ skills, collaborative learning in HEI, and an accessible research environment.

scholarly journals Artificial intelligence and machine learning: changing paradigm in diagnostics and imaging

2019 ◽  
Vol 8 (1) ◽  
pp. 382
Author(s):  
Rahul Badwaik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Setting ◽  
Digital Transformation ◽  
Current Status ◽  
Healthcare Industry ◽  
Review Of The Literature

Healthcare industry is currently undergoing a digital transformation, and Artificial Intelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machine learning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.

Technology Focus: Field Development (September 2021)

2021 ◽  
Vol 73 (09) ◽  
pp. 43-43
Author(s):  
Reza Garmeh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Development Strategies ◽  
Digital Transformation ◽  
Data Driven ◽  
Development Work ◽  
Field Development

The digital transformation that began several years ago continues to grow and evolve. With new advancements in data analytics and machine-learning algorithms, field developers today see more benefits to upgrading their traditional development work flows to automated artificial-intelligence work flows. The transformation has helped develop more-efficient and truly integrated development approaches. Many development scenarios can be automatically generated, examined, and updated very quickly. These approaches become more valuable when coupled with physics-based integrated asset models that are kept close to actual field performance to reduce uncertainty for reactive decision making. In unconventional basins with enormous completion and production databases, data-driven decisions powered by machine-learning techniques are increasing in popularity to solve field development challenges and optimize cube development. Finding a trend within massive amounts of data requires an augmented artificial intelligence where machine learning and human expertise are coupled. With slowed activity and uncertainty in the oil and gas industry from the COVID-19 pandemic and growing pressure for cleaner energy and environmental regulations, operators had to shift economic modeling for environmental considerations, predicting operational hazards and planning mitigations. This has enlightened the value of field development optimization, shifting from traditional workflow iterations on data assimilation and sequential decision making to deep reinforcement learning algorithms to find the best well placement and well type for the next producer or injector. Operators are trying to adapt with the new environment and enhance their capabilities to efficiently plan, execute, and operate field development plans. Collaboration between different disciplines and integrated analyses are key to the success of optimized development strategies. These selected papers and the suggested additional reading provide a good view of what is evolving with field development work flows using data analytics and machine learning in the era of digital transformation. Recommended additional reading at OnePetro: www.onepetro.org. SPE 203073 - Data-Driven and AI Methods To Enhance Collaborative Well Planning and Drilling-Risk Prediction by Richard Mohan, ADNOC, et al. SPE 200895 - Novel Approach To Enhance the Field Development Planning Process and Reservoir Management To Maximize the Recovery Factor of Gas Condensate Reservoirs Through Integrated Asset Modeling by Oswaldo Espinola Gonzalez, Schlumberger, et al. SPE 202373 - Efficient Optimization and Uncertainty Analysis of Field Development Strategies by Incorporating Economic Decisions in Reservoir Simulation Models by James Browning, Texas Tech University, et al.

Artificial Intelligence and Machine Learning in Higher Education

2021 ◽  
pp. 1-17
Author(s):  
Ibrahim Eren Bisen ◽  
Emin Alp Arslan ◽  
Kamil Yildirim ◽  
Yetkin Yildirim
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Machine Learning ◽  
Online Learning ◽  
Learning Experiences ◽  
Curriculum Planning ◽  
Virtual Classrooms ◽  
The Future

Artificial intelligence and machine learning have the potential to address many of the problems that have emerged in higher education due the rapid and haphazard transition to online learning brought about by the coronavirus pandemic. These problems include students' struggle to self-regulate their learning, the increase in curriculum planning and administrative workload for teachers, and the loss of personalized interaction between students and teachers. This chapter explores how artificial intelligence can be used to help students and teachers to adapt to the new realities of online learning, and how these technologies could further transform higher education in the future. By providing more personalized, flexible, inclusive, and engaging learning experiences, artificial intelligence has the potential to re-invigorate students and teachers both and to make virtual classrooms more meaningful and productive.

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