scholarly journals Artificial Intelligence Competencies for Data Science Undergraduate Curricula

2019 ◽  
Vol 33 ◽  
pp. 9746-9747
Author(s):  
Andrea Danyluk ◽  
Scott Buck
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Data Science ◽  
Task Force ◽  
Undergraduate Curriculum ◽  
Short Paper ◽  
Undergraduate Level ◽  
Programs Of Study

In August 2017, the ACM Education Council initiated a task force to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force is seeking to define what the computing contributions are to this new field, in order to provide guidance for computer science or similar departments offering data science programs of study at the undergraduate level. The ACM Data Science Task Force has completed the initial draft of a curricular report. The computing-knowledge areas identified in the report are drawn from across computing disciplines and include several sub-areas of AI. This short paper describes the overall project, highlights AI-relevant areas, and seeks to open a dialog about the AI competencies that are to be considered central to a data science undergraduate curriculum.

Applications of AI in Financial System

2017 ◽  
pp. 101-109
Author(s):  
Santosh Kumar ◽  
Roopali Sharma
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Analysis ◽  
Computer Science ◽  
Investment Decision ◽  
Analysis Tool ◽  
Changing Environment ◽  
Human Interference ◽  
Applications Of Ai

Role of computers are widely accepted and well known in the domain of Finance. Artificial Intelligence(AI) methods are extensively used in field of computer science for providing solution of unpredictable event in a frequent changing environment with utilization of neural network. Professionals are using AI framework into every field for reducing human interference to get better result from few decades. The main objective of the chapter is to point out the techniques of AI utilized in field of finance in broader perspective. The purpose of this chapter is to analyze the background of AI in finance and its role in Finance Market mainly as investment decision analysis tool.

Conclusion

2021 ◽  
pp. 127-132
Author(s):  
Simone Natale
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Computer Science ◽  
Interface Design ◽  
External World ◽  
The Social ◽  
Social Meanings ◽  
Computer Scientists ◽  
Historical Trajectory

The historical trajectory examined in this book demonstrates that humans’ reactions to machines that are programmed to simulate intelligent behaviors represent a constitutive element of what is commonly called AI. Artificial intelligence technologies are not just designed to interact with human users: they are designed to fit specific characteristics of the ways users perceive and navigate the external world. Communicative AI becomes more effective not only by evolving from a technical standpoint but also by profiting, through the dynamics of banal deception, from the social meanings humans project onto situations and things. In this conclusion, the risks and problems related to AI’s banal deception are explored in relationship with other AI-based technologies such as robotics and social media bots. A call is made for initiating a more serious debate about the role of deception in interface design and computer science. The book concludes with a reflection on the need to develop a critical and skeptical stance in interactions with computing technologies and AI. In order not to be found unprepared for the challenges posed by AI, computer scientists, software developers, designers as well as users have to consider and critically interrogate the potential outcomes of banal deception.

scholarly journals Critical Care, Critical Data

2019 ◽  
Vol 10 ◽  
pp. 117959721985656 ◽  
Author(s):  
Christopher V Cosgriff ◽  
Leo Anthony Celi ◽  
David J Stone
Keyword(s):  
Artificial Intelligence ◽  
Critical Care ◽  
Clinical Data ◽  
Continuous Monitoring ◽  
Data Science ◽  
Clinical Decision ◽  
Critical Care Medicine ◽  
Data Driven ◽  
Critical Data

As big data, machine learning, and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.

scholarly journals Remote work and human capital: new challenges and threats (international experience)

2021 ◽  
pp. 107-119
Author(s):  
Oleksandr Burov
Keyword(s):  
Artificial Intelligence ◽  
Human Capital ◽  
Data Science ◽  
Well Being ◽  
Business Environment ◽  
Remote Work ◽  
Science Data ◽  
Creative Abilities ◽  
The Impact

Keywords: human capital, remote work, cybersecurity, workforce, digital economics The article considers the role of human capital in the transitionto the remote work. The analysis of world changes in the field of safe and effectiveuse of digital business environment and qualification of workforce in the conditions ofgrowth of remote work is carried out. The analysis was conducted in the following areas:general features of the digitalizing in crisis and innovation, a new paradigm of business«Data is the new gold», the organization of the workforce in the transition to teleworking,the priorities of today's professions, the problems of cybersecurity in teleworking. It has been articulated that the main requirements for the today’s workforce are intellectualand creative abilities, competence in the field of creation and use of ICT, bigdata (data science, data mining, data analytics) and artificial intelligence, the role ofwhich has grown even more due to the COVID-19 pandemic. The human component ofintellectual capital (in the form of knowledge, skills and competencies, as well as intellectualand creative abilities) is gaining new importance in the digital economy.The analysis of relationship of the crisis and innovation made on the basis of the ClarivateDerwent report has demonstrated the impact of the pandemic on the global lifecycle of research and innovation projects in the first half of 2020, namely that COVID-19violated innovation strategy of the innovative leaders worldwide. The analysis hasdemonstrated: in the new conditions of accelerated digitalization, ingenuity and speed ofdecision-making and innovation are needed more than ever. These priorities will affectthe world economy in the coming year.Special attention in analysis has been paid to the new business paradigm related touse and role of data. It was highlighted that digitization generates vast amounts of datathat offer many opportunities for business, human well-being, and the environment. As aresult, new capabilities and opportunities arise for business with the ecosystem of cooperationand partnership, as well as collaboration of stakeholders.The core of changes in digitalization is reskilling and upskilling of the workforce accountingnew workplaces and new requirements for them. It is recognized that talentmanagement and creative people selection can be the main engine in future transformationof economics, and workforce becomes an effective pole for investments. At the sametime, it is argued that remote worker is outside the scope of corporate protection, and virtuallyany production information, like human capital, becomes much more vulnerablein such conditions and requires appropriate cybersecurity methods.As a conclusion, it is articulated that the ability of companies to use big data is beginningto play a significant role in the economy, which in turn requires the involvementand training of data processing and analysis specialists. The direction of professions thatis being actively formed recently — data science — is one of the most priority in the labormarket. At the same time, the labor market needs skills and abilities in the field of interpersonalcommunication (soft skills), which are able to ensure the effective operation ofpeople and systems of hybrid intelligence «human-artificial intelligence».For the further research it has been recommended a comprehensive study of protectionof objects and subjects of intellectual property in open networks.

scholarly journals Data science and the role of Artificial Intelligence in achieving the fast diagnosis of Covid-19

2020 ◽  
Vol 140 ◽  
pp. 110182 ◽  
Author(s):  
Dasari Naga Vinod ◽  
S.R.S. Prabaharan
Keyword(s):  
Artificial Intelligence ◽  
Data Science

scholarly journals Data Science Techniques, Tools and Predictions

2020 ◽  
Vol 8 (6) ◽  
pp. 5661-5668
Keyword(s):  
Artificial Intelligence ◽  
Data Science ◽  
Future Expectations ◽  
Science Field ◽  
Customer Needs ◽  
The People ◽  
Science Operations ◽  
Modeling Data ◽  
Vital Element

Almighty created human being with numerous wants and needs which makes them associated with their own data, choices and preferences. To grow and develop any business or organizations it is very obligatory to know their clients requests or customer needs based on their data. The evolving role of data makes it very vital element in any organization and carried with convinced operations. In this paper we are going to present a study of Data Science and its relevance with Artificial Intelligence, machine learning and deep learning. The incorporation of these intellectual sciences in data science is useful for perming numerous operations in our research we tried to demonstrate the data science operations like data cleaning, data processing, data modeling, data visualization and data presentations techniques. To grow any business it is mandatory to know their customer needs and satisfy their future expectations by smart decision makings. The intellectual algorithms or data operations in the data science make the data to be more effective in decision making and decision polices. We also focus on how data science incorporates mathematical & statistical methods, logical reasoning with applications of Artificial Intelligence techniques. We also focus on various data operations tools which exists in the market like python, SAS, R and many others. At last we focusses on how data science field going to meet the future expectations of many businesses. This research paper may become as successful reference for the people to carry out their research and meet the expectations of data science field with business growing decisions.

scholarly journals Rho AI – Leveraging artificial intelligence to address climate change: Financing, implementation and ethics

2021 ◽  
pp. 204388692096178
Author(s):  
Isabel Fischer ◽  
Claire Beswick ◽  
Sue Newell
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Data Science ◽  
Business Models ◽  
Climate Impact ◽  
Strategic Choices ◽  
Business In Society ◽  
The Impact

The case focusses on Rho AI, a data science firm, and its attempt to leverage artificial intelligence to encourage environmental, social and governance investments to limit the impact of climate change. Rho AI’s proposed open-source artificial intelligence tool integrates automated web scraping technology and machine learning with natural language processing. The aim of the tool is to enable investors to evaluate the climate impact of companies and to use this evaluation as a basis for making investments in companies. The case study allows for students to gain an insight into some of the strategic choices that need to be considered when developing an artificial intelligence–based tool. Students will be able to explore the role of ethics in decision-making related to artificial intelligence, while familiarising themselves with key technical terminology and possible business models. The case encourages students to see beyond the technical granularities and to consider the multi-faceted, wider corporate and societal issues and priorities. This case contributes to students recognising that business is not conducted in a vacuum and enhances students’ understanding of the role of business in society during new developments triggered by digital technology.

Role of Computer Assisted Instruction (CAI) in an Introductory Computer Concepts Course

1997 ◽  
Vol 25 (4) ◽  
pp. 327-345 ◽  
Author(s):  
Vincent J. Skudrna
Keyword(s):  
Computer Science ◽  
Primary Objective ◽  
Computer Assisted Instruction ◽  
Design System ◽  
Computer Assisted ◽  
Undergraduate Level ◽  
Potential Benefits ◽  
Principal Subject ◽  
Assisted Instruction

The primary objective of this article is to discuss the role of Computer Assisted Instruction (CAI) at the undergraduate level via a survey of related literature and specific applications. CAI shares many features with other instructional modes, such as traditional classrooms and programmed instruction (PI). Many characteristics of learners affect their ability to learn and acquire new knowledge. An individual's subject-specific knowledge and general knowledge both affect comprehension. With regard to instructional design, system approaches are sometimes referred to as instructional development systems (IDS). An IDS embraces several major categories. These include a statement of goals, analysis, development of instruction, and evaluation and revision. General statements on CAI can be divided into the following areas: requirements, potential benefits, state of the art, problems, CAI as a factor in society, the roles of industry, education, and government, including the role of teacher. At least two educational requirements make CAI inevitable, i.e., the trend to individualized instruction and the growth in information to be acquired. Data processing and computer science involve the teaching of computer skills in relative isolation from other disciplines. Hence, the computer is the principal subject. Student problem solving and research is where the computer is used as a tool in some field outside computer science. A specific sphere of application is the subject. This article will relate these categories as they apply to an introductory computer concepts course taught at the undergraduate level. Another phase of this course is that it is accounting-oriented.

scholarly journals APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) TO ASSESS THE ROLE OF COVID-19 LOCKDOWN IN THE DOMAIN OF ESTUARINE ACIDIFICATION

2021 ◽  
Vol 16 (12) ◽  
Author(s):  
Sitangshu Roy
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Bay Of Bengal ◽  
Ground Zero ◽  
Data Set ◽  
Estuarine Acidification

The branch of computer science that deals with the simulation of variables with the help of a computer are termed Artificial Intelligence (AI). Here we attempt to predict the pace of acidification in the Digha coast of the Bay of Bengal based on available datasets of more than three decades. The ground zero observation on the data set reveals a decreasing trend of pH since 1984 with a sudden hike in premonsoon 2020, the period coinciding with the COVID 19 lockdown phase in the Indian sub-continent.

scholarly journals Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future

2021 ◽  
Vol 13 ◽  
pp. 175628722110448
Author(s):  
B.M. Zeeshan Hameed ◽  
Gayathri Prerepa ◽  
Vathsala Patil ◽  
Pranav Shekhar ◽  
Syed Zahid Raza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Error ◽  
Data Science ◽  
Image Interpretation ◽  
X Ray ◽  
X Ray Imaging ◽  
Potential Applications ◽  
Efficiency And Productivity

Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.

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