scholarly journals A National Security Research Agenda for Cybersecurity and Artificial Intelligence

2020 ◽  
Author(s):  
Ben Buchanan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Global Stability ◽  
National Security ◽  
Research Agenda ◽  
New Technologies ◽  
Security Issues ◽  
Security Research

Machine learning advances are transforming cyber strategy and operations. This necessitates studying national security issues at the intersection of AI and cybersecurity, including offensive and defensive cyber operations, the cybersecurity of AI systems, and the effect of new technologies on global stability.

scholarly journals The Pros & Cons of Business Innovation at IT Organizations

2019 ◽  
Vol 7 (1) ◽  
pp. 82-85
Author(s):  
Geetha Swaminathan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Technology ◽  
Cloud Computing ◽  
New Technologies ◽  
Global Market ◽  
Innovative Product ◽  
Business Innovation ◽  
It Organization ◽  
Pros And Cons

In the 21st Century, the buzzword is often used in all fields is “Innovation". It is no wonder using Innovation in day to the conversation as well as striving for innovation execution at organisations in Information Technology (IT) sectors. When we need to talk about innovation in IT sectors in the fast-moving technology IT organisations, they are in a position in increasing its capability in its innovative product and services. There is a lot of benefits out of business innovations that are being reaped in IT companies; there are apparent disadvantages are also the outcome of them. It is quite common, despite all benefits and drawbacks, they are in apposition to survive in the global market. That becomes a great challenge to all IT organisations. In IT organisations which consist of departments such as Development, Testing, Consulting, Networking, Infrastructure, Process and having common platforms and legacy languages, Apart from that they are in the way of invading new technologies such as Digital, Mobile, IoT, Artificial Intelligence, Machine learning Cloud computing. In all the fields, as mentioned above and area, they need to do innovation to sustain their business. This paper will provide elaborate results on Pros and Cons of Business Innovation in IT Organization.

FinTech Adoption in China

2021 ◽  
pp. 166-173
Author(s):  
Gagan Kukreja
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Financial Services ◽  
New Technologies ◽  
Large Population ◽  
Financial Inclusion ◽  
High Penetration ◽  
Digital Payments ◽  
Almost All

Almost all financial services (especially digital payments) in China are affected by new innovations and technologies. New technologies such as blockchain, artificial intelligence, machine learning, deep learning, and data analytics have immensely influenced all most all aspects of financial services such as deposits, transactions, billings, remittances, credits (B2B and P2P), underwriting, insurance, and so on. Fintech companies are enabling larger financial inclusion, changing in lifestyle and expenditure behavior, better and fast financial services, and lots more. This chapter covers the development, opportunities, and challenges of financial sectors because of new technologies in China. This chapter throws the light on opportunities that emerged because of the large population of 1.4 billion people, high penetration, and access to the latest and affordable technology, affordable cost of smartphones, and government policies and regulations. Lastly, this chapter portrays the untapped potentials of Fintech in China.

Engineering AI Systems

2021 ◽  
pp. 1-19 ◽  
Author(s):  
Jan Bosch ◽  
Helena Holmström Olsson ◽  
Ivica Crnkovic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Case Studies ◽  
Research Agenda ◽  
Production Quality ◽  
Research Community ◽  
Design Methods ◽  
Engineering Approach ◽  
Quality Design

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry. However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this chapter, the authors provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that they have studied. The main contribution of the chapter is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.

scholarly journals A technologist view of the next decade evolution of the librarian work environment

2018 ◽  
Vol 186 ◽  
pp. 09004
Author(s):  
André Schaaff ◽  
Marc Wenger
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Work Environment ◽  
New Technologies ◽  
State Of The Art ◽  
Working Environment ◽  
Online Resources ◽  
Mining Machine ◽  
Daily Work

The work environment has deeply evolved in recent decades with the generalisation of IT in terms of hardware, online resources and software. Librarians do not escape this movement and their working environment is becoming essentially digital (databases, online publications, Wikis, specialised software, etc.). With the Big Data era, new tools will be available, implementing artificial intelligence, text mining, machine learning, etc. Most of these technologies already exist but they will become widespread and strongly impact our ways of working. The development of social networks that are "business" oriented will also have an increasing influence. In this context, it is interesting to reflect on how the work environment of librarians will evolve. Maintaining interest in the daily work is fundamental and over-automation is not desirable. It is imperative to keep the human-driven factor. We draw on state of the art new technologies which impact their work, and initiate a discussion about how to integrate them while preserving their expertise.

scholarly journals Teaching students to develop GIS applications

2021 ◽  
pp. 249-257
Author(s):  
Наталия Дмитриевна Хрулёва
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Applications ◽  
Spatial Representation ◽  
Information Technologies ◽  
New Technologies ◽  
Spatial Information ◽  
Application Development ◽  
Mobile Gis ◽  
Gis Applications

Мобильные ГИС-приложения становятся все более сложными, как решаемые с их помощью задачи. Обычное ГИС-приложение должно включать такие элементы, как искусственный интеллект, распознавание образов или машинное обучение, реляционные или нереляционные базы данных, пространственное представление и рассуждения. Такие компании, как Google и Apple, разрабатывают новые технологии, связанные с разработкой мобильных приложений. Например, Apple представила в 2019 году на WWDC2019 и WWDC2020 новую технологию под названием SwiftUI, которая направлена на сложности разработки мобильного приложения и позволяющая интегрировать такие технологии, как Mapkit, для представления пространственной информации. В данной работе представлены исследования преимуществ использования SwiftUI для интеграции Mapkit в качестве основы пространственного представления для облегчения разработки мобильных ГИС-приложений. Информационные технологии имеют большое разнообразие применений в различных областях науки. Например, искусственный интеллект и машинное обучение - это технологии, которые начинают широко использоваться в мобильных приложениях. Целью данной работы является исследования способов разработки мобильных приложений, которые могут выполнять представление и вычисления информации в соответствии с требованиями. Mobile GIS applications are becoming more and more complex, as the tasks they solve are. A typical GIS application should include elements such as artificial intelligence, pattern recognition or machine learning, relational or non-relational databases, spatial representation and reasoning. Companies such as Google and Apple are developing new technologies related to the development of mobile applications. For example, Apple introduced a new technology called SwiftUI at WWDC2019 and WWDC2020 in 2019, which aims to reduce the complexity of mobile application development and allows integrating technologies such as Mapkit to represent spatial information. This paper presents studies of the advantages of using SwiftUI to integrate Mapkit as a basis for spatial representation to facilitate the development of mobile GIS applications. Information technologies have a wide variety of applications in various fields of science. For example, artificial intelligence and machine learning are technologies that are beginning to be widely used in mobile applications. The purpose of this work is to investigate ways to develop mobile applications that can perform the presentation and calculation of information in accordance with the requirements.

scholarly journals Supervised Learning Methods and Applications in Medical Research

2022 ◽  
pp. 27-34
Author(s):  
Yung Ming ◽  
Lily Yuan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Feature Extraction ◽  
Job Performance ◽  
Computational Linguistics ◽  
New Technologies ◽  
Voice Recognition ◽  
Autonomous Driving ◽  
Automated Systems

Machine Learning (ML) and Artificial Intelligence (AI) methods are transforming many commercial and academic areas, including feature extraction, autonomous driving, computational linguistics, and voice recognition. These new technologies are now having a significant effect in radiography, forensics, and many other areas where the accessibility of automated systems may improve the precision and repeatability of essential job performance. In this systematic review, we begin by providing a short overview of the different methods that are currently being developed, with a particular emphasis on those utilized in biomedical studies.

scholarly journals 80 questions for UK biological security

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0241190
Author(s):  
Luke Kemp ◽  
David C. Aldridge ◽  
Olaf Booy ◽  
Hilary Bower ◽  
Des Browne ◽  
...  
Keyword(s):  
Research Agenda ◽  
New Technologies ◽  
Expert Elicitation ◽  
Security Strategy ◽  
Rigorous Approach ◽  
Management Actions ◽  
International Trends ◽  
Security Research ◽  
Emerging Issues ◽  
The Uk

Multiple national and international trends and drivers are radically changing what biological security means for the United Kingdom (UK). New technologies present novel opportunities and challenges, and globalisation has created new pathways and increased the speed, volume and routes by which organisms can spread. The UK Biological Security Strategy (2018) acknowledges the importance of research on biological security in the UK. Given the breadth of potential research, a targeted agenda identifying the questions most critical to effective and coordinated progress in different disciplines of biological security is required. We used expert elicitation to generate 80 policy-relevant research questions considered by participants to have the greatest impact on UK biological security. Drawing on a collaboratively-developed set of 450 questions, proposed by 41 experts from academia, industry and the UK government (consulting 168 additional experts) we subdivided the final 80 questions into six categories: bioengineering; communication and behaviour; disease threats (including pandemics); governance and policy; invasive alien species; and securing biological materials and securing against misuse. Initially, the questions were ranked through a voting process and then reduced and refined to 80 during a one-day workshop with 35 participants from a variety of disciplines. Consistently emerging themes included: the nature of current and potential biological security threats, the efficacy of existing management actions, and the most appropriate future options. The resulting questions offer a research agenda for biological security in the UK that can assist the targeting of research resources and inform the implementation of the UK Biological Security Strategy. These questions include research that could aid with the mitigation of Covid-19, and preparation for the next pandemic. We hope that our structured and rigorous approach to creating a biological security research agenda will be replicated in other countries and regions. The world, not just the UK, is in need of a thoughtful approach to directing biological security research to tackle the emerging issues.

scholarly journals New Technologies and Deterrence: Artificial Intelligence and Adversarial Behaviour

2020 ◽  
pp. 401-417
Author(s):  
Alex Wilner ◽  
Casey Babb
Keyword(s):  
Artificial Intelligence ◽  
National Security ◽  
New Technologies ◽  
Cost Benefit ◽  
Human Cost ◽  
Technological Developments ◽  
Military Engagement ◽  
Defensive Measures ◽  
Severity Of Punishment ◽  
Political Decisions

AbstractOffering a critical synthesis of extant insights into technological developments in AI and their potential ramifications for international relations and deterrence postures, this chapter argues that AI risks influencing military deterrence and coercion in unique ways: it may alter cost-benefit calculations by removing the fog of war, by superficially imposing rationality on political decisions, and by diminishing the human cost of military engagement. It may recalibrate the balance between offensive and defensive measures, tipping the scales in favour of pre-emption, and undermine existing assumptions imbedded in both conventional and nuclear deterrence. AI might altogether remove human emotions and eliminate other biological limitations from the practice of coercion. It may provide users the ability to collect, synthesize, and act upon real-time intelligence from several disparate sources, augmenting the certainty and severity of punishment strategies, both in theatre and online, compressing the distance between intelligence, political decisions, and coercive action. As a result, AI may quicken the overall pace of action across all domains of coercion, in conflict, crisis, and war, and within the related subfields of national security, counterterrorism, counter-crime, and counter-espionage.

scholarly journals The AI Triad and What It Means for National Security Strategy

2020 ◽  
Author(s):  
Ben Buchanan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
National Security ◽  
Security Policy ◽  
Learning Systems ◽  
Security Strategy ◽  
National Security Policy ◽  
Computing Power ◽  
National Security Strategy

One sentence summarizes the complexities of modern artificial intelligence: Machine learning systems use computing power to execute algorithms that learn from data. This AI triad of computing power, algorithms, and data offers a framework for decision-making in national security policy.

scholarly journals ARTIFICIAL INTELLIGENCE: AN INSTRUMENT ENSURING CYBERSECURITY OF THE FINANCIAL SPHERE OR A CYBER THREAT TO BANKS?

Banking law ◽  
2021 ◽  
Vol 1 ◽  
pp. 35-46
Author(s):  
Svetlana S. Gorokhova ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Banking Sector ◽  
New Technologies ◽  
Financial Sector ◽  
Security Threats ◽  
Cyber Threat

The article examines the growing risks and security threats faced by the financial sector. This problem is currently most relevant, as the increased demand for security in the banking sector encourages the development and introduction of new technologies (including machine learning and artificial intelligence), while at the same time creating new vulnerable areas and related problems.

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