scholarly journals AI Theory and Practice: A Discussion on Hard Challenges and Opportunities Ahead

AI Magazine ◽  
2010 ◽  
Vol 31 (3) ◽  
pp. 103 ◽  
Author(s):  
Eric Horvitz ◽  
Lise Getoor ◽  
Carlos Guestrin ◽  
James Hendler ◽  
Joseph Konstan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Theory And Practice ◽  
Challenges And Opportunities ◽  
Or Practice ◽  
Research Faculty

The Microsoft Research Faculty Summit brought together eight experts in different areas of AI to share their thoughts about the key challenges ahead in theory and/or practice in the broad constellation of artificial intelligence.  This article summarizes their conversation.

The Power of Matrix Factorization: Methods for Deconvoluting Genetic Heterogeneous Data at Expression Level

2021 ◽  
Vol 15 (8) ◽  
pp. 841-853
Author(s):  
Yuan Liu ◽  
Zhining Wen ◽  
Menglong Li
Keyword(s):  
Artificial Intelligence ◽  
Matrix Factorization ◽  
In Silico ◽  
Genetic Data ◽  
Heterogeneous Data ◽  
Expression Level ◽  
Mathematical Methods ◽  
Challenges And Opportunities ◽  
Factorization Methods ◽  
Genetic Profiles

Background:: The utilization of genetic data to investigate biological problems has recently become a vital approach. However, it is undeniable that the heterogeneity of original samples at the biological level is usually ignored when utilizing genetic data. Different cell-constitutions of a sample could differentiate the expression profile, and set considerable biases for downstream research. Matrix factorization (MF) which originated as a set of mathematical methods, has contributed massively to deconvoluting genetic profiles in silico, especially at the expression level. Objective: With the development of artificial intelligence algorithms and machine learning, the number of computational methods for solving heterogeneous problems is also rapidly abundant. However, a structural view from the angle of using MF to deconvolute genetic data is quite limited. This study was conducted to review the usages of MF methods on heterogeneous problems of genetic data on expression level. Methods: MF methods involved in deconvolution were reviewed according to their individual strengths. The demonstration is presented separately into three sections: application scenarios, method categories and summarization for tools. Specifically, application scenarios defined deconvoluting problem with applying scenarios. Method categories summarized MF algorithms contributed to different scenarios. Summarization for tools listed functions and developed web-servers over the latest decade. Additionally, challenges and opportunities of relative fields are discussed. Results and Conclusion: Based on the investigation, this study aims to present a relatively global picture to assist researchers to achieve a quicker access of deconvoluting genetic data in silico, further to help researchers in selecting suitable MF methods based on the different scenarios.

scholarly journals Applied Research, Diffractive Methodology, and the Research-Assemblage: Challenges and Opportunities

2021 ◽  
pp. 136078042110299
Author(s):  
Nick J Fox ◽  
Pam Alldred
Keyword(s):  
Analytical Approach ◽  
Applied Research ◽  
Social Research ◽  
Research Data ◽  
Critical Assessment ◽  
Policy And Practice ◽  
Challenges And Opportunities ◽  
Or Practice ◽  
Representational Approach ◽  
Oriented Research

This article offers a critical assessment of the challenges for policy- and practice-oriented social research of ‘diffractive methodology’ (DM): a post-representational approach to data analysis gaining interest among social researchers. Diffractive analyses read data from empirical research alongside other materials – including researchers’ perspectives, memories, experiences, and emotions – to provide novel insights on events. While this analytical approach acknowledges the situatedness of all research data, it raises issues concerning the applicability of findings for policy or practice. In addition, it does not elucidate in what ways and to what extent the diffractions employed during analysis have influenced the findings. To explore these questions, we diffract DM itself, by reading it alongside a DeleuzoGuattarian analysis of research-as-assemblage. This supplies a richer understanding of the entanglements between research and its subject-matter, and suggests how diffractive analysis may be used in conjunction with other methods in practice- and policy-oriented research.

Artificial intelligence research in agriculture: a review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amit Sood ◽  
Rajendra Kumar Sharma ◽  
Amit Kumar Bhardwaj
Keyword(s):  
Artificial Intelligence ◽  
Peer Review ◽  
Diffusion Of Innovation ◽  
Comprehensive Review ◽  
Content Type ◽  
Design Elements ◽  
Extant Literature ◽  
Use Of Technology ◽  
Challenges And Opportunities ◽  
Market Factors

PurposeThe purpose of this paper is to provide a comprehensive review on the academic journey of artificial intelligence (AI) in agriculture and to highlight the challenges and opportunities in adopting AI-based advancement in agricultural systems and processes.Design/methodology/approachThe authors conducted a bibliometric analysis of the extant literature on AI in agriculture to understand the status of development in this domain. Further, the authors proposed a framework based on two popular theories, namely, diffusion of innovation (DOI) and the unified theory of acceptance and use of technology (UTAUT), to identify the factors influencing the adoption of AI in agriculture.FindingsFour factors were identified, i.e. institutional factors, market factors, technology factors and stakeholder perception, which influence adopting AI in agriculture. Further, the authors indicated challenges under environmental, operational, technological, economical and social categories with opportunities in this area of research and business.Research limitations/implicationsThe proposed conceptual model needs empirical validation across countries or states to understand the effectiveness and relevance.Practical implicationsPractitioners and researchers can use these inputs to develop technology and business solutions with specific design elements to gain benefit of this technology at larger scale for increasing agriculture production.Social implicationsThis paper brings new developed methods and practices in agriculture for betterment of society.Originality/valueThis paper provides a comprehensive review of extant literature and presents a theoretical framework for researchers to further examine the interaction of independent variables responsible for adoption of AI in agriculture.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2020-0448

Effects of Deep Learning Technologies on Employment in the Field of Digital Communication Systems

2021 ◽  
Vol 12 (4) ◽  
pp. 35-42
Author(s):  
Thomas Alan Woolman ◽  
Philip Lee
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Communication Systems ◽  
Digital Communication ◽  
The United States ◽  
Learning Technologies ◽  
Technological Unemployment ◽  
Current State ◽  
Challenges And Opportunities ◽  
Digital Communication Systems

There are significant challenges and opportunities facing the economies of the United States in the coming decades of the 21st century that are being driven by elements of technological unemployment. Deep learning systems, an advanced form of machine learning that is often referred to as artificial intelligence, is presently reshaping many aspects of traditional digital communication technology employment, primarily network system administration and network security system design and maintenance. This paper provides an overview of the current state-of-the-art developments associated with deep learning and artificial intelligence and the ongoing revolutions that this technology is having not only on the field of digital communication systems but also related technology fields. This paper will also explore issues and concerns related to past technological unemployment challenges, as well as opportunities that may be present as a result of these ongoing technological upheavals.

Cyber Power in International Relations

2021 ◽  
pp. 65-81
Author(s):  
Tim Stevens ◽  
Camino Kavanagh
Keyword(s):  
International Relations ◽  
International System ◽  
Analytical Framework ◽  
Theory And Practice ◽  
Strategic Objectives ◽  
Or Practice ◽  
State Actors ◽  
Strategic Outcomes

This chapter provides a conceptual and analytical framework for the understanding of ‘cyber power’ in the theory and practice of international relations. Cyber power is the product of relationships between actors, rather than a material quantity that can be possessed and converted into strategic outcomes. This chapter identifies four forms of cyber power that arise from different configurations of state and non-state actors: compulsory, institutional, structural, and productive. Analysis of national cyber strategies shows how states develop, leverage, and exploit their relationships with the actors and structures of the international system to generate cyber power in pursuit of their strategic objectives. Cyber power should therefore be understood as a multiplicity of forms of power in and through cyberspace, not as a singular concept or practice. Moreover, cyber power should be framed within broader conceptualizations of power, rather than treated as somehow distinct and discrete.

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