Cyber Gremlin: social networking, machine learning and the global war on Al-Qaida-and IS-inspired terrorism

2019 ◽  
Vol 27 (3) ◽  
pp. 238-265
Author(s):  
Jamil Ammar
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Learning Technologies ◽  
Learning Systems ◽  
Cross Border ◽  
Law And Policy ◽  
Practical Reality ◽  
Global War ◽  
Automated Decision Making

Abstract Among our mundane and technical concepts, machine learning is currently one of the most important and widely used, but least understood. To date, legal scholars have conducted comparatively little work on its cognate concepts. This article critically examines the use of machine learning technologies to suppress or block access to al-Qaida and IS-inspired propaganda. It will: (i) demonstrate that, insofar as law and policy dictate that machine learning systems comply with desired constitutional norms, automated-decision making systems are not as effective as critics would like; (ii) emphasize that, under the current envisaged ‘proactive’ role of networking sites, equating radical and extreme ideas and ideology with ‘violence’ is a practical reality; and (iii) outline a workable strategy for cross-border legal and technical counterterrorism that satisfies the requirements for algorithmic fairness.

scholarly journals Towards Data Justice Unionism? A Labour Perspective on AI Governance

2021 ◽  
pp. 267-284
Author(s):  
Lina Dencik
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Autonomous Systems ◽  
Learning Systems ◽  
Worker Productivity ◽  
Labour Movement ◽  
Profound Impact ◽  
Data Infrastructures

The dual occurrences of constant data collection and use of artificial and autonomous systems in the workplace are having a profound impact on workers’ lives. Workers are subjected to constant surveillance that not only monitor worker productivity but factors unrelated to work. At the same time, machine learning systems are using these data to transform how work is being allocated, assessed and completed and as a result, worker lives and value in the workplace and beyond. Yet governance frameworks for AI have thus far been advanced with a noticeable absence of worker voice, unions, and labour perspectives. In this chapter I will discuss how concerns about data and data infrastructures need to be situated as part of a workers’ rights agenda, the role of the labour movement in advancing alternative governance frameworks, and the potential for data justice unionism.

Behavior Morphologies of Machine Learning Agents in Media Artworks

Leonardo ◽  
2019 ◽  
pp. 1-10
Author(s):  
Sofian Audry
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Life ◽  
Adaptive Systems ◽  
Learning Systems ◽  
Artificial Agents ◽  
Adaptive Agents ◽  
Learning Agents ◽  
The Aesthetic

Since the 1950s, a range of artists have used artificial agents in their work, in parallel with scientific research in cybernetics, artificial intelligence (AI) and artificial life (AL). In particular, an increasing number of artists work with machine learning and other adaptive systems. Through my own engagement with such systems, I analyze adaptive agents within the broader context of the aesthetic of behavior. As a result, I propose an aesthetic framework for understanding behaviors which considers the role of the observer as an adaptive perceiving agent, the unfathomable character of machine learning systems, and the morphology of behaviors as time-based phenomenon.

scholarly journals Toward to Information Security of AI-Enhanced Weapons

2021 ◽  
pp. 5-11
Author(s):  
Vadim Gribunin ◽  
◽  
Sergey Kondakov ◽  
Keyword(s):  
Machine Learning ◽  
Information Security ◽  
System Analysis ◽  
Learning Technologies ◽  
Point Of View ◽  
Learning Systems ◽  
Security Assessment ◽  
Military Equipment ◽  
The Us ◽  
Article Analysis

Purpose of the article: Analysis of intellectualized weapons using machine learning from the point of view of information security. Development of proposals for the deployment of work in the field of information security in similar products. Research method: System analysis of machine learning systems as objects of protection. Determination on the basis of the analysis of rational priority directions for improving these systems in terms of ensuring information security. Obtained result: New threats to information security arising from the use of weapons and military equipment with elements of artificial intelligence are presented. Machine learning systems are considered by the authors as an object of protection, which made it possible to determine the protected assets of such systems, their vulnerabilities, threats and possible attacks on them. The article analyzes the measures to neutralize the identified threats based on the taxonomy proposed by the US National Institute of Standards and Technology. The insufficiency of the existing regulatory methodological framework in the field of information protection to ensure the security of machine learning systems has been determined. An approach is proposed that should be used in the development and security assessment of systems using machine learning. Proposals for the deployment of work in the field of ensuring the security of intelligent weapons using machine learning technologies are presented.

scholarly journals Power to the People: The Role of Humans in Interactive Machine Learning

AI Magazine ◽  
2014 ◽  
Vol 35 (4) ◽  
pp. 105-120 ◽  
Author(s):  
Saleema Amershi ◽  
Maya Cakmak ◽  
William Bradley Knox ◽  
Todd Kulesza
Keyword(s):  
Machine Learning ◽  
Intelligent Systems ◽  
Learning Systems ◽  
Human Interaction ◽  
Interaction Patterns ◽  
Interactive Machine Learning ◽  
Effective Learning ◽  
The People ◽  
The Impact

Intelligent systems that learn interactively from their end-users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can result in better user experiences and more effective learning systems. We present a number of case studies that characterize the impact of interactivity, demonstrate ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. We argue that the design process for interactive machine learning systems should involve users at all stages: explorations that reveal human interaction patterns and inspire novel interaction methods, as well as refinement stages to tune details of the interface and choose among alternatives. After giving a glimpse of the progress that has been made so far, we discuss the challenges that we face in moving the field forward.

Strategic Role of Social Networking and Personal Knowledge Management Competencies for Future Entrepreneurs

2017 ◽  
pp. 248-266
Author(s):  
Oliana Sula ◽  
Tiit Elenurm
Keyword(s):  
Social Networks ◽  
Knowledge Management ◽  
Social Networking ◽  
Knowledge Sharing ◽  
Management Competencies ◽  
Academic Context ◽  
Cross Border ◽  
New Business ◽  
Strategic Role

The mission of this chapter is to explore the role of social networking and knowledge management competencies combined with social networking strategies as an essential component and support for the development of co-innovation and business co-creation processes for future and potential entrepreneurs enrolled in higher education programs. Business students are active users of social networks but usually do not have clear business-focus priorities when devoting their time to social networking. Social networks enable virtual communities which allow knowledge sharing and collaborative learning a different stages of new business development. These networks have the potential to create ties for cross-border business initiatives that cannot be created in face-to-face networks. Innovative ideas often emerge from combining different sources of knowledge. Social networks can be used for action learning and cross-border knowledge sharing in the academic context in order to enhance cross-border entrepreneurship.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

The role of social networking sites in the revolution of 25 January 2011 Egyptian

2019 ◽  
Vol 2 (3) ◽  
pp. 1
Author(s):  
Qassim Alwan Saeed ◽  
Khairallah Sabhan Abdullah Al-Jubouri
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Arab Spring ◽  
The World ◽  
The Revolution

Social media sites have recently gain an essential importance in the contemporary societies، actually، these sites isn't simply a personal or social tool of communication among people، its role had been expanded to become "political"، words such as "Facebook، Twitter and YouTube" are common words in political fields of our modern days since the uprisings of Arab spring، which sometimes called (Facebook revolutions) as a result of the major impact of these sites in broadcasting process of the revolution message over the world by organize and manage the revolution progresses in spite of the governmental ascendance and official prohibition.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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