scholarly journals Understanding the Role of Interactive Machine Learning in Movement Interaction Design

2019 ◽  
Vol 26 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Marco Gillies
Keyword(s):  
Machine Learning ◽  
Interaction Design ◽  
Interactive Machine Learning ◽  
Movement Interaction

Human Factors in Interactive Machine Learning: A Cybersecurity Case Study

2021 ◽  
Vol 65 (1) ◽  
pp. 1495-1499
Author(s):  
Mark H. Chignell ◽  
Mu-Huan Chung ◽  
Yuhong Yang ◽  
Greg Cento ◽  
Abhay Raman
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Financial Services ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Cyber Attacks ◽  
Transfer Data ◽  
Interactive Machine Learning

Cybersecurity is emerging as a major issue for many organizations and countries. Machine learning has been used to recognize threats, but it is difficult to predict future threats based on past events, since malicious attackers are constantly finding ways to circumvent defences and the algorithms that they rely on. Interactive Machine learning (iML) has been developed as a way to combine human and algorithmic expertise in a variety of domains and we are currently applying it to cybersecurity. In this application of iML, implicit knowledge about human behaviour, and about the changing nature of threats, can supplement the explicit knowledge encoded in algorithms to create more effective defences against cyber-attacks. In this paper we present the example problem of data exfiltration where insiders, or outsiders masquerading as insiders, who copy and transfer data maliciously, against the interests of an organization. We will review human factors issues associated with the development of iML solutions for data exfiltration. We also present a case study involving development of an iML solution for a large financial services company. In this case study we review work carried out on developing visualization dashboards and discussing prospects for further iML integration. Our goal in writing this paper is to motivate future researchers to consider the role of the human more fully in ML, not only in the data exfiltration and cybersecurity domain but also in a range of other applications where human expertise is important and needs to combine with ML prediction to solve challenging problems.

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.

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.

Dealing with Mislabeling via Interactive Machine Learning

2020 ◽  
Vol 34 (2) ◽  
pp. 271-278
Author(s):  
Wanyi Zhang ◽  
Andrea Passerini ◽  
Fausto Giunchiglia
Keyword(s):  
Machine Learning ◽  
Interactive Machine Learning

scholarly journals Tangibly understanding intangible complexities: Designing for distributed autonomous organizations

2019 ◽  
Vol 6 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Bettina Nissen ◽  
Ella Tallyn ◽  
Kate Symons
Keyword(s):  
Distributed Systems ◽  
Interaction Design ◽  
Digital Technologies ◽  
Interactive Systems ◽  
Collaborative Practice ◽  
The Internet ◽  
The Face ◽  
Future Technologies ◽  
The Internet Of Things

Abstract New digital technologies such as Blockchain and smart contracting are rapidly changing the face of value exchange, and present new opportunities and challenges for designers. Designers and data specialists are at the forefront of exploring new ways of exchanging value, using Blockchain, cryptocurrencies, smart contracting and the direct exchanges between things made possible by the Internet of Things (Tallyn et al. 2018; Pschetz et al. 2019). For researchers and designers in areas of Human Computer Interaction (HCI) and Interaction Design to better understand and explore the implications of these emerging and future technologies as Distributed Autonomous Organisations (DAOs) we delivered a workshop at the ACM conference Designing Interactive Systems (DIS) in Edinburgh in 2017 (Nissen et al. 2017). The workshop aimed to use the lens of DAOs to introduce the principle that products and services may soon be owned and managed collectively and not by one person or authority, thus challenging traditional concepts of ownership and power. This workshop builds on established HCI research exploring the role of technology in financial interactions and designing for the rapidly changing world of technology and value exchange (Kaye et al. 2014; Malmborg et al. 2015; Millen et al. 2015; Vines et al. 2014). Beyond this, the HCI community has started to explore these technologies beyond issues of finance, money and collaborative practice, focusing on the implications of these emerging but rapidly ascending distributed systems in more applied contexts (Elsden et al. 2018a). By bringing together designers and researchers with different experiences and knowledge of distributed systems, the aim of this workshop was two-fold. First, to further understand, develop and critique these new forms of distributed power and ownership and second, to practically explore how to design interactive products and services that enable, challenge or disrupt existing and emerging models.

Role of machine learning algorithms over heart diseases prediction

2020 ◽  
Author(s):  
Siva Kumar Jonnavithula ◽  
Abhilash Kumar Jha ◽  
Modepalli Kavitha ◽  
Singaraju Srinivasulu
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications

2021 ◽  
Vol 18 (4) ◽  
pp. 2121
Author(s):  
Mansoureh Maadi ◽  
Hadi Akbarzadeh Khorshidi ◽  
Uwe Aickelin
Keyword(s):  
Machine Learning ◽  
Future Research ◽  
Computational Power ◽  
Medical Field ◽  
Interactive Machine Learning ◽  
Human In The Loop ◽  
Human Interactions ◽  
Scoping Literature Review ◽  
Domain Expertise ◽  
Expertise Level

Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches. Methods: A scoping literature review is performed on Scopus and Google Scholar using the terms “human in the loop”, “human in the loop machine learning”, and “interactive machine learning”. Peer-reviewed papers published from 2015 to 2020 are included in our review. Results: We design four questions to investigate and describe human–AI interaction in ML applications. These questions are “Why should humans be in the loop?”, “Where does human–AI interaction occur in the ML processes?”, “Who are the humans in the loop?”, and “How do humans interact with ML in Human-In-the-Loop ML (HILML)?”. To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human–AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human–AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML.

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