A Survey of Ambient Intelligence

2021 ◽  
Vol 54 (4) ◽  
pp. 1-27
Author(s):  
Rob Dunne ◽  
Tim Morris ◽  
Simon Harper
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Ambient Intelligence ◽  
Ethical Issues ◽  
State Of The Art ◽  
Research Trends ◽  
Healthcare Facilities ◽  
The Past ◽  
Assistive Living ◽  
Key Terms

Ambient Intelligence (AmI) is the application and embedding of artificial intelligence into everyday environments to seamlessly provide assistive and predictive support in a multitude of scenarios via an invisible user interface. These can be as diverse as autonomous vehicles, smart homes, industrial settings, and healthcare facilities—referred to as Ambient Assistive Living. This survey gives an overview of the field; defines key terms; discusses social, cultural, and ethical issues; and outlines the state of the art in AmI technology, and where opportunities for further research exist. We guide the reader through AmI from its inception more than 20 years ago, focussing on the important topics and research achievements of the past 10 years since the last major survey, before finally detailing the most recents research trends and forecasting where this technology is likely to develop. This survey covers domains, use cases, scenarios, and datasets; cultural concerns and usability issues; security, privacy, and ethics; interaction and recognition; prediction and intelligence; and hardware, infrastructure, and mobile devices. This survey serves as an introduction for researchers and the technical layperson into the topic of AmI and identifies notable opportunities for further research.

Fairness and Ethics in Artificial Intelligence-Based Medical Imagining

2022 ◽  
pp. 71-85
Author(s):  
Satvik Tripathi ◽  
Thomas Heinrich Musiolik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Patient Care ◽  
Data Sharing ◽  
Ethical Issues ◽  
Medical Systems ◽  
Healthcare Facilities ◽  
The World ◽  
Potential Applications ◽  
Medical Imagining

Artificial intelligence has a huge array of current and potential applications in healthcare and medicine. Ethical issues arising due to algorithmic biases are one of the greatest challenges faced in the generalizability of AI models today. The authors address safety and regulatory barriers that impede data sharing in medicine as well as potential changes to existing techniques and frameworks that might allow ethical data sharing for machine learning. With these developments in view, they also present different algorithmic models that are being used to develop machine learning-based medical systems that will potentially evolve to be free of the sample, annotator, and temporal bias. These AI-based medical imaging models will then be completely implemented in healthcare facilities and institutions all around the world, even in the remotest areas, making diagnosis and patient care both cheaper and freely accessible.

Report from the 2nd Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '20)

2021 ◽  
Vol 46 (2) ◽  
pp. 28-29
Author(s):  
Benoît Vanderose ◽  
Julie Henry ◽  
Benoît Frénay ◽  
Xavier Devroey
Keyword(s):  
Artificial Intelligence ◽  
Software Engineering ◽  
Engineering Education ◽  
State Of The Art ◽  
Future Directions ◽  
The Past ◽  
Software Engineering Education ◽  
Current State ◽  
Amount Of Knowledge ◽  
Software Engineering Tools

In the past years, with the development and widespread of digi- tal technologies, everyday life has been profoundly transformed. The general public, as well as specialized audiences, have to face an ever-increasing amount of knowledge and learn new abilities. The EASEAI workshop series addresses that challenge by look- ing at software engineering, education, and arti cial intelligence research elds to explore how they can be combined. Speci cally, this workshop brings together researchers, teachers, and practi- tioners who use advanced software engineering tools and arti cial intelligence techniques in the education eld and through a trans- generational and transdisciplinary range of students to discuss the current state of the art and practices, and establish new future directions. More information at https://easeai.github.io.

scholarly journals Robotics and Artificial Intelligence in Gastrointestinal Endoscopy: Updated Review of the Literature and State of the Art

2021 ◽  
Author(s):  
Ivo Boškoski ◽  
Beatrice Orlandini ◽  
Luigi Giovanni Papparella ◽  
Maria Valeria Matteo ◽  
Martina De Siena ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Gastrointestinal Endoscopy ◽  
State Of The Art ◽  
Unmet Need ◽  
Diagnostic Techniques ◽  
Review Of The Literature ◽  
The Past ◽  
Wide Range ◽  
Near Future ◽  
Artificial Intelligence Systems

Abstract Purpose of Review Gastrointestinal endoscopy includes a wide range of procedures that has dramatically evolved over the past decades. Robotic endoscopy and artificial intelligence are expanding the horizons of traditional techniques and will play a key role in clinical practice in the near future. Understanding the main available devices and procedures is a key unmet need. This review aims to assess the current and future applications of the most recently developed endoscopy robots. Recent Findings Even though a few devices have gained approval for clinical application, the majority of robotic and artificial intelligence systems are yet to become an integral part of the current endoscopic instrumentarium. Some of the innovative endoscopic devices and artificial intelligence systems are dedicated to complex procedures such as endoscopic submucosal dissection, whereas others aim to improve diagnostic techniques such as colonoscopy. Summary A review on flexible endoscopic robotics and artificial intelligence systems is presented here, showing the m3ost recently approved and experimental devices and artificial intelligence systems for diagnosis and robotic endoscopy.

scholarly journals Improving the Speed of MRI with Artificial Intelligence

2020 ◽  
Vol 24 (01) ◽  
pp. 012-020 ◽  
Author(s):  
Patricia M. Johnson ◽  
Michael P. Recht ◽  
Florian Knoll
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
Future Perspectives ◽  
Resonance Imaging ◽  
The Past ◽  
Tremendous Progress ◽  
Accelerated Mri ◽  
Magnetic Resonance Imaging Mri ◽  
Image Modality ◽  
Resolution Data

AbstractMagnetic resonance imaging (MRI) is a leading image modality for the assessment of musculoskeletal (MSK) injuries and disorders. A significant drawback, however, is the lengthy data acquisition. This issue has motivated the development of methods to improve the speed of MRI. The field of artificial intelligence (AI) for accelerated MRI, although in its infancy, has seen tremendous progress over the past 3 years. Promising approaches include deep learning methods for reconstructing undersampled MRI data and generating high-resolution from low-resolution data. Preliminary studies show the promise of the variational network, a state-of-the-art technique, to generalize to many different anatomical regions and achieve comparable diagnostic accuracy as conventional methods. This article discusses the state-of-the-art methods, considerations for clinical applicability, followed by future perspectives for the field.

scholarly journals Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 973 ◽  
Author(s):  
Sara Saravi ◽  
Roy Kalawsky ◽  
Demetrios Joannou ◽  
Monica Rivas Casado ◽  
Guangtao Fu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
Flash Flood ◽  
Weather Forecast ◽  
Specific Pattern ◽  
The Novel ◽  
Flood Events ◽  
Ice Melt ◽  
The Past ◽  
Different Types

The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience.

Autonomous driving in urban environments: approaches, lessons and challenges

2010 ◽  
Vol 368 (1928) ◽  
pp. 4649-4672 ◽  
Author(s):  
Mark Campbell ◽  
Magnus Egerstedt ◽  
Jonathan P. How ◽  
Richard M. Murray
Keyword(s):  
Autonomous Vehicles ◽  
New Technologies ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Vehicle Safety ◽  
Paper Briefly ◽  
The Past ◽  
Current State

The development of autonomous vehicles for urban driving has seen rapid progress in the past 30 years. This paper provides a summary of the current state of the art in autonomous driving in urban environments, based primarily on the experiences of the authors in the 2007 DARPA Urban Challenge (DUC). The paper briefly summarizes the approaches that different teams used in the DUC, with the goal of describing some of the challenges that the teams faced in driving in urban environments. The paper also highlights the long-term research challenges that must be overcome in order to enable autonomous driving and points to opportunities for new technologies to be applied in improving vehicle safety, exploiting intelligent road infrastructure and enabling robotic vehicles operating in human environments.

scholarly journals Applications of artificial intelligence and machine learning in orthodontics

2020 ◽  
Vol 10 ◽  
pp. 17-24
Author(s):  
Saeed N. Asiri ◽  
Larry P. Tadlock ◽  
Emet Schneiderman ◽  
Peter H. Buschang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Quality Of Care ◽  
Treatment Outcomes ◽  
State Of The Art ◽  
Review Article ◽  
The Past ◽  
Clinical Problems ◽  
Applications Of Ai

Over the past two decades, artificial intelligence (AI) and machine learning (ML) have undergone considerable development. There have been various applications in medicine and dentistry. Their application in orthodontics has progressed slowly, despite promising results. The available literature pertaining to the orthodontic applications of AI and ML has not been adequately synthesized and reviewed. This review article provides orthodontists with an overview of AI and ML, along with their applications. It describes state-of-the-art applications in the areas of orthodontic diagnosis, treatment planning, growth evaluations, and in the prediction of treatment outcomes. AI and ML are powerful tools that can be utilized to overcome some of the clinical problems that orthodontists face daily. With the availability of more data, better AI and ML systems should be expected to be developed that will help orthodontists practice more efficiently and improve the quality of care.

scholarly journals A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Xuesong Zhai ◽  
Xiaoyan Chu ◽  
Ching Sing Chai ◽  
Morris Siu Yung Jong ◽  
Andreja Istenic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Content Analysis ◽  
Deep Learning ◽  
Adaptive Learning ◽  
Ethical Issues ◽  
Role Playing ◽  
Research Trends ◽  
Inappropriate Use ◽  
Teachers And Students ◽  
Changing Roles

This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

scholarly journals The Unfounded Bias Against Autonomous Weapons Systems

2021 ◽  
Vol 21 (2) ◽  
pp. 13
Author(s):  
Aron Dombrovszki
Keyword(s):  
Autonomous Vehicles ◽  
Ethical Issues ◽  
Just War ◽  
Just War Theory ◽  
Jus Post Bellum ◽  
The Past ◽  
Autonomous Weapons ◽  
Good Reputation ◽  
Definition Of ◽  
Weapons Systems

Autonomous Weapons Systems (AWS) have not gained a good reputation in the past. This attitude is odd if we look at the discussion of other – usually highly anticipated – AI-technologies, like autonomous vehicles (AVs); whereby even though these machines evoke very similar ethical issues, philosophers’ attitudes towards them are constructive. In this article, I try to prove that there is an unjust bias against AWS because almost every argument against them is effective against AVs too. I start with the definition of “AWS.” Then, I arrange my arguments by the Just War Theory (JWT), covering jus ad bellum, jus in bello and jus post bellum problems. Meanwhile, I draw attention to similar problems against other AI-technologies outside the JWT framework. Finally, I address an exception, as addressed by Duncan Purves, Ryan Jenkins and Bradley Strawser, who realized the unjustified double standard, and deliberately tried to construct a special argument which rules out only AWS.

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