scholarly journals Advancing Post-Genome Data and System Integration through Machine Learning

2002 ◽  
Vol 3 (1) ◽  
pp. 28-31 ◽  
Author(s):  
Francisco Azuaje
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
System Integration ◽  
Intelligent Systems ◽  
Biological Data ◽  
Markup Languages ◽  
Public And Private ◽  
Genome Data ◽  
The Creation ◽  
Biological Data Integration

Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic web. It has commonly been accepted that artificial intelligence and data mining techniques may support the interpretation of huge amounts of integrated data. But at the same time, these research disciplines are contributing to the creation of content markup languages and sophisticated programs able to exploit the constraints and preferences of user domains. This paper discusses a number of issues on intelligent systems for the integration of bioinformatic resources.

scholarly journals Artificial Intelligence & Machine Learning: a model for a new judicial system?

2020 ◽  
pp. 81-91
Author(s):  
Paula C. Arias
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
System Integration ◽  
Judicial System ◽  
Modern Life ◽  
Technological Advances ◽  
The Creation ◽  
Almost All

Artificial Intelligence and Machine Learning are a result not only of technological advances but also of the exploitation of information or data, which has led to its expansion into almost all aspects of modern life, including law and its practice. Due to the benefits of these technologies, such as efficiency, objectivity, and transparency, the trend is towards the integration of Artificial Intelligence and Machine Learning in the judicial system. Integration that is advocated at all levels and, today, has been achieved mostly under the implementation of tools to assist the exercise of the judiciary. The "success" of this integration has led to the creation of an automated court or an artificially intelligent judge as a futuristic proposal.

Research Methods to Study and Empower Crowd Workers

2021 ◽  
pp. 164-184
Author(s):  
Saiph Savage ◽  
Carlos Toxtli ◽  
Eber Betanzos-Torres
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Research Methods ◽  
Real World ◽  
Intelligent Systems ◽  
Quantitative Information ◽  
Learning Models ◽  
Professional Goals ◽  
Career Growth ◽  
Machine Learning Models

The artificial intelligence (AI) industry has created new jobs that are essential to the real world deployment of intelligent systems. Part of the job focuses on labelling data for machine learning models or having workers complete tasks that AI alone cannot do. These workers are usually known as ‘crowd workers’—they are part of a large distributed crowd that is jointly (but separately) working on the tasks although they are often invisible to end-users, leading to workers often being paid below minimum wage and having limited career growth. In this chapter, we draw upon the field of human–computer interaction to provide research methods for studying and empowering crowd workers. We present our Computational Worker Leagues which enable workers to work towards their desired professional goals and also supply quantitative information about crowdsourcing markets. This chapter demonstrates the benefits of this approach and highlights important factors to consider when researching the experiences of crowd workers.

Artificial Intelligence Based Architecture to Support Dementia Patients

2019 ◽  
Author(s):  
Fernando Luís-Ferreira ◽  
João Sarraipa ◽  
Jorge Calado ◽  
Joana Andrade ◽  
Daniel Rodrigues ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Intelligent Systems ◽  
User Interaction ◽  
Special Kind ◽  
Target Population ◽  
Interaction Data ◽  
Dementia Patients ◽  
Physiological Assessment

Abstract Artificial Intelligence is driving a revolution in the most diverse domains of computational services and user interaction. Data collected in large quantities is becoming useful for feeding intelligent systems that analyse, learn and provide insights and help decision support systems. Machine learning and the usage of algorithms are of most importance to extract features, reason over collected data so it becomes useful and preventive, exposing discoveries augmenting knowledge about systems and processes. Human driven applications, as those related with physiological assessment and user experience, are possible especially in the health domain and especially in supporting patients and the community. The work hereby described refers to different aspects where the Artificial Intelligence can help citizens and wraps a series devices and services that where developed and tested for the benefit of a special kind of citizens. The target population are those under some kind of Dementia, but the proposed solutions are also applicable to other elder citizens or even children that need to be assisted and prevented from risks.

Regulation and ethics in artificial intelligence and machine learning technologies: Where are we now? Who is responsible? Can the information professional play a role?

2020 ◽  
Vol 37 (2) ◽  
pp. 60-68
Author(s):  
Denise Carter
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
White Paper ◽  
Learning Technologies ◽  
Public And Private ◽  
General Data Protection Regulation ◽  
Oecd Principles ◽  
Information Professional ◽  
Uk Government ◽  
The Uk

Artificial intelligence (AI) and machine learning (ML) technologies are rapidly maturing and proliferating through all public and private sectors. The potential for these technologies to do good and to help us in our everyday lives is immense. But there is a risk that unless managed and controlled AI can also cause us harm. Questions about regulation, what form it takes and who is responsible for governance are only just beginning to be answered. In May 2019, 42 countries came together to support a global governance framework for AI. The Organisation for Economic Co-operation and Development (OECD) Principles on Artificial Intelligence (OECD (2019) OECD principles on AI. Available at: https://www.oecd.org/going-digital/ai/principles/ (accessed 2 March 2020)) saw like-minded democracies of the world commit to common AI values of trust and respect. In Europe, the European Commission’s (EC) new president, Ursula von der Leyen has made calls for a General Data Protection Regulation style. As a first step the EC has published a white paper: ‘On Artificial Intelligence – A European Approach to Excellence and Trust’ (European Commission (2020) Report, Europa, February). In February 2020, the UK government has published a report on ‘Artificial Intelligence in the Public Sector’ (The Committee on Standards in Public Life (2020) Artificial intelligence and public standards. Report, UK Government, February). This article discusses some of the potential threats AI may hold if left unregulated. It provides a brief overview of the regulatory activities for AI worldwide, and in more detail the current UK AI regulatory landscape. Finally, the article looks at the role that the information professional might play in AI and ML.

scholarly journals An Integrative Framework for Artificial Intelligence Education

2019 ◽  
Vol 33 ◽  
pp. 9670-9677 ◽  
Author(s):  
Pat Langley
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
System Integration ◽  
Intelligent Systems ◽  
Control Plan ◽  
Integrative Framework ◽  
Plan Generation ◽  
Knowledge Based ◽  
Intelligent Behavior ◽  
Intelligence Education

Modern introductory courses on AI do not train students to create intelligent systems or provide broad coverage of this complex field. In this paper, we identify problems with common approaches to teaching artificial intelligence and suggest alternative principles that courses should adopt instead. We illustrate these principles in a proposed course that teaches students not only about component methods, such as pattern matching and decision making, but also about their combination into higher-level abilities for reasoning, sequential control, plan generation, and integrated intelligent agents. We also present a curriculum that instantiates this organization, including sample programming exercises and a project that requires system integration. Participants also gain experience building knowledge-based agents that use their software to produce intelligent behavior.

Robots, Replicants, and Surrogates

2021 ◽  
pp. 84-111
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Science Fiction ◽  
Intelligent Systems ◽  
New World ◽  
Attitudes And Perceptions ◽  
The World ◽  
Recent Developments ◽  
Brave New World ◽  
And Robotics

The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. This has given rise to machine learning, intelligent systems, and robotics. Robots and AI might right now still seem the reserve of blockbuster science fiction movies and documentaries, but it's no doubt the world is changing. This chapter explores the origins, attitudes, and perceptions of robotics and the multiple types of robots that exist today. Perhaps most importantly, it focuses on ethical and societal concerns over the question: Are we heading for a brave new world or a science fiction horror-show where AI and robots displace or, perhaps more worryingly, replace humans?

Music, Art, Machine Learning, and Standardization

Leonardo ◽  
2021 ◽  
pp. 1-11
Author(s):  
Taylor Brook
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Culture Industry ◽  
Economic Power ◽  
The Creation

Abstract This paper explores current and hypothetical implementations of machine learning toward the creation and marketing of cultural commodities, focusing on music in popular and experimental forms. Building on Adorno and Horkheimer's critique of the culture industry, this article considers the role of machine learning and artificial intelligence as a force for stylistic standardization and further consolidation of economic power in music and art.

A Short Introduction to the Ethics of Artificial Intelligence

2020 ◽  
pp. 1-42
Author(s):  
S. Matthew Liao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intelligent Systems ◽  
Ethical Issues ◽  
Black Box ◽  
Human Beings ◽  
Key Issues ◽  
Long Term Impact ◽  
Bad Data

This introduction outlines in section I.1 some of the key issues in the study of the ethics of artificial intelligence (AI) and proposes ways to take these discussions further. Section I.2 discusses key concepts in AI, machine learning, and deep learning. Section I.3 considers ethical issues that arise because current machine learning is data hungry; is vulnerable to bad data and bad algorithms; is a black box that has problems with interpretability, explainability, and trust; and lacks a moral sense. Section I.4 discusses ethical issues that arise because current machine learning systems may be working too well and human beings can be vulnerable in the presence of these intelligent systems. Section I.5 examines ethical issues arising out of the long-term impact of superintelligence such as how the values of a superintelligent AI can be aligned with human values. Section I.6 presents an overview of the essays in this volume.

scholarly journals Towards Autonomous Driving Using Vision Based Intelligent Systems

2021 ◽  
Vol 8 (2) ◽  
pp. 1-2
Author(s):  
Julkar Nine
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Intelligent Systems ◽  
Autonomous Driving ◽  
Current Generation ◽  
Transfer Data ◽  
Safety Features ◽  
Artificial Intelligence Systems

Vision Based systems have become an integral part when it comes to autonomous driving. The autonomous industry has seen a made large progress in the perception of environment as a result of the improvements done towards vision based systems. As the industry moves up the ladder of automation, safety features are coming more and more into the focus. Different safety measurements have to be taken into consideration based on different driving situations. One of the major concerns of the highest level of autonomy is to obtain the ability of understanding both internal and external situations. Most of the research made on vision based systems are focused on image processing and artificial intelligence systems like machine learning and deep learning. Due to the current generation of technology being the generation of “Connected World”, there is no lack of data any more. As a result of the introduction of internet of things, most of these connected devices are able to share and transfer data. Vision based techniques are techniques that are hugely depended on these vision based data.

scholarly journals Clustering-Based Energy Management of Residential Loads by using Artificial Intelligence

2021 ◽  
Vol 12 (1) ◽  
pp. 15
Author(s):  
Umair Liaqat ◽  
Muhammad Yousif ◽  
Malik Shah Zeb Ali ◽  
Muhammad Afzal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Demand Response ◽  
Intelligent Systems ◽  
Supply And Demand ◽  
Demand Side Management ◽  
Demand Side ◽  
Perfect Agreement ◽  
Peak Loads ◽  
Artificial Neural Network Ann

Developing countries have witnessed a remarkable surge in the energy crisis due to the supply and demand gap. One of the solutions to overcome this problem is the optimal use of energy that can be achieved by employing demand side management (DSM) and demand response (DR) methods intelligently. Machine learning and data analysis tools help us create intelligent systems that motivate us to use machine learning to implement DSM/DR programs. In this paper, a novel DSM algorithm is introduced to implement DSM intelligently by using artificial intelligence. The results show an efficient implementation of an artificial neural network (ANN) along with demand side management, whereas the peak and off-peak loads were normalized to a certain range where a perfect agreement between supply and demand can be reached.

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