Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

Download Full-text

Machine Learning in Agriculture

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i6.713 ◽

2018 ◽

Vol 8 (6) ◽

pp. 26 ◽

Cited By ~ 1

Author(s):

Matthew N. O. Sadiku ◽

Chandra M. M Kotteti ◽

Sarhan M. Musa

Keyword(s):

Climate Change ◽

Artificial Intelligence ◽

Machine Learning ◽

Disease Diagnosis ◽

Automated Detection ◽

Disease Prediction ◽

Paper Briefly ◽

Agriculture Sector ◽

Modern Agriculture ◽

Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data. Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change. Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Download Full-text

A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

International Journal of Health Services ◽

10.1177/00207314211017469 ◽

2021 ◽

pp. 002073142110174

Author(s):

Md Mijanur Rahman ◽

Fatema Khatun ◽

Ashik Uzzaman ◽

Sadia Islam Sami ◽

Md Al-Amin Bhuiyan ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Health Care ◽

Language Processing ◽

Probabilistic Models ◽

Health Care Systems ◽

Learning Approaches ◽

Care Systems ◽

Novel Coronavirus ◽

Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

Download Full-text

Artificial Intelligence and Machine Learning Approaches For Aviation Cybersecurity: An Overview

2021 Integrated Communications Navigation and Surveillance Conference (ICNS) ◽

10.1109/icns52807.2021.9441594 ◽

2021 ◽

Author(s):

Anna Baron Garcia ◽

Radu F. Babiceanu ◽

Remzi Seker

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Learning Approaches

Download Full-text

Training commercial energy users in behavior change: A case study

10.47568/3cp104 ◽

2020 ◽

Author(s):

Sea Rotmann ◽

Beth Karlin

Keyword(s):

Behavior Change ◽

Behavioral Interventions ◽

Professional Training ◽

Energy Use ◽

Behavioral Science ◽

Research Process ◽

Commercial Sector ◽

Great Opportunity ◽

Behavior Change Interventions ◽

Commercial Energy

Within the commercial sector, energy managers and building operators have a large impact over their organizations’ energy use. However, they mostly focus on technology solutions and retrofits, rather than human or corporate behaviors, and how to change them. This gap in targeted commercial sector research and behavioral interventions provides a great opportunity which is currently not being addressed. This paper presents a field research pilot where an empirical behavior change research process was applied and taught to commercial energy users in Ontario, Canada. This course served to fill an identified market gap and to improve commercial energy managers’ literacy in behavioral science theory and techniques. A needs assessment identified a clear gap in behavioral training for energy managers, and high interest in the course further proved out the market opportunity for professional training on how to design, implement and evaluate behavior change interventions. Evaluation results identified positive feedback in terms of course reaction, self-reported learning and behavioral outcomes, and tangible results when course participants returned to work to apply their learnings. Evaluation results suggest that such training fills a vital gap in the current Strategic Energy Management (SEM) landscape, and could unlock significant savings in the commercial energy sector.

Download Full-text

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk ◽

10.5772/intechopen.93488 ◽

2021 ◽

Cited By ~ 1

Author(s):

Joseph Nyangon

Keyword(s):

Climate Change ◽

Artificial Intelligence ◽

Machine Learning ◽

Carbon Budget ◽

Energy Sector ◽

Low Carbon ◽

Low Carbon Economy ◽

Legal Challenges ◽

Nationally Determined Contributions ◽

Implementation Level

The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget. Achieving this temperature target means stranding more than 80% of all proven fossil energy reserves as well as resulting in investments in such resources becoming stranded assets. At the implementation level, governments are experiencing technical, economic, and legal challenges in transitioning their economies to meet the 2°C temperature commitment through the nationally determined contributions (NDCs), let alone striving for the 1.5°C carbon budget, which translates into greenhouse gas emissions (GHG) gap. This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies. Stranded assets are not new in the energy sector; the physical impacts of climate change and the transition to a low-carbon economy have generally rendered redundant or obsolete electricity generation and storage assets. Low-carbon electricity systems, which come in variable and controllable forms, are essential to mitigating climate change. These systems present distinct opportunities for machine learning and artificial intelligence-powered techniques. This chapter considers the background to these issues. It discusses the asset stranding discourse and its implications to the energy sector and related infrastructure. The chapter concludes by outlining an interdisciplinary research agenda for mitigating the risks of stranded assets in electricity investments.

Download Full-text

Capabilities and Challenges Using Machine Learning in Tunnelling

10.5772/intechopen.97695 ◽

2021 ◽

Author(s):

Thomas Marcher ◽

Georg Erharter ◽

Paul Unterlass

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Sharp Increase ◽

Tunnel Construction ◽

Learning Approaches ◽

Building Information ◽

Tunnel Design ◽

Mass Classification ◽

Design Analyses ◽

Operational Processes

Digitalization changes the design and operational processes in tunnelling. The way of gathering geological data in the field of tunnelling, the methods of rock mass classification as well as the application of tunnel design analyses, tunnel construction processes and tunnel maintenance will be influenced by this digital transformation. The ongoing digitalization in tunnelling through applications like building information modelling and artificial intelligence, addressing a variety of difficult tasks, is moving forward. Increasing overall amounts of data (big data), combined with the ease to access strong computing powers, are leading to a sharp increase in the successful application of data analytics and techniques of artificial intelligence. Artificial Intelligence now arrives also in the fields of geotechnical engineering, tunnelling and engineering geology. The chapter focuses on the potential for machine learning methods – a branch of Artificial Intelligence - in tunnelling. Examples will show that training artificial neural networks in a supervised manner works and yields valuable information. Unsupervised machine learning approaches will be also discussed, where the final classification is not imposed upon the data, but learned from it. Finally, reinforcement learning seems to be trendsetting but not being in use for specific tunnel applications yet.

Download Full-text

Reports of the 2013 AAAI Spring Symposium Series

AI Magazine ◽

10.1609/aimag.v34i3.2493 ◽

2013 ◽

Vol 34 (3) ◽

pp. 93-98 ◽

Cited By ~ 1

Author(s):

Vita Markman ◽

Georgi Stojanov ◽

Bipin Indurkhya ◽

Takashi Kido ◽

Keiki Takadama ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cognitive Development ◽

Behavior Change ◽

Autonomous Systems ◽

Data Driven ◽

Intelligent Robots ◽

Symposium Series ◽

Development Data ◽

Weakly Supervised

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

Download Full-text

Advanced-Level Security in Network and Real-Time Applications Using Machine Learning Approaches

Machine Learning and Cognitive Science Applications in Cyber Security - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-8100-0.ch003 ◽

2019 ◽

pp. 84-104 ◽

Cited By ~ 1

Author(s):

Mamata Rath ◽

Sushruta Mishra

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Real Time ◽

Learning Approaches ◽

Advanced Level ◽

Real Time Applications

Machine learning is a field that is developed out of artificial intelligence (AI). Applying AI, we needed to manufacture better and keen machines. Be that as it may, aside from a couple of simple errands, for example, finding the briefest way between two points, it isn't to program more mind boggling and continually developing difficulties. There was an acknowledgment that the best way to have the capacity to accomplish this undertaking was to give machines a chance to gain from itself. This sounds like a youngster learning from itself. So, machine learning was produced as another capacity for computers. Also, machine learning is available in such huge numbers of sections of technology that we don't understand it while utilizing it. This chapter explores advanced-level security in network and real-time applications using machine learning.

Download Full-text

The Usage Analysis of Machine Learning Methods for Intrusion Detection in Software-Defined Networks

Research Anthology on Artificial Intelligence Applications in Security ◽

10.4018/978-1-7998-7705-9.ch045 ◽

2021 ◽

pp. 959-975

Author(s):

Derya Yiltas-Kaplan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Intrusion Detection ◽

Network Architectures ◽

Software Defined Networks ◽

Learning Approaches ◽

Advantages And Disadvantages ◽

Machine Learning Methods ◽

Network Problems ◽

Usage Analysis

This chapter focuses on the process of the machine learning with considering the architecture of software-defined networks (SDNs) and their security mechanisms. In general, machine learning has been studied widely in traditional network problems, but recently there have been a limited number of studies in the literature that connect SDN security and machine learning approaches. The main reason of this situation is that the structure of SDN has emerged newly and become different from the traditional networks. These structural variances are also summarized and compared in this chapter. After the main properties of the network architectures, several intrusion detection studies on SDN are introduced and analyzed according to their advantages and disadvantages. Upon this schedule, this chapter also aims to be the first organized guide that presents the referenced studies on the SDN security and artificial intelligence together.

Download Full-text