scholarly journals Applied Artificial Intelligence – Making AI Work for Consumers as a Core Business Component

2020 ◽  
pp. 39-72
Author(s):  
Mikael Wiberg ◽  
Keyword(s):  
Artificial Intelligence ◽  
Study Design ◽  
Real World ◽  
Design Development ◽  
The Past ◽  
Growing Body ◽  
Business Component ◽  
Online Business ◽  
Societal Level ◽  
Applied Artificial Intelligence

Artificial Intelligence (AI) is now rapidly being applied in our society. While the breakthrough of AI in terms of its use and its applicability on a societal level has in fact been repeatedly announced since the mid 1950s, is now truer than ever. As recently acknowledged, AI has now, after three waves of developments, finally left the research labs and entered real-world contexts. Accordingly, and as AI is now increasingly and widely applied, we suggest that it is now time to address issues related to “Applied Artificial Intelligence” (AAI). In this paper we propose this term, and we define it as the study, design, development, implementation and use of Artificial Intelligence technologies to address real-world problems. In this article we present how AI has developed over the past few decades, and across three waves of developments, and we illustrated Applied Artificial Intelligence by presenting our e-Biz corp case where a global actor is now using AI as a core component of their online business. We conclude this article with a set of recommendations for moving forward with Applied Artificial Intelligence, and we present the main contributions offered by our work to the growing body of research on how to make use of AI.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

Artificial Intelligence (AI): Revolutionizing Digital Marketing

2021 ◽  
pp. 183933492110376
Author(s):  
Patrick van Esch ◽  
J. Stewart Black
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Real World ◽  
Research Process ◽  
Digital Marketing ◽  
Customer Acquisition ◽  
Special Edition ◽  
Practical Application ◽  
The Past ◽  
Acquisition Costs

Artificial intelligence (AI)-enabled digital marketing is revolutionizing the way organizations create content for campaigns, generate leads, reduce customer acquisition costs, manage customer experiences, market themselves to prospective employees, and convert their reachable consumer base via social media. Real-world examples of organizations who are using AI in digital marketing abound. For example, Red Balloon and Harley Davidson used AI to automate their digital advertising campaigns. However, we are early in the process of both the practical application of AI by firms broadly and by their marketing functions in particular. One could argue that we are even earlier in the research process of conceptualizing, theorizing, and researching the use and impact of AI. Importantly, as with most technologies of significant potential, the application of AI in marketing engenders not just practical considerations but ethical questions as well. The ability of AI to automate activities, that in the past people did, also raises the issue of whether marketing professionals will embrace AI as a means to free them from more mundane tasks to spend time on higher value activities, or will they view AI as a threat to their employment? Given the nascent nature of research on AI at this point, the full capabilities and limitations of AI in marketing are unknown. This special edition takes an important step in illuminating both what we know and what we yet need to research.

scholarly journals What can computational models learn from human selective attention? A review from an audiovisual unimodal and crossmodal perspective

2019 ◽  
Author(s):  
Di Fu ◽  
Cornelius Weber ◽  
Guochun Yang ◽  
Matthias Kerzel ◽  
Weizhi Nan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Selective Attention ◽  
Computational Modeling ◽  
Cognitive Neuroscience ◽  
Real World ◽  
Information Acquisition ◽  
Essential Role ◽  
Computational Models ◽  
The Past ◽  
Shed Light

Selective attention plays an essential role in information acquisition and utilizationfrom the environment. In the past 50 years, research on selective attention has beena central topic in cognitive science. Compared with unimodal studies, crossmodalstudies are more complex but necessary to solve real-world challenges in both humanexperiments and computational modeling. Although an increasing number of findingson crossmodal selective attention have shed light on humans’ behavioral patterns andneural underpinnings, a much better understanding is still necessary to yield the samebenefit for intelligent computational agents. This article reviews studies of selectiveattention in unimodal visual and auditory and crossmodal audiovisual setups from themultidisciplinary perspectives of psychology and cognitive neuroscience, and evaluatesdifferent ways to simulate analogous mechanisms in computational models and robotics.We discuss the gaps between these fields in this interdisciplinary review and provideinsights about how to use psychological findings and theories in artificial intelligence fromdifferent perspectives.

Modeling and using context

1998 ◽  
Vol 13 (2) ◽  
pp. 185-194 ◽  
Author(s):  
PATRICK BRÉZILLON ◽  
MARCOS CAVALCANTI
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Real World ◽  
Point Of View ◽  
Theoretical Level ◽  
The Past ◽  
System Building ◽  
Real World Applications ◽  
Different Types ◽  
Model Context

The first International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT-97) was held at Rio de Janeiro, Brazil on February 4–6 1997. This article provides a summary of the presentations and discussions during the three days with a focus on context in applications. The notion of context is far from defined, and is dependent in its interpretation on a cognitive science versus an engineering (or system building) point of view. However, the conference makes it possible to identify new trends in the formalization of context at a theoretical level, as well as in the use of context in real-world applications. Results presented at the conference are ascribed in the realm of the works on context over the past few years at specific workshops and symposia. The diversity of the attendees' origins (artificial intelligence, linguistics, philosophy, psychology, etc.) demonstrates that there are different types of context, not a unique one. For instance, logicians model context at the level of the knowledge representation and the reasoning mechanisms, while cognitive scientists consider context at the level of the interaction between two agents (i.e. two humans or a human and a machine). In the latter case, there are now strong arguments proving that one can speak of context only in reference to its use (e.g. context of an item or of a problem solving exercise). Moreover, there are different types of context that are interdependent. This makes it possible to understand why, despite the consensus on some context aspects, agreement on the notion of context is not yet achieved.

scholarly journals Evaluating artificial intelligence in medicine: phases of clinical research

JAMIA Open ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 326-331 ◽  
Author(s):  
Yoonyoung Park ◽  
Gretchen Purcell Jackson ◽  
Morgan A Foreman ◽  
Daniel Gruen ◽  
Jianying Hu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Research ◽  
Study Design ◽  
Medical Devices ◽  
Real World ◽  
In Silico ◽  
Unintended Consequences ◽  
Multifaceted Approach ◽  
Research Framework

Abstract Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need for real-world evaluations for effectiveness and unintended consequences. The complexity of healthcare, compounded by the user- and context-dependent nature of AI applications, calls for a multifaceted approach beyond traditional in silico evaluation of AI. We propose an interdisciplinary, phased research framework for evaluation of AI implementations in healthcare. We draw analogies to and highlight differences from the clinical trial phases for drugs and medical devices, and we present study design and methodological guidance for each stage.

The Future of Work and the Changing Workplace

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Political Issue ◽  
Digital Innovation ◽  
Future Of Work ◽  
The Past ◽  
The World ◽  
Public Debates ◽  
The Future ◽  
Market Opportunities

The world of work has been impacted by technology. Work is different than it was in the past due to digital innovation. Labor market opportunities are becoming polarized between high-end and low-end skilled jobs. Migration and its effects on employment have become a sensitive political issue. From Buffalo to Beijing public debates are raging about the future of work. Developments like artificial intelligence and machine intelligence are contributing to productivity, efficiency, safety, and convenience but are also having an impact on jobs, skills, wages, and the nature of work. The “undiscovered country” of the workplace today is the combination of the changing landscape of work itself and the availability of ill-fitting tools, platforms, and knowledge to train for the requirements, skills, and structure of this new age.

scholarly journals Restoring the real world records in Men’s swimming without high-tech swimsuits

2020 ◽  
Vol 16 (4) ◽  
pp. 291-300
Author(s):  
Zhenyu Gao ◽  
Yixing Li ◽  
Zhengxin Wang
Keyword(s):  
Real World ◽  
Technical Skills ◽  
High Tech ◽  
Training Methods ◽  
Strong Indication ◽  
The Real ◽  
The Past ◽  
Optimal Estimates ◽  
World Records ◽  
And Training

AbstractThe recently concluded 2019 World Swimming Championships was another major swimming competition that witnessed some great progresses achieved by human athletes in many events. However, some world records created 10 years ago back in the era of high-tech swimsuits remained untouched. With the advancements in technical skills and training methods in the past decade, the inability to break those world records is a strong indication that records with the swimsuit bonus cannot reflect the real progressions achieved by human athletes in history. Many swimming professionals and enthusiasts are eager to know a measure of the real world records had the high-tech swimsuits never been allowed. This paper attempts to restore the real world records in Men’s swimming without high-tech swimsuits by integrating various advanced methods in probabilistic modeling and optimization. Through the modeling and separation of swimsuit bias, natural improvement, and athletes’ intrinsic performance, the result of this paper provides the optimal estimates and the 95% confidence intervals for the real world records. The proposed methodology can also be applied to a variety of similar studies with multi-factor considerations.

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