Artificial intelligence: Implications for the future of work.

@article{Howard2019ArtificialII,
  title={Artificial intelligence: Implications for the future of work.},
  author={John Howard},
  journal={American journal of industrial medicine},
  year={2019}
}
  • J. Howard
  • Published 1 November 2019
  • Computer Science
  • American journal of industrial medicine
Artificial intelligence (AI) is a broad transdisciplinary field with roots in logic, statistics, cognitive psychology, decision theory, neuroscience, linguistics, cybernetics, and computer engineering. The modern field of AI began at a small summer workshop at Dartmouth College in 1956. Since then, AI applications made possible by machine learning (ML), an AI subdiscipline, include Internet searches, e-commerce sites, goods and services recommender systems, image and speech recognition, sensor… 

Guest Editorial Preface

TLDR
The marketing standard has been improved by integrating AI in the e-commerce industry, and it becomes difficult to choose sides between the positive and negative output of AI by firms and makes it further challenging to counter-argue the degree of adoption and adaptation.

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

TLDR
A new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) is introduced that highlights the role that AI plays in the anticipation and control of exposure risks in a worker’s immediate environment to optimally protect worker health, safety and well-being.

Progress and Perspective of Artificial Intelligence and Machine Learning of Prediction in Anesthesiology

TLDR
The use of AI in anesthesiology remains in development, even without extensive promotion and clinical application; moreover, it has immense potential to maintain further development in the future.

A short guide for medical professionals in the era of artificial intelligence

TLDR
The simple definition of A.I. is described, its levels, its methods, the differences between the methods with medical examples, the potential benefits, dangers, challenges, and a futuristic vision about using it in an everyday medical practice are described.

Artificial Intelligence and the Future of Work: A Functional-Identity Perspective

TLDR
It is argued that the conditions for AI to either enhance or threaten workers’ sense of identity derived from their work depends on how the technology is functionally deployed and how it affects the social fabric of work.

Critical success factors for integrating artificial intelligence and robotics

TLDR
This paper is the first of its kind that has used the CSF theory and TISM methodology for the identification and prioritization of CSFs in developing IASs and suggests a prioritization hierarchy model for building sustainable ecosystem for developing Iass.

Cognitive biases in developing biased Artificial Intelligence recruitment system

TLDR
A model to determine people’s role in developing AI recruitment systems is proposed and it is shown that identifying the sources of cognitive biases can provide insight into how to develop unbiased AI.

Commercial Use of Emotion Artificial Intelligence (AI): Implications for Psychiatry

TLDR
The successful re-integration of patients with mental illness into society must recognize the increasing commercial use of emotion AI, which will increase stigma and discrimination, and have negative consequences in daily life for people withmental illness.

Artificial Intelligence in Marketing Welcome to the New Era of Artificial Intelligence in Marketing –

Welcome to the New Era of Artificial Intelligence in Marketing – An Era in which the link between human and machine is blurring and the machine is learning without human intervention which leads to

Sources of Risk of AI Systems

TLDR
The differences between AI systems, especially those based on modern machine learning methods, and classical software were analysed, and the current research fields of trustworthy AI were evaluated, and a taxonomy was created that provides an overview of various AI-specific sources of risk.
...

References

SHOWING 1-10 OF 77 REFERENCES

Computational rationality: A converging paradigm for intelligence in brains, minds, and machines

TLDR
This work charts advances over the past several decades that address challenges of perception and action under uncertainty through the lens of computation to identify decisions with highest expected utility, while taking into consideration the costs of computation in complex real-world problems in which most relevant calculations can only be approximated.

The Wrong Kind of Ai? Artificial Intelligence and the Future of Labor Demand

Artificial intelligence (AI) is set to influence every aspect of our lives, not least the way production is organised. AI, as a technology platform, can automate tasks previously performed by

Deep Learning-A Technology With the Potential to Transform Health Care.

TLDR
The purpose of this Viewpoint is to give health care professionals an intuitive understanding of the technology underlying deep learning, used on billions of digital devices for complex tasks such as speech recognition, image interpretation, and language translation.

Machine learning: Trends, perspectives, and prospects

TLDR
The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

A Survey of the Application of Machine Learning in Decision Support Systems

TLDR
The content analysis of design-oriented research published between 1994 and 2013 suggests that the usefulness of machine learning for supporting decision-makers is dependent on the task, the phase of decision-making, and the applied technologies.

Machine Learning in Medicine.

  • R. Deo
  • Computer Science
    Circulation
  • 2015
TLDR
What obstacles there may be to changing the practice of medicine through statistical learning approaches, and how these might be overcome are identified.

Deep Learning

TLDR
Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.

The Truly Total Turing Test*

The paper examines the nature of the behavioral evidence underlying attributions of intelligence in the case of human beings, and how this might be extended to other kinds of cognitive system, in the

Is a cambrian explosion coming for robotics

TLDR
I examine some key technologies contributing to the present excitement in the robotics field and offers some thoughts about how robotics may affect the economy and some ways to address potential difficulties.
...