Artificial intelligence in optimizing environmental factors towards human performance

2012 ◽  
pp. 322-327
Author(s):  
A Ismail ◽  
B Deros ◽  
Y Yusoff
Keyword(s):  
Artificial Intelligence ◽  
Environmental Factors ◽  
Human Performance

Evaluation and Comparison of Artificial Intelligence Vision Aids: Orcam MyEye 1 and Seeing AI

2021 ◽  
pp. 0145482X2110274
Author(s):  
Christina Granquist ◽  
Susan Y. Sun ◽  
Sandra R. Montezuma ◽  
Tu M. Tran ◽  
Rachel Gage ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Performance ◽  
Visual Impairments ◽  
Light Level ◽  
Viewing Distance ◽  
Light Perception ◽  
Time To Completion ◽  
Significant Difference ◽  
Text Reading ◽  
Size Contrast

Introduction: We compared the print-to-speech properties and human performance characteristics of two artificial intelligence vision aids, Orcam MyEye 1 (a portable device) and Seeing AI (an iPhone and iPad application). Methods: There were seven participants with visual impairments who had no experience with the two reading aids. Four participants had no light perception. Two individuals with measurable acuity and one with light perception were tested while blindfolded. We also tested performance with text of varying appearance in varying viewing conditions. To evaluate human performance, we asked the participants to use the devices to attempt 12 reading tasks similar to activities of daily living. We assessed the ranges of text attributes for which reading was possible, such as print size, contrast, and light level. We also assessed if individuals could complete tasks with the devices and measured accuracy and completion time. Participants also completed a survey concerning the two aids. Results: Both aids achieved greater than 95% accuracy in text recognition for flat, plain word documents and ranged from 13 to 57% accuracy for formatted text on curved surfaces. Both aids could read print sizes as small as 0.8M (20/40 Snellen equivalent, 40 cm viewing distance). Individuals successfully completed 71% and 55% ( p = .114) of tasks while using Orcam MyEye 1 and Seeing AI, respectively. There was no significant difference in time to completion of tasks ( p = .775). Individuals believed both aids would be helpful for daily activities. Discussion: Orcam MyEye 1 and Seeing AI had similar text-reading capability and usability. Both aids were useful to users with severe visual impairments in performing reading tasks. Implications for Practitioners: Selection of a reading device or aid should be based on individual preferences and prior familiarity with the platform, since we found no clear superiority of one solution over the other.

scholarly journals Misconceptions in the health technology industry that are delaying the translation of artificial intelligence technology into relevant clinical applications

2021 ◽  
Vol 54 (4) ◽  
pp. 243-245
Author(s):  
Fabíola Macruz
Keyword(s):  
Artificial Intelligence ◽  
Human Performance ◽  
Health Technology ◽  
Medical Community ◽  
Technology Industry ◽  
Diagnosis And Prognosis ◽  
Starting Point ◽  
Causative Factors ◽  
The Status ◽  
Artificial Intelligence Technology

Abstract There is great optimism that artificial intelligence (AI), as it disrupts the medical world, will provide considerable improvements in all areas of health care, from diagnosis to treatment. In addition, there is considerable evidence that AI algorithms have surpassed human performance in various tasks, such as analyzing medical images, as well as correlating symptoms and biomarkers with the diagnosis and prognosis of diseases. However, the mismatch between the performance of AI-based software and its clinical usefulness is still a major obstacle to its widespread acceptance and use by the medical community. In this article, three fundamental concepts observed in the health technology industry are highlighted as possible causative factors for this gap and might serve as a starting point for further evaluation of the structure of AI companies and of the status quo.

scholarly journals Human factors challenges for the safe use of artificial intelligence in patient care

2019 ◽  
Vol 26 (1) ◽  
pp. e100081 ◽  
Author(s):  
Mark Sujan ◽  
Dominic Furniss ◽  
Kath Grundy ◽  
Howard Grundy ◽  
David Nelson ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Patient Care ◽  
Situation Awareness ◽  
Human Performance ◽  
Independent Evaluation ◽  
The Impact ◽  
Human Factors Research

The use of artificial intelligence (AI) in patient care can offer significant benefits. However, there is a lack of independent evaluation considering AI in use. The paper argues that consideration should be given to how AI will be incorporated into clinical processes and services. Human factors challenges that are likely to arise at this level include cognitive aspects (automation bias and human performance), handover and communication between clinicians and AI systems, situation awareness and the impact on the interaction with patients. Human factors research should accompany the development of AI from the outset.

scholarly journals Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts

2018 ◽  
Vol 62 ◽  
pp. 729-754 ◽  
Author(s):  
Katja Grace ◽  
John Salvatier ◽  
Allan Dafoe ◽  
Baobao Zhang ◽  
Owain Evans
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High School ◽  
Public Policy ◽  
Human Performance ◽  
Health Science ◽  
Modern Life ◽  
North Americans ◽  
Large Survey ◽  
The Military

Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans. These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI. This article is part of the special track on AI and Society.

scholarly journals Use of Artificial Intelligence in Healthcare and Medicine

2020 ◽  
Author(s):  
Deekshaa Khanna
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Cognitive Functions ◽  
Paradigm Shift ◽  
Human Performance ◽  
Analytical Techniques ◽  
Medical Field ◽  
Healthcare Data ◽  
Wide Range ◽  
Different Types

Artificial Intelligence is a field of computer science that mimic human cognitive functions. It has brought a paradigm shift in the medical field mostly due to the increase in healthcare data and rapid increase of analytical techniques. In recent years AI has surpassed human performance in several medical field areas, and this is a great adoption in healthcare. Also, through the use of analytical techniques, AI has the capabilities to prevent, detect, diagnose, and treat a wide range of diseases. This research paper will discuss different types of Artificial Intelligence techniques and how AI has been used in healthcare. Also, it will provide a view of the future of Artificial Intelligence in healthcare.

scholarly journals Editorial of Energy-Efficient and Reliable Information Processing: Computing and Storage

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 914 ◽  
Author(s):  
Yongjune Kim
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Energy Efficient ◽  
Human Performance ◽  
Reliable Information ◽  
And Storage

Recently, artificial intelligence (AI) systems have begun to approach and exceed human performance in many intelligent tasks: AlexNet [...]

scholarly journals Heading Toward Trusted ATCO-AI Systems: A Literature Review

2021 ◽  
Author(s):  
Jimmy Y. Zhong
Keyword(s):  
Artificial Intelligence ◽  
Traffic Management ◽  
Human Performance ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Air Traffic Controller ◽  
Current Review ◽  
Explainable Ai ◽  
Emerging Issues ◽  
Shed Light

The current review addresses emerging issues that arise from the creation of safe, beneficial, and trusted artificial intelligence–air traffic controller (AI-ATCO) systems for air traffic management (ATM). These issues concern trust between the human user and automated or AI tools of interest, resilience, safety, and transparency. To tackle these issues, we advocate the development of practical AI ATCO teaming frameworks by bringing together concepts and theories from neuroscience and explainable AI (XAI). By pooling together knowledge from both ATCO and AI perspectives, we seek to establish confidence in AI-enabled technologies for ATCOs. In this review, we present an overview of the extant studies that shed light on the research and development of trusted human-AI systems, and discuss the prospects of extending such works to building better trusted ATCO-AI systems. This paper contains three sections elucidating trust-related human performance, AI and explainable AI (XAI), and human-AI teaming.

scholarly journals THE FACTORS THAT INFLUENCE ARTIFICIAL INTELLIGENCE IMPLEMENTATION AT PT PELINDO III (Case Study in Terminal Teluk Lamong Surabaya)

2020 ◽  
Vol 18 (2) ◽  
pp. 123-131
Author(s):  
Pujiartini Pujiartini ◽  
Ulung Pribadi
Keyword(s):  
Artificial Intelligence ◽  
Human Performance ◽  
Secondary Data ◽  
Research Report ◽  
Private Companies ◽  
Related Data ◽  
Machine Technology ◽  
Government Documents ◽  
Qualitative Descriptive

This study uses qualitative descriptive methods. The type of data used in this analysis is secondary data from library analysis surveys, research-related data from various sources, websites, and government documents that can facilitate the completion of this research report. In the era of globalization, all private companies and state-owned enterprises (BMUN) already have various advanced machine technology features as one of the tools that will facilitate human performance to improve the economy. As found in PT Terminal, Teluk Lamong (PT TTL) already has various tools for unloading advanced goods or known as artificial intelligence (AI). This tool can help performance and has a unique concept, namely, Go green (No pollution).

scholarly journals Nanogenomics and Artificial Intelligence: A Dynamic Duo for the Fight Against Breast Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Batla S. Al-Sowayan ◽  
Alaa T. Al-Shareeda
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Human Performance ◽  
Estrogen Receptor Status ◽  
Automated Analysis ◽  
Breast Cancers ◽  
Fast Evaluation ◽  
Different Types ◽  
Metastasis Site ◽  
Whole Slide Images

Application software is utilized to aid in the diagnosis of breast cancer. Yet, recent advances in artificial intelligence (AI) are addressing challenges related to the detection, classification, and monitoring of different types of tumors. AI can apply deep learning algorithms to perform automated analysis on mammographic or histologic examinations. Large volume of data generated by digitalized mammogram or whole-slide images can be interoperated through advanced machine learning. This enables fast evaluation of every tissue patch on an image, resulting in a quicker more sensitivity, and more reproducible diagnoses compared to human performance. On the other hand, cancer cell-exosomes which are extracellular vesicles released by cancer cells into the blood circulation, are being explored as cancer biomarker. Recent studies on cancer-exosome-content revealed that the encapsulated miRNA and other biomolecules are indicative of tumor sub-type, possible metastasis and prognosis. Thus, theoretically, through nanogenomicas, a profile of each breast tumor sub-type, estrogen receptor status, and potential metastasis site can be constructed. Then, a laboratory instrument, fitted with an AI program, can be used to diagnose suspected patients by matching their sera miRNA and biomolecules composition with the available template profiles. In this paper, we discuss the advantages of establishing a nanogenomics-AI-based breast cancer diagnostic approach, compared to the gold standard radiology or histology based approaches that are currently being adapted to AI. Also, we discuss the advantages of building the diagnostic and prognostic biomolecular profiles for breast cancers based on the exosome encapsulated content, rather than the free circulating miRNA and other biomolecules.

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