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 Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

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.

scholarly journals Guiding the Students in High School by Using Machine Learning

TEM Journal ◽  
2021 ◽  
pp. 384-391
Author(s):  
Mustafa Ababneh ◽  
Aayat Aljarrah ◽  
Damla Karagozlu ◽  
Fezile Ozdamli
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High School ◽  
Big Data ◽  
High Efficiency ◽  
School Level ◽  
Future Success ◽  
Starting Point ◽  
Sensitive Phase ◽  
Academic Lives

Machine learning is considered the most significant technique that processes and analyses educational big data. In this research paper, many previous papers related to analysing the educational big data that uses a lot of artificial intelligence techniques were studied. The purpose of the study is to identify weaknesses and gaps in previous researches. The results showed that many researches highlighted early expectations for academic performance. Unfortunately, no one thought of finding an effective way to guide high schooled students to reach their appropriate majors that can be suitable to their abilities. Those students need to be guided to pass this sensitive phase with high efficiency and good results. Thus, this school level is considered as the starting point for students’ academic lives, professional, and future success.

Developing and Delivering a High School Artificial Intelligence Course in Blended and Online Learning Environments

2020 ◽  
pp. 255-261
Author(s):  
Antonios Karampelas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High School ◽  
High School Students ◽  
Learning Environments ◽  
Community Schools ◽  
School Students ◽  
Online Learning Environments ◽  
Online Delivery ◽  
Machine Perception

The paper outlines the development and delivery of Artificial Intelligence to high school students of the American Community Schools of Athens, either as an independent course, or as part of a S.T.E.A.M. course, and the respective instructional design. The topics developed – Impact of Artificial Intelligence, Machine Perception, and Machine Learning – are discussed, including relevant assessments. Additionally, the transition to the online delivery of Artificial Intelligence is presented, followed by reflective views on student learning and suggested future steps.

Responsible Machine Learning for Ethical Artificial Intelligence in Business and Industry

2021 ◽  
pp. 639-653
Author(s):  
Deepak Saxena ◽  
Markus Lamest ◽  
Veena Bansal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Moral Development ◽  
Ethical Framework ◽  
Self Awareness ◽  
Design Development ◽  
Economic Activities ◽  
Modern Life ◽  
Current Generation ◽  
Explainable Ai

Artificial intelligence (AI) systems have become a new reality of modern life. They have become ubiquitous to virtually all socio-economic activities in business and industry. With the extent of AI's influence on our lives, it is an imperative to focus our attention on the ethics of AI. While humans develop their moral and ethical framework via self-awareness and reflection, the current generation of AI lacks these abilities. Drawing from the concept of human-AI hybrid, this chapter offers managerial and developers' action towards responsible machine learning for ethical artificial intelligence. The actions consist of privacy by design, development of explainable AI, identification and removal of inherent biases, and most importantly, using AI as a moral enabler. Application of these action would not only help towards ethical AI; it would also help in supporting moral development of human-AI hybrid.

‘Where are the people? What are they doing? Why are they doing it?’(Mindell) Situating artificial intelligence within a socio-technical framework

2019 ◽  
pp. 144078331987304
Author(s):  
Robert Holton ◽  
Ross Boyd
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Public Policy ◽  
Big Data ◽  
Theoretical Problem ◽  
Social Actors ◽  
The People ◽  
Human Autonomy ◽  
Human Actors ◽  
Technical Framework

This article explores the sociology of artificial intelligence (AI), focusing on interactions between social actors and technological processes. The aim is to locate social actors in the key elements of Bell’s framework for understanding AI, featuring big data, algorithms, machine learning, sensors and rationale/logic. We dispute notions of human autonomy and machine autonomy, seeking alternatives to both anthropocentric and technological determinist accounts of AI. While human actors and technological devices are co-producers of the assemblages around AI, we challenge the argument that their respective contributions are symmetrical. The theoretical problem is to establish quite how human actors are positioned asymmetrically within AI processes. This challenge has strong resonances for issues of inequality, democracy, governance and public policy. The theoretical questions raised do not support the argument that sociology should respond to the rise of big data by becoming a primarily empirical discipline.

Challenges and opportunities for artificial intelligence in surgery

2021 ◽  
pp. 154851292110228
Author(s):  
Pamela Andreatta ◽  
Christopher S. Smith ◽  
John Christopher Graybill ◽  
Mark Bowyer ◽  
Eric Elster
Keyword(s):  
Artificial Intelligence ◽  
Human Performance ◽  
Medical Science ◽  
Deep Understanding ◽  
Extended Period ◽  
Military Health ◽  
Active Involvement ◽  
Performance Requirements ◽  
Challenges And Opportunities ◽  
The Military

Surgery is an exceptionally complex domain where multi-dimensional expertise is developed over an extended period of time, and mastery is maintained only through ongoing engagement in surgical contexts. Expert surgeons integrate perceptual information through both conscious and subconscious awareness, and respond to the environment by leveraging their deep understanding of surgical constructs. However, their ability to utilize these deep knowledge structures can be complicated by continuous advances in technology, medical science, pharmacology, technique, materials, operative environments, etc. that must be routinely accommodated in professional practice. The demands on surgeons to perform perfectly in ever-changing contexts increases cognitive load, which could be reduced through judicious use of accurate and reliable artificial intelligence (AI) systems. AI has great potential to support human performance in complex environments such as surgery; however, the foundational requirements for the rules governing algorithmic development of performance requirements necessitate the active involvement of surgeons to precisely model the quantitative measures of performance along the continuum of expertise. Providing the AI development community with these data will help assure that accurate and reliable systems are designed to supplement human performance in applied surgical contexts. The Military Health System’s Clinical Readiness Program is developing these types of metrics to support military medical readiness.

scholarly journals Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions

2021 ◽  
Vol 11 (1_suppl) ◽  
pp. 23S-29S
Author(s):  
Zamir A. Merali ◽  
Errol Colak ◽  
Jefferson R. Wilson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Spine Surgery ◽  
Human Performance ◽  
Automated Analysis ◽  
Learning Technologies ◽  
Current Status ◽  
Learning Technology ◽  
Spinal Disorders ◽  
Pubmed Database

Study Design: Narrative review. Objectives: We aim to describe current progress in the application of artificial intelligence and machine learning technology to provide automated analysis of imaging in patients with spinal disorders. Methods: A literature search utilizing the PubMed database was performed. Relevant studies from all the evidence levels have been included. Results: Within spine surgery, artificial intelligence and machine learning technologies have achieved near-human performance in narrow image classification tasks on specific datasets in spinal degenerative disease, spinal deformity, spine trauma, and spine oncology. Conclusion: Although substantial challenges remain to be overcome it is clear that artificial intelligence and machine learning technology will influence the practice of spine surgery in the future.

A Study on Exponential Advancement of Unmeasurable Artificial Intelligence in Destructing the Power of Human Decision Making in Near Future

2017 ◽  
Vol 6 (10) ◽  
pp. 70
Author(s):  
M. Kumarasamy ◽  
G. N. K. Suresh Babu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Significant Risk ◽  
Machine Intelligence ◽  
Health Science ◽  
Human Capabilities ◽  
Human Decision ◽  
Almost All ◽  
The Military ◽  
Near Future

Machine intelligence will in near future replace human capabilities in almost all organizations across the world. Manufacturers, Services sectors and institutions will rapidly move on to Artificial superintelligence that will surpass human decision making power to bring significant risk for humanity. Advances in artificial intelligence will transform modern life by reshaping transportation, health, science, finance, and the military. This development turns out to be ‘bad’ or ‘extremely bad’ for humanity.

Documental analysis of educational public policy: foundations of Paul de Bruyne's quadripolar approach

2020 ◽  
Vol 3 (2) ◽  
pp. 27
Author(s):  
ROSEMARY LOPES SOARES DA SILVA
Keyword(s):  
High School ◽  
Public Policy ◽  
Education Policy ◽  
Professional Education ◽  
Clear Understanding ◽  
Concepts And Categories ◽  
Research In Education

The present study seeks to explain the techniques and procedures, the concepts and categories with which the interpretation of the object studied, that is - documents related to the High School  Technical Professional Education Policy - implemented in Bahia / Brazil, specifically, the reference regarding Paul de Bryne’s quadripolar approach (1977), which provide the theoretical-methodological density of the object studied in relation to the epistemological, theoretical, technical and morphological poles. In this perspective, it is agreed with Gamboa (1987), in the sense that, the accomplishment of a research is not the fulfilment of ‘methodological ritualism’ with a ‘theoretical fad’ in order not to repeat what commonly happens in research in education, in progressively more intense way, of eclectic attempts that randomly collect techniques, methods and theoretical references without a clear understanding of the epistemological foundations and the philosophical implications of the different paths of knowledge.

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