Scenarios From Artificial Narrow Intelligence to Artificial General Intelligence—Reviewing the Results of the International Work/Technology 2050 Study

Kuusi, Osmo; Heinonen, Sirkka

doi:10.1177/19467567221101637

Cited by 19 publications

(12 citation statements)

References 8 publications

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“…Though scenarios are imaginary narratives created by the researcher, they should be plausible, that is, they should be based on current knowledge and the explanations should be understandable. They also have to be logical, that is, possible or expected in the social and cultural context (Kuusi, 2017; Linturi and Rubin, 2011). The primary function of scenarios is to provide decision makers with a range of alternative futures to consider and against which to measure alternative courses of action (Johansen, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…In this case, both the number of participants and the diverse expertise of the panellists are considered acceptable. (see Kuusi, 2017).…”

Section: The Studymentioning

confidence: 96%

“…Purposeful sampling is a relevant technique when the aim is to gain deep and comprehensive insight into the topic under study (Palinkas et al, 2013; Patton, 2002). Kuusi (2017) states that the panellists’ expertise is a more critical quality factor than the number of participants.…”

Section: The Studymentioning

confidence: 99%

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Alternative futures of Finnish comprehensive school

Nilivaara

Soini

2023

Policy Futures in Education

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Inevitable and constant change is challenging school systems worldwide, and COVID-19 has further intensified the debate on the future. This article examines the possible futures of Finnish comprehensive schools through three scenarios generated by analysing data from a Delphi panel of 30 Finnish experts in the field of education. This study contributes to two major intertwining debates: first, who or what determines the content and goals of the curriculum. The study’s theoretical framework builds on the curriculum as a social practice model, which views curriculum work as interwoven layers and sites of practice. Another topical debate concerns the tension between powerful knowledge and competences in the curricula. This is explored through Young and Muller’s model of three types of knowledge: knowledge of power, tacit knowledge and powerful knowledge. The results show that Finnish comprehensive schools have various substantially divergent trends. In the three scenarios, the role of the teacher as a curriculum maker varies from non-existent to a strong interpreter. International policy flows can be transferred to schools to varying degrees. The three types of knowledge included in Young and Muller’s model can be recognised in the three scenarios. Competences can be identified as learning outcomes in all scenarios, but the intensity varies. Scenarios are not predictions of the future or policy recommendations but an efficient tool for provoking strategic debate, generating new visionary thinking and considering the need for system-wide change in education.

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Section: Resultsmentioning

confidence: 99%

“…In this case, both the number of participants and the diverse expertise of the panellists are considered acceptable. (see Kuusi, 2017).…”

Section: The Studymentioning

confidence: 96%

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Alternative futures of Finnish comprehensive school

Nilivaara

Soini

2023

Policy Futures in Education

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“…The development of AI technologies has three stages, namely artificial narrow intelligence (ANI) (Kuusi and Heinonen, 2022), artificial general intelligence (AGI) (Goertzel and Pennachin, 2007), and artificial super intelligence (ASI) (Yampolskiy, 2015) (Figure 8). Artificial narrow intelligence assists humans in performing a specific task.…”

Section: Fully Autonomous Inspection Robotsmentioning

confidence: 99%

Artificial intelligence in civil infrastructure health monitoring—Historical perspectives, current trends, and future visions

Mondal

Chen

2022

Front. Built Environ.

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Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research.

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“…• Artificial General Intelligence (AGI), or strong AI, mimics human intelligence by learning, reasoning, generalizing, and exhibiting self-awareness [225,226]. AGI may have a significant impact on cardiovascular disease, due to its adaptability in accomplishing diverse tasks, integrating medical knowledge, and personalizing treatment planning [227]. AGI can achieve continuous learning and adaptability to ensure up-todate information for managing cardiovascular disease.…”

mentioning

confidence: 99%

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Naser,

Majeed,

Alsabah

et al. 2024

Algorithms

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Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning’s role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area.

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Scenarios From Artificial Narrow Intelligence to Artificial General Intelligence—Reviewing the Results of the International Work/Technology 2050 Study

Cited by 19 publications

References 8 publications

Alternative futures of Finnish comprehensive school

Alternative futures of Finnish comprehensive school

Artificial intelligence in civil infrastructure health monitoring—Historical perspectives, current trends, and future visions

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

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