Teaching artificial intelligence and humanity

Keating, Jennifer; Nourbakhsh, Illah

doi:10.1145/3104986

Cited by 28 publications

(14 citation statements)

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“…On this basis, combined with GIS technology, Senders applied acrcad software to the preliminary software design and development of urban land grading and evaluation [6]. Keating and Nourbakhsh made a detailed introduction and analysis of GIS technology, on the basis of which, using the statistical data of the traded plots, using Kriging interpolation method to build a threedimensional model of land price evaluation [7]. According to the characteristics of small towns, Hurlburt analyzed the influencing factors of land price, and compared the current commonly used evaluation methods [8].…”

Section: Related Workmentioning

confidence: 99%

“…There are many methods for processing land price data, including methods such as linear normalization and principal component analysis (PCA), but these methods do not adequately address the challenges that the constraint of the small samples in land price data brings to model training [6]. In consideration of the characteristics of land price data (such as a small sample size, class imbalance, and the difficulty of data acquisition) and housing price data (such as abundant data, a balanced class distribution, and the ease of acquisition), a transfer learning algorithm is introduced in this Paper [7]. Based on the high degree of correlation between housing price and land price data, the action rules of the influencing factors of housing prices are transferred to the land price data, which weakens the challenge that the defect of small samples in the land price data poses to model training [8].…”

Section: Introductionmentioning

confidence: 99%

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Notice of Violation of IEEE Publication Principles: Research on Urban Land Price Assessment Based on Artificial Neural Network Model

Cai

Wang

et al. 2019

IEEE Access

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At present, there are many problems to be solved in the study of residential land price evaluation, such as the imbalance of residential land price data categories, the small sample of residential land price data sets, the possibility of the evaluation model falling into the local optimal value, and the large error of feature quantification. This paper introduces the problems in the process of residential land price evaluation from three aspects: land price data, evaluation model and feature quantification. In this paper, an extraction method for land price features based on transfer learning is proposed in connection with defects such as the small total amount of residential land price data and unbalanced class distribution, and different land price assessment models are used to carry out a precision evaluation and explore the models and methods suitable for land price assessment. In this paper, the extraction method for residential land price features is improved. The experimental results show that the extraction algorithm for land price features based on transfer learning has better classification accuracy than the commonly used principal component analysis and extraction method and linear normalization method, with a mean average precision 10.9% and 4.73% higher, respectively. The optimal feature set classification accuracy reached 90.28%; therefore, the algorithm for residential land price assessment proposed in this paper is able to satisfy the precision requirement of actual land price assessment.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: Research on Urban Land Price Assessment Based on Artificial Neural Network Model

Cai

Wang

et al. 2019

IEEE Access

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“…Keating and Nourbakhsh () stated that there are “Emerging anxieties pertaining to the rapid advancement and sophistication of artificial intelligence…”. Anxieties can be mitigated by combining an examination of unknown AI capabilities and presenting AI/programmer teams with leadership presence.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and the Role of Leadership

Smith¹,

Green²

2018

J of Leadership Studies

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A future artificial intelligence (AI) leadership position will likely include a new follower, the AI machine. The continued rise in retired‐aged individuals illustrates the need to replace a traditional workforce with alternatives. Robots tend to be non‐functional without leaders. With this new position, AI leaders will engage in processes that focus on leading the programmers of the AI machine as well as influencing decisions made by AI machines post‐programming. Communication standards should be set for both the human and machine members. It is unclear if this new position will be referred to as leadership or management, but standards should be in place for proper supervision of these machines. Leadership research has found that behaviors such as charismatic influences and relationship building are important for leading humans. AI leadership may require adjustments to current influences used for humans. Building relationships with AI machines is expected to be altered with greater focus needed on ethical and moral mentoring. Such a focus may utilize top‐down and bottom‐up roboethics, with decreased focus on getting machine followers to feel part of an in‐group. The current paper provides ideas for leading teams of AI machines and their programmers.

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“…There is significant progress in the use and adoption of AI or machine learning, and it has been shaping socio-economic and political development of countries. Keating and Nourbakhsh (2018), , Baweja et al (2016) were of the view that increased adoption of AI will be one of the distinctive characteristics of the 4IR. There have been some developments of AI in South Africa, notably at the University of Johannesburg, where an AI-based system that can predict the failure of transformers before they break down has been developed (Marwala 2019).…”

Section: Artificial Intelligencementioning

confidence: 99%

A framework to test South Africa’s readiness for the fourth industrial revolution

Olaitan

Issah

Wayi

2021

SA Journal of Information Management

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Background: The fourth industrial revolution (4IR) is being touted as having the capacity to lift South Africa out of the challenges of poverty, unemployment and inequitable distribution of wealth. However, the state of the country’s readiness for the enabling technologies of the 4IR is in question.Objective: This article sought to investigate South Africa’s state of readiness for the technologies that enable the 4IR. The article proposed a framework which can be used to measure the country’s level of preparedness and implement the necessary processes to move the country into the 4IR era.Method: A detailed analysis of literature was carried out on the current and futuristic technologies employed by developed countries as they moved towards the 4IR. The fit – viability and the task – technology theories were employed to propose a framework to government for measuring its state of readiness for the 4IR in South Africa.Results: Based on findings from the literature study, the article proposed the 4IR readiness framework for government to measure and subsequently plans its response to the advent of 4IR technologies in the country.Conclusion: The framework revealed that South Africa has low technological capability, plummeting economic complexity and a fall in digital technological readiness. There is also a dearth of skilled workforce and technological infrastructure. It was recommended that government should focus on building and strengthening the resilience of both public and private institutions, investment in relevant 4IR education, infrastructural improvement, and promulgation and enforcement of legal framework to ensure security and privacy of data.

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Teaching artificial intelligence and humanity

Abstract: Considering rapidly evolving human-machine interactions.

Cited by 28 publications

References 1 publication

Notice of Violation of IEEE Publication Principles: Research on Urban Land Price Assessment Based on Artificial Neural Network Model

Notice of Violation of IEEE Publication Principles: Research on Urban Land Price Assessment Based on Artificial Neural Network Model

Artificial Intelligence and the Role of Leadership

A framework to test South Africa’s readiness for the fourth industrial revolution

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