Machine learning and role of artificial intelligence in optimizing work performance and employee behavior

Ramachandran, K. K.; Mary, A. Apsara Saleth; Hawladar, Shibani; Asokk, D.; Bhaskar, Bandi; Pitroda, Jayeshkumar

doi:10.1016/j.matpr.2021.11.544

Cited by 53 publications

(21 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The second body of literature is the research related to the social and economic effects generated by AI. The current research related to AI focuses on the replacement and impact of industrial robot applications on labor factors [4], the production efficiency improvements [5], etc. The "information technology productivity paradox" argues that although AI brings significant efficiency gains, they benefit only a few large firms, while the utility of other market participants remains the same.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Impact on Energy Efficiency of Firms: Evidence from Firm-level Data in China

Juan¹,

Li²,

Ma³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Impact on Energy Efficiency of Firms: Evidence from Firm-level Data in China

Juan¹,

Li²,

Ma³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These differences in AI adoption leads to disparities in production efficiency and innovation, favoring AI-advanced industries [ 11 ]. Labor-intensive sectors are slower to adopt AI, risking missed efficiency and innovation gains [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of labor and capital in AI vs. non-AI industries: A two-industry model analysis

Huang

2024

PLoS ONE

View full text Add to dashboard Cite

There is an imbalance in the development of artificial intelligence between industries. Compared to non-AI enterprise, AI- enterprise will save labor, enhance innovation capabilities, and improve production efficiency. By constructing a two-industry model of AI and non-AI enterprise, this paper finds that with the development of artificial intelligence in the same industry, the AI enterprise will occupy a dominant position, attracting labor and capital from the non-AI enterprise into the AI enterprise. In different industries, the development of artificial intelligence improves the production efficiency of the enterprise. However, due to the price effect, non-AI enterprise benefits more. Labor and capital flow from AI enterprise to non-AI enterprise. In order to promote the improvement of production efficiency in the whole society, the government can tax non-AI enterprise and subsidize them to AI enterprise. Taxation promotes the degree of automation and the improvement of production efficiency, but it has only a short-term effect on the development of AI. At the same time, taxation inhibits the development of non-AI enterprise, and there is a high risk of unemployment. When both industries use artificial intelligence for production, the labor share and the capital share of the two industries will tend to the same value. The convergence of technology measures is conducive to increasing labor income share and reducing income inequality, but it is not conducive to innovation.

show abstract

“…The aerospace industry is constantly looking for efficient ways to accelerate development to meet increasing demands and achieve superior components. Air traffic management [4], operational efficiency improvement [5], product design [6], customer service [7], pilot training [8], and research [9]- [11] are all areas where artificial intelligence can be used.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

Jurnal Polimesin

View full text Add to dashboard Cite

In designing and developing airfoils, confirmation of proper design performance under various flow conditions is vital. Experimental studies using wind tunnels or numerical simulations can often utilize. In some cases, numerical studies have a weakness in computational time. This study focuses on predicting the drag coefficient of the airfoil using the CNN machine learning architecture. Starting with a numerical simulation of 500 types of NACA airfoils with a Reynolds number of 4000 using XLRF5 software to obtain image data, lift and drag coefficients. The training, test, and validation dataset uses numerical simulation results as labels. ReLU is the activation function used in this study, with Adam optimizer and MSE loss function. It achieved a relative error of 8% in predicting the drag coefficient. With the results obtained, aircraft designers can use the method to predict the drag coefficient value from various geometries.

show abstract

Machine learning and role of artificial intelligence in optimizing work performance and employee behavior

Cited by 53 publications

References 8 publications

Artificial Intelligence Impact on Energy Efficiency of Firms: Evidence from Firm-level Data in China

Artificial Intelligence Impact on Energy Efficiency of Firms: Evidence from Firm-level Data in China

Dynamics of labor and capital in AI vs. non-AI industries: A two-industry model analysis

Untitled

Contact Info

Product

Resources

About