Inventing Artificial Intelligence in Ethiopia

Blackwell, Alan F.; Damena, Addisu; Tegegne, Tesfa

doi:10.4324/9781003242383-9

Cited by 3 publications

(2 citation statements)

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“…Based on the current research status, the research depth and application scope of NLP are still relatively low [8]. First of all, it is manifested in the poor generality of the thesaurus in the construction field, which leads to the low quality of file preprocessing, which will affect the text data segmentation and part-of-speech tagging in the NLP process.…”

Section: Natural Languagementioning

confidence: 99%

Corresponding Intelligent Calculation of the Whole Process of Building Civil Engineering Structure Based on Deep Learning

Liu

Zhang

et al. 2022

Scientific Programming

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This study proposes the first fully deep learning-based structural response intelligent computing framework for civil engineering. For the first time, from the data side to the model side, the structural information of the structure itself and any loading system is comprehensively considered, which can be applied to materials, components, and even structures, system and other multi-level mechanical response prediction problems. First, according to the characteristics of structural calculation scenarios, a unified data interface mode for structural static characteristics is formulated, which preserves the original structural information input and effectively reduces manual intervention. On this basis, an attention mechanism and a deep cross network are introduced, and a structural static feature representation learning model PADCN is proposed, which can take into account the memory and generalization of structural static features, and mine the coupling relationship of different structural information. Then, the PADCN model is integrated with the dynamic feature prediction model Mechformer and connected with the designed general data interface to form an end-to-end data-driven structural response intelligent computing framework. In order to verify the validity of the framework, numerical experiments were carried out with the steel plate shear wall structure as the carrier, in which a data augmentation algorithm suitable for the field of structural calculation was proposed to alleviate the problem of lack of structural engineering data. The results show that the deep learning model based on this framework successfully predicts the whole-process nonlinear response of specimens with different structures, the simulation accuracy is better than that of the fine finite element model, and the computational efficiency exceeds the traditional numerical method by more than 1000 times, achieving a qualitative improvement. It is proven that the intelligent computing framework has excellent accuracy and efficiency.

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Section: Natural Languagementioning

confidence: 99%

Corresponding Intelligent Calculation of the Whole Process of Building Civil Engineering Structure Based on Deep Learning

Liu

Zhang

et al. 2022

Scientific Programming

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“…This is a sequel to the paper on ‘Ethnographic Artificial Intelligence’ that was presented at the second Cambridge workshop on Science in the Forest, Science in the Past (Blackwell 2019). The goal of this programme of research, as presented in that paper, has been to offer a new cultural critique of the field of Artificial Intelligence (AI) that steps away from the investment priorities and social anxieties of the wealthy nations where AI research is typically concentrated.…”

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confidence: 99%

Inventing Artificial Intelligence in Ethiopia

Blackwell¹,

Damena

Tegegne

2021

Interdisciplinary Science Reviews

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Artificial Intelligence (AI) research has always been embedded in complex networks of cultural imagination, corporate business, and sociopolitical power relations. The great majority of AI research around the world, and almost all commentary on that research, assumes that the imagination, business, and political systems of Western culture and the Global North are sufficient to understand how this technology should develop in future. This article investigates the context within which AI research is imagined and conducted in the Amhara region of Ethiopia, with implications for public policy, technology strategy, future research in development contexts, and the principles that might be applied as practical engineering priorities.

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