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Ethiopia has significant natural resources that could make an important contribution to future developments in science and technology. For example, geothermal energy could soon be an important source of electricity nationally, [ 9 ] [ 10 ] complementing the hydropower potential of the country. [ 11 ]
The company collaborated with the Ethiopian government on some hardware and software projects. [6] On 4 October 2022, a new science museum named Ethiopia Museum of Art and Science was inaugurated by Abiy Ahmed organized by the Ethiopian Artificial Intelligence Institute and held PanAfricon Artificial Intelligence. Abiy highlighted the ...
Applications of machine learning (ML) in earth sciences include geological mapping, gas leakage detection and geological feature identification.Machine learning is a subdiscipline of artificial intelligence aimed at developing programs that are able to classify, cluster, identify, and analyze vast and complex data sets without the need for explicit programming to do so. [1]
Blue Brain Project, an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level. [1] Google Brain, a deep learning project part of Google X attempting to have intelligence similar or equal to human-level. [2] Human Brain Project, ten-year scientific research project, based on exascale ...
The Ministry of Science and Technology (MoST) was a governmental institution that was established for the first time in December 1975 by proclamation No.62/1975 as a commission. Following the change in government in 1991 and with the issuance of the new economic policy, the commission was re-established in March 1994 by Proclamation No.91/94.
STIC is hosting Ethiopia's first national science and technology digital library. It provides custom-picked educational resources that are used to meet the country's academic and knowledge needs. With emphasis on the sciences, technology, and engineering as well as humanities and arts, it boasts a large collection of e-books.
A basic example of this is quantum state tomography, where a quantum state is learned from measurement. [1] Other examples include learning Hamiltonians, [2] [3] learning quantum phase transitions, [4] [5] and automatically generating new quantum experiments.
Robert Schapire answered the question in the affirmative in a paper published in 1990. [5] This has had significant ramifications in machine learning and statistics, most notably leading to the development of boosting. [6] Initially, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner. [3]