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Some useful resources for learning about e-agriculture in practice are the World Bank's e-sourcebook ICT in agriculture – connecting smallholder farmers to knowledge, networks and institutions (2011), [2] ICT uses for inclusive value chains (2013), [3] ICT uses for inclusive value chains (2013) [4] and Success stories on information and ...
Its research also includes autonomous robots capable of operating in extreme environments, including disaster zones and deep-sea locations. Additionally, DFKI creates AI applications that promote efficiency and sustainability across sectors like agriculture, manufacturing, and energy.
Society 5.0 was designed to promote a shift toward a human-centered, knowledge-based, and data-driven society. Contrary to Germany's Industry 4.0, which focuses on industrial IT integration, Society 5.0 includes the application of IT to improve public living spaces and habits. [7]
Agroecology is defined by the OECD as "the study of the relation of agricultural crops and environment." [2] Dalgaard et al. refer to agroecology as the study of the interactions between plants, animals, humans and the environment within agricultural systems. [3]
Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition. [87]
The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.
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Data science involves the application of machine learning to extract knowledge from data. Subfields of machine learning include deep learning, supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, and active learning. Causal inference is another related component of information engineering.