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Various subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics.
Nutritional science is often combined with food science (nutrition and food science). Trophology is a term used globally for nutritional science in other languages, in English the term is dated. Today, it is partly still used for the approach of food combining that advocates specific combinations (or advises against certain combinations) of food.
A full-text aggregation of more than 180 scientific journals publishing current research in Biodiversity Conservation, Biology, Ecology, Environmental Science, Entomology, Ornithology, Plant Science, and Zoology. Free abstract & references, Open Access titles, and Subscription Available from BioOne [27] Bioinformatic Harvester: Biology ...
Recursive self-improvement (RSI) is a process in which an early or weak artificial general intelligence (AGI) system enhances its own capabilities and intelligence without human intervention, leading to a superintelligence or intelligence explosion.
Nutritional genomics, also known as nutrigenomics, is a science studying the relationship between human genome, human nutrition and health. People in the field work toward developing an understanding of how the whole body responds to a food via systems biology, as well as single gene/single food compound relationships.
The use of explainable artificial intelligence (XAI) in pain research, specifically in understanding the role of electrodermal activity for automated pain recognition: hand-crafted features and deep learning models in pain recognition, highlighting the insights that simple hand-crafted features can yield comparative performances to deep ...
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Retrieval-augmented generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.