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George Miller, former psychologist at Harvard University, believes the limits to the human brain's capacity centers around "the number seven, plus or minus two." An illustrative example of this is a test in which a person must repeat numbers read aloud. While two or three numbers are easily repeated, fifteen numbers become more difficult.
Multi-task optimization is a paradigm in the optimization literature that focuses on solving multiple self-contained tasks simultaneously. [ 1 ] [ 2 ] The paradigm has been inspired by the well-established concepts of transfer learning [ 3 ] and multi-task learning [ 4 ] in predictive analytics .
Computer multitasking, the concurrent execution of multiple tasks (also known as processes) over a certain period of time Cooperative multitasking; Pre-emptive multitasking; Human multitasking, the apparent performance by an individual of handling more than one task at the same time
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.
A lot of work has appeared offering different modifications of this algorithm. These works use other resources for analysis (thesauruses, synonyms dictionaries or morphological and syntactic models): for instance, it may use such information as synonyms, different derivatives, or words from definitions of words from definitions. [7]
New tasks can interrupt already started ones before they finish, instead of waiting for them to end. As a result, a computer executes segments of multiple tasks in an interleaved manner, while the tasks share common processing resources such as central processing units (CPUs) and main memory. Multitasking automatically interrupts the running ...
For example, a prompt may include a few examples for a model to learn from, such as asking the model to complete "maison → house, chat → cat, chien →" (the expected response being dog), [23] an approach called few-shot learning. [24] In-context learning is an emergent ability [25] of large language models.
WordNet is a lexical database of semantic relations between words that links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms are grouped into synsets with short definitions and usage examples. It can thus be seen as a combination and extension of a dictionary and thesaurus.