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LaMDA (Language Model for Dialogue Applications) is a family of conversational large language models developed by Google.Originally developed and introduced as Meena in 2020, the first-generation LaMDA was announced during the 2021 Google I/O keynote, while the second generation was announced the following year.
Data literacy is the ability to read, understand, create, and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data. [1] It is, however, not similar to the ability to read text since it requires certain skills involving reading and understanding data. [2]
Students are often taught literacy skills such as how to verify credible sources online, cite websites, and prevent plagiarism. Google and Wikipedia are frequently used by students "for everyday life research," [65] and are just two common tools that facilitate modern education. Digital technology has impacted the way materials are taught in ...
] Data analytics is used to formulate larger organizational decisions. [citation needed] Data analytics is a multidisciplinary field. There is extensive use of computer skills, mathematics, statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data through analytics.
Data for Diplomacy is a worldwide platform of data experts collaborating to create relationships in which diplomats need to understand the use of data to better facilitate the creation of such relationships, use diplomacy to access data and leverage the value of data as soft power. [2] Data can be used for diplomatic occasions in some cases.
Google Translate previously first translated the source language into English and then translated the English into the target language rather than translating directly from one language to another. [11] A July 2019 study in Annals of Internal Medicine found that "Google Translate is a viable, accurate tool for translating non–English-language ...
Data-parallelism applied computation independently to each data item of a set of data, which allows the degree of parallelism to be scaled with the volume of data. The most important reason for developing data-parallel applications is the potential for scalable performance, and may result in several orders of magnitude performance improvement.
Bigtable development began in 2004. [1] It is now used by a number of Google applications, such as Google Analytics, [2] web indexing, [3] MapReduce, which is often used for generating and modifying data stored in Bigtable, [4] Google Maps, [5] Google Books search, "My Search History", Google Earth, Blogger.com, Google Code hosting, YouTube, [6] and Gmail. [7]