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  1. 18. Mai 2021 · LaMDAs conversational skills have been years in the making. Like many recent language models, including BERT and GPT-3, it’s built on Transformer, a neural network architecture that Google Research invented and open-sourced in 2017. That architecture produces a model that can be trained to read many words (a sentence or ...

  2. Lambda-CDM-Modell ist ein kosmologisches Modell, das mit wenigen – in der Grundform sechs – Parametern die Entwicklung des Universums seit dem Urknall beschreibt. Da es das einfachste Modell ist, das in guter Übereinstimmung mit kosmologischen Messungen ist, wird es auch als Standardmodell der Kosmologie bezeichnet.

  3. The Lambda-CDM, Lambda cold dark matter, or ΛCDM model is a mathematical model of the Big Bang theory with three major components: a cosmological constant, denoted by lambda (Λ), associated with dark energy; the postulated cold dark matter, denoted by CDM; ordinary matter

  4. 20. Jan. 2022 · LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding.

  5. en.wikipedia.org › wiki › LaMDALaMDA - Wikipedia

    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.

  6. 2. Mai 2024 · Lambda-CDM-Modell ist ein kosmologisches Modell, das mit wenigen – in der Grundform sechs – Parametern die Entwicklung des Universums seit dem Urknall beschreibt. Da es das einfachste Modell ist, das in guter Übereinstimmung mit kosmologischen Messungen ist, wird es auch als Standardmodell der Kosmologie bezeichnet.

  7. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and arepre-trained on 1.56T words of public dialog data and web text. While model scaling alone canimprove quality, it shows less improvements on safety and factual grounding.