Categories: FAANG

Improving How Machine Translations Handle Grammatical Gender Ambiguity

Machine Translation (MT) enables people to connect with others and engage with content across language barriers. Grammatical gender presents a difficult challenge for these systems, as some languages require specificity for terms that can be ambiguous or neutral in other languages. For example, when translating the English word “nurse” into Spanish, one must decide whether the feminine “enfermera” or the masculine “enfermero” is appropriate. However, particularly when contextual clues are absent, such as in translating a single sentence, a model cannot determine which would be correct. This…
AI Generated Robotic Content

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what ai tool and prompts they using to get this level of perfection?

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2 hours ago

The Complete Guide to Model Context Protocol

Language models can generate text and reason impressively, yet they remain isolated by default.

2 hours ago

Improving Language Model Personas via Rationalization with Psychological Scaffolds

Language models prompted with a user description or persona are being used to predict the…

2 hours ago

AI Infrastructure and Ontology

Under the Hood of NVIDIA and PalantirTurning Enterprise Data into Decision IntelligenceOn Tuesday, October 28 in…

2 hours ago

Hosting NVIDIA speech NIM models on Amazon SageMaker AI: Parakeet ASR

This post was written with NVIDIA and the authors would like to thank Adi Margolin,…

2 hours ago

The Blueprint: How Giles AI transforms medical research with conversational AI

Welcome to The Blueprint, a new feature where we highlight how Google Cloud customers are…

2 hours ago