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Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Translation01:31

Translation

Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
Translation01:31

Translation

Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are called the...
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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Video Experimental Relacionado

Updated: Jun 19, 2026

Reversible Cooling-induced Deactivations to Study Cortical Contributions to Obstacle Memory in the Walking Cat
09:43

Reversible Cooling-induced Deactivations to Study Cortical Contributions to Obstacle Memory in the Walking Cat

Published on: December 11, 2017

IA de bajo código/sin código para traducción automática: Democratización de la IA generativa para la Transformación

Mercedes Arguello Casteleiro1, Saihong Li2, Arsenio Andrades Moreno3

  • 1BCS SGAI, UK.

Studies in health technology and informatics
|February 23, 2026
PubMed
Resumen
Este resumen es generado por máquina.

La traducción automática neuronal (TA) con IA generativa muestra potencial para romper las barreras lingüísticas en iniciativas globales como la Transformación Azul. Los modelos de código abierto, aunque accesibles, demostraron una calidad moderada en la traducción de términos clave.

Palabras clave:
IA generativaIA de bajo código/sin códigoModelos de lenguaje grandes multimodalesTraducción automática neuronalSalud planetaria

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Área de la Ciencia:

  • Salud Planetaria
  • Sistemas Alimentarios Acuáticos
  • Iniciativas de Salud Global

Sus antecedentes:

  • La malnutrición y las enfermedades impactan la salud humana.
  • La Salud Planetaria conecta el bienestar humano con la salud de la Tierra.
  • La Transformación Azul tiene como objetivo mejorar los sistemas alimentarios acuáticos globales, erradicar el hambre y mejorar los niveles de vida.

Objetivo del estudio:

  • Investigar el potencial de la traducción automática neuronal (TA) impulsada por Inteligencia Artificial (IA) generativa.
  • Evaluar si la TA impulsada por IA puede reducir las barreras lingüísticas y mejorar el acceso a información crítica.
  • Centrarse en términos clave relacionados con la iniciativa de Transformación Azul.

Principales métodos:

  • Se evaluó la calidad de la TA para un conjunto de datos de Transformación Azul utilizando métricas automáticas y juicio humano.
  • Se evaluó la calidad semántica (adecuación) de las traducciones.
  • Se empleó un enfoque de IA de bajo código/sin código con bibliotecas de Python de código abierto y modelos de lenguaje grandes (LLM), incluida la IA generativa multimodal.

Principales resultados:

  • Se confirmó la viabilidad de un enfoque simple de IA de bajo código/sin código para la traducción automática.
  • Se demostró un acuerdo moderado entre los expertos sobre la alta calidad de la traducción automática automatizada utilizando SeamlessM4T de Facebook.
  • Se destacó la efectividad de la IA generativa en la traducción de terminología especializada.

Conclusiones:

  • Los hallazgos sugieren que los modelos de lenguaje grandes (LLM) de código abierto pueden tener un rendimiento inferior en comparación con alternativas de código cerrado como GPT4 de OpenAI.
  • El estudio valida el uso de herramientas de IA accesibles para mejorar la difusión de información en los esfuerzos de salud y sostenibilidad global.
  • Se necesita investigación adicional para optimizar la TA de código abierto para iniciativas multilingües complejas.