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Related Concept Videos

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...
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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...
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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

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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.
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Proteins are called the...
Automatic Processing and Automatic Social Behavior01:28

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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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Low-Code/No-Code AI for Machine Translation: Democratising Generative AI for Blue Transformation.

Mercedes Arguello Casteleiro1, Saihong Li2, Arsenio Andrades Moreno3

  • 1BCS SGAI, UK.

Studies in Health Technology and Informatics
|February 23, 2026
PubMed
Summary
This summary is machine-generated.

Neural machine translation (MT) using generative AI shows promise in breaking down language barriers for global initiatives like the Blue Transformation. Open-source models, while accessible, demonstrated moderate quality in translating key terms.

Keywords:
Generative AILow-Code/No-Code AIMultimodal Large Language ModelsNeural Machine TranslationPlanetary Health

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Area of Science:

  • Planetary Health
  • Aquatic Food Systems
  • Global Health Initiatives

Background:

  • Malnutrition and disease impact human health.
  • Planetary Health connects human well-being with Earth's health.
  • The Blue Transformation aims to improve global aquatic food systems, eradicate hunger, and enhance living standards.

Purpose of the Study:

  • Investigate the potential of neural machine translation (MT) powered by generative Artificial Intelligence (AI).
  • Assess if AI-driven MT can reduce language barriers and improve access to critical information.
  • Focus on key terms related to the Blue Transformation initiative.

Main Methods:

  • Evaluated MT quality for a Blue Transformation dataset using automatic metrics and human judgment.
  • Assessed semantic quality (adequacy) of translations.
  • Employed a low-code/no-code AI approach with open-source Python libraries and Large Language Models (LLMs), including multimodal generative AI.

Main Results:

  • Confirmed the viability of a simple, low-code/no-code AI approach for MT.
  • Demonstrated moderate agreement among experts on the high quality of automated MT using Facebook's SeamlessM4T.
  • Highlighted the effectiveness of generative AI in translating specialized terminology.

Conclusions:

  • Findings suggest open-source Large Language Models (LLMs) may have lower performance compared to closed-source alternatives like OpenAI's GPT4.
  • The study validates the use of accessible AI tools for enhancing information dissemination in global health and sustainability efforts.
  • Further research is needed to optimize open-source MT for complex, multilingual initiatives.