Google DeepMind Introduces JEST for Enhanced AI Model Training

Google DeepMind Introduces JEST for Enhanced AI Model Training

Dmitri Ivanovski
1 min read

Google DeepMind Introduces JEST Method to Revolutionize AI Training

Google DeepMind researchers have unveiled the JEST method, a groundbreaking approach that significantly enhances the efficiency of training multimodal AI models for image and text processing. By selectively choosing data subsets based on their collective learnability, JEST utilizes two AI models to revolutionize the training process, leading to substantial reductions in training time and computing power requirements.

Key Takeaways

  • Google DeepMind's JEST method reduces AI training time by a factor of 13 and decreases computing power demand by 90%.
  • Two AI models are employed by JEST to select the most learnable data subsets, leading to enhanced efficiency in AI model training.
  • Flexi-JEST, an advanced version, achieves top performance with only 10% of the training data typically used by leading models.
  • JEST and Flexi-JEST have the potential to revolutionize AI training by significantly reducing computing power demands.


Google DeepMind's JEST method not only influences tech giants and AI startups directly but also exerts pressure on hardware manufacturers, fostering a shift towards sustainable AI development. This innovative approach has the potential to reshape industry standards and regulatory frameworks by emphasizing data quality over quantity.

Did You Know?

  • JEST Method:
    • Explanation: JEST, introduced by Google DeepMind, optimizes training multimodal AI models by intelligently selecting data subsets based on their "collective learnability." This approach significantly enhances the efficiency of the training process by identifying data that challenges the current model but is straightforward for the reference model.
  • Flexi-JEST:
    • Explanation: Flexi-JEST simplifies the data evaluation process and achieves top performance on standard tasks with only 10% of the training data typically required by leading models. This underscores the method's exceptional efficiency and potential to revolutionize AI training.
  • Data Quality Bootstrapping:
    • Explanation: This concept, stemming from the JEST and Flexi-JEST methodologies, focuses on enabling AI models to learn effectively from smaller, high-quality datasets. By training AI models with considerably less data and computing power, this approach has the potential to lead to more resource-efficient AI systems.

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