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Zero-waste machine learning

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    • Date
    9 February 2026
    Timeframe
    16:00 - 17:00 CET Geneva
    Duration
    60 minutes

    Today, both science and industry rely heavily on machine learning models, predominantly artificial neural networks, that become increasingly complex and demand a significant amount of computational resources. The problem of model computational complexity is well known to the computer science community, yet existing methods typically attempt to solve it by shrinking the models, for example by quantizing them, or limiting their access to resources. In this talk, the speaker will look holistically at the efficiency of machine learning models and draw inspiration to address their main challenges from the green sustainable economy principles. Instead of constraining some computations or memory used by the models, the speaker will focus on reusing what is available to them: computations done in the previous processing steps, partial information accessible at run-time, or knowledge gained by the model during previous training sessions in continually learned models. This new research path of zero-waste machine learning opens a plethora of research opportunities, both for academia and industry.

     

    Learning Objectives
    By the end of this session, participants will be able to:

    • Understand the key challenges related to computational complexity in modern machine learning models and their environmental implications.
    • Explain the principles of the zero-waste machine learning approach and how it differs from traditional model optimization methods such as quantization or pruning.
    • Identify and discuss practical opportunities to apply zero-waste principles to improve the efficiency and sustainability of AI systems in academic or industrial contexts.

     

    Recommended Mastery Level / Prerequisites:

    • Target audience: Participants with general awareness of artificial intelligence or data science concepts.
    • Prerequisites: Basic understanding of how machine learning models operate (for example, familiarity with neural networks at a conceptual level).
    • Technical level: Introductory to intermediate, suitable for non-specialists and decision-makers interested in sustainable AI innovation rather than algorithmic details.
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