SOURCE-LINKED INTELLIGENCE

Collaborative Machine Intelligence

CORDIS · observation · Publication date unknown

Collaborative Machine Intelligence Machine learning models are growing larger and more complex, making training increasingly resource-demanding. Concurrently, our world, and hence the training data is perpetually evolving. This requires continual model updating or retraining to address changing training data. Presently, the most reliable course to handle such distribution shifts is to retrain models from scratch on new training data. This results in substantial resource usage, increased CO2 footprint, elevated energy consumption, and limits the decisive ML progress to large-scale industry players. Imagine a world in which models help each other learn. When the data distribution changes, a complete retraining of models could be avoided if the new model could learn from the outdated one by using reliable and provably effective methods. Furthermore, the convention of relying on large, versatile monolithic models could then

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recordType

award

status

SIGNED

region

EU

value

2000000

unit

EUR