VentureBeat May 13, 2022
All machine learning models are bound by a critical factor: The quality of the data on which the model is trained.
The challenge of data curation to improve the quality of machine learning and AI models is one that is well-understood. A 2021 MIT research study found systemic issues in how training data was labeled, leading to inaccurate outcomes in AI systems. A study in the journal Quantitative Science Studies that analyzed 141 prior investigations into data labeling found that 41% of models were using datasets that had been labeled by humans.
Among the vendors trying to tackle the challenge of optimizing data curation for AI is a Swiss startup, Lightly. Founded in 2019, the company announced this week that...
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