Locations
Île-de-France, France · Paris, France · Paris, France
industry
Software
Size
51-200 employees
Stage
Series A
founded in
2018
Labeling platform for high-quality training data. One tool to label, find and fix issues, simplify DataOps, and dramatically accelerate the build of reliable AI. ___ Why Kili Technology? You might not know this, but: MNIST’s dataset has an error rate of 3.4% and is still cited by more than 38,000 papers. The ImageNet dataset, with its crowdsourced labels, has an error rate of 6%. This dataset arguably underpins the most popular image recognition systems developed by Google and Facebook. Systemic error in these datasets has real-world consequences. Models trained on error-containing data are forced to learn those errors, leading to false predictions or a need for retraining on ever-increasing amounts of data to “wash out” the errors. Every industry has begun to understand the transformative potential of AI and invest. But the revolution of ML transformers and relentless focus on ML model optimization is reaching the point of diminishing returns. What else is there?
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