Kili Technology

Build high-quality datasets, fast. Enterprises trust us to streamline their data labeling ops and build the best datasets for their custom models, generative AI, and LLMs ___ 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 of 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?