OpenTalks.AI - Иван Болохов, Гибридный интеллект для задач разметки данных

Transcript

1. Hybrid Intelligence for Data Labelling Kognitivnye Sistemy Ivan Bolokhov cogsys.company mashinist.cogsys.company
2. Data Labelling with Mashinist Studio
3. Text Audio Classification Segmentation Pairwise comparison Validation Classification Free string Segmentation Data types Images Classification Landmarks Defining boundaries
4. Data Labelling Process Tasks People Labelled Data The classic human- driven labelling system system is time consuming and poorly scalable
5. Real time results analysis Real-time task execution statistics. For each assignment the system determines a quality indicator and the results of all involved labelers.
6. Monitoring the performance of labelers The dynamic rating of each labeler is based on an quality indicator of completed tasks and the overall percentage of tasks completed.
7. HYBRID INTELLIGENCE
8. Adding AI to the process • Bots (models) are trained on partial results of human labelers • Labelling bots start pre-labeling data • Bots compete with each other for "survival " – in terms of the quality of labelling
9. Competition between humans and bots Bots label data on par with humans
10. AI considerably improves data labelling process • Labelers agree or disagree with the system, which greatly speeds up the markup process and makes the model even "smarter". • At the output, the customer receives not only the labelled data, but also the best model (bot) for future automatic labelling.
11. Results management Downloading labelling results into convenient formats. Not only a dataset, but also a ready-to-use model • CSV • JSON • XLS • Download Model
12. Dataset and model marketplace
13. Markup Cost Quality Speed Person - + - Robot + - + Hybrid + + + Comparative analysis
14. For OpenTalks.AI participants Free access invite to Mashinist labelling studio @bolokhov Still have any questions? +7-977-687-71-96