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Interactive Machine Learning



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AI is a powerful tool but it is not magic. The cornerstone of accurate results and impressive output is clean data, which doesn't really exist in the real world. Cleaning dirty data is not only time-consuming but also an expensive task. Active Machine Learning reduces the amount of labeled data required to train a model, making it less costly. Interactive learning streamlines even further the learning process.
Check out this presentation to understand the difference between Active and Interactive Learning, and see the common use cases.

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Interactive Machine Learning

  1. 1. Doculayer - Cognitive Content Management Interactive Machine Learning
  2. 2. © 2018 Onior Group B.V. Human – AI interaction Introduction • AI is a powerful tool that can achieve high performance when clean data is provided. • Real datasets are often not well-defined and noisy, thus AI by itself might struggle to provide high quality output. • Human feedback can help to improve the Machine Learner when it faces difficulties. • AI also can spot its own weaknesses and ask for feedback.
  3. 3. © 2018 Onior Group B.V. Human – AI interaction How does machine learn? Active Machine Learning and Interactive Machine Learning
  4. 4. © 2018 Onior Group B.V. Active Learning Machine is in charge • System periodically sends out workflow check-tasks to users to measure classifier performance. • Tasks are made for items with the lowest confidence in the decision and the highest expected performance gain. • User classifies manually when performance drops below threshold, system takes back control when performance is above threshold. • Corrections are added to a training set and the classifier is retrained.
  5. 5. © 2018 Onior Group B.V. Interactive Learning Synergy between man and machine • Active Learning only allows the Machine Learner to initiate a task for correction. • However, in interactive learning also the user can give advice to the Machine Learner when he spots a mistake. • Interactive learning combines the best of both worlds.
  6. 6. © 2018 Onior Group B.V. Interactive Learning Case 1: Classification (metadata) • Doculayer ML classifies content items into predefined classes (e.g. based on document types) and fills in the corresponding metadata field. • If verification of the generated metadata is needed the Machine Learner creates a workflow task. • The user verifies the metadata values marked for verification and if needed applies corrections. • Training set is updated and model is retrained.
  7. 7. © 2018 Onior Group B.V. Interactive Learning Case 2: Clustering • Clustering: grouping similar text documents & images/videos together based on attributes such as extracted keywords. • Easily extendable to a classifier. • Hierarchical clustering orders the groups top-down allowing efficient data exploration. • For clusters with the largest information gain, Interactive Learning generates tasks for a user to review. • The Machine Learner improves based on the field expert input and produces a better representation of the data.
  8. 8. © 2018 Onior Group B.V. Interactive Learning Case 3: Named Entity Recognition (NER) • Named Entity Recognition (NER) is the process of identifying specific groups of words which share common semantic characteristics (e.g. company names, dates, serial or social security numbers). • Doculayer ML identifies entities within a text document. • When the system has a low confidence the Machine Learner creates a task.​ • The user corrects the output if needed by adding missing entities, unmarking falsely identified entities or providing context. • Corrections are added to the training set and trigger a retraining of the model.
  9. 9. © 2018 Onior Group B.V. Thank you for your attention Contact info DOCULAYER INTERNATIONAL ( Prinses Catharina-Amaliastraat 5 2496 XD The Hague The Netherlands +31 (0)85 303 76 47 Meeuwenlaan 100 (Pand Noord) 1021 JL Amsterdam The Netherlands
  10. 10. Find the Real Value of your Business Content. © 2018 Onior Group B.V.