Human in-the-loop in Machine Learning

1. Human-in-the-loop in Machine Learning

2. Human-in-the-loop Machine Learning AI Classifier Confident Output Active Learning Uncertain Human Annotation 2

3. What is Human-in-the-loop? ▪ Human in the loop machine learning is a process that leverages both human and machine intelligence to create machine learning-basedAImodels. ▪ When a system is unable to provide a solution or solve a problem, human intervention is needed in the training and testing phases of an algorithm development. ▪ This creates a recurring feedback loop where every iteration of algorithm provides better solutions.Humans can also modifythealgorithmto improve accuracy. 3

4. What is HITL Machine Learning? Combination of human & machine intelligence. Incorporate human feedback into learning loop of machines. 80% of the time, the algorithm is left alone with the human involvement limited to 19% and remaining 1% left to randomness. Humans are involved in training, tuning and testing of algorithms. 4

5. Human-in-the-loop AI AI model Human expert confirms, rejects or labels output generates output Increaseefficiency of every human expert that work with the AIsystem Increasethe level of quality by collecting and sharing data and data-driveninsights Capture silent knowledge and expertise of human experts in a single system Model learns from human and accuracy constantly improves 5

6. Combining Machine Learning with Human Contributions Crowdsourcing of subjective parameters. ▪ Contextualizedata. ▪ Use user expertisein identifying and recognizing patterns. ▪ Processdata thatisdifficult to processautomatically. ▪ Ask users to improvealgorithms. ▪ Human inputto improvemachine inference. ▪ Use machine inference to monitor and improveuser performance. 6

7. Types of Data Labelling The data labelling can be categorized into different types such as ▪ Semantic segmentation to train visual perceptionannotation-basedmodel ▪ Landmark annotation to train facial recognition ▪ NLPannotation, text annotation and sentiment analysis to train language or voice recognition model. ▪ Thedata are annotated and labelledto developAI devices that communicates withhumans. ▪ HITLcan create differenttypesof training data sets for differenttypesof machine learning models builtfor differentfields. 7

8. Video Analysis Object &activity detection Person tracking Face recognition Real - time live stream Content moderation Celebrity recognition 8

9. Image Annotation for Computer Vision 9

10. Generate example data Fetch Monitor/ Collect data/ Evaluate Clean Typical Workflow for a Machine Learning Model Deploy to production Prepare Deploy the model Train model Evaluate model Train a model 10

11. Lifecycle of HITL Machine Learning ▪ Inception - Amanual workflowgetscreated. ▪ Iteration - The workflow is improvediteratively. ▪ Transition - Theworkflowtransitionsfrom a complete manual to semi-automated. ▪ Monitoring - Complete automation shiftsHITLintoa validationmechanism. 11

12. Inception ▪ Here, we follow a "Divide and Conquer" approach where we categorize the process into smaller components and train the machine learning models accordingly. ▪ The goal of the Machine learning model is defined and conceptualized. We fetch the raw unstructured data, clean, label the same and prepare for the nextstage. 12

13. Iteration ▪ We increasetheefficiency,timecost,quality and accuracy of themachine learning model to achieve better outcomes and obtain higher quality data. ▪ We train and evaluate themodel to determine itsefficiencyinan iterative way. ▪ The iterative process on the workflow components benefits us to structure our machine learning modelefficiently. 13

14. Transition ▪ We deploy the machine learning model to production once desired human efficiency and quality levels are achieved. ▪ Combining these automated mechanisms with the human component creates the actual Human-in-the-Loop machine learning system. ▪ Thetransition adopts intothefollowing. ▪ Fully manual - The human receives the automated system’s decision along with the input data, which can be used to evaluate the model’s performance before it can independentlyact. 14

15. Types of Transition ▪ Augmented manual - The human is augmented by the automation, acts as a gate-keeper for automated decisions, approving or declining — and correcting — those automated decisions. ▪ Semi-automated - A fraction of the automation’s decisions stop being monitored by a human.TherestremainsundertheAugmentedmanual model. ▪ Automated - The model reaches a desired accuracy level and humans stop being involved in theloop. 15

16. Monitoring ▪ HITL is never fully trust what is automated. Even at the Automated phase, there occurs the touch of humans to verify that the automated processes acts as intended. ▪ Once the monitoring process is operationalized, we must ensure that it is precise and accurate to avoid thepotential risksahead. 16

17. Who uses Human-in-the-loop Machine Learning? ▪ HITL can be used for manifold AI projects including NLP, computer vision, sentiment analysis, transcription, and a vast amount of other use cases. ▪ Any deep learning AIcan benefit from human intelligence involved into the loop at somepoint. 17

18. Application Areas Retail management Logistics ▪ Determine and predict demand. ▪ Patterns of the route. ▪ Characterize congestion areas. ▪ Why do users buy what theybuy? ▪ Onset of fashions ▪ Products development ▪ Targeted advertisement andinfluence 18

19. 19

20. Outofstock No visible label present cannot box as an out-of- stock item. Outofstock Outofstock 20

21. Business Address Order Number 21

22. Conclusion ▪ HITLwill significantly change the way business workflows are carried out in future by creating a pipeline that includes data collection, model training, testing, deployment andmaintenance. ▪ This isa very excitingtime to be involved in thisfieldas industry and institutions are pushing thelimitsfurthereveryday. 22

23. hello@mitosistech.com www.mitosistech.com IND: +91-78240 35173 US: +1-(415) 251-2064