Anthill Inside 2019

On infrastructure for AI and ML: from managing training data to data storage, cloud strategy and costs of developing ML models

Bandit algorithms to Reduce Cognitive Load on Customer Care Agents (Paper accepted for the demo track at SIGIR-2019)

Submitted by Hrishi Ganu (@blah) on Tuesday, 26 March 2019


Preview video

Session type: Full talk of 40 mins

Abstract

We will describe a human-in-the loop system, AgentBuddy that is helping Intuit improve the quality of search it offers to internal Customer Care Agents (CCAs). AgentBuddy aims to reduce the cognitive effort on part of the CCAs while at the same time boosting the quality of our legacy federated search system. It addresses two key pain points 1)Given several candidate query answering mechanisms, how to select the right mechanism given a question and 2)Having retrieved a set of lengthy documents how to help the agent zoom in on the content most important for the question at hand. We address #1 using an elegant approach for principled exploration based on bandit algorithms and for #2 we have several models based on supervised and unsupervised learning. Since this is a real world system deployed on AWS we will also discuss practical challenges in scaling and how we overcame them.

Outline

  • Business Problem
  • Approach
  • Formulation
  • ML algorithms used
  • Architectural challenges
  • Scaling up
  • Latency issues and sane fall-backs
  • Validation
  • Ground truth labels
  • Merics and measurement
  • Business Impact
  • Takeaways for Data Science practice

Speaker bio

Hrishi has been a regular speaker at Anthill and delivered a full-length session on “Building and driving adoption for a robust semantic search system” https://www.youtube.com/watch?v=niKXwqcTpao&t=650s&list=PL279M8GbNset5FCdcLd_ovckHE14PkIhM&index=3
Hrishi did his Master’s from Indian Institute of Science (IISc), Bangalore in 2005 where he worked on Computer Vision for studying atomization in Cryogenic Rocket Engines. Post that he did a full-time PGDM from IIM-Kozhikode. He has been working in the ML/Analytics space for over 11 years and has had long stints at Amazon Core ML and at Mu Sigma before joining Intuit’s IAT team. At Intuit, he’s working on NLP with a focus on creating algorithms that are robust to noise in user input.
Aside of work he spends time playing with his 4-year-old daughter and in solving puzzles.

Links

Preview video

https://archive.org/details/Demo1_201902

Comments

  • Anwesha Sarkar (@anweshaalt) 2 months ago

    Thank you for submitting the proposal. Submit your slides and preview video by 20th April (latest) it helps us to close the review process.

  • Zainab Bawa (@zainabbawa) Reviewer a month ago

    Hrishi,

    We’ll need to see draft slides – by 27 May – which help us understand your thinking to assess the fit of your proposal for The Fifth Elephant. Since this proposal has been submitted a while ago with no further updates, you also have to let us know if your plans have changed and if you want us to move your proposal to future editions of Rootconf and/or The Fifth Elephant.

    Other comments for your slides, when you draft them:

    1. Explain why the decision to go with Bandit algorithms? which other options were considered? Or why does Bandit algorithm serve your use case and nothing else? what other similar cases does use of Bandit algorithms make sense? What are the limitations?
    2. The concepts used Cognitiva Load, CCA, etc which will have to be explained enough for the audience to get the context. Else, participants will be searching for definitions online.
    • Hrishi Ganu (@blah) Proposer 27 days ago

      @zainab I’ve updated the submission bhy including 1)link to demo video and 2)added a slide deck

  • Abhishek Balaji (@booleanbalaji) Reviewer 10 days ago

    We’re considering this for Anthil Inside, since the talk is theory driven. Anthill Inside is currently postponed hence we’re parking this for now. We’ll get back to you when we have dates confirmed.

  • Mithun Ghosh (@mithunghosh) 4 days ago (edited 3 days ago)

    Hi @booleanbalaji this has enough depth on practical side of implementation hence it is suitable for fifth elephant too.
    This is a system working in production, designed from scratch. We can work together on slides to make it suitable for fifth elephant by adding more of system side of implementation details. Let me know your opinion.

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