##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to email@example.com
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Bandit algorithms to Reduce Cognitive Load on Customer Care Agents (Paper accepted for the demo track at SIGIR-2019)
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.
- Business Problem
- ML algorithms used
- Architectural challenges
- Scaling up
- Latency issues and sane fall-backs
- Ground truth labels
- Merics and measurement
- Business Impact
- Takeaways for Data Science practice
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.