Anthill Inside 2019

A conference on AI and Deep Learning

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Submissions are closed for this project

Taj M G Road, Bangalore, Bangalore

About the 2019 edition:

The schedule for the 2019 edition is published here:

The conference has three tracks:

  1. Talks in the main conference hall track
  2. Poster sessions featuring novel ideas and projects in the poster session track
  3. 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:

  1. Data scientists
  2. AI, DL and ML engineers
  3. Cloud providers
  4. Companies which make tooling for AI, ML and Deep Learning
  5. 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


Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.

Anthill Inside 2019 sponsors:

Bronze Sponsor

iMerit Impetus

Community Sponsor

GO-JEK iPropal
LightSpeed Semantics3
Google Tact.AI

Hosted by

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more

Piyush Makhija


Virtual Assistant for High Volume Recruitment

Submitted Oct 15, 2018

Logistics companies, both old and new, have invested heavily in building an efficient frontline workforce to provide swift and convinient services to their users. Timely delivery is often a critical deciding factor for the ever-impatient customers to choose service A over service B. Hence, operations/logistic team is the key enabler here.

The attrition rate in large frontline teams is high, close to 75 percent annually. Yet most companies have aggressive growth targets, necessitating recruitment of high volumes of workers constantly. High-growth companies in this domain like Zomato and Swiggy, which are trying to grow by 50-60 percent by the end of 2018, need to recruit tens of thousands of delivery boys every month.

At Vahan, we have developed an AI-driven virtual assistant that helps logistics companies scale and automate their hiring process by leveraging the common addiction of messaging applications like WhatsApp and FB messenger.

In this talk, I will cover in detail how we developed a complete data collection and natural language processing pipeline for indian languages and built a chatbot over Whatsapp which is currently connecting companies like Dunzo, Dominos & Ecom Express with potential frontline workers and fulfiling the hiring requirements of this industry in a scalable and autonomous fashion.


  1. Introduction to the High volume Recruitment problem
  2. Breakdown of the problem in terms of technical challenges
  3. Natural Language Processing Pipeline
  4. Building a data collection pipeline
  5. Handling Variability in Data
  6. Understanding User Needs
  7. Edge Cases
  8. Performance and Evaluation
  9. The Road Ahead


Basic Knowledge of Machine Learning and Natural Language Processing is required to understand the contents of the talk

Speaker bio

Piyush is a graduate from Georgia Institute of Techonology and is currently working as an NLP Engineer at After passing out from IITR with a Bachelors in ECE, he started out his career as a 4G protocol engineer but soon got attracted towards the fast growing ML/AI domain. Over time he switched over to this domain and, after some exploration, found his interest in working with vernacular languages.

When he is not at work, he spends his time focussing on fitness and honing his skills with the guitar.




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Sonam Srivastava

Portfolio Optimization using Deep Reinforcement Learning

What is Portfolio Management? What is Deep Learning? How does one apply deep learning to the complex problem of portfolio management? What is the intuitive interpretation of this application? What lies under the black box? more

19 Aug 2018