Graph Neural Networks with Ankit Jain

The Minhaaj's Podcast

Ankit is an experienced AI Researcher/Machine Learning Engineer who is passionate about using AI to build scalable machine learning products. In his 10 years of AI career, he has researched and deployed several state-of-the-art machine learning models which have impacted 100s of millions of users.

Currently, He works as a senior research scientist at Facebook where he works on a variety of machine learning problems across different verticals. Previously, he was a researcher at Uber AI where he worked on application of deep learning methods to different problems ranging from food delivery, fraud detection to self-driving cars. He has been a featured speaker in many of the top AI conferences and universities like UC Berkeley, IIT Bombay and has published papers in several top conferences like Neurips, ICLR. Additionally, he has co-authored a book on machine learning titled TensorFlow Machine Learning Projects. He has undergraduate and graduate degrees from IIT Bombay (India) and UC Berkeley respectively. Outside of work, he enjoys running and has run several marathons.

00:00 Intro

00:17 IIT vs FAANG companies, Competition Anxiety

05:40 Work Load between India and US, Educational Culture

07:50. Uber Eats, Food Recommendation Systems and Graph Networks

11:00 Accuracy Matrices for Recommendation Systems

12:42 Weather as a predictor of Food Orders and Pizza Fad

15:48 Raquel Urtusun and Zoubin Gharamani, Autonomous Driving and Google Brain

17:30 Graph Learning in Computer Vision & Beating the Benchmarks

19:15 Latent Space Representations and Fraud Detection

21:30 Multimodal Data & Prediction Accuracy

23:20 Multimodal Graph Recommendation at Uber Eats

23:50 Post-Order Data Analysis for Uber Eats

27:30 Plugging out of Matrix and Marathon Running

31:44 Finding Collusion between Riders and Drivers with Graph Learning

35:40 Reward Sensitivity Analysis for Drivers in Uber through LSTM Networks

42:00 PyG 2.0, Jure Leskovec, and DeepGraph, Tensorflow Support

46:46 Pytorch vs Tensorflow, Scalability and ease of use.

52:10 Work at Facebook, End to End Experiments

55:19 Optimisation of Cross-functional Solutions for Multiple Teams

57:30 Content Understanding teams and Behaviour Prediction

59:50 Cold Start Problem and Representation Mapping

01:03:30 NeurIPS paper on Meta-Learning and Global Few-Shot Model

01:07:00 Experimentation Ambience at Facebook, Privacy and Data Mine

01:09:03 Cons of working at FAANG

01:10:20 High School Math Teacher as Inspiration and Mentoring Others

01:18:25 TensorFlow Book and Upcoming Blog

01:16:40 Working at Oil Rig in the Ocean Straight Out of College

01:20:08 Promises of AI and Benefits to Society at Large

01:25:50 Facebook accused of Polarisation, Manipulation and Racism

01:28:10 Revenue Models - Product vs Advertising

01:31:15 Metaverse and Long-term Goals

01:33:10 Facebook Ray-Ban Stories and Market for Smart Glasses

01:36:40 Possibility of Facebook OS for Facebook Hardware

01:38:00 LibraCoin & Moving Fast - Breaking Things at Facebook

01:39:09 Orkut vs Facebook - A case study on Superior Tech Stack

01:42:00 Careers in Data Science & How to Get into It

01:45:00 Irrelevance of College Degrees and Prestigious Universities as Pre-requisites

01:49:50 Decreasing Attention Span & Lack of Curiosity

01:54:40 Arranged Marriages & Shifting Relationship Trends