Graph Neural Networks with Ankit Jain
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