Lightning talk 1: BuzzWords: How Bumble does Multilingual Topic Modelling at Scale, Stephen O’Farrell, Machine Learning Scientist
With the abundance of free-form text data available nowadays, topic modelling has become a fundamental tool for understanding the key issues being discussed online. We found the state-of-the-art topic modelling libraries either too naive or too slow for the amount of data a company like Bumble deals with, so we decided to develop our own solution. BuzzWords runs entirely on GPU using BERT-based models – meaning it can perform topic modelling on multilingual datasets of millions of data points, giving us significantly faster training times when compared to other prominent topic modelling libraries
Lightning talk 2: Graph Learning in a dating marketplace, Maggie Wang, Machine Learning Scientist
Abstract: Social networks form natural graphs from people and connections between people. Graph learning is to learn from such data that can be represented as graphs. The flexibility of combining neural networks with graphs gives us the ability to learn from complex data and relationships. We’ll take a high level look at what graph learning is and how it is adopted across industries.
Panel: “To see or not to see – is AI at its best when people know it’s there (and when it’s not)?”
Abstract: When we build an “intelligent” service, we have a choice: should we tell people anything about what’s running it, or preserve the mystery? For example, Reddit is open sourced and so are its sorting algorithms. By contrast, most online shopping sites include “sort by relevance” but say nothing about how they calculate relevance. Google search is somewhere in the middle – they share a lot of information about the various models and signals that drive search, but they don’t give the whole game away. Hosted by Reda Kechouri.
Supported by
