My experience at the MLconf San Francisco 2018
Tags: Machine Learning, Data Science, Uber, Facebook

I was very excited to attend the MLconf in San Francisco in November 2018. The conference has drawn a very enthusiastic crowd of machine learning practitioners and experts from companies such as Google Brain, Uber, Facebook, Baidu, Tesla, etc. The talks centered around the wide application of the machine learning techniques and various algorithms. I am very thankful to the Women in Machine Learning and Data Science (WiMLDS) for giving me an opportunity to attend!

There were 16 excellent talks lined up plus several companies had set up the booths & deligated their Data Scientists to share their experience, in-house practices & to hire.

Since there was an online voting for the best presentation, I would like to share my thoughts and notes on the talks that were highly ranked by the audience.

1st place

Ilke Demir, a Postdoc Researcher at Facebook, had a talk titled “The Geospatial Machine Learning for Urban Development”. Ilke described how the open datasets, computer vision & machine learning can be used in a collaborative environment to gain an understanding of the urban safety, socioeconomic conditions & voting patterns, and to improve the poverty & disaster mapping.

Ilke talked in details about the DeepGlobe Satellite Image Understanding Challenge, which includes public competitions for segmentation, detection, and classification tasks on satellite images. DeepGlobe aims to use the satellite imagery to extract tracks and roads, to detect buildings, and to classify land cover. In her paper, Ilke emphasizes: “Bridging modern computer vision with remote sensing data analysis could have a critical impact to the way we understand our environment and lead to major breakthroughs in global urban planning or climate change research”.

Ilke also mentioned another challenge she has been involved in: millions of people are disconnected from basic services due to lack of adequate addressing. Her research proposes an automatic generative algorithm to create street addresses from satellite imagery.

2nd place

Mike Tamir, Head of Data Science at UBER ATG talked about “How to use Deep Learning to solve the “Fake News Problem”.

Mike had explored the root problem of the fake news, they use emotional content to impact the cognitive function by engaging people emotionally. However, how we decide what is the fact and the truth and how, in the battle with the fake news, not to get into the deep censorship? He used deep-learning, neural networks, and NLP to train his algorithm to rate the article on several scales (sensational, opinion, journalism, agenda driven, etc) and providing the reader with the reasoning behind it.

You can actually try checking any news on Mike’s website by inserting the URL of the article. FakerFact is an Artificial Intelligence tool built to help understand more about what we read. It was trained on millions of documents including journalism and scientific journal articles, satire articles, narrative fiction, opinion pieces, blogs, politically leaning news and even hate speech examples.

3rd place

Franziska Bell, Data Science Director on the Platform Team at Uber talked about Natural Language (NL) use cases at Uber (Youtube video). She portrayed the Uber as a customer-centered company which uses NLP & Machine Learning in the strive for improving the Uber customer care. Her team has developed COTA, Customer Obsession Ticket Assistant, which enables quick and efficient issue resolution for more than 90% of the inbound support tickets.

Another way that Uber leverages ML and NLP techniques is by improving driver communication through One-Click Chat, Uber’s smart reply system. The system anticipates responses to common rider messages to make it easier for driver-partners to reply to in-app messages.

It was a great conference and I am very thankful to all organizers, sponsors, and experts who made the MLConf possible!