Karen Hao is the artificial intelligence reporter for MIT Technology Review, where she covers the latest developments, ethics, and social impact of the technology. She also writes the AI newsletter, The Algorithm, which thoughtfully demystifies the field’s latest news and research. Prior to joining the publication, she was a reporter and data scientist at Quartz and an application engineer at the first startup to spin out of Google X.
Senior Machine Learning Engineer
TripAdvisor is the world’s largest travel site, with an average of 490 unique monthly visitors and 730 million reviews and opinions. We provide recommendations for travelers using search log, review text, votes collected from our users, machine vision, and other machine learning techniques. This talk will cover some interesting modeling issues that arise and insights we had when building large numbers of models using terabytes of data.
Anyi Wang is a Senior Machine Learning Engineer at TripAdvisor. She has a M.S. from Columbia University School of Engineering and Applied Science with a focus on Machine Learning. Anyi has lead multiple modeling projects across the company in search engine marketing, instant booking, photo identification and machine vision, revenue optimization, hotel sorting and recommendations.
Senior Data Scientist
Working with an industry-leading manufacturer, the goal was to improve the uptime of products in use. Using more than a million repair records across 15 different datasets for three different products, we developed several solutions to identify signals that an issue might be brewing. This enabled us to solve the underlying cause well before it escalated to a problem that required widespread repairs (which would lead to downtime). During this talk Alison will share details on the use case and the solutions developed that helped identify the largest and fastest growing potential issues within product lines.
Alison (Ali) O’Connor is a Senior Data Scientist at QuantumBlack, a McKinsey Company. Ali’s advanced knowledge of predictive analytics, machine learning, and probabilistic theory allow her to lead and contribute to a number of projects that require the construction of accurate models of static and/or dynamic environments. She has significant expertise developing relational models used primarily in decision-support tools. Her modeling expertise extends beyond probabilistic modelling, and also includes standard statistical modeling. Prior to joining QuantumBlack, Ali spent five years working for Charles River Analytics, an R&D US Department of Defense contractor. At Charles River, Ali won contracts with the US Army, Navy, NASA, and DARPA. Ali holds an M.S. in Computer Science from Tufts University and a B.A. in Cognitive Science from the University of the Pennsylvania, with a concentration in Computation and Cognition.
Machine Learning Models rely on data to be able to train on and make predictions. As a result, the biases that are present in these datasets get reflected in these models. In addition, many ML systems in production are trained in a streaming fashion, where they are further adapted on some history of previous traffic data. This creates a negative feedback loop that reinforces these biases that might be present in the system, working well for groups for which abundant data is present and failing for other groups where there is a scarcity of data.
In this talk, I will highlight some of the current challenges in identifying and quantifying these biases and highlight the need for good stress test datasets to test for blindspots in these models. In addition, I will highlight the need for moving away from reporting performance metrics on a static data set towards continuous evaluation of such systems, that take into account effects of bias reinforcing feedback loops.
Pallavi Baljekar is a Software Engineer in the Google Brain team in Cambridge where her main research focus is on making Google services and products more inclusive and less biased.
Previously she obtained her PhD from the School of Computer Science at Carnegie Mellon University working with Dr. Alan Black in building speech synthesis systems for low resource languages.