Navrina Singh is Director Business Development in Artificial Intelligence at Microsoft. Her current focus is on Machine vision & Video intelligence. Prior to that Navrina spent 12 years in engineering & product management roles at Qualcomm. Navrina is a Young Global Leader of World Economic Forum and a member of the global council on Artificial Intelligence and Robotics, which will explore how developments in these fields could impact industry, governments and society in the future. Navrina is an active advocate for Diversity and Inclusion in Tech and also invests significant time in advising startups. She holds a Masters in Electrical engineering from University of Wisconsin-Madison & a MBA from University of Southern California.
In this talk, I will review recent progress in building conversational AI to complete tasks and hold general conversations. Today's dialogue systems are the fruit of decades of progress in linguistics, compute power, and machine learning. In particular, modern machine learning techniques like deep learning hold the promise of accelerating the development of dialogue systems and achieving more complex interactions but they also entail many challenges including controllability, evaluation, and data efficiency. I will describe these challenges as well as some of the promising solutions that will bring the next generation of dialogue systems.
Layla El Asri is a team lead at Borealis AI. She completed a Ph.D. in computer science at Université de Lorraine in France in 2016. Her Ph.D. was a joint project between Université de Lorraine and Orange Labs, the research and development branch of Orange, a telecommunication company in France. Her research focused on improving dialogue systems with machine learning. She developed methods to train dialogue systems faster while respecting strong industrial constraints. After her Ph.D, she joined Maluuba, a Canadian startup, in 2016 as a research scientist where she worked on user simulation, reinforcement learning, and datasets for dialogue systems. Layla then joined Microsoft, through the acquisition of Maluuba, in 2017 as a research manager leading a team focused on conversational AI and natural language processing. She is continuing her work on natural language processing at Borealis AI.
Designing AI Algorithms to Power New Experiences @ Facebook Scale
Aparna Lakshmiratan is a Technical Program Manager, Applied Machine Learning, Facebook At Facebook, Aparna works at the intersection of cutting edge technology and its deployment to products at massive scale. She is currently driving their program to build new algorithms for ranking and personalization, and shipping them to power products for over 2B people. Prior to joining Facebook, Aparna was a principal program manager at Microsoft building and shipping several products powered by machine learning including a new Click Prediction system for Ads and several enhancements to the Speller and Query Alterations engine in Bing. Aparna was also part of the CHIL (computer human interactive learning) group at Microsoft Research where she worked on an interactive machine learning platform to democratize machine learning and make it more accessible to non-experts. She has a PhD in Computer Science from the AI Lab at MIT.
Montreal Institute for Learning Algorithms
Montreal Institute for Learning Algorithms
Deep Learning Models for Personalized Medicine
In recent years, deep learning has achieved remarkable results in fields such as: computer vision, speech recognition and natural language processing. This deep learning revolution is slowly reaching the challenging problems of the medical domain, opening the doors for personalized medicine. Medical domain is characterized by high variability of data including text, imaging, and genomic data. I will discuss recent advances in two domains: imaging and genomics. First, I will briefly introduce the medical imaging segmentation problem and the contributions that we made to the standard pipeline. Second, I will present the challenges posed by genomic data and potential solutions.
Adriana Romero is a post-doctoral researcher at Montreal Institute for Learning algorithms, advised by Prof. Yoshua Bengio. Her current research revolves around deep learning techniques to tackle medical data analysis challenges, addressing impactful problems for society by paving the road towards enabling widespread usage of personalized medicine. Adriana received her Ph.D. from the University of Barcelona in 2015 with a thesis on assisting the training of deep neural networks with applications to computer vision, advised by Dr. Carlo Gatta.