Arrival & Champagne Reception
Shachi Paul - Amazon Lab126
Shachi Paul is a Machine Learning Scientist at Amazon Lab126 where she would on natural language understanding, deep learning and deep dialogue. As a graduate of Carnegie Mellon University with a Master's degree in Machine Learning, she began working at Amazon as an intern in 2016.
Julie Pitt - Netflix
Applied Machine Learning: A Netflix Production
Applied Machine Learning is about as mature as Software Engineering circa 1998. For Data Scientists, it’s hard to collaborate, hard to be productive and hard to deploy to production. In the last 20 years, Software Engineers have become far more collaborative thanks to tools like git, far more productive thanks to cloud computing and far more effective at delivering quality software thanks to CI/CD and agile development practices. The big question is: how can we apply these learnings to Data Science? At Netflix, I get to work on problems like: how do we scale Data Science innovation by making collaboration effortless? How do we enable Data Scientists to single-handedly and reliably introduce their models to production? How do we make it easy to develop ML models that humans trust? More importantly, how do we use ML to make humans BETTER?In this talk, we’ll explore how Netflix is approaching these problems to further our mission of creating joy for our 125 Million+ members worldwide!
Julie leads the Machine Learning Infrastructure at Netflix, with the goal of scaling Data Science while increasing innovation. She previously built streaming infrastructure behind the "play" button while Netflix was transitioning from domestic DVD-by-mail service to international streaming service. Julie also co-founded Order of Magnitude Labs, with a mission to build AI capable of doing things that humans find easy and today’s machines find hard: exploration, communication, creativity and accomplishing long-range goals. Early in her career, Julie developed data processing software at Lawrence Livermore National Laboratory that enabled scientists to study the newly-sequenced human genome.
Anusha Balakrishnan - Facebook
Conversational AI at Facebook
Conversational systems like Siri and Google Assistant have been around for several years now; and have recently started to play increasingly ubiquitous roles in people's daily lives, through smart home devices, phones, or social media (like Messenger). Despite this, the conversational experience that these systems provide has evolved only incrementally. At the same time, however, interest in conversational AI from the research community is growing fast, and there’s more potential than ever for using machine learning to power these systems. In this talk, I'll cover some lessons learned from research in making these systems more engaging and natural.
Anusha is a research engineer at Facebook on the Conversational AI team. She conducts research on building better conversational systems, with a specific focus on Natural Language Generation (NLG) and dialogue policy. Previously, she was a Master's student at Stanford University, where she studied Artificial Intelligence with a special focus on Natural Language Processing, and worked on research projects with Dr. Percy Liang at the Stanford NLP group. She also previously worked at Siri, where she built semantic parsing models for Siri and Spotlight Search
Sarah Laszlo - X, the moonshot factory
CEREBRO: A Neuroscience-led Effort for Stable, High-Accuracy, Brain Biometrics
In September of 2015, the New York Times reported that Chinese cyberespionage agents had stolen the fingerprint records of 5.6 million U.S. federal employees from the Office of Personnel Management (OPM). This was a severe security breach, compounded by the biometric problem that fingerprints are not "cancellable": That is, those users cannot grow new fingerprints; their fingerprint biometrics are therefore permanently compromised. This breach demonstrates two challenging facts about the current cybersecurity landscape. First, biometric credentials are vulnerable to compromise. And, second, biometrics that cannot be replaced if stolen are even more vulnerable to theft. In this talk I will discuss a novel, brain-based biometric that avoids both of these problems, and first steps towards how it can be implemented in a brain-computer-interface that identifies users of sensitive information.
Sarah leads research for an early stage biosignals project at X, at the moonshot factory. She holds a bachelor's degree in Brain and Cognitive Science from MIT, where she was Phi Beta Kappa and winner of the Hans Lukas Teuber award for scholarly excellence. She completed her master's and doctoral degree in brain and cognitive science at the University of Illinois, Urbana-Champaign. Subsequently she was an NIH-NRSA postdoctoral fellow at Carnegie Mellon University for two years, where her research focused on neurally plausible computational models of language comprehension. Sarah was a tenured professor of psychology and linguistics at Binghamton University, where she led the largest study ever conducted of brain development during reading acquisition, prior to leaving academia for industry. Sarah and her research have been featured on NPR’s Science Friday, as well as Wired, Scientific American, and the Huffington Post. Her research interests include neuromorphic computing, brain computer interface, and natural language processing.
COFFEE & DESSERT