Quantum Machine Learning - Future of AI

Quantum Machine Learning: The future scope of AI

Presented By ODSC West
ODSC West
ODSC West

Vinod Bakthavachalam

Vinod Bakthavachalam is a data scientist working with the Content Strategy and Enterprise teams where his work has recently focused on understanding the skills landscape around the world using Coursera data (see the Global Skills Index Coursera recently published for some of his work). Prior to Coursera, he majored in Economics, Statistics, and Molecular and Cell Biology at UC Berkeley, and worked in quantitative finance.


Scott J Haines

Scott Haines is a distributed systems engineer focused on real-time, highly available, trust- worthy analytics systems. He works at Twilio where he is a Principal Software Engineer on the Voice Insights team where he helped drive spark adoption, streaming pipeline architecture best practices, as well as a massive stream processing platform. Prior to Twilio, he worked writing the backend Java API’s for Yahoo Games, as well as the real- time game ranking/ratings engine (built on Storm) to provide personalized recommendations and page views for 10 million customers. He finished his tenure at Yahoo working for Flurry Analytics where he wrote the alerts/notifications system for mobile.


Jane Adams

Jane Adams is an emergent media artist, working at the intersection of visual expression and scientific inquiry. As the Data Visualization Artist in Residence at the University of Vermont Complex Systems Center, Jane builds engaging, interactive, web-based visualizations of high-dimensional data for exploratory analysis. Her visualization research topics include social network lexical analysis, healthcare morbidity and mortality modeling, and geospatial temporal dynamics, all through a lens of complexity science. In her spare time, Jane experiments with music-color synesthesia, machine learning for computational creativity, self-sustaining aquaponic sculpture, and citizen science. She is the lead community organizer of Vermont Women in Machine Learning and Data Science (WiMLDS), and holds a MFA in Emergent Media. Stay in touch on Twitter @artistjaneadams


Andrew Long, PhD

Andrew Long is a Senior Data Scientist at Fresenius Medical Care North America (FMCNA). Andrew holds a PhD in biomedical engineering from Johns Hopkins University and a Master’s degree in mechanical engineering from Northwestern University. Andrew joined FMCNA in 2017 after participating in the Insight Health Data Fellows Program. At FMCNA, he is responsible for building, piloting, and deploying predictive models using machine learning to improve the quality of life of every patient who receives dialysis from FMCNA. He currently has multiple models in production to predict which patients are at the highest risk of negative outcomes.

Presentation Description

Over the past half-century, the rapid progression in computing devices, availability of high-performance computing devices helps a researcher to do more research with high volume data. Recently IBM successfully developed quantum processor-based computing devices which very faster than the current computing devices. In general, quantum computing based computing devices integrated with a quantum bit which is faster than a binary bit. Therefore, quantum computing based computer can able to read and process high volume data in a very faster way to compare with conventional 64-bit computing devices. In a similar way, the available classical machine learning algorithms based on binary bit operation has slow performance in high volume data. It is also predicted after commercialization of quantum processor based computer, it will help many industries with maximum benefit and the field of quantum machine learning will widely open to new innovation for solving of future complex problems. This course is representing the quantum machine learning concepts, architectures and model development with quantum bit operations. 

Presentation Curriculum

Quantum Machine Learning: The future scope of AI
64:54
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