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January 14|25 2018

The future of blurred boundaries between humans and machines is a recurrent theme in science fiction. This future is today.

Join us for two weeks of discussions, lectures, workshops, exhibitions, and cultural and recreational events that will inspire our shared vision for a brighter, human-machine future. 

Is the future human?

Sensors monitor our state, health and behavior, relaying this data to configure a digital signature that is as much part of us as our biometric traits. Fed at petabyte levels, our digital signatures allow increasingly powerful machine learning algorithms to learn from the continuous data streams we generate and anticipate our needs and behaviors as consumers. 

Machine learning algorithms can increasingly mimic the function of our brain and will soon establish a direct interface that allows for the seamless control of robots. Robots are endowed with powerful sensorial capacities in order to interact with other humans, as well as their robotic avatars, through virtual networks that may eventually replace much of our human-to-human interactions. 

While this human-machine future offers to bring about unlimited potential, it also creates risks—of losing our privacy and the social traits inherent to our human nature. We live in a world where machines are increasingly embedded in our everyday lives. Machines shape our individual identities, social interactions, and society as a whole. During the 2018 Winter Enrichment Program (WEP), we invite you to immerse yourself in a diverse and stimulating program, touching on the opportunities and risks of a future where the boundaries between machines and humans will become even more diffuse. 

 



Tuesday, January 23, 2018

Random Matrix Approach for Machine Learning 1/3

Location: Bldg. 9 Lecture Hall 2322 ≤ 154

By Jamal Najim | Romain Couillet

The field of big data processing is currently percieved as a crucial tool for many scientific disciplines, including signal processing, finance, biology... Significant progress has been achieved ... more
Wednesday, January 24, 2018

Random Matrix Approach for Machine Learning 2/3

Location: Bldg. 9 Lecture Hall 2322 ≤ 154

By Jamal Najim | Romain Couillet

The field of big data processing is currently percieved as a crucial tool for many scientific disciplines, including signal processing, finance, biology... Significant progress has been achieved ... more
Thursday, January 25, 2018

Random Matrix Approach for Machine Learning 3/3

Location: Bldg. 9 Lecture Hall 2322 ≤ 154

By Jamal Najim | Romain Couillet

The field of big data processing is currently percieved as a crucial tool for many scientific disciplines, including signal processing, finance, biology... Significant progress has been achieved ... more

When Random Matrices Meet Machine Learning

Location: Bldg. 9 Lecture Hall 2322 ≤ 154

By Jamal Najim | Romain Couillet

Today's machine learning is all about "large dimensional (p) and numerous data (n)", in sharp contrast with yesterday's "small p, large n" asymptotics paradigm. In this talk, we will show that moving ... more