ETH Meets New York
New York, 15 - 16 May 2017 - ETH Zurich, the Swiss Federal Institute of Technology in Zurich, Switzerland brings top researchers to New York to unravel the mysteries of science and technology in an exchange of ideas with our counterparts in academia and industry.
From Machine Learning and Artificial Intelligence to Blockchain Technology, we invite you to participate in two free public gatherings that connect a community of computer scientists, industry leaders, international media, and students. Join ETH Zurich in New York along with its presenting partners - externe Seite Greater Zurich Area, externe Seite Open Systems and the externe Seite Swiss-American Chamber of Commerce. Follow us on social media #ETH2NY.
ETH Meets New York 2017
Blockchain Technology
Symposium - Monday, 15 May 2017, 5:00 pm
The security characteristics of blockchain technology allow a shift to well-known trust assumptions enabling society to remove the central authorities of many systems including: conventional banking, escrow, gambling and even dispute mediation. As blockchain technology is truly disruptive, it has gathered much attention across industries, academia, and startup scenes worldwide.
Blockchain Technology Symposium Speakers
Videos of talks
Please find the videos of the talks here.
externe Seite Meltem Demirors, Director, externe Seite Digital Currency Group
Arthur Gervais, Institute of Information Security, ETH Zurich
externe Seite Ashley Taylor, Community Microgrid Specialist, externe Seite LO3 Energy
externe Seite Joseph Lubin, Founder, externe Seite ConsenSys
externe Seite Elizabeth Stark, Co-Founder & CEO, externe Seite Lightning
externe Seite Daniel Doubrovkine, CTO, externe Seite Artsy
externe Seite Tadge Dryja, Research Scientist, externe Seite MIT Media Lab
externe Seite Matt Corallo, Engineer, externe Seite Chaincode Labs
Venue
National Sawdust externe Seite https://nationalsawdust.org/
80 North 6th Street, Brooklyn, NY 11249
Program
The Future is Cognitive
Symposium - Tuesday, 16 May 2017, 5:00 pm
The goal of machine learning is to enable computers to learn and excel at performing specific tasks. Examples of these tasks range from driving a car and recognizing a face in a picture, to improving a web search. It has also been argued that, through machine learning, we can get closer to achieving the overarching goal of human-level Artificial Intelligence (AI). In the future, machine learning will play a significant role in many industries and institutions.