Later this month we will have legendary researcher Bernardo Huberman visiting. And we’re lucky enough to have him giving a talk on one of the most exciting new developments in Network Science: Applying AI to networking problems.
Bernardo has been a central player throughout the rise of network theory (and mentor for field notables, such as Lada Adamic and Jure Leskovec), but that’s just a fraction of what he’s accomplished. If you care about anything related to information sciences, this is a talk you cannot miss.
Bernardo is a Fellow and vice president of the Core Innovation Team at CableLabs. He is also a Consulting Professor in the Department of Applied Physics and the Symbolic System Program at Stanford University. Previously he was Senior Fellow and Senior Vice President at Hewlett Packard Enterprise Company, and Director of the Mechanisms and Design Lab at Hewlett Packard Labs.
- Date: August 29, 2017.
- Time: 14:00
- Location: Technical University of Denmark, Building 321, 1st floor: Room 134
Title: Artificial Intelligence and the Network
Abstract: Artificial Intelligence is the attempt to make computers emulate human cognition and thought processes. It has existed for a long time and has sprouted a number of subfields, from semantic networks and common sense reasoning to robotics, logic programming and machine learning. In spite of the glacial rate of progress in AI, one subfield, machine learning, has recently taken off like wildfire. What powers this incredible growth is the availability of fast processors that have made possible computations than seemed hard to achieve a few years ago. As a result, we now have powerful systems that can easily recognize myriad images and spoken languages. This talk will describe some of the great successes of machine learning, their limitations, and their application to networking problems which pervade modern communications. I will also present a form of artificial intelligence that is distributed in nature and that mimics the ability of groups of people and social insects to solve extremely hard problems.