Name: Machine Learning and Learning from Machines
Start: 2017-05-17T
End: 2017-05-17T
Location: The Castle Bar

Artificial intelligence has made astounding progress in the last few decades, achieving feats that many previously thought impossible for a machine. But how can we use what we know of how the brain works to further push machine intelligence? Will computers ever think the way humans do? And as artificial intelligence becomes more and more human, will we be part of a convergence, or a singularity?

Please note that this event takes place on the ground floor and is accessible for those with impaired mobility, however there is no accessible toilet.

Bio-Inspired Robotics Lab

Drawing from the design of biological systems, research at the Machine Intelligence Laboratory endeavours to engineer novel robotic applications that are adaptive to real life circumstances; for example, legged robot locomotion. I will explain how biology can provide the tools and inspiration for creating practical robotic applications and explain how the concept of evolution can be used to create autonomous robots.

Artificial Intelligence: Between a tech utopia and human extinction

Will the invention of artificial superintelligence jeopardise the existence of the human race? With nanotechnology and other innovations nearer to the brink of altering the way materials are manufactured (an advance that would alter society to the same extent the digital world transformed the delivery of information); What are the implications? Some warn of machines taking our jobs and, ultimately, the atoms we're made of for their own purposes, What's real, what's hype, and what should we do about it?

From Pixels to Cats and Dogs: Vision in Animals and Computers

Knowing what you are looking at is surprisingly difficult. After five decades of research into machine vision, computers have suddenly, in just the last few years, become as good as humans at recognising objects in images. During my talk, I will describe the working of the deep neural networks behind state-of-the-art machine vision: and answer the question: are they solving the problems of seeing in the same way animal brains do?

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