This talk discards hand-wavy pop-science metaphors and answers a simple question: from a computer science perspective, how can a quantum computer outperform a classical computer? Attendees will learn the following: Representing computation with basic linear algebra (matrices and vectors) The computational workings of qbits, superposition, and quantum logic gates Solving
Tag: computers
If Brains Are Computers, Who Designs The Software? | A Talk By Daniel Dennett – Cognitive Scientist And Philosopher
Cognitive science sees the brain as a sort of computer, but how does education redesign these cerebral computers? Cognitive scientist, philosopher, and expert on consciousness Daniel Dennett explains. There is widespread agreement among researchers in cognitive science that a human brain is some kind of computer, but not much like
Should Computers Run The World? | A Talk By Hannah Fry
Algorithms are increasingly used to make decisions in healthcare, transport, finance, and security. How can they best be used and what happens when things go wrong? Hannah Fry takes us on a tour of the good, the bad and the downright ugly of the algorithms that surround us. She lifts
The P Vs NP Problem – What Computers Can’t Do | A Talk By Kevin Buzzard
Kevin Buzzard explains one of the biggest unsolved problems in theoretical computer science – the P vs NP problem. Today’s computers are lightning-fast. But sometimes we want to make sure that they can’t solve a particular task quickly (perhaps for security purposes). This issue lies at the heart of the
Will Computers Ever Think Like Human Beings? A Talk By Vinton G. Cerf
The rise of artificial intelligence has seen computers beating chess experts and performing incredibly complex tasks. But why can’t they think the same way we do? We have built incredibly powerful, multi-layered, neural networks capable of learning incredibly quickly and carrying out seemingly impossible tasks, but they still can’t always
How Computers Are Learning To Be Creative
We are on the edge of a new frontier in art and creativity – and it’s not human. Blaise Agüera Y Arcas, principal scientist at Google, works with deep neural networks for machine perception and distributed learning.
