Last night I attended the IET Turing lecture which was given by Chris Bishop, the Chief Research Scientist at Microsoft Research Cambridge. There was a great turnout, well over 400 people, and the event was fully booked! Some people may remember Chris Bishop from the 2008 Royal Institution Christmas lecture series, where he talked about the potential and limitations of computer technology to an audience of young scientists-to-be.
Here is the Promo video:
And here is the actual lecture:
IET/BCS Turing Lecture 2010 – Embracing Uncertainty: The new machine intelligence
Professor Christopher Bishop, Chief Research Scientist, Microsoft Research Cambridge Computers
From: The IET/BCS Turing Lecture
2010-02-25 00:00:00.0 IT Channel
The lecture was interesting, it focused mainly on Bayesian inference techniques and how they can help us in handling large data sets. Professor Bishop described how Microsoft have incorporated this research into a new tool called Infer.net.
I spoke to Professor Bishop after the lecture, specifically I asked him if these techniques could benefit from massively parallel architectures. He said yes they could. I then tried to ask about whether or not some of these techniques (for example the message passing part of the algorithms – watch the video at around 18:20) could potentially be mapped onto, say, an optimization approach. There is a connection here with Hopfield networks and energy minimization and the like here, but it’s not immediately obvious from the explanations given in the lecture. Unfortunately I wasn’t able to get very far with this discussion as there were lots of other people asking questions too. But it is an interesting train of thought, and as I didn’t want to take up all the speaker’s evening with this line of questioning, I thought I’d probably better buy his book and think over it a bit more instead 🙂