2010 Turing Lecture

15 March 2010

Embracing Uncertainty: The New Machine Intelligence

Christopher Bishop
Microsoft Research Cambridge
5.00pm / 5.30pm Thursday 18 March 2010
Appleton Tower

Computers are traditionally viewed as logical machines which follow precise, deterministic instructions.

The real world in which they operate, however, is full of complexity, ambiguity, and uncertainty. In this year’s Turing Lecture, Professor Chris Bishop discusses the field of machine learning, and shows how uncertainty can be modelled and quantified using probabilities.

He looks at the recent developments in probabilistic modelling which have greatly expanded the variety and scale of machine learning applications, and he explores the future potential for this technology.

In honour and recognition of Alan Turing’s contribution in the field of computing, the IET and the BCS established the Turing Lecture in 1999. It is a world leading event, presenting a topic from current research in computer science given by an acknowledged expert in the field.

Professor Bishop is Chief Research Scientist at the Microsoft Research Laboratory in Cambridge, and also holds a Chair in Computer Science in The University of Edinburgh School of Informatics. He presented the 2008 Royal Institution Christmas Lectures Hi-Tech Trek — The Quest for the Ultimate Computer.

He’s an excellent speaker, and this looks to be an interesting talk about recent advances in and applications of machine learning. There is a reception at 5pm, with the lecture at 5.30pm, and a ticket-only event afterwards. The lecture is free, but the IET ask for registration; which in turn means you need to create an account at the IET website; which means handing over address, phone number, eye colour, etc. Sorry about that.

Links: Registration; Video of this lecture in London; The British Computer Society on the Turing Lecture; The Institution of Engineering and Technology on the Turing Lecture.

Lecture 11: Markov Chains and Stochastic Logics

18 February 2010

Small Petri net example in the form of a signal cascade. Extracting from this in turn: a transition system; a DTMC with transition probabilities; and a CTMC with rates. Corresponding refinements of temporal logic: LTL/CTL; PCTL with probabilities; CSL with probabilities and duration. How this extends expressiveness, and how replacing discrete “always” with probabilistic “almost surely” gives results more closely matching intuition.

Probabilistic symbolic model checking and the PRISM tool. Examples from:


Read the remainder of the Kwiatkowska et al. tutorial, §§4–6.

Also, recall this short report:

In the meantime, however, Fisher and her fellow executable-biology enthusiasts have a lot of convincing to do, says Stephen Oliver, a biologist at the University of Cambridge, UK. “Modelling in general is regarded sceptically by many biologists,” he points out.

Do you think this is true? Can you find any evidence for or against?