Creating Intelligent Machines

I have just attended the excellent IET/BCS 2010 Turing Lecture 'Embracing Uncertainty: The New Machine Intelligence' at the University of Manchester which was given this year by Professor Chris Bishop who is the Chief Research Scientist at Microsoft Research in Cambridge and also Chair of Computer Science at the University of Edinburgh. The lecture allowed Chris to share his undoubted passion for machine learning, and although there were a number of mathematical aspects mentioned during the talk, Chris managed to ensure everyone was able to understand the key concepts being described.

Chris started by explaining that his interest is in building a framework for building intelligence into computers, something which has been a goal for many researchers for many years. This is now becoming increasingly important due to the vast amounts of data which is now available for analysis. With the amount of data doubling every 18 months, there is an increasing need to move away from purely algorithmic ways of reviewing the data to solutions which are based on learning from the data. This has traditionally been the goal for machine (or artificial) intelligence and despite what Marvin Minsky wrote in 1967 in 'Computation: Finite and Infinite Machines' that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved", the problem still does not have a satisfactory solution for many classes of problem.

A quick summary of the history of artificial intelligence showed that expert systems, which were good at certain applications but required significant investment in capturing and defining the rules, and neural networks which provide a statistical learning approach but have difficulty in capturing the necessary domain knowledge within the model, were not adequate for today's class of problems. An alternative approach which was able to integrate domain knowledge with statistical learning was required and Chris's approach was to use a combination of approaches:

1. Bayesian Learning which uses probability distributions to quantify the uncertainty of the data. The distributions are amended once 'real data' is applied to the model which results in a reduction in the uncertainty.
2. Probabilistic Graphical Models which enables domain knowledge to be captured in directed graphs with each node having a probability distribution.
3. Efficient inference which ensures efficiency in computation

To explain the approach, Chris sensibly used real-life case studies to demonstrate the application of the theory in three very diverse applications.

His first example was of Bayesian Ranking system to be used in producing a global ranking from noisy partial rankings. The conventional approaches is to use the Elo rating system which is a method for calculating the relative skill levels of players in two-player games. The Elo system could not handle team games or more than 2 players. As part of the launch of the Xbox 360 Live online playing solution, Microsoft developed the TrueSkill algorithm to match opponents of similar skill levels. The TrueSkill algorithm converges far faster than Elo by managing the uncertainty in a more efficient way; it also operates quickly so that users can find suitable opponents in a few seconds out of a user population of many million. Further details on TrueSkill(TM) are available at http://research.microsoft.com/en-us/projects/trueskill/

The next example was for a website serving adverts and how to determine which advert to show based on the probability of being clicked and the value of click. The proposed approach was to use gausian probability in order to assign a weight to a number of features which is used to determine the ranking. However it is important to ensure that the system continually learns in order to re-evaluate the ranking to ensure that the solution accurately reflects the dynamics of the adverts. If this was not the case, it would be very difficult for a new advert to be be served.

The final example was the Manchester Asthma and Allergy Study which is working with a comprehensive data set acquired over 11 years. The data set is continually being augmented with new types of data (recently genetic data has been added) and the study has been successful at establishing the important variables and features and their relationships. By defining a highly structured model of the domain knowledge, it has been possible to assign each variable a probability distribution. By placing the data at the heart of the study and applying some machine learning techniques, a number of key observations are now being reported which might not have been apparent if more traditional statistical techniques had been used.

As a closing remark, Chris promoted a product from Microsoft Research (Infer.net) which provides a framework for further experimentation in developing Bayesian models for a variety of machine learning problems.

As is now traditional with the Turing Lecture, it is presented at several locations around the country. A webcast of the version presented at the IET in London is available on the IET TV channel.