Technologists are good for business

This evening's BCS/IET Turing Lecture given by Suranga Chandratillake, founder and former chief strategy officer of Blinkx, at Manchester University was an interesting talk linking the technical excellence of an engineer with the needs of an entrepreneur. His premise was that his undergraduate course in Computer Science at Cambridge University had provided him with many of the skills he needed to have a successful business career - it was just that he wasn't aware he had the skills.

Suranga first compared the stages that an inventor and entrepreneur went through with the evolution of an idea. The inventor would go from a position where he felt he wanted to challenge the world to the point where he had a flash of inspiration  and onto the stage where the invention was now tangible  Compare this to an entrepreneur who starts by thinking 'I need money' (because ideas are not enough)  to the stage where the product or service is now a salable item up to the point where he is now making a profit. The UK is very good at educating and nurturing  many great technologists to create and innovate; unfortunately it is not always good at exploiting these ideas mainly because many of the skills to allow a entrepreneur to exploit technical ideas are not well developed.

He described how he was offered the opportunity to be the founder CEO of Blinkx, a startup spun out from Autonomy. He was reluctant (very!) at taking on this role because he felt that he didn't have the necessary skills to fulfill the role as he was essentially a technologist. He struck a deal with Mike Lynch, CEO of Autonomy, that said that if he needed help with some of the business functions such as finance, HR, Sales and marketing that Mike would help him out. What amazed me was that the skills he needed for finance, marketing and sales were all taught on his undergraduate course, it was just that they weren't expressed in business manner. For example, for marketing to determine the most effective approach to use (e.g. PR, web-page banner ads or search adverts), requires the application of some simple probabilistic modelling, a 2nd year course. I felt he stretched the analogy a bit far when he compare a HR organisation to that of a system architecture; however, I think many aspects of HR (particularly recruitment) can be covered in parts of undergraduate courses particularly with the increasing amounts of team-working forming part of the curriculum.

Suringa summarised that the attributes of a technologist of being qualitative, rigorous and analytical had actually prepared him perfectly for business in a technical organisation. He stated that it is a fallacy that technologists do not understand business, it is just that they assume that they don't have the skills. This is a mental block rather than a a lack of ability.

I found the talk provided much food for thought. Clearly the business environment that Suranga operated in was not typical of many companies but it was illuminating to see he was able to relate back to his undergraduate course. The opportunity to work in a small company with a unique technology (as blinkx)  is clearly not going to be available to everyone. However, provided the opportunities are available I am sure many more technologists should feel empowered to exploit technology to create viable and thriving businesses.

The BCS/IET Turing Lecture 2013: The IET Turing Lecture 2013: What they didn't teach me: building a technology company and taking it to market Suranga Chandratillake Play webcast The IET Prestige Lecture Series 2013, Turing Lecture, Savoy Place, London, 18 February 2013

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.