Programming a million core machine

I have just attended an excellent talk by Steve Furber, Professor of Computer Engineering at University of Manchester on the challenges on programming a million core machine as part of the SpiNNaker project. 

The SpiNNaker project has been in existence for around 15 years and has been attempting to answer two fundamental questions:

• How does the brain do what it does? Can massively parallel computing accelerate our understanding of the brain?
• How can our (increasing) understanding of the brain help us create more efficient, parallel and fault-tolerant computation?

The comparison of a parallel computer with a brain is not accidental since brains share many of the required attributes being massively parallel, have lots of interconnections, provide excellent power-efficiency, require low speed communications, is adaptable/fault-tolerant (of failure) and capable of learning autonomously. The challenges for computing as Moore's law progresses is that there will eventually come a time when further increases in speed will not be possible and as processing speed has increased, energy efficiency has become an increasingly important characteristic to address. The future is therefore parallel but the approach to handling this is far from clear. The SpiNNaker project has been established to attempt to model a brain (around 1% of a human brain) using approximately 1 million mobile phone chips with efficient asynchronous interconnections whilst also examining the approach to developing efficient parallel applications.

The project is built on 3 core principles:

• The topology is virtualised and is as generic as possible. The physical and logical connectivity are decoupled.
• There is no global synchronisation between the processing elements.
• Energy frugality such that that cost of a processor is zero (removing the need for load balancing) and the energy usage of each processor is minimised.

[As an aside, energy efficient computing is a growing interest such that when a program is constructed, how much energy is required to complete the computation is now the key factor in many systems (in terms of operational cost)]

The SpiNNaker project has designed a node which contains two chips; one chip is used for processing and consists of 18 ARM processors (1 hosts the operating system, 16 are used for application execution and 1 is spare) and the other chip is for memory (SDRAM). The nodes are connected in a 2D-mesh due to simplicity and cost. 48 nodes are assembled onto a PCB such that 864 processors are available per board. The processor only supports integer computation. The major innovation in the design is the interconnectivity within a node and between nodes on a board, A simple packet switched network is used to send very small packets around; each node has a router which is used to efficiently send the packets either within the node or to a neighbouring node. Ultimately, 24 PCBs are housed within a single 19” rack which are then housed (5) within a cabinet such that each cabinet has 120 PCBs which equates to 5760 nodes or 103680 processors. 10 cabinets would therefore result in over 1 million processors and would require around 10KW. A host machine (running Linux) is connected via Ethernet to the cabinet (and optionally each board).

Networking (and is efficiency) is the key challenge to emulate neurons. The approach by Spinnaker is to capture a simple spike (representing a neuron communication) within a small packet (40 bits) and then multicast this data around (each neuron is allocated a unique identifier, there is a theoretical limit of 4 billion neurons which can be modelled). By the use of a 3-stage associative memory holding some simple routing information, the destination of each event can be determined. If the table does not contain an entry, the packet is simply passed through to the next router. This approach is ideally suited to a static network or a (very) slowly changing network. It struck me that this simple approach could be very useful in efficient communication across the internet and maybe useful for meeting the challenge of the 'Internet of Things'.

Developing applications for SpiNNaker requires that the problem is split into two parts; one part handles the connectivity graph between nodes; the other part handles the conventional computing cycle with compile/link and deploy. Whilst the performance in terms of throughput is impressive (250 Gbps for 1024 links), it is the throughput which is exceptional at over 10 Billion packets/second.

The programming approach is to use an event-driven programming paradigm which discourages single-threaded execution. Each node runs a single application with the applications (written in C) communicating via an API to SARK (the SpiNNaker Application Runtime Kernel) which is hosted on the processor. The event model effectively maps to interrupt handlers on the processor with 3 key events handled by each application:

• A new packet (highest priority)
• A (DMA) memory transfer
• A timer event (typically 1 millisecond)

As most applications for Spinnaker have been to model the brain, most of the applications have been written in PyNN (a python neural network) which is then translated into code which can be hosted by SpiNNaker. The efficiency of the interconnections mean that brain simulations can now be executed in real-time, a significant improvement over conventional supercomputing.

In concluding, it is clear that whilst the focus has been on addressing the 'science' challenges, the are clearly insights into future computing in terms of improved inter-processor connectivity, improved energy utilization and a flexible platform. Whilst commercial exploitation has not been a major driving force for this project, I am confident that some of the approaches and ideas will find a way into main-stream computing in much the same way that 50 years ago, Manchester developed the paging algorithm which is now commonplace in all computing platforms. 

The slides are available here.

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

Windows 8 - Keep taking the tablets

So should I try the Windows 8 beta when it become available next month? That was the question following a very honest presentation given by Mike Halsey about the forthcoming Windows 8 operating system to the Manchester branch of the BCS. After having a few days to think about it, I can't see many reasons for upgrading from my current Windows 7 setup (and various Linux distributions running as virtual machines and an Android smartphone). This is very different to when Windows 7 came out, as it was a significant improvement from Windows Vista - I used the Beta version on my main machine until it was officially released when I then upgraded all of my machines to Windows 7.


Whilst there are probably many significant improvements behind the scenes, including support for new and emerging technologies such as USB3 and Thunderbolt, Windows 8's main evolution appears to be with the user interface, and the introduction of the new Metro interface. Mike had a pre-beta version of Windows 8 loaded onto a tablet and having a quick play on the tablet (start up time from cold was most impressive), the user interface was very similar to both Android and IoS. However, one significant change was that the application icons (or tiles as Microsoft would call them) could be different sizes and could also be live (so that they could show the weather or a stock price) without having to launch the application. This was first launched on the Windows Phone but I still think that this was a neat idea (although there are now similar products available for both Android and IoS).


2012 is clearly going to be interesting year for operating systems for tablets with Google's Ice-Cream sandwich (aka Android 4), Apples's latest IoS and Microsoft's entry in tablet space (Windows 8). There is clearly room for all of them but it is now very clear that operating systems that cater for  tablets must also work seamlessly with smartphones and other devices. With Microsoft being the last to release a tablet based OS, they are clearly playing catch-up and their success will clearly depend on the quality and takeup of the Apps in the recently announced Windows Store. Windows 8 is blatantly aimed at the consumer market, and is clearly trying to be a common platform across a variety of different platform types (desktops/laptops, smartphones, tablets and games consoles). This is is a bold strategy which no one has yet mastered. It is also promoting connected 'experiences' (with the cloud being a key part of the strategy) and clearly expects a 'touch' interface to become increasingly the primary form of interaction. Having said that, I understand it is still possible to get to the good old DOS window so that traditional user interface (command line) can still be experienced.


Will Windows 8 be a success? I don't know, but I think Windows 9 (scheduled for late 2015) might be the better bet as it will have the benefit of seeing how the integrated desktop/smartphone/tablet/games console world works. Microsoft are clearly betting on trying to develop a platform which can be common across a range of platform types, a laudable aim which will certainly deliver benefits in terms of product management (assuming it works!). However, I can't see any attraction for large corporates, many of which have yet to migrate from Windows XP. A big problem is in the application space in which applications developed specifically for Windows 8 cannot be run on Windows 7 (or predecessors). It was not clear to me if existing Windows 7 (or earlier) applications could run on Windows 8; if not this will be a huge mistake unless Windows 8 apps becomes priced at typical app prices (i.e. free or typically less than £1) rather than the several £100's that Microsoft applications typically cost.


Will I download the Windows 8 beta when it is available later this month? Maybe but only out of curiosity and it will be running on some old equipment as it don't see it as replacement OS for my primary machine.

Open Source Software - the legal implications

I attended the recent BCS Manchester/Society for Computers and Law event on the use of Open Source Software and the legal implications. It was given by Dai Davis, an information technology lawyer who is also a chartered engineer, a very unusual combination but he clearly knew his material.

Dai started by explaining the basic aspects of copyright law and the implications that this

had on software. It was clear that some of the original purposes of copyright law (to prevent

copying) were applicable to software but that the period that copyright lasts (70 years after

the authors death) clearly made little sense for software. However a number of points caught my eye:

• Copyright protects the manifestation and not the subject matter. This means that look and feel is not normally subject to copyright although fonts are.
• Copyright infringement also includes translating the material. Translation in the software case includes compiling source code as well as rewriting the source code into another language.
• Copyright protects copying some or all of the material. The amount does not normally matter.
• Moral rights do not extend to software but do apply to documentation
• Copyright infringement is both a civil and criminal offence with a maximum of 10 years imprisonment and an unlimited fine.

Dai then explained that the first owner (or creator) of the material owns the copyright. Misunderstanding this is the major cause of disputes about copyright. Clearly there are exceptions if the material is created in the course of employment (copyright rests with the employer) or if the contract under which the material is being created 'assigns' the copyright to the purchaser.

All software licences grants the purchaser permission to use the software otherwise the purchaser would be in breach of the copyright. Licences can be restrictive e.g. by time, number of concurrent users and all licences are transferable according to EU law.

Copyright of Open Source Software is no different to normal copyright of software but the approach to licencing is very different:

• Nearly all OSS do not require payment to acquire
• Free relates to restrictions on use (non-OSS can place restrictions)
• Open access to source and usage is required (not normally available with non-OSS)

However, the licences are very difficult to enforce mainly because there has been no loss in terms of monetary value. There has never been a successful prosecution in the UK although there are a number of examples in Germany (where litigation is cheaper than the UK) and an example in the US (Jacobson v Katzer in 2009) where a 'token' settlement of $100000 was awarded.

Whilst there may be little prospect of getting sued for use of Open Source Software the biggest issue often comes when businesses are sold and OSS is found within a product - this often affects the eventual purchase price of the company. Many businesses don't know where Open Source Software is being used and included within its own products because it is very difficult to police and manage.

A video of the session was filmed by students from Manchester Metropolitan University the resulting video being made available via the BCS Manchester website.

Computer Forensics

BCS Manchester recently hosted an interesting evening on the growing importance of computer forensics. The session was led by Sam Raincock, an experienced expert in performing forensic analyses. Whilst the session did not reveal the secrets behind a successful analysis (or give hints of how to make an analysis more difficult), it did explore some of the approaches (in general) that can be used in establishing evidence. Whilst a typical forensic investigation does include a significant amount of technical information this only accounts for about 20% of an analysis as the remaining time is concerned with communication with lawyers and report writing. As in all legal cases, it is crucial to review and present all the evidence to piece together a coherent case rather than circumstantancial evidence.

While Computer forensics is primarily the examination of any device that permanently stores data (the range of devices is ever-expanding from the traditional hard disc drives, CD-ROMS and USB memory sticks to mobile phones and cameras), it also includes reviewing system functionality in its goal to try to establish what happened and who did it. It is used in a variety of cases include criminal, civil and fraud cases.

It was stated that 'Every contact leaves a trace' by Edmond Locard, an early pioneer of forensic science. This is very true with all computer usage as every time a file is created, every web page that is browsed, every document that is printed is recorded somewhere although computer usage is unique to everyone.

Some key points that I took away from the session included:

1. Never assume anything
2. All humans are unpredictable, and different
3. Personnel cause more damage than they discover
4. Do not assume that common sense prevails
5. The IT department are not forensically trained and don't necessarily understand the value of every piece of data
6. Forensics is not about data recovery
7. Ownership of data must be established

A forensic examination is looking at where the offence was allegedly committed, how the event occurred and who performedthe activity. A typical examination can normally be performed on a single device (once a forensic image has been taken) by an appropriate expert and does not normally need to consult with outside agencies (e.g. internet service providers) to obtain specific information. The examination will review such data as cookies, the various system logs, network connections (IP addresses, type of connection particularly whether it was local, remote, fixed, wireless etc). The usage patterns of a computer will reveal a significant amount as every human has particular behaviour traits. The use of the various system logs that reside on a computer or within a network can reveal significant and valuable data; these logs should be actively monitored as they can often be the first sign that something unusual is being performed that may merit investigation. The sooner something is detected, the greater the chance of limiting the damage (or increasing the evidence in order to establisha conviction). In the case of an incident being detected within a business, the primary aim is to return a business to normal as quickly as possible. This is where policies are vitally important; it is equally important that they are actively used, policed and maintained.

Whilst there is no formal qualification required to become a forensic expert (an inquiring mind would probably be useful), it is clearly a growing and important aspect of computing. There are clearly many challenges with the continually evolving usage of computers; the growing importance of the cloud will clearly require different techniques to those employed when examining a physical item such as a laptop. The session left me wondering what traits my computer usage would reveal about me but also wanting to find out more about what is being recorded without me having any knowledge.

Open Source Document and Content Management

BCS Manchester recently hosted a meeting on open source content management and document management in the Public Sector. The speaker. Graham Oakes, explained that the catalyst for this had been an article in the Guardian about the use of commercial software by Birmingham City Council and its attempt at building websites. They had already spent £2.8M and the question was asked ‘Why not use Open Source Software instead?’ Graham stated that there is an awful lot of mis-information around, in particular that the use of OSS does not mean it costs nothing! This led the BCS Open Source Software Group to run a conference in January 2010 looking at the use and adoption of OSS in the public sector.

After briefly outlining what OSS is (source code owned by the community which can derive new works), Graham outlined a typical software stack found in many organisations. At every level, there are good open source solutions available. Content Management normally fits in at the application layer below portals and webservers. Content Management has a number of very strong options including Plone, Hippo, TYPO3, Drupal, Joomla, eZ and Umbraco. Many of these have already been adopted by public sector in developing websites, for example a number of Police forces.Document Management is not as well developed as Content Management and there are fewer options.

Gartner and Forrester enterprise software reviews both report that OSS should be adopted and it is becoming more amenable to use in the UK but it is still necessary to consider the full life-cycle costs. OSS should be considered equivalent to proprietary – public sector should now consider and event contribute to OSS projects. However UK is some way behind other european nations (specifically Netherlands, Germany, Italy and Denmark) with the OSOSS project in Netherlands urging public administration to use more OSS and open standards.

Advantages

The key advantages of adopting OSS within the public sector were identified as

• The reason for adoption is low up-front costs. Low costs of initial ownership, but the organisation needs to consider normal software selection processes and consider the risks, requirements for the software etc. OSS should be considered no differently to commercial software. It is still necessary to look at the total cost of ownership (TCO) and an organisation may need to still involve a system integrator in order to deploy effectively.
• OSS applications do not constrain the design. The public sector can can use it to start small, think big.
• Many OSS is easier to work with because access to source code (only useful if you have skills to use it!) is always available. This can provide an additional option to documentation. OSS is also increasingly important with the cloud as proprietary license models don’t adapt readily.
• Good to help the public sector to demonstrate openness (committed to visibility and open to the public)

Apart from the last one, the advantages are not particularly specific for public sector adoption.

Risks

Of course, there is always a downside, commonly called risks in procurement circles. The risks identified included

• Getting over the perception that everything is free. This misunderstands the true costs of OSS. Most costs in using software are not in the licenses – it is actually in the content created/managed by the software. Most project costs are less than 10% on technology/license and the migration costs must always be considered as these can often be many more times more expensive than the base software costs.
• Misperception around content management and reusability of OSS. Just because some OSS can be used securely, does not mean that all OSS is secure.
• Public sector are use to working with large organisations. OSS needs a different approach which the public sector may not readily adopt due to the mismatch of scale. OSS developers often move at a far faster pace than the public sector does (or can). This can be difficult at the procurement stage as the existing procurement model (level playing field) is broken. The procurement approach needs to recognise that OSS is no more less secure than proprietary solutions
• Unreasoned decisions still dictate the major procurement decisions. OSS might not be a perfect match to the requirements but may provide a suitable solution.

Conclusions

Graham presented a set of conclusions, which if I am honest are no different to proprietary software.

• Remember not all OSS are not the same, different quality levels and capabilities
• Always chose carefully – consider usages scenarios
• Look beyond licensing costs, user adoption, change management, migration
• A team still creates the success over the technology. Choose the right team!
• OSS supports evolutionary delivery, try before buy, which encourages innovation and supports agile and lean practices. However, this is good practice for all software.
• License fees for software bring costs forward and commit project for the duration (unless trial licenses are available). OSS does not have this commitment and it is (relatively) easy to change OSS software without excessive upfront costs.

So would OSS have solved Birmingham’s problem. No. The problem was not a cost of licences issue; it was not understanding the issue well enough. OSS would have helped to examine the problem in the small before the initial financial commitment was identified which might have produced a more realistic budget.

Lessons in measurement and data analysis

Recently I attended a very interesting and entertaining lecture by Peter Comer, from Abellio Solutions, to the BCS Quality Management Specialist Interest Group (NW) on lessons learnt in measurement and data analysis following a recent quality audit of an organisation’s quality management system (QMS).

The talk started by highlighting the requirements for measurement in ISO9001 (section 8). Key aspects that were highlighted included

• Measure process within QMS to show conformity with and effectiveness of QMS
• Monitoring and measurement of processes, products and customer satisfaction with QMS
• Handle and control defects with products and services
• Analyse data to determine suitability and effectiveness of QMS
• Continual improvements through corrective and preventative actions

It was noted that everyone has a KPI (Key Performance Indicator) to measure the effectiveness of products and services although every organisation will use the KPIs slightly differently.

Peter outlined the context of the audit, which was an internal audit in preparation for a forthcoming external audit. The audit was for a medium sized organisation with small software group working in transport domain. A number of minor non-conformances which were relatively straightforward to correct. However, after the audit an interesting discussion ensued regarding the software development process which stated that they were finding more bugs in bespoke software development than anticipated and a lot harder to fix. Initial suggestions included:

• Look at risk management practices. However, the organisation had already done this by reviewing a old (2002) downloaded paper looking at risk characteristics.
• Look at alternative approaches to software development.

It was the approach to risk which intrigued Peter. The quality of the paper was immediately considered. What was the quality of the paper? Has it been peer-reviewed? Is it still current and relevant?

Peter then critiqued the paper. The paper proposed a number of characteristics supplemented by designators; it was quickly observed that there was considerable overlap between the designators. The analysis of the data was across a number of different sources although no indication of what the counting rules are (and no indication if they were rigorous and consistent). The designators were not representative of all risk factors that may affect a development and said nothing about their relevance to the size of development. The characteristics focused on cultural issues rather than technical issues - risk characteristics should cover both. Just counting risk occurrences does not demonstrate the impact that the risk could have on the project.

Turning to the conclusions, Peter considered if the conclusions were valid. What would happen if you analysed the data in a different way, would the conclusions be different? Can we be assured that the data has been analysed by someone with prior experience in software development? It was observed that designators were shaped to criteria which is appropriate, but one size doesn’t fit all. Only by analysing the data in a number of different ways can the significance of the data can be established. It can also show if the data is not balanced which can in turn lead to skewed results. In the paper under review, it was clear that qualitative data was being used quantatively.

Peter concluded by stating that by ignoring simple objective measures can lead to the wrong corrective approach which might not be appropriate to their process and product. This is because ‘you don’t know what you don’t know’. It is essential to formally define what to count (this is a metric) with an aim to make the data to be objective. Whatever the method for collection, it must be stated to ensure that it is consistent.

The talk was very informative and left much food for thought. I have always aimed to try and automate the collection process to try and make this consistent. However this does nothing if the data is interpreted incorrectly or inconsistently. It is also difficult to know if you are collecting the right data but that is what experience is for!

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.