Originally posted on my uSpace blog on May 20th 2012.
Computer science has evolved as a subject. The early days of computer science focused on languages and compilers. Ease of programming and reusability of code were key objectives. Ensuring the quality of the resulting software for reliability and safety concerns was a cornerstone of computer science research. The early days of the field were dominated by breakthroughs in these areas. The needs of modern Computer Science are very different. The very success of Computer Science has meant that computing is now pervasive, the consequence is vast realms of data. Automatically extracting knowledge from this data should now be the main goal of modern Computer Science.
I cannot say when I became a computer scientist as my undergraduate was in Mechanical Engineering, and whilst my PhD in Machine Learning was in a Computer Science department I was isolated there in terms of my research field and was closer related in my research to colleagues in Engineering and Physics.
My first postdoctoral position was with Microsoft, but I programmed only in MATLAB, and my second postdoctoral position was as a Computer Science Lecturer in Sheffield, but I still felt somewhat out of place. At the time Sheffield was rare in that it had a speech processing group based in Computer Science, and there was also a large and successful language processing group which overlapped more with my research.
My initial focus on arriving in the department was refining my own knowledge of computer science, I taught Networks and two classic books on Operating Systems (Tanenbaum) and Compilers (Aho, Sethi and Ullman) still sit on my shelf. I still thought of myself as an Engineer in the classical sense, only one who was interested in data. A Data Engineer, if you will. My contemporaries in machine learning research are mostly from Physics, Engineering or Mathematics backgrounds. There were more Psychologists than Computer Scientists.
Machine Learning Today
Today the situation has very much changed. I am a convinced computer scientist. So what has happened in the intervening 10 years. Did I dedicate myself so much to the teaching of Networks and the reading of Operating Systems and Compilers that I forsook my original research field? No, in fact it turned out that I didn’t have to conquer the mountain of computer science, the mountain chose to come to me. Today machine learning is at the core of Computer Science. The big four US institutions in Computer Science: MIT, Stanford, Berkeley and CMU all have very large groups in machine learning. In all of these cases these groups have grown in size significantly since 1996 when I started my PhD. Whilst MIT was active then, that was mostly through their Brain Sciences unit. CMU already had a very large group, and has since moved machine learning to a separate department, but Berkeley and Stanford were also yet to grow such large groups.
At the first NIPS (the premier machine learning conference) I attended there were no industry stands, in recent years we have had stands from Google, Yahoo!, Microsoft as well as a range of financial institutions and even airline booking companies.
Machine learning is now at the core of computer science. Of my current cohort of PhD students 4 out of 5 have computer science undergraduate degrees. The fifth has an undergraduate statistics education. The quality of these students is excellent. They combine mathematical strengths with an excellent technical understanding of their machines and what they are physically capable of. They are trained in programming, but they use their programming like they use their ability to write English, as a means to an end: not as the end in itself.
Modern Computer Science
The research effort to standardize machines, simplify language, encourage code reuse and formalize software specification has to a large extent been successful. Whilst it is not the case that everyone can program (as was envisaged by the inventors of BASIC). Today you do not need a degree in Computer Science to implement very complex systems. You can capitalize on the years of experience integrated in modern high level programming languages and their associated software libraries. There is a large demand for programmers who can combine php with MySql to provide a complex retail interface. But there are many individuals that implement these systems without ever having attended a University. Indeed, the prevailing wisdom seems to be that such skills (implementation of a well specified system in standard programming languages) will be subject to a worldwide labour market causing UK IT workforce to be undercut costwise by countries with a large portion of highly educated people, where labour costs are lower (e.g. India). In the UK (and more widely in Europe and the US) our target should not be to produce graduates who can only implement software to known specifications. What, then, is the role of computer science in a developed country like the UK? What graduates should we be producing?
Historically we would have hoped to produce graduates who had a developed understanding of operating systems and compilers, graphics, and perhaps formal methods. We would have produced people that could have designed the next generation of computer languages. We would have produced people who could conceive and design protocols for the internet. That would have been our target. These goals are still at the computer science core. But today we need to be much more ambitious. The success of the preceding generations has now meant that computer science is pervasive, far beyond the technical domain where it has previously dominated. The internet and social networking means that computers are affecting our every day lives in ways that were only imagined even 15 years ago.
This prevents a major technical challenge. In the past, some of the most advanced uses of computers were in other technical fields (engine management systems, control etc.) Those fields had technical expertise which they were able to bring to bear. The software engineer provided a service role to the engineering experts. Today, there are very few technical experts in the vast realms of data that computers have facilitated. Even in technical domains such as Formula 1, the amount of data being produced means that it is technical expertise that is required in data analysis rather than engineering systems. To a large extent, we made this mess, and now it is time for us to clean it up.
A modern Computer Science degree must retain a very large component of analysis of unstructured data. What do I mean by unstructured data? Data that is not well curated, it was not collected with a particular question in mind, it is just there. Traditional statistics worked by designing an experiment: carefully deciding what to measure in order to answer a specific question. The need for modern data analysis is very different. We need to be able to assimilate unstructured data sources to translate them into a system that can be queried. It may not be clear what questions we’ll be able to answer with the resulting system, and we are only likely to have minimal control over what variables are measured.
Examples of data of this type include health records of patients and associated genomic information. Connectivity data: links between friends in social networks or links between documents such as web pages. Purchase information for customers of large supermarkets or web retailers. Preference information for consumers of films. These data sets will contain millions or billions of records that will be `uncurated’ in the sense that the size of the data sets means that no single individual will have been through all the data consistently removing outliers or dealing with missing or corrupted values. The data may also not be in a traditional vectorial form, it could be in the form of images, text or recorded speech. We need algorithms that deal with these challenges automatically.
The Next Generation of Graduates
To address this situation we need to train a generation of computer scientists to deal with these challenges. The fundamentals they will require are language processing: extracting information from unstructured documents. Speech processing: extracting information from informal meetings, conversations or direct speech interaction with computer. Bioinformatics: extracting information from biological experiments or medical tests. Computer vision: extracting information from images or videos. Sitting at the core of each of these areas is machine learning: the art of processing and assimilating a range of unstructured data sources into a single model.
These areas must form the basis of a modern computer science course if we are to provide added value over what will be achievable by farming out software engineering. At the core of each of the areas outlined above is a deep mathematical understanding. Mathematics is more important to computer science than at any time previously. The algorithms used in all the areas developed above are derived and implemented through mathematics. The modern computer science education needs to be based on solid principles: probability and logic. These areas are at the core of mathematics and it is the responsibility of computer science to drive forward research in these areas. A modern computer science graduate must be fluent in programming languages and systems. Not as an end in themselves, but as a means to an end: the construction of a complex interacting systems for extracting knowledge from data. Teaching programing alone is like teaching someone how to write without giving them something to say.
It must be the target of a leading Computer Science undergraduate course to produce students that can address these challenges. All modern Computer Science courses should have a significant basis of data analysis beginning from the very first year. Computer science graduates should understand text and language processing: extracting meaning from documents. Rapid evolution of the language through internet mediums requires flexible algorithms for decoding meaning. The large volume of text on the internet presents major analysis challenges, but the wider challenge of understanding video: images and speech, gesture and emotion recognition hardly had its surface scratched.
A depth of understanding of probabilistic modelling, language modelling and signal processing must be built up over the second and third years of the degree. Our best graduates would have at least a four year education where they are given an opportunity in their final year to put the ideas they have learned into practice through thesis work on cutting edge research questions. Our graduates must be adaptable, they need to be able to build on the analysis skills we equip them with to address new challenges. If Computer Science doesn’t produce graduates in this mold there is no other field that will.
Many grand visions of Computer Science have largely been realized to, perhaps, a greater degree than was even anticipated: a computer on every desktop has become a computer in every pocket. Social interfaces through the internet that connect across the world and through generations. International commerce conducted with a click of the mouse. All of these successes have created an enormous challenge in processing of uncurated data. Computer Science Research has developed the first generation of tools to address these challenges, it is time for Computer Science departments to produce the first generation of graduates who will wield these tools with confidence.
The computer has changed the world, and I believe now it is time for the world to change the way we study computers.