Future developments in computers

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I am writing my first blog post by sharing my views about the future developments in computers that may happen within next few years.

• The introduction of multi-core processors into standard desktop PCs means that making software work in parallel has become a very high priority. For many years attempts have been made to solve the issue of how to easily and efficiently ‘parallelize’ code to work on multiple processors. This is continuing, but there is a debate between those who favour using a few complex CPU cores (the present situation) and those who argue for the use of many simple CPU cores.

• Computer science needs to ‘get a grip’ with regard to ubiquitous computing. In a few years computers will be leaving our desks and merging into our physical environments, cars and even clothing. There may well be millions of these computing devices spread out across our urban environments and this will obviously include schools and colleges. Understanding how these devices will all interact with each other and with us, in essence, how they will ‘behave’, is one of the big research questions at the moment. They will create an enormous information space and this space is likely to be the greatest challenge for computer science in 21st century.

• Mobile phones are starting to incorporate forms of sensor, e.g. location-based sensors (GPS), awareness of user’s status (walking, running, sitting etc.). These devices will form networks with other devices in the near future and exchange information via the Internet, forming a global mobile sensor network. There are potentially many applications (and social implications) for this in the education arena.

• There is a lot of work going on into human facial recognition and expression/emotion detection which, in the long run, will feed into the kinds of human-computer interfaces that we will be using at home and in the educational setting.

• Research is being undertaken into what’s called the outlier detection problem. This is the process of automatically detecting anomalies or unusual events from massive streams of real-time experimental data that can now be generated by scientific experiments. This work will have obvious implications for the research community as enormous data sets become more common through the work of the e-science community.