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Solving real-world problems require deeper knowledge of AI, ML than simply accessing smartphone apps

Posted on Monday, June 10, 2019

John S Fitzgerald, head, School of Computing, Newcastle University on how AI, ML, and IoT involve a convergence of disciplines to solve real-world problems, writes Rajlakshmi Ghosh

Q. What does AI, Big Data, ML and IoT mean for students’ careers and future workplaces? 

A.Future generations will be used to having Artificial Intelligence (AI), Big Data and Internet of Things (IoT) in their work and home environments. They will need to have a high level of ‘literacy’ in these technologies, appropriate to their needs. For students wanting to enter this field, the core foundation skills of computer science are key to creating the next generation of technical systems.

Q. Do you think only computer science students can qualify for skilling in these emerging technologies? 

A. No, and in fact, it is important that these technologies are not just left to computer scientists. In order to make the most of them, we need to have specialists who work alongside those skilled in engineering, science, math, business, and a whole host of other disciplines. At Newcastle University (NCL), some of our best computer scientists come from backgrounds in areas as diverse as business, history and languages.

Q. Since future-readiness would involve a multidisciplinary approach towards futuristic technologies, how is it possible to bring about their convergence?

A. New generations of products and systems require multidisciplinary teams to develop and maintain them. Computing today is not only about the apps on your smartphone or the computer, it is about whole products and systems that use computing technology intensively. If you think about a modern building or a piece of infrastructure such as a road system, there is computing technology and software embedded alongside mechanical, electronic and other elements. The teams who will innovate in this area will include specialists in all these disciplines, and they will need to be able to work together, even though they might use different tools, techniques and methods in their work.

Q. Will new-age technological disruptions reduce human interface?

A. When we talk about new technologies, we often do so in slightly fearful terms, and of course, we know very well that these things can be used for good or for ill. However, it is vital to see the good that these technologies can provide. We have done work in areas such as rehabilitative computer games, which help people to regain limb movement after having a stroke. The big data analytics to monitor the patient’s movements helps to ensure that the human interactions between the patient and their therapist are much richer than before because we can see and analyse the areas where improvement is limited, and explore the patient’s concerns much better.

Q. What is the kind of research taking place at your university in the field of Big Data towards problem-solving?

A. At NCL, work in data science is spread widely across different fields. In data gathering, our Urban Observatory collects information dynamically about the city (noise, air quality traffic, water levels, etc) from a large range of sensors all across our environment, creating one of the biggest live and publically accessible urban data streams in the world. In data analytics, even within my own School of Computing, we apply AI techniques in areas as diverse as bio-computing and human-computer interaction. 

Since we want to do research that addresses real-world challenges, we link our research to industry engagement. In Newcastle, with direct investment from the UK government and the University, we have created the UK’s National Innovation Centre for Data. This is a place (a new building next to the Computing School) where businesses can work with us on upskilling their staff in data science, identifying and solving real problems with the large data sets they are gathering in their organisations. The problems that we cannot solve define new research challenges for the next generations of researchers and students.


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