How Data and AI May Reshape Education
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How Data and AI May Reshape Education

Alessandro Curioni

The primary role of the university is and always has been to equip its students with the fundamental knowledge required to become experts in their professions, and to teach new skills in emerging fields to master future challenges. Major technology advances will require new curricula that provide students with a solid understanding of the technology and its broader implications – also from a business, economic, or societal point of view. To succeed here the creators of new technologies need to engage closely with the universities to bring the advances into the curricula.

Tracking the historical development of different areas of technical science and the waves of innovation can provides us with insightful examples of how universities can best support industry through education and training.

In 1930, the CEO of IBM Thomas J Watson Jr discussed with people in Columbia how the tabulator machine could be used for the automated rating of university tests. This established a first step towards creating a new scientific field that is today known as Computer Science. In 1945, IBM created the Watson Scientific Computing Laboratory at Columbia University, its first laboratory devoted to pure science. Around 1950, the first Computer Science courses were established at universities and in 1970 it became an established discipline.

Around the turn of the century, the first digital revolution accelerated very strongly. New digital businesses emerged and novel services were created. During this period more value started to be created out of services than with classical manufacturing. This development created the need for a deeper understanding of services and their optimization, which in turn lead to the introduction of the discipline of service science in academia. The key to service science is its interdisciplinarity, focusing on service as a system of interacting components including people, technology, business, etc. Service science integrates aspects of multiple disciplines – including computer science, cognitive science, economics, organizational behavior, human resources management, marketing, operations research, etc. Within about 10 years this discipline was established in academia with over 400 courses offered in 2010 and was driven by a strong collaboration between academia and the Computer Science Industry. Today, we find ourselves in the middle of another big innovation wave, fueled by the rapid increase in data from various sources such as Internet-of-things devices, social media, or computers. Every month over 50 Exabytes of data is produced (Note: one exabyte could hold a hundred thousand times the printed material at the Library of Congress). This extremely large pool of data demands automated techniques that efficiently extract and aggregate the contained knowledge and thus enable humans to take informed decisions and actions. It is the Artificial Intelligence (AI) technology that has the potential to handle such large data volumes automatically and to change not only the technology landscape, but to have a fundamental impact on people’s lives and professions. Humans are on the cusp of augmenting their lives in extraordinary ways with AI. Next-generation AI enabled systems will work side-by side with humans, accelerating our ability to create, learn, make decisions, and think.

These systems will become pervasive in many areas and already have applications in cancer research, financial decision-making, oil exploration or education.

Many new challenges need to be addressed to fully exploit the potential of this technology, including ethical questions, the need for new ways of human-machine interactions, the ability to make AI decisions understandable and acceptable by humans, all the way to changing characteristics of today’s professions.

These requirements lead to the need for new curricula at the universities and possibly new majors, if not departments, which enable students to build learning machines, interact with them, and, more importantly, to address the much broader challenges in collaborative, interdisciplinary ways.

The rate of change at which these technological changes happen is a real challenge for universities, requiring them to adopt more agile forms of education. A report by LinkedIn shows that two of the top four majors in 2014 were not in the list by 2016. Can universities adapt changes at the rate of technology pace? They have to answer the question, if a three or four-year degree is valid with today’s rapid changes.

As AI systems become much smarter in their specialized fields, it becomes crucial that students navigate proficiently in these interdisciplinary domains and are enabled to “connect the dots”. In the past, typically a successful expert was one who combined deep theoretical expertise with excellent practical skills in a specialized area and the ability to collaborate across disciplines with experts in other areas. Consider a material scientist who first had to acquire, digest and summarize the relevant knowledge from the literature for a particular field, and then use his experience to gain new insights and extend the existing knowledge.

Now, that AI systems can scan millions of new publications for any new insights, the task of summarizing the existing knowledge from literature can be completed much faster and at a much larger scale. The role and required skills of the material scientist will change significantly. Instead of spending on literature studies, he/ she will need to take additional and complementary aspects of the problem into account, such as the final use of the material in a product, the production process itself, or the business case. Tackling the problem from a broader knowledge base and in a much more holistic way will lead to improved products and to new professional challenges and opportunities for the material scientist.

The skills that will define a successful expert in the future will be centered around the expert’s ability to work across disciplines, to understand and connect multiple fields, and to create value in interdisciplinary areas that couldn’t be created in a siloed, specialized environment.

It will be the joint responsibility of the industry and universities to work together to develop those cross-disciplinary curricula and to prepare the future experts for successful carriers in rapidly changing professional environments.

 

Dr. Alessandro Curioni is an IBM Fellow, Vice President of IBM Europe and director of the IBM Research Lab in Zurich, Switzerland. In parallel, he serves as the Watson IoT Research Relationship Executive. Dr. Curioni is an internationally recognized leader in the area of high-performance computing and computational science, where his innovative thinking and seminal contributions have helped solve some of the most complex scientific and technological problems in healthcare, aerospace, consumer goods and electronics.
He was a member of the winning team recognized with the prestigious Gordon Bell Prize in 2013 and 2015. Alessandro started at IBM Research – Zurich as a PhD student in 1993 before officially joining as a research staff member in 1998, where his most recent position was Head of the Cognitive Computing and Computational Sciences department

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