30 Jun 2020
AI in Academia: How the Need for Future Data Scientists & the Availability of Big Data is Transforming Universities
Today, the world of academia and education is undergoing one of its most substantial transformations. Undoubtingly, COVID-19 can be identified as a major catalyst for a technological shift at universities around the world. Millions of students, tutors and professors have had to adapt to online tutoring and collaboration to continue their research and studies. Nevertheless, recent research and developments suggest technological transformation was already underway in academia before the global pandemic. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have gained substantial momentum, both the way we learn and study, as well as the crucial role universities play in fulfilling demand for data scientists and innovative scientific research.
How will AI change academia?
Over the past decade, AI and ML technologies have improved dramatically and their field of application continues to grow by the day. Throughout the globe, data-savvy individuals that seek to tackle academic, business, and societal challenges with the support of AI technologies are being recruited by universities and scientific institutions. As these technologies advance and become more sophisticated, experts are certain that they will revolutionise academia. Elsevier’s 2019 Research Futures report identifies three main areas in which AI-enabled technologies will change the game in science and academia:
- AI and ML innovations offer scientists and researchers across all fields and disciplines an enhanced access to data in an unprecedented speed and volume.
- These adaptive technologies will also support peer review procedures and enable data-driven scientific hypotheses.
- AI-enabled technologies are paving the way for open-source science. An inclusive approach offers an easy to access approach that allows academia to join forces through global scientific platforms.
These new methods of scientific collaboration and impact assessments will transform the world of academia as we know it throughout the next ten years. Although these visions are yet to be met, AI has already taken up a crucial role in scientific research. Two recent studies illustrate the value of AI-enabled technologies for science.
Powering AI initiatives in Academia
Amidst global scrutiny of news and in particular its authenticity a 2018 Massachusetts Institute of Technology (MIT) paper found that the application of ML tools, concerning the identification and detection of fake news, should still be further improved. This example showcases not only the progressive approach the academic community displays in relation to AI and ML technology but further provides for a best-practice example of how societal issues can be tackled through these technologies. Indeed, the MIT study resulted in the development of a tool that is able to detect language patterns that characterise real and fake news and can differentiate between the two with the support of ML and natural language processing (NLP) techniques.
AI-enabled processes have further allowed researchers to quantify and validate gender stereotypes and sexist language in literature. An unsupervised ML study, which was presented at the 2019 meeting of the Association for Computational Linguistics, concluded that negative terms in literature and language are disproportionately identifying female characters or subjects. In a collaborative effort, researchers from different institutions and private entities made use of AI and ML-based technologies to emphasise the impact literature and language has on the protection of gender inequalities. The benefits AI and ML technologies provide in these cases are but two examples. AI also benefits the scientific community by fighting plagiarism, as well as identifying flawed results and statistics.
AI expertise on the move
The increasing demand in AI and ML concepts has led to a hard-fought competition between academic institutions and the private sector to nurture and invest in junior AI researchers. Unsurprisingly, careers in AI have dominated recent LinkedIn employment rankings and will continue to do so since the need for field-specific knowledge only is in an early stage. According to a 2019 Worcester Polytechnic Institute study, the percentage of ads seeking expertise in AI, ML and data mining have almost doubled over the course of the last five years. The present scarcity of AI researchers across all academic career levels has also benefited those involved in this industry. Higher salaries, improved resources allocation and enhanced data sets—both in academia and the private sector—have substantially transformed the industry.
Cooperation not confrontation
Fostering collaborative efforts is critical for developing an inclusive approach towards AI and ML technologies and their application in academia and beyond. In early 2019, Dutch Prime Minister Mark Rutte inaugurated Elsevier’s TechHub which supports efforts to streamline the development of AI-enabled technologies between the government, academia, and the private sector. Linked to Elsevier’s TechHub, the Amsterdam-based Innovation Center for Artificial Intelligence is an additional example of how cooperation between the private sector and academic institutions can enhance the application of AI and ML technologies.
Without doubt, AI is going to substantially influence the world of science in the years and decades to come. It will make academia more accessible, while at the same time more cohesive, reducing current limitations researchers often face.
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