At the Global AI Summit in Riyadh, experts discussed how artificial intelligence (AI) might bridge the longstanding gap between academia and industry. Historically seen as having conflicting priorities—academia focused on advancing knowledge and industry on profit—the panelists argued that AI could facilitate greater collaboration.
Ahmed Serag, director of the AI Innovation Lab at Weill Cornell Medicine in Qatar, highlighted the traditional differences between the two sectors. Academia often emphasizes long-term knowledge advancement and peer recognition, while industry is driven by immediate returns on investment. This divergence has sometimes led to delays in applying academic research to practical, profitable applications. The dominance of resources in the private sector, particularly in AI, has exacerbated this issue.
Chuck Yoo, executive vice president for research affairs at Korea University, suggested that AI's rapid progress could help bridge this divide. He noted that the era of AI is fostering new forms of collaboration between academia and industry. Programs that allow students and researchers to work in industry settings, such as internships and fellowships, can help by providing real-world perspectives and bridging the communication gap.
Serag also emphasized the importance of establishing shared intellectual property agreements early in collaborations. This would address the fundamental conflict where academia seeks publication and recognition, while industry focuses on protecting and commercializing technology. One approach could be to initially patent technology and allow universities to publish later.
Abdulmuhsen Al-Ajaji, vice president of cloud software and services at Ericsson Saudi Arabia, observed that academia is increasingly emulating industry practices. Universities are launching their own accelerators and venture capital funds to take a more active role in commercializing research and owning intellectual property.
Despite these positive steps, Serag cautioned that the exploitation of academic research by industry is a deeply entrenched issue. He cited the example of the breakthrough in AI vision algorithms from the ImageNet competition, which started in academia but was scaled up by companies like Google. This exemplifies the ongoing challenge of translating academic innovations into practical, commercially viable technologies.