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Artificial intelligence (AI) and machine learning (ML) advances draw headlines around the globe and spur speculation about the future and about innovations that may impact both the workplace and personal life, all while promising exciting and unexpected changes.

To Dr. Alvis Fong, professor of computer science and a leader in AI and ML research, these changes represent an opportunity for “smart” machines to eventually take mundane tasks away from human operators in industry and elsewhere. But they also represent uncertainty.

“While it is exciting to envision a future of harmony, with people and machines collaboratively solving complex research problems that are unsolvable today, there are unknown variables,” says Fong. “Many things can potentially go horribly wrong.”

And to address the unknown requires both technical research and studies into teaching and learning.

“My technical research revolves around machine-assisted knowledge discovery from big data, knowledge representation and reasoning, and pitfalls to avoid when using AI,” explains Fong. “A common thread that binds my projects is that there must be clearly demonstrable practical implications.”

Having amassed hundreds of technical publications, Fong covers a multitude of aspects of AI and ML from neural networks for filtering harmful web content and phishing to automatic fuzzy ontology and sustainable micromobility management. Fong has a particular interest in evolutionary computation and quantum computing. Recently, he demonstrated efficacy in applying AI deep learning for early disease detection with findings published in Multimedia Tools and Applications, 2023.

But leading technical research is just part of this expert’s research profile. He has an intense interest in teaching and learning research that revolves around combining technical education with skills �鶹��ýs and workers need for effectively applying AI skills.

“Being AI ready means more than knowing how to use AI/ML methods,” says Fong. “It also means knowing when other approaches are more appropriate—better, cheaper, simpler, realizable, more explainable. Because AI has far reaching societal impact, readiness training must cover non-technical issues like ethics.”

All this became the key driver of two consecutive NSF-funded projects aimed at getting STEM �鶹��ýs AI ready in a holistic way. The first, “Modular experiential learning for safe, secure, and reliable AI” (2020-2022), was a pilot study that entailed development and launch of 12 experiential learning modules.

The second project, “Promoting AI readiness for machine-assisted secure data analysis” (2023-2027), is developing flexible micromodules and immersive learning experiences compatible with the proposed metaverse. This project is led by  an entire team at Western that includes Fong and Dr. Shameek Bhattacharjee, assistant professor; Dr. Steve Carr, chair; and Dr. Ajay Gupta, professor, all in the Department of Computer Science.

It’s clear that AI and ML are impacting the world and that experts will continue to develop new applications. So how should individuals adapt?

“It is critically important for society as a whole to have a reasonable grasp of where AI research is potentially taking us. We need to be informed, so we can contribute meaningfully to debate surrounding the broad and deep societal impacts of AI.”

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