Advancing AI Across Disciplines: A Roundtable Discussion with the Deans of Northeastern University

by Tyler Wells Lynch

The ten deans of Northeastern University discuss how AI spans their domains at Discover Experiential AI. (Photo by Heratch Ekmekian)

At Discover Experiential AI, the inaugural event of the Institute for Experiential AI, ten deans representing the nine colleges (and the library) at Northeastern gathered to discuss how AI spans each of their domains, and how practitioners can go about creating the foundations for a cross-disciplinary field.

Engineering: Scaling solutions in engineering contexts requires evermore data, which requires more sophisticated means of processing. Gregory Abowd, dean of the College of Engineering, said it bluntly: “Engineering pervades AI and vice versa, and AI needs much more than engineering to be successful.” That’s a job only AI can fulfill.

Library Studies: Dan Cohen, dean of the Libraries, pointed to text extraction as an ongoing challenge: handwritten documents, daguerreotype photographs, library collections written in hundreds of languages — are huge challenges for AI. But great strides have been made to help process, digitize, and search the wealth of knowledge housed in humanity’s first home for information — the library.

Professional Studies: Citing a study predicting an AI-driven $15.7 trillion increase in global GDP by 2030, David Fields, dean of the College of Professional Studies, explained how AI is already pervasive. In professional settings, AI applications are found at the intersection of different professions. For example, Human resources, leadership, and project management are better served by leaders equipped with training that meets the demands of a data-driven economy.

Law: James Hackney, dean of the School of Law, pointed to ways AI has transformed how lawyers do their jobs, assisting them in document review and legal discovery. He also spoke about the ways AI has transformed people’s experiences of the legal system positively and negatively. Biased data, mistranslations, and algorithmic errata have contributed to legal decisions that have greatly impacted their lives.

Art and Design: The arts span a vast range of literacies, for which AI offers a suite of creative tools. Elizabeth Hudson, dean of the College of Arts, Media and Design, explained how these tools expand access and allow creators to reach beyond their human limits: musical generators, AI-powered choreography, and even script ideation, to name a few.

Computer Science: Acknowledging the transformative power of AI, Beth Mynatt, dean of the Khoury College of Computer Sciences, stressed how AI needs to become more than what it currently is. “AI is much more than the modern infrastructure of our daily lives,” she said. “It’s also the societal infrastructure.” For that reason, computer scientists need to do a better job setting expectations and focusing on the huge ethical and technical challenges that lie ahead.

Humanities: Uta Poiger, dean of the College of Social Sciences and Humanities, argued that the humanities are uniquely positioned to critique AI while also proposing new applications. She also stressed the need for community. Educational programs focused on community stand a better chance of formulating outcomes that reflect society in all its diversity — not just in academia, but also in industry, philanthropy, and public policy.

Health: Health is complicated and messy, spanning genetics, biology, the environment, and social contexts. Any attempt to understand health must be interdisciplinary from the start. Carmen Sceppa, dean of the Bouvé College of Health Sciences, explained how AI technologies help bridge those divides by supplying robust data and digital assistance in clinical and research settings. It promises to make sense of complex data sets while pointing to solutions that reach well beyond personal health.

Science: Hazel Sive, dean of the College of Science, also pointed to data as the cornerstone of AI. “Most scientific data sets are huge,” she said. “And often they are not properly analyzed.” The human genome, disease risk, ecosystems, climate change, particle physics — all these areas of study depend on sophisticated data analysis that’s increasingly only possible through AI.

Business: Emery Trahan, interim dean of the D’Amore-McKim School of Business, sees AI as a key element of the digital convergence, where success depends on combining data analytics with human judgment. “Business is the epitome of experiential,” he said. In that world, Trahan explained, AI partnerships are needed to solve business problems and improve the human condition.

Unlikely Allies: AI and Creativity

If you imagine the scientific branches as limbs on a tree, you’ll see three great trunks representing the formal, natural, and social sciences. As they grow upward, they branch off into ever more distant fields, ranging from mathematics to geology to anthropology. But nature doesn’t distinguish between domains, only people do. And the barriers we create to assert domain expertise too often result in hierarchies that may or may not be just.

In non-academic settings, is it wise to assume those hierarchies no longer exist? Probably not. If you look at the field of artificial intelligence, you’ll see how those borders sometimes lead to real-world harm. It’s no wonder trust in AI is so low. It’s fitting then that the first question put to the panel dealt with what is perhaps the most cross-disciplinary feature of academic pursuit: creativity.

But can machines be creative?

Dean Hudson chose to flip the question on its head: While pointing to AI programs that could generate works of art, she asked what it means for a machine to create? Perhaps a more fruitful question would be: How can AI be used to enhance creativity?

For Dean Mynatt, it’s a matter of shifting from the representative problem of machine creativity to the productive problem of co-creation. Human-machine interactions in, for example, live jazz performances can force creative challenges that might otherwise never have occurred. Dean Abowd broadened the scope of the topic, pointing out that, insofar as creativity can be understood as exploring a space, humans are deficient: “A machine is far better at exploring the entirety of that space than any human would be.”

The point is that creativity, like intelligence, is hard to define. It makes sense that our efforts to pin down machine creativity would be even more difficult. David Fields pointed to alternative models to help illustrate the challenge: In the west, we tend to think of creativity as a blank canvas, while in the east, art is more about the strategic removal of content to reveal a deeper meaning. Might AI then be an entirely new model of creativity?

Watch the fascinating discussion between the deans via our Discover Experiential AI event replay. Contact the institute for more information about how we can help you achieve your AI goals.