Higher Learning: AI, ML, and Speech Tech in Academia
Google, Amazon, Facebook, and other big tech players in the industry continue to expand the boundaries of artificial intelligence (AI) and speech technology as well as reap the profits earned from related products and services. But while these private sector giants—and the scientists, researchers, and innovators who work for them—continue to grab headlines and market share, arguably the most cutting-edge and important discoveries in the field are coming from the hallowed halls of academia.
That’s because many universities, colleges, and other institutions of higher learning have ramped up their curricula related to AI, machine learning (ML), and speech technology in recent years and bolstered their programs and departments with impressive faculty and resources. The reason is simple: growing demand from students seeking to earn a degree in these tech fields and fill the strong demand for high-paying high-tech jobs. Armed with bright minds, smart tools, and a determination to innovate, these educational establishments are pushing technology forward faster thanks to groundbreaking research, testing, and inventions.
But even as the learning opportunities at schools have increased, so have the challenges—including recruitment and retention of skilled teachers, lack of funding, inability to meet enrollment demand, and keeping up with the pace of progress.
A Growing Presence on Campus
AI first came to the classroom in 1956, when Dartmouth offered a Summer Research Project on Artificial Intelligence workshop. The following decades spawned many related discoveries by researchers at universities, labs, and private companies—research often funded by government agencies like DARPA.
However, due to limited computing capacity and other difficulties, the hopes and promises of AI proved elusive to academics until relatively recently. Thanks to 21st-century advancements in theory, algorithms, and computing power that have fueled interest in AI and ML among faculty and students alike, the dreams of scientists past are coming to life.
“Today, AI is being taught to more than just engineering and computer science students. Students in the liberal arts and business are getting exposure to AI,” says Joseph Wilck, faculty director of business analytics in the Mason School of Business at William & Mary. “Basically, it has morphed from a strictly theoretical domain and curriculum to a more applied and applications-driven curriculum.”
Additionally, institutions now have many more courses that deal with “how to” rather than just “what is,” according to Nava Shaked, head of the department of multidisciplinary studies at Israel’s Holon Institute of Technology and adjunct professor at the Graduate School and University Center of the City University of New York.
“Advancements in algorithms such as deep neural networks,” she says, “as well as conversational systems like bots and conversational virtual agents have created new interests and new ecosystems to explore.” New courses are also part of the equation. For example, AI classes offered at HIT include “Autonomous Cars and IOT,” “ML Algorithms and Deep NN Analytics,” “Wearable Computing,” and “Social Robots.”
“Excellent progress is being made in universities in the area of new learning algorithms, robustness of such algorithms—especially for mission-critical applications—and AI hardware architecture and circuits,” says Kaushik Roy, professor and director of Purdue University’s Center for Brain-Inspired Computing.
According to research by Diffbot, the top 10 universities producing graduates skilled in machine learning (more than 45,000) are Stanford; the University of California, Berkeley; Carnegie Mellon; Georgia Tech; MIT; University of Illinois; University of Southern California; University of Washington; University of Michigan; and Columbia University.
But AI classes aren’t solely for graduate students any longer, and they’re no longer strictly offered at schools specializing in high-tech, notes Houwei Cao, assistant professor of computer science at New York Institute of Technology.
“Nowadays, more and more schools start offering introductory-level AI and machine learning course for undergraduate students,” says Cao.
Consider that last year Carnegie Mellon University and the Milwaukee School of Engineering became the first two schools in America to offer an undergraduate degree in AI. There are also plenty of online AI courses accessible to all, including classes offered by Coursera and MIT.
Even schools and departments that don’t necessarily specialize in technology are now offering AI/ML learning opportunities. Case in point: Fordham Law School’s Center on Law and Information Policy launched the Intellectual Property Artificial Intelligence & Blockchain Project, directed by Shlomit Yanisky Ravid, which conducts research and offers courses, seminars, and conferences that address the discourse regarding AI and future policymaking from the legal regime perspective; it also produces works of art, like jazz songs entirely created by AI, to further study intellectual property law.
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