Higher Learning: AI, ML, and Speech Tech in Academia
Wilck sees the investment costs for starting a program as the highest hurdle to clear. “Faculty and staff, buildings and labs, and equipment and software are expensive. At some institutions, money spent on a new program means that other programs are left with smaller budgets,” says Wilck.
Simsarian agrees. “Academics need a minimum of $2 million a year to run a lab with graduate students and equipment,” he says.
Teaching Will Change with the Technology
Looking ahead, many are optimistic and excited about the potential for AI/ML’s growth in academia. “Universities will play a pivotal role in advancing AI primarily from the perspective of developing the theoretical underpinnings and explaining the decisions of the current AI systems, and improving their robustness against adversarial attacks,” Roy says. “These institutions will continue to offer specialized AI programs to cope with the increasing demand for such courses both from the students and industries alike.”
Bianconi seconds that assessment. “Given the growing demand from industry and students, universities are going to continue to increase their AI offerings,” Bianconi says. “We’ll see more people coming to school for advanced degrees as well as people retraining on the job.”
But, Bianconi predicts, most of the academic interest in the near term “will be on vision and natural language processing for text, not as much on speech.”
While an increasing number of schools may offer introductory and general classes on AI/ML, Wilck believes more universities “are going to try to find their niche and specialize. And I think we may also see degree programs that are created that merge AI into an already existing application area. Many students will find the most value is not only becoming an expert in AI, but also having a foundation in another field, such as business, engineering, or public policy.”
In addition, the debate over ethics and AI will increasingly be played out on college campuses. Shaked says, “Academia is a good place to raise, discuss, and educate on the importance of ethical questions, which must be addressed as more AI technologies become part of our lives.”
Erik J. Martin is a Chicago area-based freelance writer and public relations expert whose articles have been featured in EContent Magazine, Reader’s Digest, The Chicago Tribune, Los Angeles Times, and other publications. He often writes on topics related to technology, real estate, business and retailing, healthcare, insurance, and entertainment.
Sidebar: Graduating with Speech Tech Honors
Universities are proving to be instrumental in advancing speech technology, thanks to pioneering research and studies.
Exhibit A: Columbia University researchers have developed a way to convert brain waves into identifiable verbal speech employing AI and speech synthesizers—a potential boon to patients who have lost their voice due to stroke, injury, or disease.
“We combined the state of the art in artificial intelligence with advanced speech processing algorithms to reconstruct sounds from the brain that were much more intelligible compared to previous research,” says Nima Mesgarani, neuroengineer at Columbia University’s Zuckerman Institute. “Our algorithm is the first to generate a sound that is actually intelligible to human listeners—therefore bringing this concept a step closer to reality.”
In the future, the Columbia team plans to test more complicated words and sentences and run the same tests on brain signals emitted when a person speaks or imagines speaking. “Our ultimate goal is to develop technologies that can decode the internal voice of a patient who is unable to speak, such that it can be understood by any listener. We hope our system can be part of an implant, similar to those worn by some epilepsy patients, that translates the wearer’s imagined voice directly into words,” Mesgarani adds.
Exhibit B: A recent study at the University of Vermont is the first to apply speech analysis and machine learning techniques to child mental health; researchers there have developed a system that can identify markers for depression and anxiety in young kids based on their speech patterns.
“In the mental health field, we are in need of quick, feasible, objective tools that can help screen for anxiety and depression in this vulnerable population who often are overlooked,” says Ellen McGinnis, postdoctoral fellow in child psychiatry at the University of Vermont Medical Center. “Our results motivate additional development that can serve to translate this promising technology into a scalable screening tool for identifying young children who have anxiety and depression and directing them to appropriate psychological/psychiatric services.”
Co-author Ryan McGinnis, assistant professor at the University of Vermont, notes that this work advances speech technology by applying it to address a new and important problem related to human health.
“Childhood mental health is an emerging area of study, and with our society’s focus on preventive healthcare, the types of objective screening tools enabled by this advance have the potential to make a significant impact on how children with internalizing psychopathology are identified and referred for psychological assessment and treatment,” he says.
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