Higher Learning: AI, ML, and Speech Tech in Academia

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Speech Tech Advancements via Academia

Still, it’s the technical-engineering schools—where AI and ML systems are being developed in labs—that have fostered the most innovation, particularly when it comes to speech tech. “Universities like these have always been leaders in developing cutting-edge technology. Great minds and the search for innovations, promotion, and fame as well as other factors have played an important role in the development of AI, similar to the role academic institutions play in developing other areas such as biomedical and computer science,” says Ravid, a visiting professor of law at Fordham University Law School in addition to directing the school’s AI-blockchain project.

For proof, consider the following speech technology breakthroughs made at institutions with more established and renowned AI/ML faculty and programs:

  • Columbia University neuroengineers converting brain waves into recognizable verbal speech using AI and speech synthesizers (see sidebar);
  • an AI-based system at the University of Vermont that can recognize signs of depression and anxiety in young children via their speech patterns (see sidebar);
  • turning brain signals into natural speech with the help of a brain-machine interface created by neuroscientists at the University of California, San Francisco;
  • the development of Kaspar, a talking robot that helps autistic children hone crucial social skills, by the University of Hertfordshire, in the United Kingdom;
  • the creation of synthesized voices that sound more natural, aiding patients who use communication aids, thanks to the University of Edinburgh’s Centre for Speech Technology Research, also in the U.K.;
  • research from Washington State University which shows, with the help of automatic speech recognition technology, that young children, not their moms, initiate conversation;
  • safeguarding computer speech recognition from malicious messages concealed in speech, courtesy of research at the University of Illinois, Urbana-Champaign; and
  • Rochester Institute of Technology deep learning research that has created an automatic speech recognition system to help preserve the language of the Seneca Indian Nation.

Anima Anandkumar, Bren professor at Caltech’s CMS department and director of machine learning research at NVIDIA, says AI breakthroughs like these are being accomplished at universities, colleges, and institutions of higher learning through a combination of academic exploration funded by grants and industry collaborations.

“For example, NVIDIA has AI labs in Seattle and Toronto that partner with many universities like Stanford, University of California [at] Berkeley, Caltech, University of Washington, and the University of Toronto. These are great examples of the collaboration that happens between academia and industry to accelerate breakthroughs with state-of-the-art GPUs from NVIDIA,” says Anandkumar.

These alliances between companies and colleges create a win-win synergy.

“Academia develops the foundations that allow industry to adapt and apply at scale,” Anandkumar adds. “Collaborations between academia and industry help speed up innovations and close gaps that allows both to make new discoveries at a faster pace while at the same time adapting the learnings to industries such as healthcare, retail, finance, automotive, and more.”

Meeting the Challenges

It isn’t easy to serve as ground zero for tomorrow’s hot technology, however. Institutions large and small face plenty of obstacles on the path to providing proper instruction. “The prevalence of AI and machine learning has increased dramatically in industry in the last few years, which has created a shortage of qualified talent in the field,” says Cao. “In order to address this problem, more and more universities are trying to develop new curricula and new courses in artificial intelligence and machine learning.”

Kristian Simsarian, owner of Collective Creativity and professor at California College of the Arts in San Francisco, says it’s also hard for universities to continue cutting-edge research in ML “because companies are offering incredible salaries and bounties for professors and leaders to join industry.”

In addition to recruitment and retention of highly valued human resources, “you need faculty focused on multiple areas of AI—such as vision, natural language processing, applications, robotics, and learning theory,” says Gabriel Bianconi, founder of Scalar Research and a former researcher at Stanford’s AI Lab. “It’s also difficult meeting student demand. Some classes at Stanford have around 1,000 students enrolled.”

Introducing inexperienced students to AI/ML can be particularly tricky for universities aiming to offer undergraduate programs.

“Most of the students start from zero and have no background, which is required to reach a high level of academic research in AI. That’s why top graduate students often benefit the most,” Shaked notes. “It’s also challenging to apply AI teaching in non-science and engineering programs and classes, such as design and industrial management.”

Another inherent problem? Staying relevant and not letting your AI/ML courses become outdated, as the technologies and theories can evolve quickly.

“Even if schools have offered a miniscule number of courses about AI and ML for years, these past courses were different due to significant progress achieved in research in programming and its results,” Ravid says.

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