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Schools focused on computer science and engineering should take steps to prepare to teach AI, robotics, and automation

AI is the future–is your school ready to teach it?

Schools focused on computer science and engineering should take steps to prepare to teach AI, robotics, and automation

By Cheryl Martin, Director of Higher Education Research, NVIDIA November 21st, 2022

Artificial intelligence is one of the most important and transformative technologies of our time. From assisting pathologists and discovering new drugs to recommending products and automating entire factories, more and more organizations are adopting AI to solve some of the world’s most important problems.

As young people grow up in an increasingly connected and technologically dependent world, they’re naturally interested in learning how to harness those technologies. But many higher education institutions do not have the capacity to meet the surging demand for computer science majors.

At the same time, schools face a number of challenges in preparing students for careers in AI and computer science. These challenges include limited faculty familiar with AI and data science, undeveloped curriculum, and limited computing resources.

Some schools are already successfully navigating these challenges. The University of Florida has worked in partnership with the public and private sector to develop a curriculum, hire teachers, and offer computing resources. The interdisciplinary collaboration will deploy AI across the curriculum to address challenges such as rising seas, aging populations, data security, personalized medicine, urban transportation, and food insecurity.

Oregon State University just announced the Collaborative Innovation Complex—a 150,000 square foot facility scheduled to open in 2025. The CIC will include a new supercomputer as well as a robotics and drone playground and an extended-reality theater. That’s alongside what people would think of as traditional laboratory settings.

Last year, Southern Methodist University launched a supercomputing research system, which is an $11.5 million investment that will increase SMU’s current supercomputer memory tenfold, allowing for AI and machine learning 25 times faster than current levels.

Not every institution will be able to take such big leaps. But every school focused on computer science and engineering should take appropriate steps to prepare to teach AI, robotics, and automation.

A Roadmap for AI Universities

The Center for Data Innovation developed a precise, detailed roadmap—one that can be utilized by colleges and universities across the country.

  • Increase AI computing capacity. GPU-accelerated computing is essential for researchers and faculty to train and test AI models. Securing government funding and partnering with industry can help.
  • Articulate realistic goals for partnerships based on a clear understanding of institutional capacity. Universities should carefully evaluate their own institutional capabilities and set goals that play to their unique strengths and missions.
  • Spread the benefits across disciplines. Make every department part of the process of building the core AI curriculum for their respective departments. Colleges of business, medicine, art, and economics can also benefit from AI. By extending opportunities throughout disciplines, institutions can overcome resistance, give students powerful new tools, and strengthen the program’s value.
  • Develop an ongoing assessment program. The benefits may be difficult to quantify initially, but benchmarks and measurable impact are vital for future partners, prospective faculty, or researchers to understand the program’s value.

Benefits of Widespread Access to AI

UF presents the best use case for showcasing what AI can do—not just for a university, but for an entire region. Since deploying its AI supercomputer and beginning to introduce AI curriculum-wide in 2020, UF has experienced several positive developments.

The university has recruited more than 130 faculty from diverse backgrounds to teach AI techniques throughout the school, including the College of Arts, the College of Medicine, and the Institute of Food and Agricultural Sciences.

Beyond the diversity in curriculum, UF’s program has brought diversity to those who can access AI research and education. The UF supercomputer is available to 12 public universities in Florida, including Florida A&M—the largest HBCU in the country – and Miami-Dade College, which has the country’s largest college undergraduate enrollment (165,000 students), 75 percent of whom are Hispanic and 15 percent of whom are Black.

UF is building an AI-fluent community at the school and beyond. So far, more than 1,000 faculty, students and researchers have received certified, instructor-led AI training. These teachers and students will be able to spread this knowledge to thousands more.

Investments like these help attract attention and research funds. By building an AI supercomputer and integrating AI across the university, UF has already attracted more research projects. This year, for the first time, UF surpassed $1 billion in research funding, a milestone that places it among 15 public research universities nationwide.

Widening the Funnel

For too long, AI innovation has been limited to an elite few universities with huge resources. But to prepare students for some of the biggest challenges and opportunities facing business and society, more students need access to AI training and compute resources. That includes students from HBCUs and other minority and underrepresented groups.

Limited access to compute resources and training not only restrains research, but it also limits educational opportunities and will ultimately make the U.S. less competitive.

Even small steps can make a difference. Not every school is ready to build an AI supercomputer, but they can engage faculty in discussions on how best to include AI in the curriculum, understand what would be required, and consider the resources and investment that would be needed. It’s an objective too important to postpone.

About the Author:

Cheryl Martin is the director of higher education research at NVIDIA.

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