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The Historical Hype Cycle of AI

Before talking about the current and projected impact of AI in education and other industries, Ramsey explained the concept of the AI winter.

He showed a graph on the historical hype cycle of AI that featured peaks and drops over a 70-year period.

There was a big peak in the mid-1960s, when there was an emergence of symbolic AI research and new insights into the possibility of training two-layer neural networks. A resurgence came in the 1980s with the invention of certain algorithms for training three-plus layer neural networks.

The graph showed a drop in the mid-1990s, as the computational horsepower and data did not exist to develop real-world applications for AI a situation he calls an AI winter. We are in the middle of another resurgence today, he said.

There has been a huge increase in the amount of data and computer power that we have available, sparking research, Ramsey said. People have been able to start inventing algorithms and training not just three-layer neural networks but a 100-layer one.

The question now is where we will go next, he said. His answer? We will sustain progress, leading to true or strong AI the point at which a machines intellectual capability is functionally equal to a humans.

The number of researchers working on this, the amount of money thats being spent on this and the amount of research publications its all growing, he said. And where Google is right now is on a plateau of productivity because were using AI in everything that we do, at scale.

MORE ON EDTECH:Learn how data-powered AI tools are helping universities drive enrollment and streamline operations.

During his presentation, Ramsey showed an infographic that featured what machine learning could look like across a students journey through higher education, starting from their college search and ending with employment.

For example, he said, colleges and universities can apply machine learning when targeting quality prospective students to attend their schools. They can even automate call center operations to make contacting prospective students more efficient and deploy AI-driven assistants to engage with applicants in a personalized way, he said.

Once students are enrolled, they can also useAI chatbotsto improve student support services, assisting new students in their adjustment to college. They can leverage adaptive learning technology topredictperformance as they choose a path through school, and they can tailor material to their knowledge levels and learning styles.

For example, a machine learning algorithm helped educators at Ivy Tech Community College in Indianapolis identify at-risk students and provide early intervention, Ramsey said.

Ivy Tech shifted toGoogleCloud Platform, which allowed the school to manage 12 million data points from student interactions and develop aflexible AI engineto analyze student engagement and success. For instance, a student who stops logging in to their learning management system or showing up to class would be flagged as needing assistance.

The predictions were 83 percent accurate, Ramsey said. It worked quite well, and they were actually able to save students from dropping out, which makes a big difference because their funding is based on how many students they have, he said.

As students near graduation and start their job searches, schools can also use AI to understand career trends and match them to a students competencies and skills. Machine learning can be used to better understand job listings and a jobseekers intent, matching candidates to their ideal jobs more quickly.

At the end of the day, what were doing with these technologies is trying to understand who we are and how our minds work, Ramsey said. Once we fully understand that, we can build machines that function in the same way, and the possibilities are endless.

Excerpt from:
A 5-Year Vision for Artificial Intelligence in Higher Ed - EdTech Magazine: Focus on Higher Education