Research using machine learning and artificial intelligence — tools that allow computers to learn about and predict outcomes from massive datasets — has been booming at the University of Michigan.
The potential societal benefits being explored on campus are numerous, from on-demand transportation systems to self-driving vehicles to individualized medical treatments to improved battery capabilities.
The ability of computers and machines generally to learn from their environments is having transformative effects on a host of industries — including finance, health care, manufacturing, and transportation — and could have an economic impact of $15 trillion globally according to one estimate.
But as these methods become more accurate and refined, and as the datasets needed become bigger and bigger, keeping up with the latest developments and identifying and securing the necessary resources — whether that means computing power, data storage services, or software development — can be complicated and time-consuming. And that’s not to mention complying with privacy regulations when medical data is involved.
“Machine learning tools have gotten a lot better in the last 10 years,” said Matthew Johnson-Roberson, assistant professor of naval architecture and marine engineering and of electrical engineering and computer science. “The field is changing now at such a rapid pace. It takes a lot of time and energy to stay current.”
Johnson-Roberson’s research is focused on getting computers and robots to better recognize and adapt to the world, whether in driverless cars or deep-sea mapping robots.
“The goal in general is to enable robots to operate in more challenging environments with high levels of reliability,” he said.
Johnson-Roberson said that U-M has many of the crucial ingredients for success in this area — a deep pool of talented researchers across many disciplines ready to collaborate, flexible and personalized support, and the availability of computing resources that can handle storing the large datasets and heavy computing load necessary for machine learning.
“The people is one of the reasons I came here,” he said. “There’s a broad and diverse set of talented researchers across the university, and I can interface with lots of other domains, whether it’s the environment, health care, transportation or energy.”
“Access to high powered computing is critical for the computing-intensive tasks, and being able to leverage that is important,” he continued. “One of the challenges is the data. A major driver in machine learning is data, and as the datasets get more and more voluminous, so does the storage needs.”
Yuekai Sun, assistant professor of statistics, develops algorithms and other computational tools to help researchers analyze large datasets, for example, in natural language processing. He agreed that being able to work with scientists from many different disciplines is crucial to his research.
“I certainly find the size of Michigan and the inherent diversity that comes with it very stimulating,” he said. “Having people around who are actually working in these application areas helps guide the direction and the questions that you ask.”
Sun is also working on analyzing the potential discriminatory effects of algorithms used in decisions like whether to give someone a loan or to grant prisoners parole.
Jason Mars, assistant professor of electrical engineering and computer science and co-founder of a successful spinoff called Clinc, is applying artificial intelligence to driverless car technology and a mobile banking app that has been adopted by several large financial institutions. The app, called Finie, provides a much more conversational interface between users and their financial information than other apps in the field.
“There is going to be an expansion of the number of problems solved and number of contributions that are AI-based,” Mars said. He predicted that more researchers at U-M will begin exploring AI and ML as they understand the potential.
He added that U-M does a “phenomenal job” in supporting researchers conducting AI and ML research.
“The level of support and service is awesome here,” he said. “Not to mention that the infrastructure is state of the art. We stay relevant to the best techniques and practices out there.”
Advanced Research Computing at U-M, in part through resources from the universitywide Data Science Initiative, provides computing infrastructure, consulting expertise, and support for interdisciplinary research projects to help scientists conducting artificial intelligence and machine learning research.
For example, Consulting for Statistics, Computing and Analytics Research, an ARC unit, has several consultants on staff with expertise in machine learning and predictive analysis with large, complex, and heterogeneous data. CSCAR recently expanded capacity to support very large-scale machine learning using tools such as Google’s TensorFlow.
CSCAR consultants are available by appointment or on a drop-in basis, free of charge.
CSCAR also provides workshops on topics in machine learning and other areas of data science, including sessions on Machine Learning in Python, and an upcoming workshop in March titled “Machine Learning, Concepts and Applications.”
The computing resources available to machine learning and artificial intelligence researchers are significant and diverse. Along with the campuswide high-performance computing cluster known as Flux, the recently announced Big Data cluster Cavium ThunderX will give researchers a powerful new platform for hosting artificial intelligence and machine learning work. Both clusters are provided by Advanced Research Computing – Technology Services.
All allocations on ARC-TS clusters include access to software packages that support AI/ML research, including TensorFlow, Torch, and Spark ML, among others.
ARC-TS also operates the Yottabyte Research Cloud, a customizable computing platform that recently gained the capacity to host and analyze data governed by the HIPAA federal privacy law.
Also, the Michigan Institute for Data Science has supported several AI/ML projects through its Challenge Initiative program, which has awarded more than $10 million in research support since 2015.