MSOE Innovation Center and Data Center

The Milwaukee School of Engineering is building a $76.5 million engineering building—the Robert D. Kern Engineering Innovation Center—at the southeast corner of State and Milwaukee streets. The builder will include flexible labs, AI, and robotics. This will be a space that encourages collaboration and firsthand learning in the Innovation Center.

The roadmap for the AI future involves technological advancements, such as the emergence of AI agents and self-improving systems, alongside a focus on policy and societal integration, including setting safety standards, upskilling the workforce, and managing job displacement. Rapid technological advancements, industry investment, and increasing AI adoption across sectors are shaping the future of AI. Artificial intelligence is changing every sector of society, but its rapid growth comes with a real footprint in energy, water, and carbon, according to a study by David Nutt from Cornell University.

 “I think the reality is nobody knows for sure. I can come up with some guesses, but that’s about as far as I can go. So, it’s clear that AI, specifically generative AI, will continue to find its way into our lives in many different ways. And so, yeah, it will continue to grow. “But it’s clear that technology is continuing to solve more complex problems, and those problems tend to be useful ones that we get value from. So, it will continue to grow. Just a matter of how,” said Derek Riley, Ph.D. Director of Computer Science, Milwaukee School of Engineering.

The impact of AI on society is complex. AI can deliver positive outcomes, including improved and personalized services. However, it raises concerns regarding job displacement, biased decision-making, privacy infringement, and social inequalities. The net impact of AI on society will depend on ethical considerations, responsible deployment, and inclusiveness, according to a study by Mike Trojecki from IIot World. AI can lead to job loss. AI can be biased in how it is used and in how jobs should be done.

“But AI more broadly has been demonstrated to be useful, and so I think it will continue to find its way into our lives in lots of different ways. And so, I think it will continue to grow; how rapidly it will grow is really a business question. However, it’s clear that technology continues to solve increasingly complex problems, and these problems tend to be useful ones that yield value. So, I think it will continue to grow. Just a matter of how,” said Riley.

Wisconsin is becoming a hub for data centers due to its abundant water for cooling, available land, cool climate, and existing infrastructure, combined with a favorable business environment and lower costs than in other regions. There are at least 47 data centers currently up and/or proposed in Wisconsin. Businesses rush to take advantage of accessible water, affordable electricity, and the state’s cool climate. Wisconsin has robust infrastructure, including secure and reliable fiber optic connections, and a large and skilled construction industry, which are some of the things that make Wisconsin a hub for data centers.

“People are using more data, are more demanding, and require more computational power. The companies that provide that, the major cloud service providers, are trying to meet that demand. And so, they do what businesses do,” Riley said.

“They try to do it in the most cost-effective way possible. The high costs for data centers are electricity and water. And so Wisconsin offers, you know, a good combination of those two sorts of price points, and so that’s. The reality is, there’s a data center boom everywhere that isn’t hostile towards data centers. So Wisconsin is necessarily leading the pack, but we’ve got a few high-profile ones recently because there’s been huge growth in that space. So Wisconsin is just, you know, part of that growth,” said Riley.

Data centers can reduce their long-term digital footprint by transitioning to renewable energy, improving energy and water efficiency through power management systems and advanced cooling, optimizing data storage and virtualization, and leveraging AI for dynamic workload management.

The initial energy concerns in computing were consumer-driven, such as improving battery life in mobile devices. Today, the focus is shifting to environmental sustainability, carbon footprint reduction, and making AI models more energy efficient. AI, particularly large language models, requires enormous computational resources. Makers. The implementation of power management systems to automatically shut down idle servers and continuously monitor and optimize the facility’s energy usage effectiveness at the data center.

In addition, “Yes, so I would say that, for AI to work, there are plenty of AI algorithms that don’t require a data center to operate. In fact, on a modern cell phone, there are dozens, if not hundreds, of AI applications that can run right on the device. So, data centers themselves aren’t explicitly needed for AI. But they’re really used when there are large AI models that are very capable, the models behind ChatGPT or Gemini, one of those. They are big enough to run in a data center. If you’re training new AI models, those are also generally large enough to require a data center,” said Riley.

The required level of human oversight for an AI data center depends on the risk involved, with high-risk systems needing continuous oversight and the ability to intervene. This may include a technology that can monitor the system, verify inputs and outputs, and have the authority to stop or correct the data. The extent of oversight also varies based on factors such as the system’s purpose and the safety, control, and security measures in place. The less oversight a human can exert over an AI system, the more testing and governance are needed to ensure the system produces accurate and reliable outputs. It will be a designated person who must be able to oversee the system’s operation, so nothing goes wrong with the system.