Duration: 1 hour
Cost: No Fee

Description:

Artificial intelligence is one of those terms that has been thrown out there for several years now. But what is AI? How do you define it? Are we just talking about predictive analytics? Are we just talking about machine learning?

John McCarthy, one of the founders of artificial intelligence research and one of first researchers to use the term “artificial intelligence,” described AI as machines that could think autonomously. He described the threshold as “getting a computer to do things which, when done by people, are said to involve intelligence.”

The Brookings Institution says today that definition evolved to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.” Brookings says these systems have three qualities that constitute the essence of artificial intelligence: intentionality, intelligence and adaptability.

No matter how you define AI, agencies are excited about the potential and real benefits.

Researchers at Stanford and New York universities found in a report released in February 2020 that 45% of federal agencies have experimented with AI and related machine learning tools. Those agencies are using AI tools to monitor risks to public health and safety, enforce regulations on environmental protection and many other mission areas ranging from NASA to the SEC to the Justice Department.

As agencies continue to add new AI capabilities to their toolset, one big consideration is ensuring the network, the data and the workforce are ready to handle these algorithms and capabilities.

Brian Carnell, the chief AI architect in the office of the federal CTO at Dell Technologies, said federal agencies are trying to figure out how to democratize AI for the organization.

The big challenges is the technical debt many agencies face. Their infrastructures and systems can’t handle the ever-increasing amount of data that is coming in.

“We’ve started to really focus on two things with data that’s coming from the edge for the AI projects. Number one, is it immediately actionable or is it historically valuable and not all data that is historically valuable is immediately actionable. But pretty much all data that is immediately actionable is historically valuable. So once you start to get that data and now how do you apply it against an infrastructure? How do you use the cycles that you have created? If you’ve got an AI ready architecture already? Or if you’ve got a cluster in your data center? How do you get the data to it?” Carnell said. “Those are the things that we really focused on with our customers, more so than the point projects that they’ve already run. Over the last few years, they’ve had huge success with using AI and with creating models, and with getting value from it. We do a lot of our supply chain management with AI. We do a lot of our manufacturing with AI. We understand the business value that we can drive out of the data. And that’s where the big government agencies are headed.”

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