A.I. and Innovation - Costello

It takes three types of thinking to be smart

Katelynn C Hipolito — Tue, 10 Feb 2026 19:07:24 +0000

It takes three types of thinking to be smart Katelynn C Hipolito Tue, 02/10/2026 - 14:07

Balancing and combining different kinds of intelligence may be even more important than how much you know, or how you think.

Do you know what it means to be smart? It’s a more complicated question than it may seem. There are several ways to think about intelligence—as the well-known “book-vs.-street smart” binary illustrates. By most people’s definition, a truly smart person would be someone who not only thinks well but is also able to translate thought into concrete steps toward positive and practical goals. Balancing and combining different kinds of intelligence may be even more important than how much you know, or how you think.

Matthew A. Cronin. Photo by Hannah Patterson/Costello College of Business.

In a recently published technical note, Matthew A. Cronin, professor of management at Costello College of Business at 911��, and his co-author Lillien M. Ellis of the University of Virginia, deconstruct intelligence into three modalities, which they label the Scientist, the Artist and the Judge (or “SAJ,” pronounced “sage”).

As Cronin says, the Scientist is about “logic and evidence…how we know stuff.” The Artist stands for imagination, the ability to conceive possibilities outside what we are given. The Judge is responsible for weighing the morality, appropriateness, etc. of an action or direction.

Cronin contends that we all have a Scientist, Artist and Judge in our minds, but they are often out of balance. “Most people have one of the three that they like the most, and they have that guy command everybody else. And that’s when you have problems,” he says.

As an example, Cronin’s technical note cites Judge-heavy cybersecurity protocols—they valued security but did not account for how memory worked—that forced users to create passwords that were impossible to remember. People wrote them down near their computers, defeating the whole purpose of security. Adding the Scientist and Artist into the mix resulted in innovation, e.g. long passphrases that more easily stick in the mind without needing to be noted down.

“AI can tell you whatever you want to know. But that doesn’t guarantee it’s correct—that’s the Scientist. It doesn’t tell you what you should do—that’s the Judge. And it will predict only from the most likely outcomes—definitely not the Artist.”

— Matthew A. Cronin, professor of management at Costello College of Business at 911��

According to the SAJ framework, the Artist-Scientist pairing produces discoveries about the world, by relating novel information or situations to what is already known. Collaboration between Artist and Judge is required to formulate a vision, or an imagined realization of desires or ideals deemed worth pursuing. The Scientist and Judge can work together to build skill, or the productive application of knowledge toward a chosen objective.

“You cycle through these roles,” Cronin says. “We can start with what we want, which is the Judge, and how things work, which is the Scientist—but that’s likely only to maximize what we’re already doing. So we have to find a vision that might be better, but we’re almost certainly not going to be able to get that to work without some discovery.”

Building upon Cronin’s 2018 book (co-authored by Jeffrey Loewenstein) The Craft of Creativity, the SAJ framework formalizes how creativity (the Artist) works alongside other cognitive tools to help us think, work, and live better. “People think of creativity as this magical ability. No, it’s a skill that can be developed. If you can think about a subject, you can think creatively about it,” Cronin says.

By extension, there’s hope for anyone striving to achieve intellectual balance by strengthening their Scientist, Artist, or Judge—whichever might be a bit undernourished. “Step one is just to make people aware,” Cronin says. “They think either thinking is one undifferentiated blob, or the three are totally remote and separated from one another.”

The SAJ framework can also be an active tool for structuring decision-making in a balanced way. “If I have an idea for the way things could be, I know that I need to also know how they actually work…And when you encounter unbalanced thinking, like a bureaucrat who lacks the imagination (i.e. Artist) to conceive that there may be other ways to deal with a situation, you can remind that person how everything we now take for granted was once thought impossible.”

Cronin has been teaching the SAJ framework in his management courses for more than five years, and he feels it will only grow more impactful as AI challenges organizations to define the value-add that human minds can bring to a problem set.

“AI can tell you whatever you want to know,” he says. “But that doesn’t guarantee it’s correct—that’s the Scientist. It doesn’t tell you what you should do—that’s the Judge. And it will predict only from the most likely outcomes—definitely not the Artist.”

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911�� researcher is using AI to identify human trafficking hot spots

Katelynn C Hipolito — Mon, 29 Sep 2025 18:20:46 +0000

911�� researcher is using AI to identify human trafficking hot spots Katelynn C Hipolito Mon, 09/29/2025 - 14:20

Illicit massage businesses (IMBs) run by human trafficking rings are rampant in the United States. A 911�� professor has helped build what may be the best artificial intelligence (AI)-driven tool to root them out.

Human trafficking rings are at their most dangerous when they masquerade as legitimate commercial activity. IMBs are one of the most common ways in which exploitive networks operate in plain sight.

The Network, an anti-trafficking nonprofit, estimates that there are more than 13,000 IMBs active in the United States, raking in annual total revenue of more than $5 billion.

Abhishek Ray

“You are stuck in a massage business. You’re not allowed to go out,” says Abhishek Ray, assistant professor of information systems and operations management at the Donald G. Costello College of Business at 911��, describing the plight of IMB workers. “Your passports are taken away, and you’re supposed to do a certain amount of business every day and give the money to the trafficker. It’s a really abhorrent form of abuse.”

Ray is one of a growing group of researchers exploring how various forms of AI could help resource-constrained law enforcement agencies differentiate between IMBs and the legitimate enterprises they try to mimic. His ongoing research using graph neural networks has yielded more promising results than rival approaches, when put to the test in a recent experiment.

His co-researchers on the IMB project are Lumina Albert and Swetha Varadarajan of Colorado State University.

According to Ray, “Graph neural networks are just a fancy way of saying that if I get a graph of a city or locality at one point in time, and I add data to it, can I predict future patterns on this graph if I know the past?”

This approach made sense for detecting IMBs, because try as they might to appear above board, they have geographical needs that conventional businesses don’t. “IMBs don’t allow their trafficked employees to go out of the parlor,” Ray says. “But since they’re humans, they need sustenance. They have to be near groceries, gas stations, where they can get stuff and come back.”

The researchers combined several graph neural networks into a framework called IMBWatch. The training data-set for IMBWatch comprised publicly available information such as online customer reviews, arrest and raid data for known IMBs, and advertisements from websites promoting illicit activities (e.g., the infamous Backpage). The result, in essence, was a series of snapshots mapping the evolution of the IMB network in a given city or county over a period of time. This could then be overlaid on geographical maps to tease out hidden patterns.

To gauge IMBWatch’s relative performance, the researchers let it loose on a testing data-set alongside four other AI models, which were not as sensitive to the nuanced interplay of spatial and temporal factors. Of the five models, IMBWatch provided the most accurate, precise and informative predictions. In other words, it outperformed the others at spotting IMBs among a larger mass of local businesses.

“Graph neural networks are just a fancy way of saying that if I get a graph of a city or locality at one point in time, and I add data to it, can I predict future patterns on this graph if I know the past?”
— Abhishek Ray, assistant professor of information systems and operations management

While encouraging, these outcomes require further confirmation with a larger data-set. “IMBWatch was trained on data from Georgia and Louisiana, not the entire United States,” Ray says. “These were small, manageable data-sets, but we will now scale up to major states such as New York and California.”

The researchers are also looking at enhancing IMBWatch with data related to how workers end up wandering into trafficking webs. These might include “proximity to hospitals, religious places, etc. because a lot of times people are coerced by religious compulsions, or because they’re pregnant and need some care,” Ray says.

This is not Ray’s first foray into the field of AI-fueled anti-trafficking. Previously, Ray co-developed a model for improving machine learning-based detection of human trafficking activity at transit stations and on fishing vessels.

However, law enforcement agencies and other pertinent stakeholders (e.g., business owners) are often wary of adopting AI-based solutions, due to a lack of trust in the technology. Ray and his co-researchers are currently devising a framework that will clarify how these stakeholders can work together with tech experts and, perhaps most importantly, human trafficking survivors to make the best possible use of AI.

“This qualitative piece is required to make sure that people who are on the sidelines, on the fences about using this actually start using it, because that’s the need right now,” Ray says.

Learn more about Artificial Intelligence at 911��

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This content appears in the Spring 2026 print edition of the Mason Spirit Magazine with the title "Using AI to Identify Human Trafficking Hot Spots."

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It takes three types of thinking to be smart

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