Uploaded May 2025 | Updated September 2026, 2 days ago
As artificial intelligence becomes increasingly integrated into our lives, the way machines “think” is beginning to mirror how humans make decisions. The recent rise of large language models (LLMs) has brought renewed attention to a style of intelligence that resembles what psychologists call “thinking fast”—quick, intuitive, and context-driven responses. But human intelligence also relies on a slower, more deliberate system of reasoning—”thinking slow”—which allows for reflection, planning, and logical analysis.
In "Machines Thinking Fast and Slow," Dr. Brent Venable explores how insights from dual-system theories of human cognition can help us build smarter, more adaptable AI. Drawing on examples from AI agents performing complex tasks, Dr. Venable highlights how combining fast, data-driven neural approaches with slower, symbolic reasoning leads to more robust performance. This dual-systems perspective not only provides a promising path for integrating symbolic and neural methods in AI, but also offers a framework for understanding the next generation of intelligent systems—machines that can reason, reflect, and react, all in the flow of thought.
Dr. Brent Venable's research sits at the intersection of AI-based preference reasoning and cognitive modeling of human decision making, with the goal of designing intelligent systems that are both adaptive and human-aware. Her work focuses on developing automated models of preference to support decision-making in applications such as recommender systems, human-machine teaming, and behavioral modeling. More recently, she has explored the ethical implications of AI technologies and their potential for advancing social good. She is the co-author of two books and has published more than 100 peer-reviewed articles in leading AI journals and conferences. She collaborates with a number of premier research institutions, including NASA Ames, IBM Research, and NICTA, and her work has been supported by agencies such as NASA, ONR, ARL, NIH, NSF, the Gulf of Mexico Research Initiative, and the Future of Life Institute. Brent graduated Magna Cum Laude in Pure Mathematics from the University of Padova, Italy, and earned her Ph.D. in Computer Science and Artificial Intelligence with a dissertation on automatically learning preferences over schedules, developed at NASA Ames in support of the Mars Rovers Spirit and Opportunity. She is currently a Senior Research Scientist at the Florida Institute for Human and Machine Cognition (IHMC) and serves as Director of the IHMC–University of West Florida Ph.D. program in Intelligent Systems and Robotics. She is also a Professor of Computer Science at the University of West Florida. Previously, she held a joint appointment with Tulane University, where she joined as an Associate Professor in 2012 and was promoted to Professor in 2018. Before moving to the U.S., she was a tenured Assistant Professor of Computer Science at the University of Padova.
As artificial intelligence becomes increasingly integrated into our lives, the way machines “think” is beginning to mirror how humans make decisions. The recent rise of large language models (LLMs) has brought renewed attention to a style of intelligence that resembles what psychologists call “thinking fast”—quick, intuitive, and context-driven responses. But human intelligence also relies on a slower, more deliberate system of reasoning—”thinking slow”—which allows for reflection, planning, and logical analysis.
In "Machines Thinking Fast and Slow," Dr. Brent Venable explores how insights from dual-system theories of human cognition can help us build smarter, more adaptable AI. Drawing on examples from AI agents performing complex tasks, Dr. Venable highlights how combining fast, data-driven neural approaches with slower, symbolic reasoning leads to more robust performance. This dual-systems perspective not only provides a promising path for integrating symbolic and neural methods in AI, but also offers a framework for understanding the next generation of intelligent systems—machines that can reason, reflect, and react, all in the flow of thought.
Dr. Brent Venable's research sits at the intersection of AI-based preference reasoning and cognitive modeling of human decision making, with the goal of designing intelligent systems that are both adaptive and human-aware. Her work focuses on developing automated models of preference to support decision-making in applications such as recommender systems, human-machine teaming, and behavioral modeling. More recently, she has explored the ethical implications of AI technologies and their potential for advancing social good. She is the co-author of two books and has published more than 100 peer-reviewed articles in leading AI journals and conferences. She collaborates with a number of premier research institutions, including NASA Ames, IBM Research, and NICTA, and her work has been supported by agencies such as NASA, ONR, ARL, NIH, NSF, the Gulf of Mexico Research Initiative, and the Future of Life Institute. Brent graduated Magna Cum Laude in Pure Mathematics from the University of Padova, Italy, and earned her Ph.D. in Computer Science and Artificial Intelligence with a dissertation on automatically learning preferences over schedules, developed at NASA Ames in support of the Mars Rovers Spirit and Opportunity. She is currently a Senior Research Scientist at the Florida Institute for Human and Machine Cognition (IHMC) and serves as Director of the IHMC–University of West Florida Ph.D. program in Intelligent Systems and Robotics. She is also a Professor of Computer Science at the University of West Florida. Previously, she held a joint appointment with Tulane University, where she joined as an Associate Professor in 2012 and was promoted to Professor in 2018. Before moving to the U.S., she was a tenured Assistant Professor of Computer Science at the University of Padova.
