Robert Lufkin and Artificial intelligence

Robert Lufkin has had an enduring interest in artificial intelligence. From his days in high school, Robert developed rule based computer programming that would generate music in a certain style as specified by the code. He supplemented this knowledge by attending classes at the Massachusetts Institute of Technology [MIT] on the weekends. During that time he also helped in the laboratories of David Hubel and Torsten Wiesel at Harvard University who studied the mammalian visual system. They later received the Nobel Prize for their revolutionary work describing the neurophysiology of the visual cortex. Their initial results showed both the transformations that occur from one level of processing to the next and how a sequence of these transformations might lead to at least the elements of pattern perception. Their experiments immediately provided a structure for conceptualizing how cortical neurons could be organized to produce perception. This work would foreshadow the development of convolutional neural networks as the basis for deep learning artificial intelligence years later.

         In college at Brown University, Robert did original experimental research in the neurophysiology of the mammalian visual system, specifically the superior colliculus. He also minored in computer science and worked part-time in the main campus computer center as a machine operator to earn extra money. He continued to work in artificial intelligence, using the LISP language to produce natural language processing systems that would simulate the verbal patient-physician interactions of a psychiatric session. 

            While attending the University of Virginia School of Medicine, also studied computer science in healthcare at the National Institutes of Health in Bethesda, Maryland. He completed an internship in internal medicine at the University of Oregon Center for Health Sciences and a residency in diagnostic radiology [where he served as Chief Resident] at the University of California, Los Angeles[UCLA] School of Medicine. After residency he did a fellowship in Neuroradiology/Head and Neck Radiology with Dr. William Hanafee and was recruited to join the faculty at UCLA where he eventually became a tenured Professor of Radiology. He continued to work in artificial intelligence, developing software that would automatically simulate the parameters within an MRI scan. He also used the AI technique of rule based production systems to create a device that would warn against potential adverse drug reactions that a patient might encounter.

            He continues his work in the area of machine learning with neural networks and deep convolutional neural networks. His team went on to be one of the top 10 winning entries for the 2018 RSNA Medical Imaging Artificial Intelligence challenge that had almost 1500 entries from the top artificial intelligence groups around the world. He now also works with social media to educate other professionals and the public about the risks and potentials of artificial intelligence and technology in general.