During my last interview I had a great talk with Daniel McDuff. Daniel's research is at the intersection of psychology and computer science. He is interested in designing hardware and algorithms for sensing human behavior at scale, and in building technologies that make life better. Applications of behavior sensing that he is most excited about are in: understanding mental health, improving online learning and designing new connected devices (IoT). Listen to more about why it is important to collect data from much larger scales and help computers read our emotional state. Key Learning Points: 1. Understanding the impact, intersection, and meaning of Psychology and Computer Science 2. Facial Expression Recognition 3. How to define Artificial Intelligence, Deep Learning, and Machine Learning 4. Applications of behavior sensing with Online Learning, Health, and Connected Devices 5. Visual Wearable sensors and heart health 6. The impact of education and learning 7. How to build computers to measure phycology, our reactions, emotions, etc 8. Daniel is building and utilizing scalable computer vision and machine learning tools to enable the automated recognition and analysis of emotions and physiology. He is currently Director of Research at Affectiva, a post-doctoral research affiliate at the MIT Media Lab and a visiting scientist at Brigham and Womens Hospital. At Affectiva Daniel is building state-of-the-art facial expression recognition software and leading analysis of the world's largest database of human emotion responses. Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR, the Center for Integrated Medicine and Innovative Technology (CIMIT) and several IEEE conferences. His work has been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine. Daniel has been named a 2015 WIRED Innovation Fellow.
Marvin Minsky, an American scientist working in the field of artificial intelligence (AI) who co-founded vthe Massachusetts Institute of Technology (MIT) AI laboratory, wrote several books on AI and philosophy, and was honored with the ACM A.M. Turing Award, passed away on Sunday, Jan. 24, 2016 at the age of 88. Born in New York City, Minsky attended the Ethical Culture Fieldston School, the Bronx High School of Science, and Phillips Academy, before entering the U.S. Navy in 1944. After leaving the service, he attended Harvard University, where he earned a bachelor's degree in mathematics in 1950. He then went to Princeton University, where he built the first randomly wired neural network learning machine, the Stochastic Neural Analog Reinforcement Calculator (SNARC), before earning his Ph.D in mathematics there in 1954. Doctorate in hand, Minsky was admitted to the group of Junior Fellows at Harvard, where he invented the confocal scanning microscope for thick, light-scattering specimens, decades in advance of the lasers and computer power needed to make it useful; today, it is in wide use in the biological sciences.
Virtual patients are viewed as a cost-effective alternative to standardized patients for role-play training of clinical interviewing skills. However, training studies produce mixed results. Students give high ratings to practice with virtual patients and feel more self-confident, but they show little improvement in objective skills. This confidence-competence gap matches a common cognitive illusion, in which students overestimate the effectiveness of training that is too easy. We hypothesize that cost-effective training requires virtual patients that emphasize functional and psychological fidelity over physical fidelity. We discuss 12 design decisions aimed at cost-effective training and their application in virtual patients for practicing brief intervention in alcohol abuse. Our STAR Workshop includes 3 such patients and a virtual coach. A controlled experiment evaluated STAR and compared it to an easier E-Book and no-training Control. E-Book subjects displayed the illusion, giving high ratings to their training and self-confidence, but performing no better than Control subjects on skills. STAR subjects gave high ratings to their training and self-confidence and scored better higher than E-Book or Control subjects on skills. We invite other researchers to use the underlying Imp technology to build virtual patients for their own work.